{"id":6087,"date":"2019-07-01T17:59:08","date_gmt":"2019-07-01T08:59:08","guid":{"rendered":"http:\/\/www.is.doshisha.ac.jp\/news\/?p=6087"},"modified":"2021-03-09T11:09:42","modified_gmt":"2021-03-09T02:09:42","slug":"%e3%80%90%e9%80%9f%e5%a0%b1%e3%80%91ohbm2019","status":"publish","type":"post","link":"https:\/\/is.doshisha.ac.jp\/news\/?p=6087","title":{"rendered":"\u3010\u901f\u5831\u3011OHBM2019"},"content":{"rendered":"<p>OHBM2019\u306b\u3066\u767a\u8868\u3057\u307e\u3057\u305f\u3002<br \/>\n<!--more--><br \/>\n<strong>\u5b66\u4f1a\u53c2\u52a0\u5831\u544a\u66f8<\/strong><\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"147\"><strong>\u5831\u544a\u8005\u6c0f\u540d<\/strong><\/td>\n<td width=\"373\">\u6749\u91ce\u68a8\u7dd2<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u767a\u8868\u8ad6\u6587\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n<td width=\"373\">Behavioral and functional connectivity analysis of Kanizsa illusory contour perception<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u8457\u8005<\/strong><\/td>\n<td width=\"373\">Rio Sugino, Satoru Hiwa, Keisuke Hachisuka, Fumihiko Murase, Tomoyuki Hiroyasu<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u4e3b\u50ac<\/strong><\/td>\n<td width=\"373\">Organization for Human Brain Mapping<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u8b1b\u6f14\u4f1a\u540d<\/strong><\/td>\n<td width=\"373\">25th Annual Meeting of the Organization for Human Brain Mapping<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u4f1a\u5834<\/strong><\/td>\n<td width=\"373\">Auditorium Parco Della Musica<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u958b\u50ac\u65e5\u7a0b<\/strong><\/td>\n<td width=\"373\">2019\/06\/9-2019\/06\/14<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<br \/>\n&nbsp;<\/p>\n<ol>\n<li>\u8b1b\u6f14\u4f1a\u306e\u8a73\u7d30<\/li>\n<\/ol>\n<p>2019\/06\/9\u304b\u30892019\/06\/14\u306b\u304b\u3051\u3066\uff0cAuditorium Parco Della Musica\u306b\u3066\u958b\u50ac\u3055\u308c\u307e\u3057\u305f25th Annual Meeting of the Organization of Human Brain Mapping\u306b\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u3053\u306e25th Annual Meeting of the Organization of Human Brain Mapping\u306f\uff0cOrganization for Human Brain Mapping\u306b\u3088\u3063\u3066\u4e3b\u50ac\u3055\u308c\u305f\u56fd\u969b\u4f1a\u8b70\u3067\uff0c\u30d2\u30c8\u306e\u8133\u7d44\u7e54\u304a\u3088\u3073\u8133\u6a5f\u80fd\u306e\u30de\u30c3\u30d4\u30f3\u30b0\u306b\u95a2\u3059\u308b\u7814\u7a76\u306b\u643a\u308f\u308b\u69d8\u3005\u306a\u80cc\u666f\u3092\u6301\u3064\u7814\u7a76\u8005\u3092\u96c6\u3081\uff0c\u3053\u308c\u3089\u306e\u79d1\u5b66\u8005\u306e\u30b3\u30df\u30e5\u30cb\u30b1\u30fc\u30b7\u30e7\u30f3\uff0c\u304a\u3088\u3073\u6559\u80b2\u3092\u4fc3\u9032\u3059\u308b\u3053\u3068\u3092\u76ee\u7684\u306b\u958b\u50ac\u3055\u308c\u3066\u3044\u307e\u30591\uff09\uff0e<br \/>\n\u79c1\u306f10\uff5e14\u65e5\u306b\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u672c\u7814\u7a76\u5ba4\u304b\u3089\u306f\u4ed6\u306b\u5ee3\u5b89\u5148\u751f\uff0c\u65e5\u548c\u5148\u751f\uff0c\u53e4\u5bb6\uff0c\u5927\u585a\uff0c\u5965\u6751\uff0c\u5c71\u672c\uff0c\u5409\u7530\uff0c\u98a8\u5442\u8c37\uff0c\u4e39\u304c\u53c2\u52a0\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<ol start=\"2\">\n<li>\u7814\u7a76\u767a\u8868\n<ul>\n<li>\u767a\u8868\u6982\u8981<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<p>\u79c1\u306f11\u65e5\u306e\u5348\u5f8c\u306e\u30bb\u30c3\u30b7\u30e7\u30f3\u300cPoster session\u300d\u306b\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u767a\u8868\u306e\u5f62\u5f0f\u306f\u30dd\u30b9\u30bf\u30fc\u767a\u8868\u3067\uff0c\u767a\u8868\u6642\u9593\u306f1\u6642\u9593\u3068\u306a\u3063\u3066\u304a\u308a\u307e\u3057\u305f\uff0e<br \/>\n\u4eca\u56de\u306e\u767a\u8868\u306f\uff0cBehavioral and functional connectivity analysis of Kanizsa illusory contour perception\u3067\u3057\u305f\uff0e\u4ee5\u4e0b\u306b\u6284\u9332\u3092\u8a18\u8f09\u81f4\u3057\u307e\u3059\uff0e<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">Background: Illusory contour (IC) is one of the common illusions encountered in our daily lives. The neural basis involved in IC perception has been studied so far (Ritzl Afra et al 2003), but the relationship between behavioral response and brain activity has remained unexplored. In this study, we considered IC perception speed as a behavioral measure, and assumed a functional connectivity (FC) to be correlated with it. Kanizsa figures were used to induce IC perception, and a dot localization task was used to measure reaction time (RT) to the IC. Brain activity during IC perception was measured and functional connections correlated with RT to IC were extracted by FC analysis.<br \/>\nMethods: Eighteen healthy adults (23.1\u00b11.2 y\/o, 4F\/14M) were instructed to perform a dot localization task in an fMRI scanner (Chen Siyi et al 2018) . In the task, they judged whether the dots were inside or outside the contour, and pressed a button to answer. Fig.1 shows the experimental design. The dots were presented in a pseudo-random position with the same inside and outside probability. RT from the instance of target presentation to that of response was used as a behavioral metric reflecting the contour perception difference between the participants. The difference in average RT between IC and real contour (RC) tasks was used as an indicator of IC perception speed, and on the basis of the median value the participants were divided into FAST and SLOW groups. These two groups were compared to investigate how the difference in RT was reflected on FC network. The whole brain was parcellated into 116 regions based on the AAL atlas. A correlation coefficient matrix was calculated from region-of-interest-wise BOLD signal of each task using CONN toolbox. FC matrices for each group were compared between two tasks; and FC which was higher in the IC task than the RC task, was extracted and compared among the two groups. Furthermore, a correlation analysis was performed between FC of these connections and the difference in RTs between the two tasks.<br \/>\nResults: Mean values of RT differences between IC and RC tasks of FAST and SLOW groups were 131.2\u00b128.0 ms and 218.5\u00b169.0 ms, respectively, and there was a significant difference between the two groups (p&lt;0.05). Fig.2a shows functional-connection differences between IC and RC tasks, which significantly differed between the two groups (p&lt;0.05, FDR). The red line indicates the connection whose difference between IC and RC tasks is higher in the FAST group than that in the SLOW group, while the blue line shows those with a higher difference in the SLOW group. In addition, Fig.2b shows a significant correlation between the functional connection differences and average RT differences (p&lt;0.05). The functional connections between the right supplementary motor area (SMA.R) and the orbital parts of left\/right superior frontal gyri (ORBsup.L\/R) were the highest among the extracted connections. SMA is the region belonging to the salient network and is related to awareness (Power Jonathan D. et al. 2011), while ORBsup is related to top-down attention (Aboitiz Francisco et al 2014). Since FC is higher in IC task than RC task, it is conceivable that these connections are related to IC perception. Furthermore, it was suggested that higher connections among these regions in the FAST group than in the SLOW group could be associated with an increase in IC perception speed.<br \/>\nConclusions: In this study, FC correlated with IC perception speed was investigated using Kanizsa figures. Participants were divided into FAST and SLOW groups according to the IC perception speed, and FC was found to be higher in IC task when compared between the groups. Thus, it was shown that six functional connections in the FAST group had higher connectivity in the IC task than in the RC task. Among these connections, FC between the regions related to awareness and attention was high. These results suggest that a higher temporal synchronization is involved between these regions in IC perception.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<ul>\n<li>\u8cea\u7591\u5fdc\u7b54<\/li>\n<\/ul>\n<p>\u4eca\u56de\u306e\u8b1b\u6f14\u767a\u8868\u3067\u306f\uff0c\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u8cea\u7591\u3092\u53d7\u3051\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9\uff11<\/strong><br \/>\n\u6b63\u7b54\u7387\u306f\u3069\u306e\u30bf\u30b9\u30af\u306b\u5bfe\u3057\u3066\u7b97\u51fa\u3057\u3066\u3044\u308b\u306e\u304b\u306b\u3064\u3044\u3066\u8cea\u554f\u3092\u53d7\u3051\u307e\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\uff0cIC\u30bf\u30b9\u30af\u3067\u306e\u6b63\u7b54\u7387\u3092\u7b97\u51fa\u3057\u3066\u3044\u308b\u3068\u7b54\u3048\u307e\u3057\u305f\uff0e<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9\uff12<\/strong><br \/>\n\u306a\u305c5\u672c\u306e\u7d50\u5408\u306e\u307f\u304c\u62bd\u51fa\u3055\u308c\u305f\u306e\u304b\u306b\u3064\u3044\u3066\u8cea\u554f\u3092\u53d7\u3051\u307e\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\u6709\u610f\u6c34\u6e960.1%\u3067\u7121\u76f8\u95a2\u691c\u5b9a\u3092\u884c\u3063\u305f\u305f\u3081\uff0cp\u5024\u304c0.001\u3088\u308a\u5c0f\u3055\u3044\u7d50\u5408\u3092\u62bd\u51fa\u3057\u305f\u305f\u3081\u3068\u7b54\u3048\u307e\u3057\u305f\uff0e<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9\uff13<\/strong><br \/>\nFC\u89e3\u6790\u306f\u5168\u8133\u3067\u884c\u308f\u308c\u3066\u3044\u308b\u306e\u304b\uff0c\u7d50\u5408\u306f\u3044\u304f\u3064\u3042\u308b\u306e\u304b\u306b\u3064\u3044\u3066\u8cea\u554f\u3092\u53d7\u3051\u307e\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\uff0c\u5168\u8133\u306f116\u9818\u57df\u306b\u5206\u5272\u3055\u308c\uff0c\u305d\u306e\u5168\u3066\u306e\u9818\u57df\u9593\u3067FC\u306f\u7b97\u51fa\u3055\u308c\u305f\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9\uff14<\/strong><br \/>\nRC\u30bf\u30b9\u30af\u3092\u57fa\u6e96\u3068\u3057\u3066\u7528\u3044\u308b\u3053\u3068\u306b\u3064\u3044\u3066\u8cea\u554f\u3092\u53d7\u3051\u307e\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066RC\u306fIC\u306b\u5bfe\u3059\u308b\u30b3\u30f3\u30c8\u30ed\u30fc\u30eb\u30bf\u30b9\u30af\u3067\u3042\u308b\u305f\u3081\uff0c\u57fa\u6e96\u3068\u3057\u3066\u7528\u3044\u305f\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>5<\/strong><br \/>\nsharpness\u304c\u9ad8\u3044\u7fa4\u3068\u4f4e\u3044\u7fa4\u306e2\u3064\u7fa4\u304c\u3042\u308b\u306e\u304b\u306b\u3064\u3044\u3066\u8cea\u554f\u3092\u53d7\u3051\u307e\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\u88ab\u9a13\u800522\u540d\u306e\u4e2d\u306b\u9ad8\u3044\u4eba\u3082\u3044\u308c\u3070\u4f4e\u3044\u4eba\u3082\u3044\u308b\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>6<\/strong><br \/>\n\u8003\u5bdf\u306e\u89e3\u91c8\u306b\u3064\u3044\u3066\u8cea\u554f\u3092\u53d7\u3051\u307e\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066Sharpness\u304c\u4f4e\u3044\u4eba\u306f\u6b63\u7b54\u7387\u304c\u4f4e\u304f\uff0c\u30bf\u30b9\u30af\u3092\u884c\u3046\u305f\u3081\u306b\u3088\u308a\u30a8\u30cd\u30eb\u30ae\u30fc\u3092\u4f7f\u3063\u3066\u3044\u308b\u305f\u3081FC\u304c\u9ad8\u304f\u306a\u308b\u3068\u8003\u3048\u3066\u3044\u308b\u3068\u7b54\u3048\u307e\u3057\u305f\uff0e<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>6<\/strong><br \/>\n\u5b9f\u9a13\u8a2d\u8a08\u306f\u4e8b\u8c61\u95a2\u9023\u30c7\u30b6\u30a4\u30f3\u304b\u306b\u3064\u3044\u3066\u8cea\u554f\u3092\u53d7\u3051\u307e\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\u30d6\u30ed\u30c3\u30af\u30c7\u30b6\u30a4\u30f3\u3092\u7528\u3044\u3066\u3044\u308b\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>7<\/strong><br \/>\n\u6241\u6843\u4f53\u304c\u7d50\u679c\u306b\u542b\u307e\u308c\u3066\u3044\u308b\u3053\u3068\u306b\u3064\u3044\u3066\u8cea\u554f\u3092\u53d7\u3051\u307e\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\u8996\u899a\u30b7\u30b9\u30c6\u30e0\u306f\u6241\u6843\u4f53\u3068\u306e\u69cb\u9020\u7684\u7d50\u5408\u3092\u6301\u3063\u3066\u304a\u308a\uff0c\u6241\u6843\u4f53\u306f\u60c5\u52d5\u306b\u95a2\u308f\u308b\u9818\u57df\u3067\u3042\u308b\u305f\u3081\uff0c\u60c5\u52d5\u306e\u5909\u5316\u304c\u611f\u899a\u306e\u77e5\u899a\u306b\u95a2\u308f\u308b\u306e\u3067\u306f\u306a\u3044\u304b\u3068\u89e3\u91c8\u3057\u3066\u3044\u308b\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n2.3.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u611f\u60f3<br \/>\n2\u56de\u76ee\u306e\u56fd\u969b\u5b66\u4f1a\u306e\u53c2\u52a0\u3060\u3063\u305f\u305f\u3081\u524d\u56de\u3088\u308a\u3082\u843d\u3061\u7740\u3044\u3066\u767a\u8868\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u305f\uff0e\u4eca\u56de\u306f\u65b9\u6cd5\u304c\u6bd4\u8f03\u7684\u30b7\u30f3\u30d7\u30eb\u3067\u3042\u3063\u305f\u305f\u3081\uff0c\u524d\u56de\u3088\u308a\u3082\u6b63\u78ba\u306b\u4f1d\u3048\u308b\u3053\u3068\u304c\u3067\u304d\u305f\u3068\u611f\u3058\u3066\u3044\u308b\uff0e\u826f\u304b\u3063\u305f\u70b9\u306f\uff0c\u5b66\u4f1a\u3092\u901a\u3057\u3066\u9854\u898b\u77e5\u308a\u306b\u306a\u3063\u305f\u65b9\u306b\u30dd\u30b9\u30bf\u30fc\u3092\u805e\u304d\u306b\u304d\u3066\u6b32\u3057\u3044\u3068\u4f1d\u3048\u305f\u3053\u3068\u306b\u3088\u308a\uff0c\u89aa\u8eab\u306b\u767a\u8868\u3092\u805e\u3044\u3066\u304f\u308c\u305f\u3053\u3068\u3067\uff0c\u8aac\u660e\u3068\u8cea\u554f\u3092\u7e70\u308a\u8fd4\u3057\u3066\u7814\u7a76\u306e\u9685\u3005\u307e\u3067\u4f1d\u3048\u308b\u3053\u3068\u304c\u3067\u304d\u305f\u3053\u3068\u304c\u6319\u3052\u3089\u308c\u308b\uff0e\u53cd\u7701\u70b9\u306f\u540d\u523a\u3092\u8981\u6c42\u3055\u308c\u305f\u304c\u6301\u3063\u3066\u3044\u306a\u304b\u3063\u305f\u3053\u3068\u304c\u6319\u3052\u3089\u308c\u308b\uff0e\u53bb\u5e74\u540c\u69d8\u306b\u82f1\u8a9e\u3092\u805e\u304d\u53d6\u308b\u306e\u306f\u82e6\u624b\u3067\u306f\u3042\u3063\u305f\u304c\uff0c\u8ae6\u3081\u305a\u306b\u805e\u304d\u76f4\u3057\uff0c\u81ea\u5206\u3067\u8a00\u3044\u76f4\u3057\u5408\u3063\u3066\u3044\u308b\u304b\u3092\u78ba\u8a8d\u3057\u306a\u304c\u3089\u76f8\u624b\u306e\u767a\u8a00\u3092\u805e\u304f\u3053\u3068\u304c\u51fa\u6765\u305f\u3053\u3068\u304c\u3067\u304d\u305f\uff0e<br \/>\n&nbsp;<br \/>\n&nbsp;<\/p>\n<ol start=\"3\">\n<li>\u8074\u8b1b<\/li>\n<\/ol>\n<p>\u4eca\u56de\u306e\u8b1b\u6f14\u4f1a\u3067\u306f\uff0c\u4e0b\u8a18\u306e\uff14\u4ef6\u306e\u767a\u8868\u3092\u8074\u8b1b\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aTask-dependent functional organizations of the visual ventral stream<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Han-Gue Jo, Thilo Kellermann, Junji Ito, Sonja Gru\u0308n, Ute Habel<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Poster session<br \/>\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a<br \/>\nBackground: The visual ventral stream is a series of hierarchical processing stages from the primary visual cortex V1 to inferior temporal cortex IT, in which neural interactions along this hierarchy enable us to recognize visual objects. However, its complex and diverse connectivity make it difficult to illustrate the functional organization, particularly when top-down cognition is involved. Depending on task-goal, the ventral stream may require different functional structure of the hierarchy to incorporate visual features of interest into object recognition [1,2]. Here we identified context-dependent functional structures of the ventral stream.<br \/>\nMethods: Twenty-eight participants performed three types of visual cognition task during fMRI measurement. The three task conditions that required distinct cognitive processes for object recognition were used in order to drive the visual ventral stream: searching for a target object, memorizing objects in natural scenes, or free viewing of the same natural scenes. We identified a task-dependent connectivity network of the ventral stream, utilizing a hierarchical seed-based connectivity approach that explicitly compared task-specific BOLD time-series. Seed-based analysis was performed within the ventral stream, and the first cortical processing stage V1 was subjected as a seed region. Voxel clusters that revealed significant task effect were identified as regions of interest (ROIs) and these ROIs were further subjected as seeds for subsequent seed-based analyses. On the basis of the identified ROIs, we demonstrated task-dependent connectivity to which extent the connectivity increases or decreases during each of the visual search, memory, and free viewing conditions.<br \/>\nResults: The hierarchical seed-based connectivity approach identified five ROIs in the visual ventral stream (Figure 1), representing a task-dependent functional network. The connections across the identified ROIs were organized into correlated and anti-correlated structures according to the context of visual cognition. Searching for a target object separated the visual area V1 and V4 from the high-order visual area PIT (the posterior part of the IT), while memorizing objects strengthened the coupling of V4 with PIT. Furthermore, task-dependent activation was found in V1 and V4, while the PIT showed deactivation.<br \/>\nConclusions: The present study demonstrated context-dependent functional structures of the visual ventral stream. In particular, while the ventral stream was organized into correlated and anti-correlated structures during searching for a target object, memorizing objects manifested a correlated structure. Our results further suggest a putative boundary between V4 and PIT, which divides the visual hierarchy into two subdivisions that interact competitively or cooperatively depending on task demand. These results highlight the context-dependent nature of the ventral stream and shed light on how the visual hierarchy is selectively mediated to bias object recognition toward features of interest.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u3053\u306e\u767a\u8868\u306f\u8179\u5074\u76ae\u8cea\u8996\u899a\u8def\u306e\u30b3\u30f3\u30c6\u30ad\u30b9\u30c8\u4f9d\u5b58\u6a5f\u80fd\u69cb\u9020\u3092\u8b58\u5225\u306b\u95a2\u3059\u308b\u7814\u7a76\u3067\u3057\u305f\uff0e 3 \u7a2e\u985e\u306e\u8996\u899a\u8a8d\u8b58\u8ab2\u984c\uff08\u76ee\u6a19\u5bfe\u8c61\u7269\u306e\u63a2\u7d22\uff0c\u81ea\u7136\u30b7\u30fc\u30f3\u306b\u304a\u3051\u308b\u5bfe\u8c61\u7269\u306e\u8a18\u61b6\uff0c\u307e\u305f\u306f\u540c\u3058\u81ea\u7136\u30b7\u30fc\u30f3\u306e\u81ea\u7531\u8996\u8074\uff09\u3092\u7528\u3044\u308b\u3053\u3068\u306b\u3088\u308a\uff0c\u8179\u5074\u76ae\u8cea\u8996\u899a\u8def\u306e\u6a5f\u80fd\u3067\u3042\u308b\u7269\u4f53\u7279\u5b9a\u3084\u7269\u4f53\u8a8d\u8b58\u306e\u50cd\u304d\u3092\u898b\u3066\u3044\u307e\u3057\u305f\uff0e\u3053\u306e3\u7a2e\u985e\u306e\u30bf\u30b9\u30af\u304c\u975e\u5e38\u306b\u8208\u5473\u6df1\u304b\u3063\u305f\u3067\u3059\uff0e\u307e\u305f\u3053\u306e\u8996\u899a\u8def\u306f\u795e\u7d4c\u76f8\u4e92\u4f5c\u7528\u306b\u3088\u3063\u3066\u8996\u899a\u7684\u5bfe\u8c61\u3092\u8a8d\u8b58\u3059\u308b\u3053\u3068\u304c\u53ef\u80fd\u3067\u3042\u308b\u3068\u8a00\u308f\u308c\u3066\u304a\u308a\uff0c\u5148\u65e5\u8abf\u67fb\u3057\u305f\u3070\u304b\u308a\u3060\u3063\u305f\u306e\u3067\uff0c\u307e\u305f\u65b0\u305f\u306a\u77e5\u898b\u3092\u5f97\u308b\u3053\u3068\u304c\u3067\u304d\u975e\u5e38\u306b\u52c9\u5f37\u306b\u306a\u308a\u307e\u3057\u305f\uff0e\u89e3\u6790\u306b\u304a\u3044\u3066\u306f\uff0c\u8179\u5074\u76ae\u8cea\u8996\u899a\u8def\u306e\u51e6\u7406\u6bb5\u968e\u306e\u968e\u5c64\u306b\u304a\u3044\u30661\u756a\u521d\u3081\u306e\u9818\u57df\u3067\u3042\u308bV1\u3092Seed\u9818\u57df\u3068\u3057\u3066Seed-based analysis\u3092\u884c\u3044\uff0c\u6709\u610f\u306a\u30bf\u30b9\u30af\u52b9\u679c\u3092\u6301\u3064\u3053\u3068\u304c\u308f\u304b\u3063\u305f\u9818\u57df\u3092ROI\u3068\u3057\u3066\u540c\u5b9a\u3057\uff0c\u3055\u3089\u306b\u305d\u306eROI\u3092Seed\u9818\u57df\u3068\u3057\u3066Seed-based analysis\u3092\u884c\u3046\u3068\u3044\u3046\u65b9\u6cd5\u3067\u3057\u305f\uff0e\u3053\u306e\u89e3\u6790\u65b9\u6cd5\u306f\u771f\u4f3c\u3066\u307f\u308b\u3053\u3068\u304c\u51fa\u6765\u305d\u3046\u3060\u3063\u305f\u306e\u3067\uff0c\u4eca\u5f8c\u306e\u7814\u7a76\u306b\u3082\u6d3b\u304b\u3057\u305f\u3044\u3068\u601d\u3063\u3066\u3044\u307e\u3059\uff0e<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a10,000 Social Brains: Charting sexual dimorphism in the UK Biobank<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Hannah Kiesow<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Oral session: Population Neuroscience<br \/>\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Reliance on one&#8217;s social network provides many advantages. The social brain hypothesis (Byrne &amp; Whiten, 1988; Humphrey, 1976) argues that neocortex volume in primates co-evolved with the cognitive costs required to maintain complex social environments. Various complex social indices have structural implications in the brain. For example, larger social network size was associated with increases in gray matter volume in regions engaged in social processing in the human (Lewis, Rezaie, Brown, Roberts, &amp; Dunbar, 2011) and nonhuman (Sallet et al., 2011) primate brain. Individual variation in social brain volumes may be expressed according to sex. Sex has been argued to be the phenotypical distinction that explains most behavioral variability in most species. The different behavioral profiles of males and females presumably rely on distinct topographical brain circuits that are anatomically or functionally dimorphic. Thus, the aim of our study was to investigate the neural manifestations of sexual dimorphism in different indices of social behavior.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u3053\u306e\u767a\u8868\u306f10000\u4eba\u306e\u5206\u306e\u8133\u69cb\u9020\u30c7\u30fc\u30bf\u3092\u7528\u3044\u305f\u6027\u5dee\u7814\u7a76\u3067\u3057\u305f\uff0e\u6027\u5225\u306f\u307b\u3068\u3093\u3069\u306e\u7a2e\u306b\u304a\u3044\u3066\uff0c\u3082\u3063\u3068\u3082\u884c\u52d5\u306e\u591a\u69d8\u6027\u3092\u8aac\u660e\u3059\u308b\u8868\u73fe\u578b\u306e\u9055\u3044\u3067\u3042\u308b\u3068\u4e3b\u5f35\u3055\u308c\u3066\u304a\u308a\uff0c\u7537\u6027\u3068\u5973\u6027\u306e\u7570\u306a\u308b\u884c\u52d5\u306e\u30d7\u30ed\u30d5\u30a1\u30a4\u30eb\u306f\u89e3\u5256\u5b66\u7684\u307e\u305f\u306f\u6a5f\u80fd\u7684\u306b\u7570\u306a\u308b\u8133\u56de\u8def\u306b\u4f9d\u5b58\u3057\u3066\u3044\u308b\u3068\u8003\u3048\u3089\u308c\u3066\u3044\u308b\uff0e\u305d\u306e\u305f\u3081\u793e\u4f1a\u7684\u884c\u52d5\u306e\u7570\u306a\u308b\u6307\u6a19\uff08\u793e\u4f1a\u7684\u5185\u8f2a\u6307\u6a19\uff0c\u793e\u4f1a\u7684\u30a2\u30a6\u30c8\u30ec\u30c3\u30c8\u6307\u6a19\uff0c\u793e\u4f1a\u7684\u7d4c\u6e08\u6307\u6a19\uff0c\u793e\u4f1a\u6587\u5316\u7684\u79fb\u884c\u6307\u6a19\uff09\u3092\u7528\u3044\u3066\u6027\u7684\u4e8c\u5f62\u6027\u306e\u795e\u7d4c\u57fa\u76e4\u306b\u3064\u3044\u3066\u7814\u7a76\u304c\u3055\u308c\u3066\u3044\u305f\uff0e\u89e3\u6790\u3068\u3057\u3066\u793e\u4f1a\u7684\u6307\u6a19\u306e\u8133\u5bb9\u7a4d\u306e\u5206\u5e03\u3092\u660e\u793a\u7684\u306b\u30e2\u30c7\u30eb\u5316\u3059\u308b\u78ba\u7387\u7684\u968e\u5c64\u30e2\u30c7\u30ea\u30f3\u30b0\u3092\u7528\u3044\u3066\u3044\u305f\uff0e\u3053\u306e\u30e2\u30c7\u30eb\u306f\u4e88\u60f3\u3055\u308c\u308b\u793e\u4f1a\u4eba\u53e3\u7d71\u8a08\u5b66\u7684\u5909\u52d5\u3092\u8868\u3059\u305f\u3081\u306b\u5e74\u9f62\u3068\u6027\u5225\u306b\u6761\u4ef6\u3065\u3051\u3089\u308c\u3066\u304a\u308a\uff0c\u9577\u671f\u53ef\u5851\u6027\u52b9\u679c\u306e\u7d50\u679c\u3092\u53cd\u6620\u3057\u3066\u3044\u308b\uff0e\u305d\u306e\u7d50\u679c\uff0c\u793e\u4f1a\u7684\u306a\u8133\u306e\u7279\u5b9a\u306e\u9818\u57df\u306e\u4f53\u7a4d\u3068\u95a2\u9023\u3057\u3066\u3044\u308b\u3053\u3068\uff0c\u6a5f\u80fd\u7684\u7d50\u5408\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3092\u901a\u3057\u3066\u9055\u3044\u304c\u691c\u51fa\u3055\u308c\u305f\u3053\u3068\uff0c\u985e\u4f3c\u3057\u305f\u751f\u6d3b\u7fd2\u6163\u56e0\u5b50\u306e\u793e\u4f1a\u7684\u6307\u6a19\u306e\u4e2d\u3067\u8b58\u5225\u3067\u304d\u308b\u8907\u96d1\u306a\u793e\u4f1a\u5909\u6570\u306b\u304a\u3051\u308b\u6027\u95a2\u9023\u306e\u5f71\u97ff\u304c\u793a\u5506\u3055\u308c\u305f\u3068\u767a\u8868\u3057\u3066\u3044\u305f\uff0e\u79c1\u305f\u3061\u306e\u7814\u7a76\u5ba4\u3067\u3082\u30c6\u30fc\u30de\u3068\u3057\u3066\u7528\u3044\u3089\u308c\u3066\u3044\u308b\u3053\u3068\u304b\u3089\u6027\u5dee\u306b\u95a2\u3059\u308b\u7814\u7a76\u306f\u8eab\u8fd1\u3067\u3042\u308b\u3082\u306e\u306e\uff0c\u3053\u308c\u307b\u3069\u306e\u30d3\u30c3\u30b0\u30c7\u30fc\u30bf\u306b\u5bfe\u3057\u3066\u7814\u7a76\u304c\u884c\u308f\u308c\u3066\u3044\u308b\u3053\u3068\u306b\u9a5a\u304d\u307e\u3057\u305f\uff0e\u3057\u304b\u3057\u6700\u8fd1\u8abf\u67fb\u3092\u884c\u3063\u305f\u30b5\u30f3\u30d7\u30eb\u30b5\u30a4\u30ba\u8a2d\u8a08\u3092\u8003\u616e\u3059\u308b\u3068\uff0c\u30b5\u30f3\u30d7\u30eb\u30b5\u30a4\u30ba\u304c\u5927\u304d\u3044\u3053\u3068\u306e\u30c7\u30e1\u30ea\u30c3\u30c8\u304c\u5b58\u5728\u3059\u308b\u306e\u3067\u306f\u3068\u611f\u3058\u307e\u3057\u305f\uff0e\u666e\u6bb5\u306f\u8133\u6a5f\u80fd\u7814\u7a76\u306e\u767a\u8868\u3092\u4e2d\u5fc3\u306b\u8074\u8b1b\u3057\u3066\u3044\u308b\u306e\u3067\uff0c\u8133\u69cb\u9020\u7814\u7a76\u306e\u8a71\u306f\u975e\u5e38\u306b\u65b0\u9bae\u3067\u8208\u5473\u6df1\u3044\u3068\u611f\u3058\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aChanges in pRFs during perceptual filling-in of an artificial scotoma in humans<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Joana Carvalho, Remco Renken, Frans cornelissen<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Poster session<br \/>\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a<br \/>\nBackground: When the information extracted from a visual scene is incomplete, the visual system attempts to predict what is missing by extrapolating from nearby information. This is filling-in. Despite its clinical and scientific relevance, the neuronal mechanisms underlying filling-in are still ill-understood. Here, we used fMRI in combination with population receptive field (pRF) mapping to determine how and where in the visual hierarchy filling-in takes place. In our experiment, we measured pRF properties during and in the absence of perceptual filling-in, while observers viewed band-pass filtered textures on which artificial scotomas could be superimposed.<br \/>\nMethods: Seven observers (3 females; age-range: 26\u201332) with normal or corrected to normal vision were scanned using a Siemens Prisma 3T scanner. Retinotopic mapping was performed using:1) spatial frequency retinotopy (SFR) &#8211; the contrast between the carrier and the bar was only perceived on the basis of spatial frequency; and 2) SFR with four artificial scotomas superimposed (SFR_scot). The scotomas were centred at each quarter field at 5 deg eccentricity, the diameter of the scotomas was 3 deg, as depicted in figure 1A. High contrast localiser scans were used to obtain the locations of the artificial Scotoma Projection Zones (aSPZ). During scanning, participants were asked to perform a fixation task: they had to press a button each time the fixation point changed from red to green. For both SFR and SFR_scot, a single run consisted of 136 functional images (204 s). The pRF estimation was performed using the mrVista (VISTASOFT) Matlab toolbox and using a custom implementation of Bayesian pRF. Data was thresholded by retaining the pRF models that explained at least 15% of the variance.<br \/>\n&nbsp;<br \/>\nThe interaction between the presence of scotomas and the pRFs was modelled via a gain field (GF) model, Klein et al, 2014. A GF shifts the position and size of a pRF. The GF was centred at the edge of the scotoma. Its size was estimated by minimising the error between predicted and measured position shifts (SFR_scot vs SFR). Data was split in a training (50% of the data) and test set.<br \/>\nResults: By comparing the pRFs estimated using SFR and SFR_scot we observed a change in position towards the scotoma, figure 1A. This effect was present throughout the six visual areas tested and it scales with visual hierarchy, figure 1B. The change in position is minimum near the edge of the scotoma and gradually increases with the distance from the edge, figure 1D. pRFs originally within the aSPZ shift radially towards the edge of the scotoma, see figure 1E. Regarding the pRF size, no significant changes were measured between SFR and SFR_scot, see figure 1C.<br \/>\n&nbsp;<br \/>\nThe common GF explained on average 65 % of the measured position changes. GF sizes tend to increase with visual hierarchy.<br \/>\nConclusions:<br \/>\n&nbsp;<br \/>\nThe presence of artificial scotomas resulted in pRFs shifts towards the scotoma&#8217;s edge throughout the visual cortex. We interpret this as an evidence of perceptual filling-in. The pRF shifts of neurons within the aSPZ resulted from an extrapolation process- it enables the stimulation by spared portions of the visual field. Surprisingly, the pRFs outside the aSPZ were also attracted towards the scotoma&#8217;s edge. This process was modeled using a GF. This suggests that attention directed to the edge of the scotomas modulates the pRFs position. Furthermore our results are in agreement with previous studies which suggested that filling-in depends on local processes generated at the edge of the scotoma in early visual areas, Komatsu, H. (2006). However in contrast with previous findings, we did not find evidence for expansion of the receptive fields. We conclude that, in response to an artificial scotoma \u2013 and most likely filling-in \u2013 there is a short-term reorganization not only in the aSPZ but throughout the visual cortex.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u3053\u306e\u767a\u8868\u306f\uff0c \u30d5\u30a3\u30ea\u30f3\u30b0\u30a4\u30f3\u3068\u547c\u3070\u308c\u308b\u9762\u306e\u5f62\u6210\u306b\u95a2\u3059\u308b\u7814\u7a76\u3067\u3042\u308a\uff0c\u79c1\u304c\u7528\u3044\u3066\u3044\u308bKanizsa figure\u3067\u3082\u30d5\u30a3\u30ea\u30f3\u30b0\u30a4\u30f3\u306e\u8981\u7d20\u304c\u5b58\u5728\u3059\u308b\u305f\u3081\uff0c\u975e\u5e38\u306b\u8fd1\u3044\u7814\u7a76\u3067\u3057\u305f\u3002\u307e\u305f\u53bb\u5e74\u306eOHBM\u3067\u77e5\u3063\u305fpRF\u3092\u7528\u3044\u3066\u304a\u308a\u8996\u899a\u7684\u968e\u5c64\u306e\u3069\u306e\u9818\u57df\u3067\u3069\u3053\u306e\u88dc\u9593\u3092\u884c\u3063\u3066\u3044\u308b\u306e\u304b\u3092\u691c\u8a0e\u3057\u3066\u3044\u307e\u3057\u305f\uff0e\u3053\u306epRF\u306f\u8133\u9818\u57df\u3068\u8996\u91ce\u9818\u57df\u306e\u95a2\u9023\u3092\u898b\u308b\u3053\u3068\u304c\u3067\u304d\u308b\u89e3\u6790\u65b9\u6cd5\u3067\u3042\u308b\u3053\u3068\u306f\u7406\u89e3\u3057\u3066\u3044\u305f\u304c\uff0c\u5176\u306e\u305f\u3081\u306b\u5fc5\u8981\u306a\u8a2d\u8a08\u3084\u30c4\u30fc\u30eb\u306f\u7406\u89e3\u3067\u304d\u306a\u3044\u307e\u307e\u3067\u3042\u3063\u305f\uff0e\u3057\u304b\u3057\uff0cMATLAB\u4e0a\u3067\u4f7f\u7528\u53ef\u80fd\u306a\u30c4\u30fc\u30eb\u30dc\u30c3\u30af\u30b9\u3092\u6559\u3048\u3066\u3044\u305f\u3060\u304f\u3053\u3068\u304c\u3067\u304d\uff0c\u5b9f\u9a13\u8a2d\u8a08\u3068\u3057\u3066\u6ce8\u8996\u70b9\u306e\u8272\u304c\u5909\u5316\u3059\u308b\u3053\u3068\u3078\u306e\u53cd\u5fdc\u3092\u542b\u3093\u3067\u3044\u305f\u3053\u3068\u304b\u3089\uff0c\u5b9f\u9a13\u8a2d\u8a08\u306b\u5fc5\u8981\u306a\u8981\u7d20\u3092\u77e5\u308b\u3053\u3068\u304c\u3067\u304d\u305f\uff0e\u975e\u5e38\u306b\u8208\u5473\u6df1\u3044\u89e3\u6790\u65b9\u6cd5\u306a\u306e\u3067\u8abf\u67fb\u3057\u81ea\u8eab\u306e\u7814\u7a76\u306b\u3082\u4f7f\u3046\u3053\u3068\u304c\u3067\u304d\u308b\u306e\u3067\u3042\u308c\u3070\u6311\u6226\u3057\u3066\u307f\u305f\u3044\u3068\u601d\u3063\u3066\u3044\u308b\uff0e<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Neural substrates of human facial emotion processing: evidence from an ALE meta-analysis<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a shaoling peng, Xinyu Liang, Chenxi Zhao, Gaolang Gong<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Poster session<br \/>\nAbstruct \uff1a Background: There are six basic emotions: anger, fear, sad, happy, disgust and surprise[1]. To date, fMRI results regarding the human facial emotion processing for each of the basic emotions are inconsistent, possibly due to differences in subject group, stimulus materials, and experimental paradigms across studies. To address this, we here applied a coordinate-based activation likelihood estimation (ALE) meta-analysis [2, 3] to investigate neural substrates underlying human emotional face processing.<br \/>\nMethods: Candidate articles were from 3 sources: 1) PubMed dataset; 2) studies listed by other related meta-analysis studies[4-6] and 3)references of studies retrieved by 1 and 2. Eligible studies were selected using following inclusion criteria: (1) fMRI or PET studies; (2) healthy subjects with age older than 18; (3) whole brain analysis; (4) emotional facial stimuli as experimental condition, with neutral face as control condition; (5) includes activating, rather than deactivating coordinates.<br \/>\nOnce the eligible studies were obtained, coordinates were extracted and analyzed using the ALE meta-analytic tools, where a cluster-level FWE threshold of p &lt; 0.05 and a cluster-forming threshold of p &lt; 0.001 were applied as the significant level. We first located brain areas that are activated under each basic emotion by analyzing studies with neutral faces as control stimuli. Next, studies of all basic emotions are combined to investigate emotional face processing in general.<br \/>\nResults: 125 studies with a total of 2675 subjects were selected. The &#8220;surprise&#8221; condition was excluded, because of the very small number of eligible studies (i.e., 4).<br \/>\nThe ALE results for each basic emotion are shown in Figure 1. All basic emotions except &#8220;disgust&#8221; showed activation in the amygdala. Both &#8220;sad&#8221; and &#8220;happy&#8221; groups have only one significant cluster in the left amygdala, while &#8220;angry&#8221; and &#8220;fear&#8221; groups activated both the left and right amygdala. In addition to activation in the amygadala, watching &#8220;angry&#8221; faces also triggered activation in the right fusiform gyrus, while &#8220;fear&#8221; face processing triggered activation in the bilateral fusiform gyrus, right occipital lobe and left insula. In contrast, &#8220;Disgust&#8221; facial processing activated bilateral middle and inferior occipital gyrus.<br \/>\nComparing all emotional faces with neutral face, which putatively involves only the general emotional cognitive processing component, showed activation in the bilateral amygdala, bilateral fusiform gyrus, bilateral insula, bilateral thalamus, and bilateral middle and inferior occipital gyrus.<br \/>\nConclusions: Our results show shared and distinct patterns of activation when processing human faces with different basic emotions. As expected, the amygdala is pivotal to human emotional processing. Processing human emotional faces not only recruits brain regions involved in emotion processing, but also areas that are known to be engaged in face processing, e.g., the fusiform gyrus [7].<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u3053\u306e\u767a\u8868\u306f\uff0c \u30e1\u30bf\u30a2\u30ca\u30ea\u30b7\u30b9\u3092\u7528\u3044\u305f\u60c5\u52d5\u306e\u7814\u7a76\u3067\u3057\u305f\uff0ePubMed\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u7528\u3044\u3066\u30e1\u30bf\u30a2\u30ca\u30ea\u30b7\u30b9\u306e1\u3064\u3067\u3042\u308bALE\u3092\u7528\u3044\u3066\u60c5\u52d5\u306b\u95a2\u308f\u308b\u8133\u8ce6\u6d3b\u9818\u57df\u3092\u62bd\u51fa\u3057\u3066\u3044\u307e\u3057\u305f\uff0e125\u500b\u306e\u7814\u7a76\u5206\uff0c\u8a082675\u4eba\u5206\u306e\u30c7\u30fc\u30bf\u3092\u7528\u3044\u3066happy\uff0csad\uff0cdisgust\uff0cangry\uff0cfear\u306e\u5404\u60c5\u52d5\u306b\u3064\u3044\u3066\u691c\u8a0e\u3092\u884c\u3063\u3066\u3044\u307e\u3057\u305f\uff0e\u307e\u305f\u60c5\u52d5\u305d\u306e\u3082\u306e\u306b\u95a2\u9023\u3059\u308b\u8133\u9818\u57df\u306e\u7279\u5b9a\u3082\u884c\u306a\u3063\u3066\u3044\u307e\u3057\u305f\uff0e\u53c2\u8003\u6587\u732e\u3092\u9078\u629e\u3059\u308b\u57fa\u6e96\u3068\u3057\u3066\uff0cfMRI\u307e\u305f\u306fPET\u7814\u7a76\u3067\u3042\u308b\u3053\u3068\uff0c18\u6b73\u4ee5\u4e0a\u306e\u5065\u5e38\u4eba\u3067\u3042\u308b\u3053\u3068\uff0c\u5168\u8133\u5206\u6790\u3067\u3042\u308b\u3053\u3068\uff0c\u5b9f\u9a13\u6761\u4ef6\u3068\u3057\u3066\u306e\u611f\u60c5\u7684\u9854\u9762\u523a\u6fc0\u3001\u5bfe\u7167\u6761\u4ef6\u3068\u3057\u3066\u4e2d\u7acb\u9762\u3092\u7528\u3044\u3066\u3044\u308b\u3053\u3068\uff0c\u5ea7\u6a19\u304c\u6709\u52b9\u3067\u3042\u308b\u3053\u3068\u3068\u8a2d\u5b9a\u3057\u3066\u3044\u305f\uff0e\u305d\u306e\u7d50\u679c\uff0c\u7570\u306a\u308b\u57fa\u672c\u7684\u306a\u611f\u60c5\u3092\u6301\u3064\u4eba\u9593\u306e\u9854\u3092\u8a8d\u8b58\u3059\u308b\u51e6\u7406\u3092\u884c\u3046\u3068\u304d\u306e\u6d3b\u6027\u5316\u306f\u7570\u306a\u308b\u30d1\u30bf\u30fc\u30f3\u3092\u793a\u3057\u3066\u304a\u308a\uff0c\u6241\u6843\u4f53\u304c\u4eba\u9593\u306e\u611f\u60c5\u51e6\u7406\u306b\u3068\u3063\u3066\u975e\u5e38\u306b\u91cd\u8981\u3067\u3042\u308b\u3053\u3068\u304c\u308f\u304b\u3063\u305f\u3068\u767a\u8868\u3057\u3066\u3044\u307e\u3057\u305f\uff0e\u30e1\u30bf\u30a2\u30ca\u30ea\u30b7\u30b9\u3092\u8abf\u67fb\u3057\u3066\u3044\u305f\u3068\u304d\u306b\uff0cPICO\u3068\u3044\u3046\u9078\u629e\u57fa\u6e96\u306e\u5b58\u5728\u3092\u77e5\u3063\u305f\u304c\uff0c\u5b9f\u969b\u306b\u30e1\u30bf\u30a2\u30ca\u30ea\u30b7\u30b9\u3092\u7528\u3044\u305f\u7814\u7a76\u304c\u3069\u306e\u3088\u3046\u306b\u8ad6\u6587\u3092\u9078\u629e\u3057\u3066\u3044\u308b\u306e\u304b\u306b\u3064\u3044\u3066\u306f\u3042\u307e\u308a\u8abf\u67fb\u3067\u304d\u3066\u3044\u306a\u304b\u3063\u305f\u306e\u3067\uff0c\u4eca\u56de\u3053\u306e\u8074\u8b1b\u3092\u53d7\u3051\u3066\u7406\u89e3\u304c\u6df1\u307e\u3063\u305f\u3068\u601d\u3044\u307e\u3059\uff0e<br \/>\n\u53c2\u8003\u6587\u732e<br \/>\n1) OHBM2018 Annual meeting,<br \/>\nhttps:\/\/www.humanbrainmapping.org\/i4a\/pages\/index.cfm?pageID=3821<br \/>\n<strong>\u5b66\u4f1a\u53c2\u52a0\u5831\u544a\u66f8<\/strong><\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"158\"><strong>\u5831\u544a\u8005\u6c0f\u540d<\/strong><\/td>\n<td width=\"403\">\u53e4\u5bb6\u77e5\u6a39<\/td>\n<\/tr>\n<tr>\n<td width=\"158\"><strong>\u767a\u8868\u8ad6\u6587\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n<td width=\"403\">\u4f4e\u5468\u6ce2\u632f\u52d5\u632f\u5e45\u5f37\u5ea6\u306b\u57fa\u3065\u304f\u7791\u60f3\u6642\u8133\u72b6\u614b\u306e\u6a5f\u80fd\u7684\u8133\u5206\u5272<\/td>\n<\/tr>\n<tr>\n<td width=\"158\"><strong>\u767a\u8868\u8ad6\u6587\u82f1\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n<td width=\"403\">Functional brain parcellation of the meditative brain based on amplitude of low-frequency fluctuation<\/td>\n<\/tr>\n<tr>\n<td width=\"158\"><strong>\u8457\u8005<\/strong><\/td>\n<td width=\"403\">\u53e4\u5bb6\u77e5\u6a39, \u65e5\u548c\u609f, \u5ee3\u5b89\u77e5\u4e4b<\/td>\n<\/tr>\n<tr>\n<td width=\"158\"><strong>\u4e3b\u50ac<\/strong><\/td>\n<td width=\"403\">Organization of Human Brain Mapping<\/td>\n<\/tr>\n<tr>\n<td width=\"158\"><strong>\u8b1b\u6f14\u4f1a\u540d<\/strong><\/td>\n<td width=\"403\">25th Annual Meeting of the Organization of Human Brain Mapping<\/td>\n<\/tr>\n<tr>\n<td width=\"158\"><strong>\u4f1a\u5834<\/strong><\/td>\n<td width=\"403\">Auditorium Parco Della Musica<\/td>\n<\/tr>\n<tr>\n<td width=\"158\"><strong>\u958b\u50ac\u65e5\u7a0b<\/strong><\/td>\n<td width=\"403\">2019\/06\/09-2019\/06\/13<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<br \/>\n&nbsp;<\/p>\n<ol>\n<li>\u8b1b\u6f14\u4f1a\u306e\u8a73\u7d30<\/li>\n<\/ol>\n<p>2019\/06\/09\u304b\u30892019\/06\/13\u306b\u304b\u3051\u3066\uff0c\u30ed\u30fc\u30de\u306eAuditorium Parco Della Musica\u306b\u3066\u958b\u50ac\u3055\u308c\u305f25th Annual Meeting of the Organization of Human Brain Mapping\u306b\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u3053\u306e\u5b66\u4f1a\u306f\uff0c\u3053\u306e\u5b66\u4f1a\u306f\uff0cOrganization of Human Brain Mapping\u306b\u3088\u3063\u3066\u4e3b\u50ac\u3055\u308c\u305f\u56fd\u969b\u5b66\u4f1a\u3067\uff0c\u30d2\u30c8\u306e\u8133\u5730\u56f3\u4f5c\u6210\u306e\u305f\u3081\u306e\u6700\u65b0\u306e\u7814\u7a76\u306b\u3064\u3044\u3066\u5b66\u3076\u3053\u3068\u3092\u76ee\u7684\u306b\u958b\u50ac\u3055\u308c\u3066\u3044\u307e\u3059\uff0e\u3053\u306e\u5206\u91ce\u306e\u5c02\u9580\u5bb6\u3068\u30dd\u30b9\u30bf\u30fc\u767a\u8868\u3092\u901a\u3058\u3066\u30c7\u30a3\u30b9\u30ab\u30c3\u30b7\u30e7\u30f3\u3092\u884c\u3044\uff0c\u4e16\u754c\u4e2d\u306e\u7814\u7a76\u8005\u3068\u4ea4\u6d41\u3059\u308b\u3053\u3068\u304c\u53ef\u80fd\u3067\u3059\uff0e<br \/>\n\u79c1\u306f\u5168\u65e5\u7a0b\u306b\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u672c\u7814\u7a76\u5ba4\u304b\u3089\u306f\u4ed6\u306b\u5ee3\u5b89\u5148\u751f\uff0c\u65e5\u548c\u5148\u751f\uff0c\u5927\u585a\uff0c\u6749\u91ce\uff0c\u5965\u6751\uff08\u99ff\uff09\uff0c\u5c71\u672c\uff0c\u5409\u7530\uff0c\u98a8\u5442\u8c37\uff0c\u4e39\u304c\u53c2\u52a0\u3057\u307e\u3057\u305f\uff0e<\/p>\n<ol start=\"2\">\n<li>\u7814\u7a76\u767a\u8868\n<ul>\n<li>\u767a\u8868\u6982\u8981<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<p>\u79c1\u306f11\u65e5\u306b\u958b\u50ac\u3055\u308c\u305fPoster Session\u306b\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u767a\u8868\u306e\u5f62\u5f0f\u306f\u30dd\u30b9\u30bf\u30fc\u767a\u8868\u30672\u6642\u9593\u306e\u767a\u8868\u6642\u9593\u3068\u306a\u3063\u3066\u304a\u308a\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n\u4eca\u56de\u306e\u767a\u8868\u306f\uff0cMapping the brain state behavior during meditation in low dimensional feature space\u3067\u3059\uff0e\u4ee5\u4e0b\u306b\u6284\u9332\u3092\u8a18\u8f09\u81f4\u3057\u307e\u3059\uff0e<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\"><strong>Introduction<\/strong>: Mindfulness meditation has the positive effects as it improves well-being by reducing stress and improves concentration. However, novices without meditation experience find it difficult to arrive at the meditation state. The purpose of this study is to examine the neural basis that makes meditation successful. The region of interest (ROI) used to determine the meditation state depends on brain region segmentation. Therefore, we propose a method for evaluating the meditation state based on brain activity intensity during meditation. In previous studies, the methods employed to divide brain regions were not based on brain activity. because it was thought that brain activity during meditation could not be evaluated correctly. However, in this study, characteristics of activity during meditation were examined based on brain segmentation defined by fractional amplitude of low frequency<br \/>\nfluctuations (fALFF).<br \/>\n<strong>\u00a0<\/strong><br \/>\n<strong>Methods<\/strong>: Fig.1 shows a brain segmentation method that quantitatively evaluates the participants meditation state based on fALFF (Zou et al 2008). Twenty-nine novices of meditation (22.9 \u00b1 2.3 years; 6 females) and 7 practitioners of meditation (43.0 \u00b1 9.1 years; 1 females) participated in this experiment. They performed 5-minute breath-counting meditation after 5-minute rest periods in an fMRI scanner. Brain activity of all participant s during rest and meditation was used for analysis. First, the z score (zfALFF) of fALFF, which is an indicator of local spontaneous brain activity in each voxel of 7 meditation practitioners, was calculated, and the brain region during meditation was segmented by simple linear iterative clustering (SLIC) (Achanta et al 2012). Furthermore, the correlation between the questionnaire and the area where fALFF increased was calculated for the 29 beginners. Finally, brain functions in the automated anatomical labeling (AAL) in regions with high correlation were examined.<br \/>\n<strong>\u00a0<\/strong><br \/>\n<strong>Results<\/strong>: Fig.2 shows the results that division from the fALFF of the group of meditating practitioners. The number of divisions was 189 and it was shown that fALFF in 12 areas increased during meditation. The fALFF increased, there were left frontal sup (dlPFC), left Insula, left Cingulum Ant (ACC) and right Parietal Sup (SPL) all of which are areas of the task positive network (TPN) involved in meditation. In addition, Frontal Sup Medial (mPFC) and right Parietal Inf (IPL) were involved both of which are the areas of the default mode network (DMN) involved in mind wandering. Correlation between the questionnaire and the fALFF of the 29 beginners was calculated in the area where the fALFF increased. A significant correlation between fALFF and the questionnaire was shown in one area. Thus, participants who did not get distracted during meditation tended to show high activity intensity. Furthermore, a significant correlation was found in left SPL which is the region of the central executive network (CEN) involved in meditation. In addition, the left IPL and left precuneus were involved, which are areas of the DMN involved in mind wandering. From these results, it is suggested that participants with a high evaluation of the degree of realization of meditation have increased activity intensity of the TPN and the DMN during meditation. By changing segmentation based on the brain activity, the proposed method could determine brain-state during meditation. Thus, it is possible to determine the region of the brain involved during meditation.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<ul>\n<li>\u8cea\u7591\u5fdc\u7b54<\/li>\n<\/ul>\n<p>\u4eca\u56de\u306e\u8b1b\u6f14\u767a\u8868\u3067\u306f\uff0c\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u8cea\u7591\u3092\u53d7\u3051\u307e\u3057\u305f\uff0e\u672c\u767a\u8868\u3067\u306f\u8cea\u554f\u8005\u306e\u540d\u524d\u306f\u805e\u3044\u3066\u3044\u307e\u305b\u3093\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u6d3b\u52d5\u5f37\u5ea6\u3068\u306f\u3069\u3046\u3044\u3046\u6307\u6a19\u306a\u306e\u304b<\/strong><br \/>\n\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3059\u308b\u79c1\u306e\u56de\u7b54\u306f\u81ea\u767a\u7684\u306a\u8133\u6d3b\u52d5\u3092\u793a\u3059\u6307\u6a19\u3067\uff0c\u30d2\u30c8\u306f0.008\u304b\u30890.09Hz\u306e\u5468\u6ce2\u6570\u5e2f\u3067\u81ea\u767a\u7684\u306a\u8133\u6d3b\u52d5\u304c\u73fe\u308c\u308b\u3068\u3055\u308c\u3066\u304a\u308a\uff0c\u305d\u306e\u5468\u6ce2\u6570\u5e2f\u3067\u306e\u6d3b\u52d5\u304c\u5168\u5468\u6ce2\u6570\u5e2f\u306b\u305f\u3044\u3057\u3066\u3069\u306e\u304f\u3089\u3044\u542b\u307e\u308c\u3066\u3044\u308b\u304b\u3067\u7b97\u51fa\u3055\u308c\u307e\u3059\u3068\u56de\u7b54\u3044\u305f\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb<\/strong><strong>SLIC<\/strong><strong>\u3068\u306f\u4f55\u304b<\/strong><br \/>\n\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066SLIC\u3068\u306f\u8272\u3084\u7a7a\u9593\u7684\u7279\u5fb4\u91cf\u3092\u7528\u3044\u3066\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u3059\u308b\u624b\u6cd5\u3067\uff0c\u4e3b\u306b\u753b\u50cf\u51e6\u7406\u306a\u3069\u3067\u7528\u3044\u3089\u308c\u3066\u304a\u308a\uff0ck-means\u3092\u57fa\u306b\u4f5c\u6210\u3055\u308c\u305f\u624b\u6cd5\u3067\u3042\u308b\u3068\u56de\u7b54\u81f4\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u30d1\u30fc\u30bb\u30ec\u30fc\u30b7\u30e7\u30f3\u306f\u4f55\u3092\u7528\u3044\u3066\u884c\u3063\u305f\u306e\u304b<\/strong><br \/>\n\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\uff0cPython\u306escikit-learn\u3092\u7528\u3044\u3066\u884c\u3063\u3066\u3044\u308b\u3068\u56de\u7b54\u3044\u305f\u3057\u307e\u3057\u305f\uff0e<br \/>\nPython\u306b\u3042\u307e\u308a\u306a\u3058\u307f\u306e\u7121\u3044\u65b9\u3082\u304a\u308a\uff0c\u3082\u3063\u3068\u8a73\u3057\u304f\u8aac\u660e\u3059\u308b\u5fc5\u8981\u304c\u3042\u3063\u305f\u306a\u53cd\u7701\u3044\u305f\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306f\u898b\u306a\u3044\u306e\u304b<\/strong><br \/>\n\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306f\u5354\u8abf\u306e\u6307\u6a19\u306a\u306e\u3067\uff0c\u307e\u305a\u6d3b\u52d5\u304c\u5897\u52a0\u3057\u3066\u3044\u308b\u9818\u57df\u304c\u7791\u60f3\u6642\u306e\u7279\u5fb4\u3092\u793a\u3057\u3066\u3044\u308b\u3068\u8003\u3048\u308b\u305f\u3081\u672c\u5b9f\u9a13\u3067\u306f\u6d3b\u52d5\u3092\u307f\u3066\u3044\u307e\u3059\u3068\u56de\u7b54\u3044\u305f\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u3053\u306e\u8133\u5730\u56f3\u306f\u4f55\u306b\u826f\u3044\u306e\u304b<\/strong><br \/>\n\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3059\u308b\u79c1\u306e\u56de\u7b54\u306f\u4eca\u56de\u306e\u8133\u5730\u56f3\u3067\u5b9f\u8df5\u8005\u306b\u5171\u901a\u3057\u3066\u6d3b\u52d5\u3059\u308b\u9818\u57df\u3092\u898b\u3064\u3051\u308b\u3053\u3068\u304c\u3067\u304d\u305f\u306e\u3067\uff0c\u5c06\u6765\u306f\u3053\u306e\u3088\u3046\u306a\u9818\u57df\u306e\u7528\u3044\u3066\u521d\u5fc3\u8005\u306e\u8133\u72b6\u614b\u306e\u30d5\u30a3\u30fc\u30c9\u30d0\u30c3\u30af\u3092\u884c\u3044\u305f\u3044\u3068\u8003\u3048\u3066\u3044\u308b\u3068\u56de\u7b54\u3044\u305f\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u30d1\u30fc\u30bb\u30ec\u30fc\u30b7\u30e7\u30f3\u3059\u308b\u969b\u306e\u8133\u306e\u30c6\u30f3\u30d7\u30ec\u30fc\u30c8\u306f\u4f55\u3092\u4f7f\u7528\u3057\u3066\u3044\u308b\u306e\u304b<\/strong><br \/>\nAAL\u3067\u9818\u57df\u304c\u5272\u308a\u5f53\u3066\u3089\u308c\u3066\u3044\u308b\u8133\u30c6\u30f3\u30d7\u30ec\u30fc\u30c8\u3092\u7528\u3044\u3066\u30d1\u30fc\u30bb\u30ec\u30fc\u30b7\u30e7\u30f3\u3092\u884c\u3063\u3066\u3044\u308b\u3068\u56de\u7b54\u3044\u305f\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb<\/strong><strong>fMRI<\/strong><strong>\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u306b\u3064\u3044\u3066<\/strong><br \/>\n<strong>\u3000<\/strong>\u3053\u306e\u8cea\u554f\u306f\u3046\u307e\u304f\u805e\u304d\u53d6\u308c\u306a\u304b\u3063\u305f\u306e\u3067\u3059\u304c\uff0cTR\u306b\u5bfe\u3059\u308b\u30b9\u30e9\u30a4\u30b9\u306e\u539a\u307f\u304c\u8584\u3044\u3068\u3044\u3046\u3088\u3046\u306a\u30a2\u30c9\u30d0\u30a4\u30b9\u3068\uff0c\u3084\u306f\u308a1.5T\u3067\u306f\u3044\u307e\u3044\u3061\u3068\u3044\u3046\u3088\u3046\u306a\u3054\u6307\u6458\u3092\u3044\u305f\u3060\u304d\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb<\/strong><strong>ROI<\/strong><strong>\u304b\u3089\u4f55\u304c\u8a00\u3048\u308b\u306e\u304b<\/strong><br \/>\n<strong>\u3000<\/strong>\u3053\u306e\u9818\u57df\u306f\u3044\u305a\u308c\u7791\u60f3\u6642\u306e\u795e\u7d4c\u57fa\u76e4\u306e\u89e3\u660e\u306b\u5f79\u7acb\u3064\u9818\u57df\u3067\u3042\u308b\u3068\u56de\u7b54\u3044\u305f\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb<\/strong><strong>Dice<\/strong><strong>\u4fc2\u6570\u3068\u306f\u306a\u306b\u304b\uff0e\u3069\u306e\u3088\u3046\u306b\u6bd4\u8f03\u3057\u3066\u3044\u308b\u306e\u304b\uff0e<\/strong><br \/>\n\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3059\u308b\u56de\u7b54\u3068\u3057\u3066\uff0cDice\u4fc2\u6570\u306f\u3042\u308b\u9818\u57df\u306e\u30dc\u30af\u30bb\u30eb\u306e\u7a7a\u9593\u7684\u306a\u91cd\u306a\u308a\u3092\u6e2c\u308b\u6307\u6a19\u3067\u3059\uff0e\u3068\u56de\u7b54\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u3069\u306e\u3088\u3046\u306b\u6bd4\u8f03\u3057\u3066\u3044\u308b\u304b\u3068\u3044\u3046\u3068\uff0c\u5168\u9818\u57df\u306edice\u4fc2\u6570\u3092\u88ab\u9a13\u8005\u9593\u3067\u7b97\u51fa\u3057\uff0c\u9818\u57df\u306edice\u4fc2\u6570\u304c\u9ad8\u3044\u4e0a\u4f4d5%\u306e\u9818\u57df\u3092\u672c\u7814\u7a76\u306eROI\u3068\u5b9a\u7fa9\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<ul>\n<li>\u611f\u60f3<\/li>\n<\/ul>\n<p>\u30dd\u30b9\u30bf\u30fc\u30bb\u30c3\u30b7\u30e7\u30f3\u306e\u59cb\u3081\u306f\u805e\u304d\u306b\u6765\u3066\u3082\u3089\u3048\u308b\u4eba\u304c\u5c11\u306a\u304b\u3063\u305f\u306e\u3067\u3059\u304c\uff0c\u6642\u9593\u304c\u7d4c\u3064\u306b\u3064\u308c\u3066\u591a\u304f\u306e\u4eba\u306b\u6765\u3066\u3044\u305f\u3060\u304d\u304a\u3082\u3057\u308d\u3044\u3068\u8a00\u3063\u3066\u3044\u305f\u3060\u3051\u307e\u3057\u305f\uff0e\u81ea\u5206\u306e\u7814\u7a76\u306e\u30a2\u30d7\u30ed\u30fc\u30c1\u65b9\u6cd5\u304c\u65b0\u3057\u304f\u8208\u5473\u3092\u6301\u3063\u3066\u3082\u3089\u3048\u308b\u5185\u5bb9\u3060\u3068\u81ea\u4fe1\u3092\u5f97\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3057\u305f\uff0e\u3057\u304b\u3057\uff0c\u82f1\u8a9e\u3067\u306e\u8cea\u554f\u306b\u306f\u5185\u5bb9\u3092\u7406\u89e3\u3059\u308b\u307e\u3067\u6642\u9593\u304c\u304b\u304b\u3063\u3066\u3057\u307e\u3044\u82e6\u6226\u3057\u307e\u3057\u305f\uff0e\u3067\u3059\u304c\uff0c\u81ea\u5206\u306e\u56de\u7b54\u306b\u7d0d\u5f97\u3057\u3066\u3044\u305f\u3060\u3051\u308b\u5834\u9762\u3082\u3042\u308a\u3001\u53c2\u52a0\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u826f\u304b\u3063\u305f\u3068\u611f\u3058\u3066\u3044\u307e\u3059\uff0e\u540c\u6642\u306b\u3082\u3063\u3068\u9ad8\u3044\u30ec\u30d9\u30eb\u3067\u30c7\u30a3\u30b9\u30ab\u30c3\u30b7\u30e7\u30f3\u3092\u3059\u308b\u305f\u3081\u306b\u52c9\u5f37\u304c\u5fc5\u8981\u3067\u3042\u308b\u3068\u5f37\u304f\u611f\u3058\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<ol start=\"3\">\n<li>\u8074\u8b1b<\/li>\n<\/ol>\n<p>\u4eca\u56de\u306e\u8b1b\u6f14\u4f1a\u3067\u306f\uff0c\u4e0b\u8a18\u306e3\u4ef6\u306e\u767a\u8868\u3092\u8074\u8b1b\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aThe effects of Vipassana meditation on brain network: a magnetoencephalography study.<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Anna Lardone, Marianna Liparoti, Pierpaolo Sorrentino, Rosaria Rucco, Fabio Baselice, Francesca Jacini, Matteo Pesoli, Arianna Polverino, Roberta Minino, Emahnuel Troisi Lopez, Pietro Cipresso, Giuseppe Sorrentino, Laura Mandolesi<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Posters Session<br \/>\nAbstract\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a<br \/>\n<strong>Introduction:<\/strong> In recent decades there has been growing attention to meditation. This interest is probably due to the increasing number of evidences showing how the practice of meditation may improve higher level of well-being and specific cognitive functions. Neuroimaging and electroencephalographic studies have shown that the brain connectivity of meditators changes as they meditate as well as in the resting-state. Furthermore, the constant practice of meditation causes widespread long-term changes in structural connectivity, suggesting that meditation might induce neural plasticity. Main areas involved are right orbito-frontal cortex, anterior cingulate cortex, right anterior insula, left inferior temporal gyrus, right hippocampus and amygdala (Newberg et al. 2014).<br \/>\nThe aim of our study is to compare the resting-state brain activity of mindfulness meditators with more than one year of experience and meditation-na\u00efve controls recorded using a magnetoencephalography (MEG) in order to clarify the neural circuits that benefits of mindfulness meditation.<br \/>\n&nbsp;<br \/>\n<strong>Methods:<\/strong> Twenty-six meditation practitioners, and twenty-nine controls matched for age, gender, education, race, and handedness were recruited. All participants were right-handed adults, had no general or mental illness, and were native Italian speakers. Meditators were trained to Vipassana Meditation (mindfulness meditation) and had an average of 6,41 (SE = 1,489) years of meditation experience. Both groups underwent five minutes of closed eyes resting-state MEG acquisition. The row data were cleaned from environmental noise, physiological and system related artifacts using Principal Component Analysis, Independent Component Analysis and visual inspection (Figure 1 a). Subsequently, the time series of neuronal activity were reconstructed in ninety regions of interests (ROIs) using the beamformer based on a template MRI (Figure 1 b) and then filtered in the classical frequency bands (delta, theta, alpha, beta, gamma) (Figure 1 c). To estimate the connectivity between ROIs, we calculated the Phase Lag Index (PLI) and the Minimum Spanning Tree (MST) was reconstructed (Figure 1 d &amp; e) Finally, we compared topological metrics in meditators and non-meditators using permutation testing corrected for multiple comparisons through false discovery rate.<br \/>\n&nbsp;<br \/>\n<strong>Results:<\/strong> Our findings reveal differences between the two groups in the resting-state condition. Compared to the non-meditator group, meditators show a higher degree (P = 0.009) in the right hippocampus in the theta band (Figure 2). The degree represents the number of connections incident upon a given node.<br \/>\n&nbsp;<br \/>\n<strong>Conclusions:<\/strong> Previous studies have suggested that the right hippocampus is engaged during the creation of future events and in spatial memory processes (Bohbot et al. 1998). Furthermore, navigation abilities fully rely on the metric of theta oscillation (Buzs\u00e1ki 2005). On this evidence the result of increased activation of the right hippocampus in theta band in meditators during resting state condition, supports the real possibility that meditation causes changes in brain networks and may improve specific cognitive functions, as spatial abilities and prospective memory. These changes could be seen in the perspective of treating degenerative pathologies characterized by alteration in the hippocampal areas and functional deficit in spatial orienting, such as Alzheimer disease.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u3053\u306e\u767a\u8868\u306f\uff0cMEG\u3092\u7528\u3044\u3066\u7791\u60f3\u6642\u306e\u795e\u7d4c\u57fa\u76e4\u3092\u89e3\u660e\u3059\u308b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u30c7\u30fc\u30bf\u306e\u6b21\u5143\u524a\u6e1b\u65b9\u6cd5\u3068\u3057\u3066PCA\u3084ICA\u3092\u884c\u3044\uff0cMinimum Spanning Tree\uff08MST\uff09\u3092\u7528\u3044\u3066\u72b6\u614b\u306e\u63a8\u5b9a\u3092\u884c\u3063\u3066\u3044\u307e\u3057\u305f\uff0eMST\u306f\u4ee5\u524d\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u52c9\u5f37\u4f1a\u3067\u3082\u53d6\u308a\u4e0a\u3052\u305f\u624b\u6cd5\u3060\u3063\u305f\u305f\u3081\uff0c\u4eca\u5f8c\u79c1\u305f\u3061\u306e\u7814\u7a76\u306b\u3082\u53c2\u8003\u306b\u3067\u304d\u308b\u3088\u3046\u306b\u52c9\u5f37\u3057\u305f\u3044\u3068\u8003\u3048\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Notizia dell&#8217;AFNI! AFNI now makes templates from your subjects easily!<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a John Lee, Paul Taylor, Robert Cox, Daniel Glen<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Posters Session<br \/>\nAbstract\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a<br \/>\n<strong>Introduction:<\/strong> MRI and FMRI studies have standardized on just a few brain templates over the years. Commonly these have been limited to just the well known individual template, the N27 brain template from 27 scans of a single individual, or one of several MNI human brain templates, such as the 2009 version made by combining 152 (mostly young adult) subjects. Additional contributions have occasionally been made for other age groups, like pediatric templates[1], or other species, like the macaque with similar individual (D99)[2] and group-based templates (NMT)[3]. Templates serve two purposes-correspondence+reference. Group templates have an advantage over a selected individual template because idiosyncratic structures are avoided, and common features are extracted. Earlier group templates were limited by their alignment algorithms to simple affine transformations, and a mean computed across subjects. The result was a blurry combination retaining only the most major features of a brain. Recent implementations make use of nonlinear warping that dramatically improves feature correspondence across subjects.<br \/>\nHere we introduce a new AFNI tool, make_template_dask.py, for the creation of group-based templates, across a desired group or species. Importantly, the program uses Dask [4] to efficiently run this intensive, multilevel processing efficiently on any computer setup (single CPU, multicore or cluster). Much of the processing repeats similar processing for every subject in the group, and, consequently, is &#8220;embarrassingly parallel&#8221;. The script makes use of existing AFNI tools and wraps them in the Dask parallelization toolbox.<br \/>\n&nbsp;<br \/>\n<strong>Methods:<\/strong> The script does a series of steps for each subject and then for the means across subjects:Unifize (i.e., bias correct) subjects to standardize intensities<br \/>\nSkullstrip Align centers to some starting base template or subject Rigidly align (rigid component of an affine alignment) to the initial template Compute mean across all rigidly aligned subjects -&gt; rigid template Affine align all rigid align subjects to rigid template Compute mean across all affinely aligned subjects -&gt; affine template<br \/>\nNonlinearly align all affinely aligned subjects to affine template in large voxel neighborhood (patch size 101mm) Compute mean across all nonlinearly aligned subjects -&gt; nl0 template Repeat nonlinear alignment and mean computation of two previous steps four more times with successively finer voxel patch sizes (49, 23, 13, 9 mm). -&gt; nl1,2,3,4 templates Dask provides a convenient and scalable framework for using Python scripts, parallelizing the processing into multiple threads to be executed on a variety of platforms. make_template_dask.py is written to be run in parallel on a server or desktop with many CPUs or on a computing cluster like the NIH Biowulf SLURM cluster [5] While Dask simplifies this task, there are still inherent issues regarding individual commands that are written to use specific filenames with each instance. This problem was solved with parallelization by subject and processing each subject&#8217;s data in a separate directory, until individual results are brought together for each averaging step. There are additional options for anisotropic smoothing,unifizing of the nonlinear mean results, and for skipping any of the steps. There are further options to allow for restarting the script with already existing datasets in case of server or cluster failures or timeouts.<br \/>\n&nbsp;<br \/>\n<strong>Results:<\/strong> Results will be shown for test group of 15 subjects from OpenFMRI, Indian brain template groups, Toddler age group with example workflow plots.<br \/>\n&nbsp;<br \/>\n<strong>Conclusions:<\/strong> make_template_dask.py simplifies the process of making group templates with a command line interface with an efficient use of compute services, largely independent of number of subjects. This script can be applied to a variety of subject groups and compute platforms. New templates can be created for groups based on age, geography, species&#8230; with the goal of making these templates more relevant to the research study.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u3053\u306e\u767a\u8868\u306ffMRI\u7814\u7a76\u3067\u7528\u3044\u308b\u65b0\u3057\u3044\u8133\u30c6\u30f3\u30d7\u30ec\u30fc\u30c8\u3092\u4f5c\u6210\u3057\u305f\u7814\u7a76\u3067\u3057\u305f\uff0e\u3053\u308c\u307e\u3067\u591a\u304f\u306e\u7814\u7a76\u3067\u7528\u3044\u3089\u308c\u3066\u3044\u308bMNI\u30c6\u30f3\u30d7\u30ec\u30fc\u30c8\u306a\u3069\u306f\u5c11\u4eba\u6570\u304b\u3064\u5e74\u9f62\u5c64\u3092\u504f\u3063\u305f\u3082\u306e\u304c\u591a\u304f\uff0c\u3053\u306e\u7814\u7a76\u306f\u5b50\u4f9b\u306e\u8133\u304b\u3089\u4e00\u822c\u7684\u306b\u4f7f\u7528\u53ef\u80fd\u306a\u30c6\u30f3\u30d7\u30ec\u30fc\u30c8\u3092\u4f5c\u6210\u3057\u3066\u3044\u307e\u3057\u305f\uff0e\u5185\u5bb9\u3082\u8208\u5473\u6df1\u304b\u304b\u3063\u305f\u306e\u3067\u3059\u304c\uff0c\u5927\u304d\u304f\u66f8\u304b\u308c\u305f\u201dOne(brain) for All, All(brains) for One \u201d\u3068\u3044\u3046\u30ad\u30e3\u30c3\u30c1\u2015\u306a\u30d5\u30ec\u30fc\u30ba\u304c\u76ee\u3092\u60f9\u304d\u307e\u3057\u305f\uff0e\u307e\u305a\u306f\u4eba\u306b\u6ce8\u76ee\u3057\u3066\u3082\u3089\u3048\u308b\u3088\u3046\u306a\u30dd\u30b9\u30bf\u30fc\u4f5c\u6210\u306e\u91cd\u8981\u6027\u3092\u5b66\u3073\u4eca\u5f8c\u30dd\u30b9\u30bf\u30fc\u3092\u4f5c\u6210\u3059\u308b\u969b\u306b\u306f\u53c2\u8003\u306b\u3057\u305f\u3044\u3068\u8003\u3048\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Assessing multisite reproducibility of parcellation methods using traveling subjects<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Giuseppe Lisi, Ayumu Yamashita, Noriaki Yahata, Takashi Itahashi, Takashi Yamada, Naho Ichikawa, Masahiro Takamura, Yujiro Yoshihara, Akira Kunimatsu, Naohiro Okada, Hirotaka Yamagata, Koji Matsuo, Ryu-ichiro Hashimoto, Go Okada, Yuki Sakai, Jin Narumoto, Yasuhiro Shimada, Kiyoto Kasai, Nobumasa Kato, Hidehiko Takahashi, Yasumasa Okamoto, Saori Tanaka, Okito Yamashita, Hiroshi Imamizu, Mitsuo Kawato, Jun Morimoto<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Posters Session<br \/>\nAbstract\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a<br \/>\n<strong>Introduction:<\/strong> When collecting large neuroimaging data associated with brain disorders, images must be acquired from multiple sites (e.g. hospitals) because of the limited capacity of a single site. Site differences represent a great barrier, making data harmonization necessary (Yamashita et al. 2018). This is especially true when considering the problem of building a connectivity-based classifier that generalizes from one site to another (Yahata et al. 2016). In this context, brain parcellation is a critical step, since brain regions computed (e.g. by independent component analysis, ICA) on data from site A, may not generalize well to data from site B. Previous works have systematically analyzed the impact of different parcellation methods, by comparing either classification accuracy (Dadi et al 2018), or reproducibility of parcellations across scans (using the same scanner) within a subject (Arslan et al. 2018). In this study, we take one step further, by investigating the reproducibility of common parcellation methods across sites using a travelling subject dataset.<br \/>\n<strong>Methods:<\/strong> We use the travelling-subject dataset collected in Yamashita et al. 2018. Nine healthy participants were scanned at each of 12 different sites in Japan, 3-4 times each, producing a total of 411 scan sessions. Data were preprocessed according to the procedure described in Yamashita et al. 2018. We take into consideration all the parcellation methods in Dadi et al. 2018, excluding the structural and functional pre-computed atlases. As a result, we include in the analysis two linear decomposition methods (i.e. Canonical ICA and Dictionary Learning) and two clustering methods (K-means and Ward clustering) (Abraham et al. 2014). The number of parcellations is set to 100 for every method, as Dadi et al. 2018 found it to be a sufficient number for good prediction. Then regions are extracted by breaking out clusters in their connected components. During this procedure, we remove spurious regions of size &lt; 1500mm3 (Dadi et al. 2018).We apply the above four methods to the whole group of subjects, separately for each site, computing a total of 48 (4 methods x 12 sites) different parcellations. Separately for each parcellation method, we compare the reproducibility across sites using parcellation similarity scores (Dice Similarity and Joined Dice Similarity, Arslan et al. 2018) for each pairwise combination of sites.<br \/>\n&nbsp;<br \/>\n<strong>Results:<\/strong> The Dice Similarity scores (Fig. 1a) show that CanICA produces the most reproducible parcellations across sites, followed by Dictionary Learning, Ward Clustering and k-means clustering. When using the Joined Dice Similarity score (Fig. 1b), Dictionary Learning gets closer to CanICA. These quantitative results are confirmed by visual inspection. For example, Fig. 2 shows a better similarity across sites in the regions extracted by CanICA, compared to k-means.<br \/>\n&nbsp;<br \/>\n<strong>Conclusions:<\/strong> Linear decomposition methods provide better reproducibility scores. These methods also achieved the best classification performance on the benchmarking study by Dadi et al. 2018. On the other hand, Ward and k-means clustering show relatively poor reproducibility scores, which is in agreement with Arslan et al. 2018. As suggested in Dadi et al. 2018, the linear decomposition methods provide soft assignments that capture uncertainty, allowing to define overlapping regions. It should be noted that the dice scores of our dataset are generally lower than those reported in Arslan et al. 2018, suggesting that scanner differences in the travelling subject dataset add uncertainty that should be taken into account in order to build truly generalizable classifiers. In future, we will analyze additional measures such as homogeneity (i.e. correlation among the vertices of a region), that would allow the inclusion of pre-computed atlases in the analysis. This study provides the basis for building parcellation methods that are robust against site differences.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u3053\u306e\u767a\u8868\u3067\u306f\uff0c\u6700\u826f\u306a\u8133\u5206\u5272\u624b\u6cd5\u306b\u3064\u3044\u3066\u306e\u7814\u7a76\u304c\u884c\u308f\u308c\u3066\u304a\u308a\uff0c\u30d1\u30fc\u30bb\u30ec\u30fc\u30b7\u30e7\u30f3\u306fK-means\u3084canica\u306a\u3069\u306e\u624b\u6cd5\u3092\u7528\u3044\u3066\u884c\u3063\u3066\u3044\u307e\u3057\u305f\uff0eK-means\u306f\u3044\u307e\u3044\u3061\u3068\u3044\u3046\u7d50\u679c\u304c\u793a\u3055\u308c\u3066\u3044\u305f\u3053\u3068\u304b\u3089\uff0ck-means\u3092\u57fa\u306b\u3057\u3066\u3044\u308bSLIC\u306b\u3064\u3044\u3066\u3082\u3046\u5c11\u3057\u7406\u89e3\u3092\u6df1\u3081\u308b\u5fc5\u8981\u304c\u3042\u308b\u3068\u611f\u3058\u307e\u3057\u305f\uff0e\u30a2\u30c8\u30e9\u30b9\u9593\u985e\u4f3c\u5ea6\u306e\u8a55\u4fa1\u65b9\u6cd5\u3068\u3057\u3066\uff0cdice\u4fc2\u6570\u306e\u307b\u304b\u306b\u3082\u3055\u307e\u3056\u307e\u306a\u6307\u6a19\u3092\u7528\u3044\u3066\u884c\u3063\u3066\u304a\u308a\uff0c\u53c2\u8003\u306b\u306a\u308b\u3082\u306e\u304c\u591a\u3044\u3068\u611f\u3058\u307e\u3057\u305f\uff0e<br \/>\n<strong>\u5b66\u4f1a\u53c2\u52a0\u5831\u544a\u66f8<\/strong><\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"147\"><strong>\u00a0<\/strong><br \/>\n<strong>\u5831\u544a\u8005\u6c0f\u540d<\/strong><br \/>\n<strong>\u00a0<\/strong><\/td>\n<td width=\"373\">&nbsp;<br \/>\n\u98a8\u5442\u8c37\u4f91\u5e0c<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u767a\u8868\u8ad6\u6587\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n<td width=\"373\">\u4f4e\u30e9\u30f3\u30af\u8fd1\u4f3c\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u624b\u6cd5\u3092\u7528\u3044\u305f\u6a5f\u80fd\u7684\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u69cb\u9020\u306e\u63a8\u5b9a<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u767a\u8868\u8ad6\u6587\u82f1\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n<td width=\"373\">Extracting functional network structures using low-rank matrix factorization-based matrix clustering<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u8457\u8005<\/strong><\/td>\n<td width=\"373\">\u98a8\u5442\u8c37\u4f91\u5e0c, \u65e5\u548c\u609f\uff0c\u8c37\u5ca1\u5065\u8cc7\uff0c\u5bbf\u4e45\u6d0b\uff0c\u5ee3\u5b89\u77e5\u4e4b<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u4e3b\u50ac<\/strong><\/td>\n<td width=\"373\">Organization for human brain mapping<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u8b1b\u6f14\u4f1a\u540d<\/strong><\/td>\n<td width=\"373\">25th Annual Meeting of the Organization of Human Brain Mapping<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u4f1a\u5834<\/strong><\/td>\n<td width=\"373\">auditorium parco della musica<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u958b\u50ac\u65e5\u7a0b<\/strong><\/td>\n<td width=\"373\">2019\/06\/09-2019\/06\/13<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<br \/>\n&nbsp;<\/p>\n<ol>\n<li>\u8b1b\u6f14\u4f1a\u306e\u8a73\u7d30<\/li>\n<\/ol>\n<p>2019\/6\/9\u304b\u30892019\/6\/13\u306b\u304b\u3051\u3066\uff0c\u30a4\u30bf\u30ea\u30a2\u306eauditorium parco della musica\u306b\u3066\u958b\u50ac\u3055\u308c\u307e\u3057\u305f25th Annual Meeting of the Organization of Human Brain Mapping\u306b\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e2019 OHBM annual meeting\u306f\uff0cOrganization for human brain mapping\u306b\u3088\u3063\u3066\u4e3b\u50ac\u3055\u308c\u305f\u56fd\u969b\u4f1a\u8b70\u3067\uff0c\u30d2\u30c8\u306e\u8133\u7d44\u7e54\u304a\u3088\u3073\u8133\u6a5f\u80fd\u306e\u30de\u30c3\u30d4\u30f3\u30b0\u306b\u95a2\u3059\u308b\u7814\u7a76\u306b\u643a\u308f\u308b\u69d8\u3005\u306a\u80cc\u666f\u306e\u7814\u7a76\u8005\u3092\u96c6\u3081\uff0c\u3053\u308c\u3089\u306e\u79d1\u5b66\u8005\u306e\u30b3\u30df\u30e5\u30cb\u30b1\u30fc\u30b7\u30e7\u30f3\uff0c\u304a\u3088\u3073\u6559\u80b2\u3092\u4fc3\u9032\u3059\u308b\u3053\u3068\u3092\u76ee\u7684\u306b\u958b\u50ac\u3055\u308c\u3066\u3044\u307e\u3059\uff0e\u79c1\u306f\u5168\u65e5\u7a0b\u306b\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u672c\u7814\u7a76\u5ba4\u304b\u3089\u306f\u4ed6\u306b\u5ee3\u5b89\u5148\u751f\uff0c\u65e5\u548c\u5148\u751f\uff0cM2\u306e\u5b66\u751f\u3068\u3057\u3066\u53e4\u5bb6\u3055\u3093\uff0c\u5927\u585a\u3055\u3093\uff0c\u5965\u6751\uff08\u99ff\uff09\u3055\u3093\uff0c\u6749\u91ce\u3055\u3093\uff0c\u5c71\u672c\u3055\u3093\uff0c\u5409\u7530\u3055\u3093M1\u306e\u5b66\u751f\u3068\u3057\u3066\u4e39\u304c\u53c2\u52a0\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<ol start=\"2\">\n<li>\u7814\u7a76\u767a\u8868\n<ul>\n<li>\u767a\u8868\u6982\u8981<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<p>\u79c1\u306f10\u65e5\u304b\u308913\u65e5\u306e4\u65e5\u9593\u958b\u50ac\u3055\u308c\u305fPoster Session\u306e\u3046\u3061\uff0c12\u65e5\u306ePoster Session\u306b\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e \u767a\u8868\u5f62\u5f0f\u306f\u30dd\u30b9\u30bf\u30fc\u767a\u8868\u3067\uff0c1\u6642\u9593\u306e\u767a\u8868\u6642\u9593\u3068\u306a\u3063\u3066\u304a\u308a\u307e\u3057\u305f\uff0e<br \/>\n\u4eca\u56de\u306e\u767a\u8868\u306f\uff0cExtracting functional network structures using low-rank matrix factorization-based matrix clustering\u3067\u3059\uff0e\u4ee5\u4e0b\u306b\u6284\u9332\u3092\u8a18\u8f09\u81f4\u3057\u307e\u3059\uff0e<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\"><strong>Background<\/strong>: Working memory (WM), which is a temporary storage system that processes information, is necessary for daily life (A.D. Baddeley 2000). In the present study, the influence of the WM load on the changes in functional connectivity (FC) networks was examined. Given the high-dimensionality of the brain function data, it is difficult to find a characteristic pattern among participants using such as the arithmetic average because the variability of the data is high. In contrast, an analysis involving dimensionality reduction is necessary to determine latent feature patterns. Therefore, a method for extracting a characteristic function network structure which changes depending on the WM load based on low-rank factorization-based matrix clustering (D.J. Simon and J. Abell 2010) was proposed and applied to fMRI data measured during an N-back task.<br \/>\n&nbsp;<br \/>\n<strong>Methods<\/strong>: A total of 29 healthy adults (22.3 \u00b1 0.17 years; 10 females, 19 males) performed N-back (N = 1, 2, 3) tasks in an fMRI scanner. Furthermore, the FC matrix between the brain areas was calculated for all the 116 regions defined by the<br \/>\nautomated anatomical labeling (AAL). As illustrated in Fig.1, a method was proposed to estimate a single FC matrix expressing the brain functional network characteristic of plural FC matrices under the same experimental condition through low-rank matrix factorization-based matrix clustering. The squared error between the deriving matrix and the input matrices was minimized using a numerical optimization algorithm. The method enabled the extraction of the latent features from the data in a high dimensional space. Additionally, our method performed clustering of the brain regions in the derivation process of the matrix factorization so as to extract the module structure existing in the network represented by the derived low-rank matrix.<br \/>\n&nbsp;<br \/>\n<strong>Results<\/strong>: The proposed method was performed on the FC matrix at each N-back load of 29 participants, while a single representative FC matrix associated with each load was estimated. Fig. 2 indicates the brain function network structure estimated for the 1-, 2-, and 3-back tasks. Specifically, the FC of the prefrontal cortex was confirmed in both the 1- and 2-back tasks, whereas other brain areas involved in the WM activity, including the left superior frontal gyrus (SFG), left middle frontal gyrus (MFG), the left\/right medial frontal gyrus (SFGmed), functioned as the same module in the 1-back task, and FC that formed a single module in the area of somatosensory cortex was also confirmed. In contrast, in addition to the right SFG, left\/right MFG, the FC of the left\/right opecular part of inferior frontal gyrus (IFGoper) and left\/right triangular part of inferior frontal gyrus (IFGtriang) belonging to the Broca&#8217;s area was also confirmed in the 2-back task. Moreover, the left\/right inferior parietal lobule (IPL) and the left\/right supramarginal gyrus (SMG) were confirmed to present the same module to the prefrontal cortex (PFC). Finally, the FC of the brain regions mainly related to the visual cortex (VC) and cerebellum was confirmed in the 3-back task, while that of the PFC was not estimated. The regions belonging to multiple clusters were derived for all WM loads and were considered to represent hubs connecting the different modules.<br \/>\n&nbsp;<br \/>\n<strong>Conclusions<\/strong>: In the present study, the FC matrix of 29 healthy adults during the N-back tasks was measured using fMRI. Additionally, the single representative FC matrix was estimated for each N-back load from the FC matrices of the multiple participants using the proposed method. The results suggested that the WM load-dependent changes in a functional network and its module structure could be identified by our proposed method.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<ul>\n<li>\u8cea\u7591\u5fdc\u7b54<\/li>\n<\/ul>\n<p>\u4eca\u56de\u306e\u8b1b\u6f14\u767a\u8868\u3067\u306f\uff0c\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u8cea\u7591\u3092\u53d7\u3051\u307e\u3057\u305f\uff0e\u672c\u767a\u8868\u3067\u306f\u8cea\u554f\u8005\u306e\u540d\u524d\u306f\u805e\u3044\u3066\u3044\u307e\u305b\u3093\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb<\/strong><strong>connectivity<\/strong><strong>\u7b97\u51fa\u306b\u304a\u3051\u308b\u7a93\u95a2\u6570\u306f\u3069\u308c\u304f\u3089\u3044\u306a\u306e\u304b<\/strong><br \/>\n\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3059\u308b\u79c1\u306e\u56de\u7b54\u3068\u3057\u3066\uff0c\u300c\u672c\u7814\u7a76\u3067\u306f\uff0c\u30c0\u30a4\u30ca\u30df\u30c3\u30af\u89e3\u6790\u306f\u884c\u3063\u3066\u304a\u3089\u305a\uff0cconnectivity\u7528\u306b\u8a08\u6e2c\u3057\u305f250\u79d2\u306e\u8840\u6d41\u5909\u5316\u91cf\u306e\u6642\u7cfb\u5217\u76f8\u95a2\u3092\u7b97\u51fa\u3057\u3066\u3044\u308b\u300d\u3068\u8aac\u660e\u81f4\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u305d\u308c\u305e\u308c\u306e\u88ab\u9a13\u8005\u30c7\u30fc\u30bf\u304b\u3089\u96c6\u56e3\u306e\u30e2\u30b8\u30e5\u30fc\u30eb\u69cb\u9020\u3092\u63a8\u5b9a\u3059\u308b\u610f\u5473\u306f<\/strong><br \/>\n\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3059\u308b\u79c1\u306e\u56de\u7b54\u3068\u3057\u3066\uff0c\u300c\u96c6\u56e3\u9593\u306e\u30e2\u30b8\u30e5\u30fc\u30eb\u69cb\u9020\u3092\u63a8\u5b9a\u3059\u308b\u3053\u3068\u3067\u30ef\u30fc\u30ad\u30f3\u30b0\u30e1\u30e2\u30ea\u306b\u304a\u3051\u308b\u8133\u6a5f\u80fd\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3092\u89e3\u91c8\u3057\u3084\u3059\u3044\u5f62\u3067\u8868\u73fe\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u308b\u300d\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u8a2d\u8a08\u5909\u6570\u306e\u5b9f\u884c\u53ef\u80fd\u9818\u57df\u306f\u4f55\u306a\u306e\u304b<\/strong><br \/>\n\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\u79c1\u306f\u305d\u306e\u5834\u3067\u304a\u7b54\u3048\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u305b\u3093\u3067\u3057\u305f\uff0e\u30dd\u30b9\u30bf\u30fc\u767a\u8868\u7d42\u4e86\u5f8c\u8003\u3048\u305f\u3068\u3053\u308d\uff0c\u5b9f\u884c\u53ef\u80fd\u9818\u57df\u306f\u8a2d\u8a08\u5909\u6570\u00a0<em>p<\/em>=\u8133\u9818\u57df\u6570\uff0cc=\u30af\u30e9\u30b9\u30bf\u6570\u3068\u8aac\u660e\u3059\u308b\u5fc5\u8981\u304c\u3042\u308b\u3068\u611f\u3058\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u3053\u306e\u63d0\u6848\u624b\u6cd5\u306f\u30bd\u30d5\u30c8\u30a6\u30a7\u30a2\u3092\u7528\u3044\u3066\u3044\u308b\u306e\u304b\u81ea\u4f5c\u306e\u30b3\u30fc\u30c9\u306a\u306e\u304b<\/strong><br \/>\n\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\u306e\u79c1\u306e\u56de\u7b54\u3068\u3057\u3066\uff0c\u300c\u5171\u540c\u7814\u7a76\u8005\u306e\u65b9\u3005\u3068\u7d44\u3093\u3060\u81ea\u4f5c\u306e\u30bd\u30fc\u30b9\u30b3\u30fc\u30c9\u3067\u3042\u308b\u300d\u3068\u56de\u7b54\u3044\u305f\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u524d\u982d\u524d\u91ce\u306e\u90e8\u5206\u304c\u63a8\u5b9a\u3055\u308c\u3066\u3044\u306a\u3044\u306e\u306f\u306a\u305c\u304b<\/strong><br \/>\n\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3059\u308b\u56de\u7b54\u3067\u3059\u304c\u300c\u79c1\u306e\u624b\u6cd5\u306f\u30e2\u30b8\u30e5\u30fc\u30eb\u69cb\u9020\u3092\u5f37\u8abf\u3059\u308b\u624b\u6cd5\u3067\u3042\u308b\u305f\u3081\uff0c\u8ab2\u984c\u6642\u306e\u524d\u982d\u524d\u91ce\u306e\u6a5f\u80fd\u306f\u30e2\u30b8\u30e5\u30fc\u30eb\u69cb\u9020\u3092\u6709\u3057\u3066\u3044\u306a\u3044\u53ef\u80fd\u6027\u304c\u3042\u308b\uff0e\u300d\u3068\u56de\u7b54\u3044\u305f\u3057\u307e\u3057\u305f\uff0e<br \/>\n<strong>\u00a0<\/strong><br \/>\n<strong>\u30fb<\/strong><strong>1-back<\/strong><strong>\u3068<\/strong><strong>2-back<\/strong><strong>\u3067\u51fa\u3066\u3044\u308b\u30af\u30e9\u30b9\u30bf\u306f\u540c\u3058\u30af\u30e9\u30b9\u30bf\u306a\u306e\u304b<\/strong><br \/>\n\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3059\u308b\u79c1\u306e\u56de\u7b54\u3068\u3057\u3066\uff0c\u300c\u540c\u3058\u3088\u3046\u306a\u30af\u30e9\u30b9\u30bf\u3082\u5b58\u5728\u3059\u308b\u304c\uff0c\u7570\u306a\u308b\u30af\u30e9\u30b9\u30bf\u69cb\u9020\u3082\u5b58\u5728\u3059\u308b\u300d\u3068\u56de\u7b54\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u307e\u305f\u88dc\u8db3\u3068\u3057\u3066\uff0c\u300c\u56f3\u306e\u30ce\u30fc\u30c9\u306e\u8272\u306f\u540c\u3058\u30af\u30e9\u30b9\u30bf\u3092\u8868\u3057\u3066\u304a\u308a\uff0c\u30ce\u30fc\u30c9\u306e\u5927\u304d\u3055\u306f\u4f55\u500b\u306e\u30af\u30e9\u30b9\u30bf\u306b\u5c5e\u3059\u308b\u304b\u8868\u73fe\u3057\u3066\u3044\u308b\u3068\u8aac\u660e\u3057\uff0c1-back\uff0c2-back\u306e\u30ce\u30fc\u30c9\u306e\u8272\u306b\u4f9d\u5b58\u95a2\u4fc2\u306f\u306a\u3044\u300d\u3068\u8aac\u660e\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n&nbsp;<\/p>\n<ul>\n<li>\u611f\u60f3<\/li>\n<\/ul>\n<p>\u4eca\u56de\u306e\u5b66\u4f1a\u304c\uff0c\u521d\u306e\u5b66\u4f1a\u53c2\u52a0\u304b\u3064\u521d\u306e\u56fd\u969b\u5b66\u4f1a\u3067\u3042\u308a\uff0c\u3068\u3066\u3082\u7dca\u5f35\u3057\u307e\u3057\u305f\uff0e\u307e\u305f\u82f1\u8a9e\u3067\u306e\u30dd\u30b9\u30bf\u30fc\u767a\u8868\u3067\u3042\u308a\uff0c\u82f1\u8a9e\u3067\u7814\u7a76\u5185\u5bb9\u3092\u4f1d\u3048\u308b\u3053\u3068\u306e\u96e3\u3057\u3055\u3092\u75db\u611f\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u3057\u304b\u3057\uff0c\u7814\u7a76\u5185\u5bb9\u306b\u8208\u5473\u3092\u6301\u3063\u3066\u9802\u3051\u305f\u65b9\u3005\u3068\u306e\u30b3\u30df\u30e5\u30cb\u30b1\u30fc\u30b7\u30e7\u30f3\u3092\u901a\u3057\u3066\uff0c\u82f1\u8a9e\u3067\u8aac\u660e\u3059\u308b\u3053\u3068\u3078\u306e\u62b5\u6297\u304c\u306a\u304f\u306a\u308a\uff0c\u3088\u308a\u82f1\u8a9e\u304c\u3046\u307e\u304f\u8a71\u305b\u308b\u3088\u3046\u306b\u306a\u308a\u305f\u3044\u3068\u3044\u3046\u30e2\u30c1\u30d9\u30fc\u30b7\u30e7\u30f3\u306b\u5909\u308f\u308a\u307e\u3057\u305f\uff0e\u4eca\u56de\uff0c\u8cea\u554f\u304c\u3042\u307e\u308a\u805e\u304d\u53d6\u308c\u305a\uff0c\u805e\u304d\u76f4\u3059\u3053\u3068\u304c\u591a\u304f\uff0c\u307e\u305f\u539f\u7a3f\u306b\u52a9\u3051\u501f\u308a\u308b\u3053\u3068\u304c\u591a\u304b\u3063\u305f\u306e\u3067\uff0c\u4eca\u5f8c\u306f\u82f1\u8a9e\u306e\u52c9\u5f37\u306b\u3088\u308a\u4e00\u5c64\u53d6\u308a\u7d44\u3093\u3067\u3044\u304d\u305f\u3044\u3068\u601d\u3044\u307e\u3059\uff0e\u672c\u5b66\u4f1a\u3092\u901a\u3057\u3066\uff0c\u6163\u308c\u306a\u3044\u7570\u56fd\u306e\u5730\u3067\u69d8\u3005\u306a\u6587\u5316\u3084\u4fa1\u5024\u89b3\u306b\u89e6\u308c\u308b\u3053\u3068\u3067\uff0c\u81ea\u5206\u306e\u30ec\u30d9\u30eb\u30a2\u30c3\u30d7\u306e\u305f\u3081\u306b\u3059\u3079\u304d\u3053\u3068\u3084\u65e5\u672c\u3084\u6d77\u5916\u305e\u308c\u305e\u308c\u306e\u826f\u3057\u60aa\u3057\u306a\u3069\u304c\u5206\u304b\u308a\uff0c\u30b0\u30ed\u30fc\u30d0\u30eb\u306a\u8996\u91ce\u304c\u5c11\u3057\u306f\u4ed8\u3044\u305f\u3068\u611f\u3058\u3068\u3066\u3082\u7d20\u6674\u3089\u3057\u3044\u7d4c\u9a13\u306b\u306a\u308a\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<ol start=\"3\">\n<li>\u8074\u8b1b<\/li>\n<\/ol>\n<p>\u4eca\u56de\u306e\u8b1b\u6f14\u4f1a\u3067\u306f\uff0c\u4e0b\u8a18\u306e3\u4ef6\u306e\u767a\u8868\u3092\u8074\u8b1b\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Persistent hippocampal neural firing and hippocampal cortical coupling predict working memory load<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Ece Boran, Tommaso Fedele, Johannes Sarnthein<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Poster Session<br \/>\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a<br \/>\n&nbsp;<br \/>\n<strong>Introductions<\/strong>: Hippocampal activity is known for its role in cognitive tasks involving episodic memory or spatial navigation, but its role in working memory and its sensitivity to workload is still under debate. The maintenance of items in working memory relies on persistent neural activity in a widespread network of brain areas. It remains, however, unclear how load on working memory influences persistent neural activity, particularly in the hippocampus and in its functional connections.<br \/>\n&nbsp;<br \/>\n<strong>Methods<\/strong>: Here, we investigated hippocampal activity while subjects maintained sets of letters in verbal working memory for a few seconds to guide action. We used the Sternberg paradigm as a standard tool to study working memory. Subjects were presented with sets of 4,6 or 8 letters for 2 seconds (encoding), maintained them in working memory for 3 seconds (maintenance), and &#8211; upon viewing a probe letter &#8211; responded by pressing &#8220;in&#8221; or &#8220;out&#8221; (retrieval). In nine patients with epilepsy, we recorded single neuron firing and intracranial EEG in the medial temporal lobe and EEG from the scalp. We used machine learning tools to predict the subject&#8217;s behavior on a trial-by-trial basis. We calculated the phase locking index over time and spectral frequency to characterize the functional connectivity between hippocampal EEG and scalp EEG.<br \/>\n&nbsp;<br \/>\n<strong>Results<\/strong>: Hippocampal neural firing distinguished between the periods of a trial and increased with the number of letters to be memorized (Figure 1). Along the periods of a trial, hippocampal firing differentiated between success and error trials during stimulus encoding, predicted workload during memory maintenance, and predicted the subjects&#8217; behavior during retrieval. The hippocampal neurons did not encode for individual items of memory content, but rather the number of letters maintained in memory. This property was known from frontal cortex and is shown here for neurons within the medial temporal lobe for the first time.<br \/>\nAfter having demonstrated that the hippocampus is involved in the processing of verbal working memory load at the single neuron level, we studied how the hippocampus is embedded in the working memory network. We found that hippocampal oscillations coupled to scalp EEG in the theta-alpha range, exclusively during maintenance, and with increasing workload (Figure 2). This demonstrates a network for working memory that is bound by coherent oscillations involving cortical areas and persistent hippocampal neuron firing.<br \/>\n&nbsp;<br \/>\n<strong>Conclusions<\/strong>: We are the first to combine hippocampal single-unit activity with scalp-recorded EEG, all in a task that is feasible only for human subjects. Connecting the single-neuron scale to the cortical population scale represents a breakthrough in the study of human functional neuroanatomy. These findings allowed us to clarify the role of the hippocampus with single units and characterize its functional connectivity in the working memory network.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u3053\u306e\u767a\u8868\u306f\u30ef\u30fc\u30ad\u30f3\u30b0\u30e1\u30e2\u30ea\u8ab2\u984c\u304a\u3051\u308b\u6d77\u99ac\u306e\u6a5f\u80fd\u7684\u7d50\u5408\u306b\u6ce8\u76ee\u3057\u305f\u7814\u7a76\u3067\u3057\u305f\uff0e\u6d77\u99ac\u306e\u6d3b\u52d5\u306f\uff0c\u30a8\u30d4\u30bd\u30fc\u30c9\u8a18\u61b6\u3092\u542b\u3080\u8a8d\u77e5\u8ab2\u984c\u306e\u5f79\u5272\u3068\u3057\u3066\u77e5\u3089\u308c\u3066\u3044\u307e\u3059\u304c\uff0c\u30ef\u30fc\u30ad\u30f3\u30b0\u30e1\u30e2\u30ea\u306b\u304a\u3051\u308b\u5f79\u5272\u304a\u3088\u3073\u8ca0\u8377\u306b\u5bfe\u3059\u308b\u5909\u5316\u306f\u3044\u307e\u3060\u306b\u8b70\u8ad6\u4e2d\u3067\u3042\u308a\uff0c\u672c\u7814\u7a76\u306e\u7d50\u679c\u3068\u3057\u3066\u306f\u30ef\u30fc\u30ad\u30f3\u30b0\u30e1\u30e2\u30ea\u306b\u4e0e\u3048\u308b\u8ca0\u8377\u91cf\u306e\u5897\u52a0\u306b\u4f34\u3063\u3066\u6d77\u99ac\u306e\u795e\u7d4c\u767a\u706b\u3082\u5897\u52a0\u3059\u308b\u3068\u3044\u3046\u7d50\u679c\u3067\u3057\u305f\uff0e\u3053\u306e\u7814\u7a76\u7d50\u679c\u306e\u5185\u5bb9\u306f\u81ea\u5206\u306e\u7814\u7a76\u3068\u7167\u3089\u3057\u5408\u308f\u305b\u3066\u8003\u5bdf\u3059\u308b\u5fc5\u8981\u304c\u3042\u308b\u3068\u611f\u3058\u307e\u3057\u305f\uff0e\u307e\u305f\uff0c\u3053\u306e\u7814\u7a76\u3067\u306f\u30ef\u30fc\u30ad\u30f3\u30b0\u30e1\u30e2\u30ea\u3092\u7814\u7a76\u3059\u308b\u305f\u3081\u306e\u57fa\u672c\u7684\u306a\u30c4\u30fc\u30eb\u3068\u3057\u3066Sternberg paradigm\u3068\u3044\u3046\u3082\u306e\u3092\u4f7f\u3063\u3066\u3044\u3066\u8abf\u67fb\u3059\u308b\u5fc5\u8981\u304c\u3042\u308b\u3068\u611f\u3058\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aModeling and Analysis Methods \u2013 Uni\/multi-variate analysis<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aCedric Huchuan Xia, Zongming Ma, Danielle Bassett, Theodore Satterthwiate, Russell Shinohara, Daniela Witten<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a ORAL SESSION: Modeling and Analysis Method \u2013 Uni\/multi-variate analysis<br \/>\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a<br \/>\n&nbsp;<br \/>\n<strong>Introductions<\/strong>: Relating connectomic features to subject-level measures is a popular approach to study the brain-behavior relationships. However, most studies have relied on mass univariate methods, which are vulnerable to Type II errors due to extensive comparisons that must be accounted for. On the other hand, studies using common multivariate analyses often ignore the innate structure of brain network data, producing results that are difficult to interpret. Here, we introduce a new penalized regression method, specifically designed to analyze the relationship between high-dimensional connectomic data and covariates of interest.<br \/>\n&nbsp;<br \/>\n<strong>Methods<\/strong>: Given n subjects, let Ai denote the connectivity matrix for the i-th subject, where p is the number of nodes. The p nodes can be sorted into K communities. For each subject, q covariates have been measured, so that Xfi is the f-th covariate measurement for the i-th subject (Fig 1a). We consider the model Ai = \u0398 + \u03a3qf=1Xfi\u2022(W\u0393fWT), where the mean connectivity matrix \u0398pxp captures the edge-level information, WpxK indicates the community membership for each node, and \u0393qxKxK captures the community-level information for each covariate. We make two assumptions: 1)\u0398 is low-rank, i.e. it can be represented by a low-dimensional subspace, and 2)\u0393 is sparse, i.e. some of the elements are exactly zero. They can be implemented via a nuclear norm (\u03bb1) and a L1 norm (\u03bb2) penalty, respectively. Finally, the convex optimization problem can be solved by a block coordinate descent algorithm. As this method is designed to incorporate node-, edge-, and community-level information, we refer to it as multi-scale network regression (MSNR).<br \/>\nWe exercised MSNR by applying it to data from the Philadelphia Neurodevelopmental Cohort. We analyzed 1015 subjects (age 8-22), who completed a resting-state fMRI acquisition, had adequate data quality, and were not excluded for medical co-morbidity. We used a validated preprocessing pipeline to minimize motion artifact. We constructed functional connectivity matrices using a commonly-used parcellation. To evaluate the performance of all models, we randomly selected 10% of the data as a validation set. In the remaining 90% of the data, we tuned \u03bb1 and \u03bb2 via a 9-fold cross-validation (Fig 1b). We evaluated the final model in three approaches: 1) out-of-sample prediction, 2) permutation testing, and 3) bootstrapping (Fig 1c). Finally, we also benchmarked the MSNR&#8217;s predictability and interpretability against other common methods, such as linear models using community-mean and individual edges as features (Fig 2a).<br \/>\n&nbsp;<br \/>\n<strong>Results<\/strong>: We successfully trained a multi-scale network regression model linking high-dimensional functional connectivity to three covariates: motion, age, and sex. MSNR performed well in the validation set, with a prediction error comparable to the mean error in the training set (Fig 2b). Notably, its performance was also significantly better than the null distribution generated by permuted data (p &lt; 0.001). In contrast, the community-based model generalized poorly to the validation set, whereas the edge-based model was on par with MSNR. However, both common methods had substantially less interpretable coefficients than MSNR (Fig 2c, d). Finally, MSNR revealed known connectivity-covariate relationships, such as distance-dependent motion artifact, developmental effects, and sex differences (Fig 2e).<br \/>\n&nbsp;<br \/>\n<strong>Conclusions<\/strong>: By taking into account of node-, edge-, and community-level information, we developed a multi-scale network regression approach that achieves a balance between prediction and interpretability. Empirically, we demonstrated its advantages over traditional methods and its ability to uncover meaningful relationships. This new method could prove useful in future studies of brain-behavior relationships.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u3053\u306e\u767a\u8868\u3067\u7740\u76ee\u3057\u305f\u306e\u306f\u81ea\u5206\u3068\u540c\u3058\u3088\u3046\u306b\u591a\u5909\u91cf\u89e3\u6790\u306b\u304a\u3051\u308b\u30e2\u30c7\u30eb\u3084\u65b0\u305f\u306a\u89e3\u6790\u624b\u6cd5\u3092\u7d39\u4ecb\u3057\u3066\u3044\u308b\u3068\u3053\u308d\u3067\u3059\uff0e\u4e00\u822c\u7684\u306a\u591a\u5909\u91cf\u89e3\u6790\u3092\u7528\u3044\u305f\u7814\u7a76\u3067\u306f\uff0c\u8133\u6a5f\u80fd\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u672c\u8cea\u7684\u306a\u69cb\u9020\u3092\u7121\u8996\u3059\u308b\u3053\u3068\u304c\u591a\u304f\uff0c\u89e3\u91c8\u304c\u56f0\u96e3\u306a\u7d50\u679c\u304c\u751f\u3058\u308b\u5834\u5408\u304c\u3042\u308a\u307e\u3059\uff0e\u672c\u7814\u7a76\u3067\u306f\uff0c\u305d\u306e\u89e3\u6c7a\u65b9\u6cd5\u3068\u3057\u3066\u7279\u306b\u9ad8\u6b21\u5143\u306econnectomic\u30c7\u30fc\u30bf\u3068\u95a2\u5fc3\u306e\u3042\u308b\u5171\u5909\u91cf\u9593\u306e\u95a2\u4fc2\u3092\u5206\u6790\u3059\u308b\u305f\u3081\u306b\u8a2d\u8a08\u3055\u308c\u305f\uff0c\u65b0\u305f\u306a\u5236\u7d04\u4ed8\u304d\u56de\u5e30\u6cd5\u3092\u7d39\u4ecb\u3057\u3066\u304a\u308a\uff0c\u89e3\u6790\u624b\u6cd5\u304c\u79c1\u305f\u3061\u306e\u7814\u7a76\u306b\u3082\u53c2\u8003\u306b\u3067\u304d\u308b\u3068\u611f\u3058\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aCommunity organization of brain networks and hub disruptions in chronic pain<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aCamille Fauchon, David Meunier, Kasey Hemington, Joshua Cheng, Rachael Bosma, Natalie Osborne, Anton Rogachov, Andrew Kim, Robert Inman, Karen Davis<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Poster SESSION<br \/>\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a<br \/>\n&nbsp;<br \/>\n<strong>Introductions<\/strong>: We previously reported that patients with chronic pain exhibit abnormalities of within- and cross-network functional connectivity, and regional dynamics within the dynamic pain connectome [1-5]. However, the extent and nature of changes in brain networks topology is poorly understood. Thus, the aim of this study was to model the brain as a modular network [6] using community network graph analysis based on resting-state fMRI (rsfMRI) data in patients with chronic low back pain.<br \/>\n&nbsp;<br \/>\n<strong>Methods<\/strong>: Forty-five right-handed male chronic pain patients with ankylosing spondylitis (AS) and age\/sex-matched healthy controls (HC) were recruited and provided informed consent to the study approved by our local research ethics board. AS predominantly occurs in young men with relatively few co-morbidities. Inclusion criteria for the patients were 18-65 years old, pain for &gt;6 months, stable medications, and absence of other major diseases. Each subject had a 3T MRI session to acquire high resolution T1 anatomical and 10-minute rsfMRI scans. Data were preprocessed using FSL and parcelled based on the HCP atlas [7] using the open-source python package Nipype [8] to create 180 anatomical regions (nodes) in each hemisphere. For each subject, the raw time-series data were averaged over the voxels within the HCP areas and extracted using the graphpype functions of the neuropypcon package. Individual functional connectivity matrices were generated by computing the Pearson correlations between every pair of nodes (360 x 360). A network density threshold of 10% was applied to remove weak correlations. We then conducted a modular analysis using Radatools software [9] as previously described [6] based on the average Z-correlation matrix for each group. This segregated the network into modules of regions (i.e., communities) that work tightly together as a function of their level of mutual inter-connectivity [6]. We computed the networks metrics to characterize regions which acted as an inter-modular hub (e.g., betweenness centrality), or as an intra-module hub (e.g., degree Z-score). Permutation tests (n=500) were applied at the level of nodes and subjects to determine statistical significance of a network (p&lt; 0.05; corrected for multiple comparisons).<br \/>\n&nbsp;<br \/>\n<strong>Results<\/strong>: In both the HC and AS groups, there was a significant network structure of six connected communities compared to a random network, with similar modularity and no inter-hemispheric differences: 1) fronto-parietal regions (i.e., precuneus, medial and dorsolateral prefrontal cortex) including areas of the default mode network, 2) regions of the occipital cortex (i.e., calcarine, cuneus) of the visual network, 3) sensorimotor and salience areas including the insula, and mid\/anterior cingulate cortex, 4) parietal sensorimotor areas (i.e., SMA, S1, M1), 5) temporal cortices and subcortical regions, and 6) orbito- and ventro-medial prefrontal cortex (vmPFC). Furthermore, the chronic pain AS group showed inter- and intra-modular hub disruption compared to the HC group as follows: 1) low betweenness centrality in the rostral anterior cingulate cortex and the anterior intra-parietal area, 2) high betweenness centrality in the precuneus\/posterior cingulate cortex (PCC), the anterior mid cingulate cortex, and the vmPFC, and 3) high intra-modular degree in the inferior parietal area, PCC, posterior mid-cingulate cortex, and visual cortex.<br \/>\n&nbsp;<br \/>\n<strong>Conclusions<\/strong>: Chronic pain patients had a normal number of communities but with abnormal membership and hub disruptions. These results provide evidence of brain network reorganization in chronic pain, and provide a framework to study the effects of pain on the brain from a network perspective. Our future analysis will assess the impact of individual differences (e.g., sex differences) in brain network topology and its interaction with chronic pain.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u3053\u306e\u767a\u8868\u3067\u7740\u76ee\u3057\u305f\u306e\u306f\u8133\u6a5f\u80fd\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u30e2\u30b8\u30e5\u30fc\u30eb\u69cb\u9020\u3092\u30e2\u30c7\u30eb\u5316\u3057\u3066\u3044\u308b\u3053\u3068\u3067\u3059\uff0e\u672c\u7814\u7a76\u3067\u306f\u5f93\u6765\u3068\u306f\u7570\u306a\u308a\uff0cRadatools\u3068\u3044\u3046\u30bd\u30d5\u30c8\u30a6\u30a7\u30a2\u3092\u4f7f\u7528\u3057\u3066\u30e2\u30b8\u30e5\u30fc\u30eb\u69cb\u9020\u306e\u5206\u6790\u3092\u884c\u3063\u3066\u304a\u308a\uff0c\u307e\u305f\u30cf\u30d6\u3068\u3057\u3066\u6a5f\u80fd\u3057\u3066\u3044\u308b\u9818\u57df\u3092\u7279\u5fb4\u4ed8\u3051\u3066\u3044\u308b\u306e\u304c\u81ea\u8eab\u306e\u7814\u7a76\u3068\u8fd1\u304f\u79c1\u306e\u7814\u7a76\u306e\u53c2\u8003\u306b\u306a\u308b\u3068\u611f\u3058\u307e\u3057\u305f\uff0e\u30c7\u30fc\u30bf\u3068\u3057\u3066\u306f\u6162\u6027\u75bc\u75db\u306e\u60a3\u8005\u306e\u30c7\u30fc\u30bf\u3092\u4f7f\u3063\u3066\u304a\u308a\uff0c\u7d50\u679c\u3068\u3057\u3066\uff0c\u6b63\u5e38\u8005\u3068\u30e2\u30b8\u30e5\u30fc\u30eb\u306e\u6570\u306f\u5909\u308f\u3089\u306a\u3044\u304c\u30cf\u30d6\u306e\u9818\u57df\u3084\u30e2\u30b8\u30e5\u30fc\u30eb\u5185\u306e\u69cb\u9020\u304c\u5909\u308f\u308b\u3068\u3044\u3046\u7d50\u679c\u3067\u3057\u305f\uff0e<br \/>\n<strong>\u5b66\u4f1a\u53c2\u52a0\u5831\u544a\u66f8<\/strong><\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"147\"><strong>\u5831\u544a\u8005\u6c0f\u540d<\/strong><\/td>\n<td width=\"373\">\u5409\u7530\u65e9\u7e54<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u767a\u8868\u8ad6\u6587\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n<td width=\"373\">\u30c0\u30fc\u30c4\u6295\u3066\u304d\u6642\u306e\u8133\u6d3b\u52d5\u9818\u57df\u306e\u305f\u3081\u306efNIRS\u306e\u8a08\u6e2c\u30c7\u30fc\u30bf\u306e\u4f53\u52d5\u9664\u53bb\u624b\u6cd5\u306e\u78ba\u7acb<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u767a\u8868\u8ad6\u6587\u82f1\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n<td width=\"373\">Motion artifacts removal method for fNIRS data to examine brain activity during dart throwing<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u8457\u8005<\/strong><\/td>\n<td width=\"373\">\u5409\u7530\u65e9\u7e54, \u65e5\u548c\u609f, \u7af9\u7530\u6b63\u6a39\uff0c\u5ee3\u5b89\u77e5\u4e4b<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u4e3b\u50ac<\/strong><\/td>\n<td width=\"373\">Organization for human brain mapping<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u8b1b\u6f14\u4f1a\u540d<\/strong><\/td>\n<td width=\"373\">OHBM2019<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u4f1a\u5834<\/strong><\/td>\n<td width=\"373\">Auditorium Parco Della Musica \/ Rome<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u958b\u50ac\u65e5\u7a0b<\/strong><\/td>\n<td width=\"373\">2019\/06\/9-2019\/06\/13<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<br \/>\n&nbsp;<\/p>\n<ol>\n<li>\u8b1b\u6f14\u4f1a\u306e\u8a73\u7d30<\/li>\n<\/ol>\n<p>2019\/06\/9\u304b\u30892018\/06\/13\u306b\u304b\u3051\u3066\uff0c\u30a4\u30bf\u30ea\u30a2\u306e\u30ed\u30fc\u30de\u306b\u3066\u958b\u50ac\u3055\u308c\u307e\u3057\u305f2019 annual meeting\u306b\u53c2\u52a0\u3057\u307e\u3057\u305f\uff0e\u3053\u306e\u5b66\u4f1a\u306f\uff0cOrganization for human brain mapping\u306b\u3088\u3063\u3066\u4e3b\u50ac\u3055\u308c\u305f\u56fd\u969b\u4f1a\u8b70\u3067\uff0c\u30d2\u30c8\u306e\u8133\u7d44\u7e54\u304a\u3088\u3073\u8133\u6a5f\u80fd\u306e\u30de\u30c3\u30d4\u30f3\u30b0\u306b\u95a2\u3059\u308b\u7814\u7a76\u306b\u643a\u308f\u308b\u69d8\u3005\u306a\u80cc\u666f\u3092\u6301\u3064\u7814\u7a76\u8005\u3092\u96c6\u3081\uff0c\u3053\u308c\u3089\u306e\u79d1\u5b66\u8005\u306e\u30b3\u30df\u30e5\u30cb\u30b1\u30fc\u30b7\u30e7\u30f3\uff0c\u304a\u3088\u3073\u6559\u80b2\u3092\u4fc3\u9032\u3059\u308b\u3053\u3068\u3092\u76ee\u7684\u306b\u958b\u50ac\u3055\u308c\u3066\u3044\u307e\u3059<sup>\uff08\uff11<\/sup>\uff0e\u672c\u7814\u7a76\u5ba4\u304b\u3089\u306f\u4ed6\u306b\uff0c\u5ee3\u5b89\u5148\u751f\uff0c\u65e5\u548c\u5148\u751f\uff0cM2\u306e\u5b66\u751f\u3068\u3057\u3066\u53e4\u5bb6\uff0c\u5927\u585a\uff0c\u5965\u6751\uff08\u99ff\uff09\uff0c\u5c71\u672c\uff0c\u6749\u91ce\uff0cM1\u306e\u5b66\u751f\u3068\u3057\u3066\u98a8\u5442\u8c37\uff0c\u4e39\u304c\u53c2\u52a0\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<ol start=\"2\">\n<li>\u7814\u7a76\u767a\u8868\n<ul>\n<li>\u767a\u8868\u6982\u8981<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<p>\u79c1\u306f13\u65e5\u306e\u30dd\u30b9\u30bf\u30fc\u30bb\u30c3\u30b7\u30e7\u30f3\u304a\u3088\u3073\u30dd\u30b9\u30bf\u30fc\u30ec\u30bb\u30d7\u30b7\u30e7\u30f3\u306b\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u767a\u8868\u306e\u5f62\u5f0f\u306f\u30dd\u30b9\u30bf\u30fc\u767a\u8868\u3067\uff0c\u8a083\u6642\u9593\u53c2\u52a0\u8005\u306e\u65b9\u3068\u8b70\u8ad6\u3092\u884c\u3044\u307e\u3044\u3057\u305f\uff0e<br \/>\n\u4eca\u56de\u306e\u767a\u8868\u306f\uff0c\u300cMotion artifacts removal method for fNIRS data to examine brain activity during dart throwing\u300d\u306b\u3064\u3044\u3066\u767a\u8868\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u4ee5\u4e0b\u306b\u6284\u9332\u3092\u8a18\u8f09\u81f4\u3057\u307e\u3059\uff0e<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">Introduction<br \/>\nHabitual darts training is said to have the potential to improve cognitive function (Takeda et al. 2017). Furthermore, using the cognitive function test, it has been shown to increase the short-term memory ability. It is necessary to examine this from the aspect of brain function. In this study, we measured brain activity in participants while throwing darts, using functional near-infrared spectroscopy (fNIRS). However, the measurement data include many motion artifacts (MAs) due to the throwing motion. Although several types of MA removal methods have been developed, their effectiveness for removing MAs due to dart throwing has not yet been investigated. Here, we performed removal of MAs in dart throwing using multiple methods and compared the results.<br \/>\n&nbsp;<br \/>\nMethods<br \/>\nTwelve healthy adults participated in the experiment. We measured measure oxy- and de-oxy hemoglobin (Hb) concentration changes during dart throwing using LABNIRS (Shimadzu, 38 CH, 37 Hz). Each subject repeatedly tried to throw darts 9 times in total. A voice instruction system told the subject when to begin the action. The oxy-Hb data were analyzed using Homer2 (Huppert et al. 2009) NIRS processing package functions in MATLAB (Mathworks, MA USA). The four different MA removal methods, targeted principal component analysis (tPCA), principal component analysis (PCA), MA reduction algorithm (MARA), and kurotsis wavelet (kWavelet) were applied to the oxy-Hb signals measured. Then, they were bandpass filtered (0.01 &#8211; 0.5 Hz). The function of hmrMotionArtifact implemented in Homer2 was used to identify the sections with MAs. Three sets of control parameters for the MA removal methods used in previous studies were set and compared each other. Additionally, we examined the differences in activated regions between the methods using general liner model (GLM).<br \/>\n&nbsp;<br \/>\nResults<br \/>\nAfter applying the MA removal methods, the section specified as the MA component was no longer observed. The number of the identified MA components differed depending on the parameter value and type of the MA removal methods (Fig. 1). The GLM analysis was applied to the oxy-Hb data after the MA removal (Figure 2). No regions were activated for data to which PCA was applied. The results of the data to which tPCA was applied varied depending on the parameter value. The results of MARA and kWavelet were similar to those only bandpass filtered.<br \/>\n&nbsp;<br \/>\nConclusion<br \/>\nOxy-Hb changes during dart throwing were measured using fNIRS. The measured data included MAs due to dart throwing. Several removal methods were applied and their results were compared each other. We have revealed that the result of each method differed from other methods and highly depends on its parameter setting. It is necessary to optimize it for the experiments.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<ul>\n<li>\u8cea\u7591\u5fdc\u7b54<\/li>\n<\/ul>\n<p>\u4eca\u56de\u306e\u8b1b\u6f14\u767a\u8868\u3067\u306f\uff0c\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u8cea\u7591\u3092\u53d7\u3051\u307e\u3057\u305f\uff0e\u8cea\u554f\u8005\u306e\u6c0f\u540d\u3092\u63a7\u3048\u640d\u306d\u3066\u3057\u307e\u3044\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>1<\/strong><br \/>\n\u8cea\u554f\u306f\uff0cLABNIRS\u306e\u30c7\u30fc\u30bf\u3092Homer2\u306e\u30c7\u30fc\u30bf\u5f62\u5f0f\u306b\u5909\u63db\u3059\u308b\u306e\u304c\u96e3\u3057\u304b\u3063\u305f\u306e\u3067\u306f\u306a\u3044\u304b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\u306fShimadzu2nirs\u3092\u4f7f\u7528\u3059\u308b\u3068\u5bb9\u6613\u3067\u3057\u305f\u3068\u7b54\u3048\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>2<\/strong><br \/>\n\u8cea\u554f\u306f\uff0c\u30c1\u30e3\u30f3\u30cd\u30eb\u914d\u7f6e\u3092\u4f5c\u308b\u306e\u306f\u3069\u3046\u3057\u305f\u306e\u304b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\u306f\uff0c\u57fa\u6e96\u70b9\u306e\u5024\u306e\u5fae\u8abf\u6574\u304c\u96e3\u3057\u304b\u3063\u305f\u3068\u7b54\u3048\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>3<\/strong><br \/>\n\u8cea\u554f\u306f\uff0c\u3069\u306eMA\u9664\u53bb\u624b\u6cd5\u304c\u6709\u52b9\u3067\u3042\u3063\u305f\u306e\u304b\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\u306f\uff0c\u30a6\u30a7\u30fc\u30d6\u30ec\u30c3\u30c8\u30d5\u30a3\u30eb\u30bf\u3067\u3057\u305f\u3068\u7b54\u3048\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>4<\/strong><br \/>\n\u8cea\u554f\u306f\uff0c\u30a6\u30a7\u30fc\u30d6\u30ec\u30c3\u30c8\u30d5\u30a3\u30eb\u30bf\u3092\u884c\u3046\u969b\u306eiqr\u306e\u8a2d\u5b9a\u306f\u3044\u304f\u3064\u306b\u3057\u305f\u306e\u304b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\u306f\uff0c1.5\u306b\u8a2d\u5b9a\u3057\u307e\u3057\u305f\u3068\u7b54\u3048\u307e\u3057\u305f\uff0e<br \/>\n<strong>\u00a0<\/strong><br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>5<\/strong><br \/>\n\u8cea\u554f\u306f,\u00a0 fNIRS\u3068fMRI\u306e\u9055\u3044\u306b\u3064\u3044\u3066\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\u306f\uff0cfNIRS\u306f\u81ea\u7136\u306a\u72b6\u614b\u3067\u8a08\u6e2c\u53ef\u80fd\u3067\u3042\u308b\u305f\u3081\uff0c\u30c0\u30fc\u30c4\u6295\u3066\u304d\u6642\u306e\u8133\u6d3b\u52d5\u3092\u8a08\u6e2c\u3059\u308b\u3053\u3068\u304c\u53ef\u80fd\u3067\u3059\u3068\u7b54\u3048\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>6<\/strong><br \/>\n\u8cea\u554f\u306f\uff0cfMRI\u3067\u306f\u6570mm\u3067\u3082\u52d5\u304f\u3068\u30e2\u30fc\u30b7\u30e7\u30f3\u30a2\u30fc\u30c1\u30d5\u30a1\u30af\u3060\u304cfNIRS\u3067\u306f\u3069\u308c\u3050\u3089\u3044\u52d5\u304f\u3068\u5f71\u97ff\u3059\u308b\u306e\u304b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\u306f\uff0c\u4f55mm\u3068\u3044\u3046\u5024\u3067\u306f\u308f\u304b\u3089\u306a\u3044\u3068\u7b54\u3048\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>7<\/strong><br \/>\n\u8cea\u554f\u306f\uff0cmotion artifact detection algorithm\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u306f\u81ea\u5206\u306e\u8a08\u6e2c\u30c7\u30fc\u30bf\u306b\u5408\u308f\u305b\u308b\u3079\u304d\u3067\u3042\u308b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\u306f\uff0c\u4eca\u56de\u306f\u8ad6\u6587\u3092\u53c2\u8003\u306b\u3057\u307e\u3057\u305f\u304c\u4eca\u5f8c\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u691c\u8a0e\u3057\u305f\u3044\u3068\u601d\u3044\u307e\u3059\u3068\u7b54\u3048\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>8<\/strong><br \/>\n\u8cea\u554f\u306f\uff0c\u3053\u308c\u304b\u3089\u3069\u3046\u3059\u308b\u306e\u304b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\u306f\uff0c\u88ab\u9a13\u8005\u6570\u3092\u5897\u3084\u3057\u307e\u3059\u3068\u7b54\u3048\u307e\u3057\u305f\uff0e<\/p>\n<ul>\n<li>\u611f\u60f3<\/li>\n<\/ul>\n<p>\u4eca\u56de\uff0c\u79c1\u306f\u521d\u3081\u3066\u306e\u5b66\u4f1a\u53c2\u52a0\u3067\u3057\u305f\uff0e\u6e96\u5099\u304c\u9593\u306b\u5408\u308f\u305a\uff0c\u5148\u751f\u306e\u529b\u3092\u501f\u308a\u308b\u3053\u3068\u306b\u306a\u308a\u51fa\u767a\u524d\u304b\u3089\u53cd\u7701\u3059\u308b\u3053\u3068\u3070\u304b\u308a\u3067\u3057\u305f\uff0e\u307e\u305f\uff0c\u767a\u8868\u6642\u306f\u65b9\u6cd5\u3084\u7d50\u679c\u304b\u3089\u5c0b\u306d\u3089\u308c\u308b\u3053\u3068\u304c\u591a\u304f\uff0c\u6700\u521d\u306f\u614c\u3066\u3066\u3057\u307e\u3044\u307e\u3057\u305f\u304c\uff0c\u767a\u8868\u3057\u3066\u3044\u308b\u3046\u3061\u306b\u843d\u3061\u7740\u3044\u3066\u7b54\u3048\u308b\u3053\u3068\u304c\u3067\u304d\u305f\u3088\u3046\u306b\u601d\u3044\u307e\u3059\uff0e\u3057\u304b\u3057\uff0c\u82f1\u8a9e\u3092\u805e\u304d\u53d6\u308b\u80fd\u529b\u3084\u4f1d\u3048\u308b\u80fd\u529b\u304c\u4e4f\u3057\u304f\uff0c\u8b70\u8ad6\u304c\u9032\u307e\u306a\u304b\u3063\u305f\u3053\u3068\u304c\u3042\u3063\u305f\u306e\u304c\u6094\u3057\u304b\u3063\u305f\u3067\u3059\uff0e\u4eca\u56de\u306e\u5b66\u4f1a\u3067\u306f\uff0c\u6e96\u5099\u4e0d\u8db3\u3084\u82f1\u8a9e\u529b\u306e\u4e0d\u5341\u5206\u3055\u306a\u3069\u53cd\u7701\u3059\u308b\u3053\u3068\u304c\u591a\u304f\u3042\u3063\u305f\u305f\u3081\uff0c\u4eca\u5f8c\u306b\u6d3b\u304b\u3057\u3066\u3044\u304d\u305f\u3044\u3068\u601d\u3044\u307e\u3059\uff0e<br \/>\n&nbsp;<\/p>\n<ol start=\"3\">\n<li>\u8074\u8b1b<\/li>\n<\/ol>\n<p>\u4eca\u56de\u306e\u8b1b\u6f14\u4f1a\u3067\u306f\uff0c\u4e0b\u8a18\u306e4\u4ef6\u306e\u767a\u8868\u3092\u8074\u8b1b\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a A clinical fMRI protocol for cognitive-motor dual task<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Oana Rus-Oswald1,2, Julia Reinhardt1,3, C\u00e9line B\u00fcrki2, Stephanie Bridenbaugh2, Christoph Stippich1, Reto Kressig2, Maria Blatow1<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Poster Session<br \/>\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a<br \/>\n<strong>Introduction<\/strong>:<br \/>\nGait analysis involving cognitive-motor dual task (DT) is used as a diagnostic tool in geriatric populations (e.g. &#8220;walking while talking&#8221;)[1]. Cognitive-motor interference effects, measured as decrease of walking speed and increase of step variability during DT as well as decreases in cognitive performance have a high predictive value for future fall risk and cognitive decline[5]. In our previous study, we demonstrated the feasibility of performing the cognitive-motor DT in the fMRI environment using an MRI-compatible stepping device and evaluated the neural correlates of the DT costs [2]. In the present study, we aimed to optimize the DT fMRI protocol with respect to task difficulty, duration and signal robustness in order to be able to apply this fMRI protocol in clinical context. Furthermore, we compared the stepping DT paradigm to a finger tapping DT paradigm, to evaluate if the latter brings similar results in terms of DT difficulty and signal robustness.<br \/>\n&nbsp;<br \/>\n<strong>Methods<\/strong>:<br \/>\n30 elderly healthy subjects (mean age \u00b1 SD: 70.2 \u00b1 4.97) participated in the fMRI study which included the performance of a cognitive task (verbal fluency and serial subtraction) and a motor single task (ST; stepping or finger tapping) and the combination of both, i.e. a cognitive-motor DT. Data analysis was performed using standardized routines in BrainVoyager. First, the group level analysis based on the contrast task vs. baseline, was performed using a separate subjects fixed effects analysis. Second, a region-of-interest analysis (ROI) at the individual level of each subject was performed, employing a dynamic threshold technique[3, 4]. Further, the ROI based DT costs were computed based on the individual difference of activation between ST and DT.<br \/>\n&nbsp;<br \/>\n<strong>Results<\/strong>:<br \/>\nDuring cognitive-motor DT the primary and secondary motor as well as parietal and prefrontal areas were active at group level. Activation of motor areas was decreased in DT as compared to the motor ST, according to our previous findings[3]. Activation of parietal and prefrontal areas was on average equivalent or increased in DT as compared to ST. The stepping DT paradigm was more distinctive (higher ROI occurence and activation strength on individual level) between ST and DT than the finger tapping DT paradigm. At the individual level the following ROIs showed robust activations in terms of occurence probability and signal strength, measured in the left hemisphere: primary motor cortex (M1), supplementary motor area (SMA) and superior parietal lobule\/intraparietal sulcus (SPL\/IPS). The neural correlates of DT costs computed in the stepping condition in SPL\/IPS enabled to descriptively separate the subjects into two groups, one with high and one with low DT costs based on their individual activation differences.<br \/>\n&nbsp;<br \/>\n<strong>Conclusions<\/strong>:<br \/>\nWith this study, we propose an optimized cognitive-motor DT fMRI protocol and a standardized individual analysis routine to measure the neural correlates of cognitive-motor interference effects during DT. In the future this fMRI protocol may be evaluated in the clinical setting, i.e. in patients with mild cognitive impairment and might enable the early detection of motor and cognitive decline based on the obtained neurofunctional markers and preferably before the structural degeneration process occurs.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u3053\u306e\u767a\u8868\u306f\uff0cfMRI\u3092\u7528\u3044\u3066\u904b\u52d5\u30bf\u30b9\u30af\u3068\u8a8d\u77e5\u30bf\u30b9\u30af\u306e\u30c7\u30e5\u30a2\u30eb\u30bf\u30b9\u30af\u6642\u306e\u8133\u6d3b\u52d5\u9818\u57df\u306b\u3064\u3044\u3066\u691c\u8a0e\u3055\u308c\u305f\u3082\u306e\u3067\u3057\u305f\uff0e\u904b\u52d5\u30bf\u30b9\u30af\u306b\u306f\u8db3\u306e\u30b9\u30c6\u30c3\u30d7\uff0c\u8a8d\u77e5\u30bf\u30b9\u30af\u306b\u306f\u5f15\u304d\u7b97\u307e\u305f\u306f\u8a00\u8a9e\u8a18\u61b6\u304c\u7528\u3044\u3089\u308c\u3066\u3044\u307e\u3057\u305f\uff0e\u672c\u7814\u7a76\u3067\u306f\uff0c\u9ad8\u9f62\u800530\u540d\u3092\u8a08\u6e2c\u3057\u3066\u304a\u308a\uff0c\u8133\u9818\u57df\u306fPFC\uff0cM1\uff0cSMA\uff0cSPL\uff0cIPS\u306b\u6ce8\u76ee\u3057\u3066\u3044\u305f\uff0e\u7d50\u679c\u3067\u306fM1\u306f\u5168\u88ab\u9a13\u8005\u30c7\u30e5\u30a2\u30eb\u30bf\u30b9\u30af\u6642\u306e\u307b\u3046\u304c\u30b7\u30f3\u30b0\u30eb\u30bf\u30b9\u30af\u6642\u3088\u308a\u8133\u6d3b\u52d5\u304c\u4f4e\u4e0b\u3057\u3066\u3044\u305f\u304c\uff0cSPL\u306f\u534a\u5206\u306e\u88ab\u9a13\u8005\u306fM1\u3068\u540c\u69d8\u306b\u8133\u6d3b\u52d5\u304c\u4f4e\u4e0b\u3057\u3066\u3044\u305f\u304c\uff0c\u534a\u5206\u306e\u88ab\u9a13\u8005\u3067\u306f\u8133\u6d3b\u52d5\u304c\u5897\u52a0\u3057\u3066\u3044\u305f\u3053\u3068\u304c\u793a\u3055\u308c\u3066\u3044\u305f\uff0e\u30c7\u30e5\u30a2\u30eb\u30bf\u30b9\u30af\u6642\u306e\u9ad8\u9f62\u8005\u306e\u8133\u6d3b\u52d5\u3092\u8a08\u6e2c\u3059\u308b\u3053\u3068\u306f\uff0c\u79c1\u306e\u7814\u7a76\u306e\u6700\u7d42\u76ee\u6a19\u3068\u4e00\u81f4\u3057\u3066\u304a\u308a\uff0c\u6ce8\u76ee\u3059\u308b\u8133\u9818\u57df\u306a\u3069\u3092\u53c2\u8003\u306b\u3057\u305f\u3044\u3068\u601d\u3044\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Motor imagery and visual neurofeedback to activate the swallowing network<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Guilherme Wood1, Doris Gr\u00f6ssinger1, Silvia Kober1<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Poster Session<br \/>\nAbstruct \uff1a<br \/>\n<strong>Introduction<\/strong>:<br \/>\nMotor imagery of movements is used as mental strategy in neurofeedback applications to gain voluntary control over activity in motor areas of the brain. In the present fMRI study, we first addressed the question whether motor imagery and execution of swallowing activate comparable brain areas, which has been already proven for hand and foot movements. Prior near-infrared spectroscopy (NIRS) studies provide evidence that this is the case in the outer layer of the cortex. With the present fMRI study, we want to expand these prior NIRS findings to the whole brain. Second, we used motor imagery of swallowing as mental strategy during visual neurofeedback to investigate whether one can learn to modulate voluntarily activity in brain regions, which are associated with active swallowing, using real-time fMRI.<br \/>\n&nbsp;<br \/>\n<strong>Methods<\/strong>:<br \/>\nEleven right-handed, healthy young adults (4 male, 7 female, mean age = 29.18 years, SD = 5.62) took part in this study. All participants gave written informed consent. During the first session, participants performed the functional localizer task to identify brain areas, which are active during mental imagery and motor execution of swallowing. This task consisted of 4 motor execution trials and 4 mental imagery trials. The data of the localizer session was analyzed offline using Brain Voyager QX v.2.3.1 and used to identify the region of interest (ROI) for the NF task, which was performed during the second session. The ROI for the NF task was extracted individually for each participant. Clusters of activity in the left lateral precentral gyrus were observed in all participants. During the second session, participants received real-time feedback of activation changes in the left lateral precentral gyrus using Turbo-BrainVoyager. Each run included 10 resting trials and 10 NF trials. All trials had a duration of 30 s. Functional images were acquired using a T2* weighted gradient-echo pulse imaging sequence (TR = 2400 ms; TE = 30 ms; flip angle = 90\u25e6; matrix = 68 x 68; slice thickness = 3.5 mm; voxel dimensions = 3.5 x 3.5 x 3.5 mm) providing whole brain coverage in 36 slices. Anatomical images were recorded using a T1-weighted MPRAGE sequence (TR = 2530 ms; TE = 2.26 ms; flip-angle = 9\u00b0; slice thickness = 1 mm; 256 x 256 acquisition matrix; voxel dimensions = 1 x 1 x 1 mm; TI = 900 ms). SPM 8 was used to preprocess and analyze functional whole brain data with the purpose of analyzing group effects. The derived spatial transformation was then applied to the realigned T2\u2217 volumes, which were spatially smoothed with a Gaussian kernel of 8-mm FWHM.<br \/>\n&nbsp;<br \/>\n<strong>Results<\/strong>:<br \/>\nLocalizer: To examine brain activation patterns during ME and MI of swallowing, we contrasted both conditions with the resting condition. During ME of swallowing, a large network of brain areas was active including the bilateral cerebellum, bilateral pre- and postcentral gyrus, basal ganglia, the insula, motor areas and the SMA. During MI of swallowing when no real-time feedback was provided (MI_offline), comparable brain areas were active than during ME of swallowing (Table 1). Neurofeedback training: Only the right precuneus showed a stronger activation during the second compared to the first run (voxels: 21, peak: x: 2, y: -64, z: 54, T-value: 5.94). Activity levels observed during the mental imagery offline task and the first feedback run in the feedback ROI correlated significant positively (r = 0.61, p &lt; 0.05). Hence, the higher the activation during the offline task, the higher the ability to up-regulate activation during feedback.<br \/>\n&nbsp;<br \/>\n<strong>Conclusions<\/strong>:<br \/>\nDuring neurofeedback training, participants were able to increase the activity in the left lateral precentral gyrus and in other brain regions, which are generally active during swallowing, compared to the motor imagery offline task. Our results indicate that motor imagery of swallowing is an adequate mental strategy to activate the swallowing network of the whole brain, which might be useful for future treatments of swallowing disorders.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u3053\u306e\u767a\u8868\u306f\uff0c \u753b\u50cf\u306b\u3088\u308b\u52d5\u304d\u306e\u30a4\u30e1\u30fc\u30b8\u3068\u5b9f\u969b\u306e\u5b9f\u884c\u6642\u306e\u8133\u306e\u904b\u52d5\u9818\u57df\u306e\u6d3b\u52d5\u306e\u6bd4\u8f03\u3067\u3042\u3063\u305f\uff0e\u3053\u308c\u306f\uff0cfNIRS\u3067\u624b\u3084\u8db3\u306e\u52d5\u304d\u3067\u7814\u7a76\u3055\u308c\u3066\u3044\u305f\u5185\u5bb9\u3092fMRI\u3092\u7528\u3044\u3066\u8133\u5168\u4f53\u3067\u89e3\u660e\u3057\u3088\u3046\u3068\u3059\u308b\u3082\u306e\u3067\u3042\u3063\u305f\uff0e\u3053\u306e\u7814\u7a76\u3067\u306f\uff0c\u5b9f\u884c\u6642\u306e\u307b\u3046\u304c\u30a4\u30e1\u30fc\u30b8\u3057\u3066\u3044\u308b\u3068\u304d\u3088\u308a\u3082\u591a\u304f\u306e\u8133\u9818\u57df\u304c\u6d3b\u52d5\u7684\u3067\u3042\u3063\u305f\u304c\uff0cleft-IFG\u306f\u30a4\u30e1\u30fc\u30b8\u6642\u306e\u307b\u3046\u304c\u5f37\u3044\u6d3b\u6027\u304c\u898b\u3089\u308c\u305f\u3068\u3044\u3046\u7d50\u679c\u3067\u3042\u3063\u305f\uff0e\u79c1\u306f\uff0cfMRI\u306e\u7814\u7a76\u3092fNIRS\u306b\u9069\u7528\u3059\u308b\u3053\u3068\u306f\u591a\u3044\u3088\u3046\u306b\u601d\u3063\u3066\u3044\u305f\u304c\uff0c\u9006\u306e\u30d1\u30bf\u30fc\u30f3\u3067\u3042\u3063\u305f\u305f\u3081\u8208\u5473\u3092\u6301\u3063\u305f\uff0e<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aAbstract and concrete conceptual representation in the inferior parietal lobe: fNIRS<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Maria\u00a0Montefinese1,2, Paola\u00a0Pinti3,4, Ettore\u00a0Ambrosini1,5,6, Ilias\u00a0Tachtsidis3, David Vinson2<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Poster Session<br \/>\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a<br \/>\n<strong>Introduction<\/strong>:<br \/>\nSimilarity measures can inform us about the nature of semantic representation: the knowledge we have of the world. Numerous theories of semantic representation exist, some based upon our sensorimotor experience as inferred from verbal features (e.g.\u00a0featural\u00a0similarity) or property ratings (e.g. affective content), others based on regularities in spoken and written language (e.g. lexical co-occurrence) [1]. Similarity measures may also be estimated from lexical measures (e.g. orthographic similarity) [2] and\u00a0behavioural\u00a0studies (e.g. word association) [3].\u00a0\u00a0A meta-analysis of 120 fMRI studies showed a left lateralized brain network in semantic processing [4], including the angular and\u00a0supramarginal\u00a0gyri. In particular, the left inferior parietal lobe (IPL) is one of the most informative regions for abstract and concrete word classification [5].\u00a0\u00a0However, no\u00a0neuroimaging\u00a0study has investigated which similarity measure best predicts patterns of brain activity in IPL, and whether this differs for abstract and concrete words. Here, we test this using fNIRS [6], which ensures a higher degree of ecological validity than fMRI. We expect the IPL to code different kinds of similarity measure depending on the word concreteness.<br \/>\n&nbsp;<br \/>\n<strong>Methods<\/strong>:<br \/>\n13 native English speakers (9 M, mean age: 26.7 years) performed a semantic decision task (is a word abstract or concrete?) on 160 visually presented words (80 abstract, 80 concrete) (Figure 1A). Stimuli were repeated over 6 runs. Hemodynamic changes in IPL were monitored bilaterally using a 20-channel fNIRS system\u00a0(LIGHTNIRS, sampling rate = 13.33 Hz; Figure 1B). Channels&#8217; locations were digitized and co-registered onto a standard MNI brain template. fNIRS data were corrected for motion artifacts and band-pass filtered (.01-.6 Hz) to remove physiological noise.\u00a0Oxy- and\u00a0deoxy-hemoglobin\u00a0(HbO2 and HbR) signals were then down-sampled to 3 Hz and analyzed with a General Linear Model (GLM) approach [7] to derive the beta-estimates for each word. This was carried out on the channels covering the IPL (green-filled circles in Figure 1B).\u00a0\u00a0The analysis involved: (i) a Representational Similarity (RS) Analysis [8] based on Spearman partial correlations, performed between the similarity matrices based on both neural activity patterns (Brain) and similarity measures across abstract and concrete concepts (Models). HbO2 and HbR were analyzed separately. We derived 5 theoretical models based on\u00a0featural\u00a0similarity, word association, lexical co-occurrence, affective content, and orthographic similarity (control model); (ii) a leave-one-out item-level multivariate classification analysis, carried out using the procedure in [9], to decode the concreteness category of single words (trial-level decoding). Subject-wise RSs and mean trial-level decoding accuracies were compared between hemispheres and abstract and concrete categories with GLM analysis.<br \/>\n&nbsp;<br \/>\n<strong>Results<\/strong>:<br \/>\nFor HbO2, we found different Brain-Model RSs for co-occurrence between abstract and concrete words, regardless of the hemisphere. This was due to a significant RS for concrete words only. Moreover, we found different Brain-Model RSs for affective content between hemispheres and word concreteness, with a significant Brain-Model RS for abstract words in the left hemisphere only. No significant Brain-Model RS were found for HbR, probably due to HbR smaller changes than HbO2 and consequent lack of statistical power [6].\u00a0\u00a0For HbO2, we also found lower accuracies for word association-based decoding for concrete words as compared to abstract ones, regardless of the hemisphere.<br \/>\n<strong>\u00a0<\/strong><br \/>\n<strong>Conclusions<\/strong>:<br \/>\nThese results indicate that IPL represents semantic-affective information depending on word concreteness, implying that semantic representations of abstract and concrete words are governed by different organizational principles. Moreover, they suggest the feasibility of multivariate methods as a tool for fNIRS decoding of pattern-based neural correlates of cognition.<br \/>\n&nbsp;<br \/>\n&nbsp;<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u3053\u306e\u767a\u8868\u306f\uff0c LIGHTNIRS\u3092\u7528\u3044\u3066\u7814\u7a76\u3055\u308c\u3066\u3044\u307e\u3057\u305f\uff0e\u672c\u7814\u7a76\u306f\uff0c\u63d0\u793a\u3055\u308c\u305f\u5358\u8a9e\u306b\u5bfe\u3057\u3066\u610f\u5473\u6c7a\u5b9a\u30bf\u30b9\u30af\u3092\u5b9f\u884c\u3059\u308b\u3053\u3068\u306b\u3088\u308a\uff0c\u795e\u7d4c\u6d3b\u52d5\u30d1\u30bf\u30fc\u30f3\u3068\u5358\u8a9e\u306e\u985e\u4f3c\u5ea6\u3092\u6e2c\u5b9a\u3059\u308b\u3082\u306e\u3067\u3057\u305f\uff0e\u307e\u305f\uff0c\u591a\u5909\u91cf\u30d1\u30bf\u30fc\u30f3\u89e3\u6790\uff08Multivariate pattern analysis: MVPA\uff09[2]\u306b\u3088\u308a\uff0c\u30b0\u30eb\u30fc\u30d7\u89e3\u6790\u304c\u3055\u308c\u3066\u3044\u307e\u3057\u305f\uff0e\u3053\u306e\u624b\u6cd5\u306e\u5b9f\u73fe\u53ef\u80fd\u6027\u3092\u793a\u5506\u3057\u3066\u304a\u308a\uff0c\u4eca\u5f8c\u306e\u7814\u7a76\u306b\u4f7f\u7528\u3059\u308b\u3053\u3068\u306f\u53ef\u80fd\u304b\u8abf\u67fb\u3057\u305f\u3044\u3068\u601d\u3044\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Monitoring Brain Activity during Rhythmic Music Therapy: an fNIRS Investigation<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aSabrina Brigadoi1 , Federico Curzel1 , Simone Cutini1<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Poster Session: NIRS<br \/>\nAbstruct \uff1a<br \/>\n<strong>Introduction<\/strong>: Music therapy is a method used in movement disorders, such as Parkinson disease (Thaut et al., 1996), relying on the influence of rhythmic stimulation on movement coordination (Chen et al., 2006; Palomar-Garc\u00eda et al., 2016). Functional near-infrared spectroscopy (fNIRS) is a non-invasive and portable optical imaging technique that can be used to monitor brain activity in tasks involving participants&#8217; movements (Cutini et al., 2014). In this study, we used fNIRS to monitor brain activity of healthy participants whilst performing a rhythmic music therapy session playing the drums. The aim was to detect modulations of hemodynamic activity in areas involved in music perception and movement control depending on the type of exercise performed to elucidate the mechanisms of movement facilitation.<br \/>\n<strong>\u00a0<\/strong><br \/>\n<strong>Methods<\/strong>: Twenty-six healthy volunteers (mean age 21.27\u00b12.74 years) performed a paradigm composed of 8 parts. The first and the eighth part were a passive listening of a track with a harmonic and rhythmic line, the other parts were rhythmic-motor tasks. Ten blocks of 18 s were administered for each task type, followed by a rest period (13-18 s). Participants were asked to play two MIDI pads placed in front of them (Fig. 1a). In the second task (ex2), participants were asked to produce a constant rhythm of 1 Hz with no external support. In the thir For each participants and task condition, the average time interval between consecutive beats was computed. The difference between this interval and 55 bpm was computed as behavioral metric. Hemodynamic data were acquired with the ISS Imagent system (8 detectors-32 sources). A symmetric probe was created covering motor-temporal areas, with a total of 44 standard channels (3 cm) and 2 short-separation (SS) channels (Fig. 1b).Homer2 (Huppert et al., 2009) was used to preprocess the data using spline interpolation and wavelet methods for motion artifact correction and a band-pass filter (0.01-0.5 Hz). SS channels were used to remove physiological noise. The average value of the HRF in the interval 5-15 s from stimulus onset was computed and submitted to statistical analyses. Both behavioral and hemodynamic data were analyzed with repeated measures ANOVAs, followed by paired t-tests.<br \/>\n&nbsp;<br \/>\n<strong>Results<\/strong>: Participants improved their rhythmic ability during and after the training (F(6,192)=37.476; p&lt;.001), producing in ex7 a closer rhythm to the expected one than in the first two tasks (ex2 vs. ex7: t(32)=4.49, p &lt; .001; ex3 vs. ex7: t(32)=3.16, p = .003; Fig. 2a). \u00a0Hemodynamic data showed a different involvement of the motor and temporal areas during the different tasks, with less channels being activated during ex5, ex6 and ex7 compared to the initial tasks. Type of task had a significant impact on modulation of hemodynamic activity depending on the channel (F(126,3150)=1.557, p &lt; .001). Seven channels broadly located in the supplementary motor area (SMA, Fig. 2b,c,d) resulted to be modulated depending on the task.<br \/>\n&nbsp;<br \/>\n<strong>Conclusions<\/strong>: Results demonstrate that the proposed paradigm modulates brain activity mainly in the SMA, suggesting that this could be a suitable paradigm to test the effects of music therapy on patients with pathologies affecting movements. SMA was found to have a fundamental predictive role in motor synchronization (Chen et al., 2006). Furthermore, our results suggest the importance and power of rhythm in movement facilitation, how melody can further strength this effect and our ability to internalize the rhythm once learnt. Next step will be the use of this paradigm on Parkinson&#8217;s patients to understand how music therapy can modulate brain activity in their &#8220;damaged&#8221; brain, aiming to achieve an individual treatment.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u3053\u306e\u767a\u8868\u306f\uff0c fNIRS\u3092\u7528\u3044\u3066\u904b\u52d5\u4fc3\u9032\u306e\u30e1\u30ab\u30cb\u30ba\u30e0\u3092\u660e\u3089\u304b\u306b\u3059\u308b\u305f\u3081\u306b\u884c\u308f\u308c\uff0c\u904b\u52d5\u306e\u7a2e\u985e\u306b\u5fdc\u3058\u3066\u97f3\u697d\u306e\u77e5\u899a\u3068\u904b\u52d5\u5236\u5fa1\u306b\u95a2\u3059\u308b\u9818\u57df\u306e\u8840\u884c\u52d5\u614b\u6d3b\u52d5\u306e\u5909\u8abf\u3092\u691c\u51fa\u3057\u305f\u3082\u306e\u3067\u3057\u305f\uff0eHomer2\u3092\u7528\u3044\u3066\u89e3\u6790\u3055\u308c\u3066\u304a\u308a\uff0c\u4f53\u52d5\u9664\u53bb\u624b\u6cd5\u306b\u306f\u30b9\u30d7\u30e9\u30a4\u30f3\u88dc\u9593\u3068wavelet\u306e2\u7a2e\u985e\u304c\u7528\u3044\u3089\u308c\u3066\u3044\u307e\u3057\u305f\uff0e\u307e\u305f\uff0c\u8ce6\u6d3b\u89e3\u6790\u306b\u306f\u523a\u6fc0\u5f8c5-15[s]\u306e\u9593\u306eHRF\u304c\u7528\u3044\u3089\u308c\u3066\u3044\u307e\u3057\u305f\uff0e\u672c\u7814\u7a76\u3067\u306f\u904b\u52d5\u306b\u306fSMA\u304c\u5f71\u97ff\u3092\u4e0e\u3048\u308b\u3053\u3068\u304c\u793a\u3055\u308c\u3066\u3044\u305f\uff0e\u904b\u52d5\u306b\u95a2\u3059\u308b\u7814\u7a76\u3067\u3042\u308a\uff0c\u540c\u3058\u89e3\u6790\u30bd\u30d5\u30c8\u304c\u7528\u3044\u3089\u308c\u3066\u3044\u305f\u305f\u3081\uff0c\u81ea\u8eab\u306e\u7814\u7a76\u306e\u53c2\u8003\u306b\u3057\u3066\u3044\u304d\u305f\u3044\u3068\u601d\u3044\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n\u53c2\u8003\u6587\u732e<br \/>\n[1]\u3000OHBM2019 Annual meeting,<br \/>\n<a href=\"https:\/\/www.humanbrainmapping.org\/i4a\/pages\/index.cfm?pageID=3882&amp;activateFull=true\">https:\/\/www.humanbrainmapping.org\/i4a\/pages\/index.cfm?pageID=3882&amp;activateFull=true<\/a><br \/>\n&nbsp;<br \/>\n[2]\u3000 Emberson, Lauren L., et al. &#8220;Decoding the infant mind: Multivariate pattern analysis (MVPA) using fNIRS.&#8221;\u00a0<em>PloS one<\/em>12.4 (2017):\u00a0e0172500.<br \/>\n<strong>\u5b66\u4f1a\u53c2\u52a0\u5831\u544a\u66f8<\/strong><\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"147\"><strong>\u00a0<\/strong><br \/>\n<strong>\u5831\u544a\u8005\u6c0f\u540d<\/strong><\/td>\n<td width=\"373\">&nbsp;<br \/>\n\u5927\u585a\u53cb\u6a39<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u767a\u8868\u8ad6\u6587\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n<td width=\"373\">\u5b89\u9759\u6642\u304a\u3088\u3073\u7791\u60f3\u6642\u306b\u304a\u3051\u308b\u6a5f\u80fd\u7684\u63a5\u7d9a\u6027\u306e\u500b\u4eba\u9593\u304a\u3088\u3073\u500b\u4eba\u5185\u5909\u52d5\u306e\u691c\u8a0e<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u767a\u8868\u8ad6\u6587\u82f1\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n<td width=\"373\">Intra- and inter-individual variation in the resting- and<br \/>\nmeditative-state functional connectivity<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u8457\u8005<\/strong><\/td>\n<td width=\"373\">\u5927\u585a\u53cb\u6a39, \u65e5\u548c\u609f, \u5ee3\u5b89\u77e5\u4e4b<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u4e3b\u50ac<\/strong><\/td>\n<td width=\"373\">Organization for human brain mapping<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u8b1b\u6f14\u4f1a\u540d<\/strong><\/td>\n<td width=\"373\">OHBM2019<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u4f1a\u5834<\/strong><\/td>\n<td width=\"373\">Auditorium Parco Della Musica<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u958b\u50ac\u65e5\u7a0b<\/strong><\/td>\n<td width=\"373\">2019\/06\/09-2019\/06\/13<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<br \/>\n&nbsp;<\/p>\n<ol>\n<li>\u8b1b\u6f14\u4f1a\u306e\u8a73\u7d30<\/li>\n<\/ol>\n<p>2019\/06\/09\u304b\u30892019\/06\/13\u306b\u304b\u3051\u3066\uff0c\u30ed\u30fc\u30de\u306b\u3066\u958b\u50ac\u3055\u308c\u307e\u3057\u305f2019 OHBM annual meeting\u306b\u53c2\u52a0\u3057\u307e\u3057\u305f\uff0e\u3053\u306e2019 OHBM annual meeting\u306f\uff0cOrganization for human brain mapping\u306b\u3088\u3063\u3066\u4e3b\u50ac\u3055\u308c\u305f\u56fd\u969b\u4f1a\u8b70\u3067\uff0c\u30d2\u30c8\u306e\u8133\u7d44\u7e54\u304a\u3088\u3073\u8133\u6a5f\u80fd\u306e\u30de\u30c3\u30d4\u30f3\u30b0\u306b\u95a2\u3059\u308b\u7814\u7a76\u306b\u643a\u308f\u308b\u69d8\u3005\u306a\u80cc\u666f\u3092\u6301\u3064\u7814\u7a76\u8005\u3092\u96c6\u3081\uff0c\u3053\u308c\u3089\u306e\u79d1\u5b66\u8005\u306e\u30b3\u30df\u30e5\u30cb\u30b1\u30fc\u30b7\u30e7\u30f3\uff0c\u304a\u3088\u3073\u6559\u80b2\u3092\u4fc3\u9032\u3059\u308b\u3053\u3068\u3092\u76ee\u7684\u306b\u958b\u50ac\u3055\u308c\u3066\u3044\u307e\u3059<sup>\uff08\uff11<\/sup>\uff0e\u672c\u7814\u7a76\u5ba4\u304b\u3089\u306f\u4ed6\u306b\uff0c\u5ee3\u5b89\u5148\u751f\uff0c\u65e5\u548c\u5148\u751f\uff0cM2\u306e\u5b66\u751f\u3068\u3057\u3066\u53e4\u5bb6\uff0c\u5965\u6751(\u99ff)\uff0c\u5409\u7530\uff0c\u5c71\u672c\uff0c\u6749\u91ce\uff0cM1\u306e\u5b66\u751f\u3068\u3057\u3066\u98a8\u5442\u8c37\uff0c\u4e39\u304c\u53c2\u52a0\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<ol start=\"2\">\n<li>\u7814\u7a76\u767a\u8868\n<ul>\n<li>\u767a\u8868\u6982\u8981<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<p>\u79c1\u306f11\u65e5\u306e\u30dd\u30b9\u30bf\u30fc\u30bb\u30c3\u30b7\u30e7\u30f3\u304a\u3088\u3073\u30dd\u30b9\u30bf\u30fc\u30ec\u30bb\u30d7\u30b7\u30e7\u30f3\u306b\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u767a\u8868\u306e\u5f62\u5f0f\u306f\u30dd\u30b9\u30bf\u30fc\u767a\u8868\u3067\uff0c\u8a083\u6642\u9593\u53c2\u52a0\u8005\u306e\u65b9\u3068\u8b70\u8ad6\u3092\u884c\u3044\u307e\u3044\u3057\u305f\uff0e<br \/>\n\u4eca\u56de\u306e\u767a\u8868\u306f\uff0c\u300cIntra- and inter-individual variation in the resting- and meditative-state functional connectivity\u300d\u306b\u3064\u3044\u3066\u767a\u8868\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u4ee5\u4e0b\u306b\u6284\u9332\u3092\u8a18\u8f09\u81f4\u3057\u307e\u3059\uff0e<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">Introduction<br \/>\nConventional studies on the neural basis of mindfulness meditation did not consider the intra-individual variation in the brain state, as they assumed it to be small compared with the inter-individual variation. However, the brain functional connectivity (FC) networks present variability within an individual as well. As shown in Fig. 1, result reproducibility would be poor in a brain region showing larger intra- than inter-individual variation, as the group analyses would be strongly influenced by intra-individual variation. Therefore, inter- and intra-individual variation during meditation were investigated in the present study through repeated measures of the same participant on different days to determine the reliability of the estimates on the brain functional network involved in meditation.<br \/>\n&nbsp;<br \/>\nMethods<br \/>\nA total of 21 healthy novice meditators (22.9 \u00b1 2.5 years; 6 females, 15 males) performed breath-counting meditation for 5 minutes, which consisted of the mental counting of breaths, following a 5-minute resting state in an fMRI scanner. Specifically, the same task was performed 10 times on different days in 3 subjects (i.e., subjects A, B and C). Furthermore, the FC matrix among the brain regions was calculated for the 116 regions defined by the automated anatomical labeling (AAL). In addition, the Pearson&#8217;s correlation coefficient between the FC matrices of participants in the group of novices and those of participants A, B, C during both the resting and meditative state was calculated and defined as the similarity matrix. The similarity matrix was converted into the dissimilarity matrix. It was used for the distance measure of classical multidimensional scaling (MDS) to visualize the FC matrices during the resting and meditative state on a two-dimensional space (M.L. Davison 1983). Finally, the variation of the functional connectivity matrix between the experimental conditions and within individuals were compared.<br \/>\n&nbsp;<br \/>\nResults<br \/>\nAs illustrated in Fig. 2, the brain functional connectivity matrix during both the resting and meditative states were visualized for each subject implanted in the 2D space by MDS and the distance between the 21 participants of the novice group and the 3 subjects (i.e., A, B, C) was measured. While the solid ellipse represents the meditative state, the dotted ellipse shows the 95% confidence interval in the resting state bivariate normal distribution, whereas the comparison between the intra-individual variation of the subjects A, B and C and the group variation is displayed in Fig. 2 (b-d). These results indicate a smaller intra-individual variation of subjects A, B, C within the resting and meditative states during the day than the inter-individual one of the group. Our finding suggests that participants&#8217; inter-individual variation has a greater influence on the brain states than the daily fluctuation. Furthermore, as reported in Fig. 2 (e), the distance between subjects at rest and during meditation was significantly larger (p &lt;0.05) than that between the rest and meditation states in the same subject. This result revealed that the inter-individual variation in both the resting and meditative states is greater than the change in FC networks associated with meditation.<br \/>\nConclusion<br \/>\nIn the current study, the intra- and inter-individual variation in the brain functional network involved in meditation were investigated using MDS. Our results indicated that the inter-individual variation in the resting and meditative states is significantly larger than the fluctuation from resting to meditation in the same individual. This suggests that the variation among individuals is larger than that associated with the quality of meditation in a given individual.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<ul>\n<li>\u8cea\u7591\u5fdc\u7b54<\/li>\n<\/ul>\n<p>\u4eca\u56de\u306e\u8b1b\u6f14\u767a\u8868\u3067\u306f\uff0c\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u8cea\u7591\u3092\u53d7\u3051\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>1<\/strong><br \/>\n\u8cea\u554f\u306f\uff0c\u88ab\u9a13\u8005\u306f\u7791\u60f3\u5b9f\u8df5\u8005\u304b\u521d\u5fc3\u8005\u304b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\u306f\u521d\u5fc3\u8005\u3067\u3059\u3068\u7b54\u3048\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>2<\/strong><br \/>\n\u8cea\u554f\u306f\uff0c\u3069\u3046\u3084\u3063\u3066\u89e3\u6790\u3057\u3066\u3044\u308b\u306e\u304b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\u306f\uff0c\u5404\u88ab\u9a13\u8005\u306eFC matrix\u3092MDS\u3092\u7528\u3044\u3066\u4e8c\u6b21\u5143\u306b\u843d\u3068\u3057\u8fbc\u307f\u305d\u308c\u305e\u308c\u306eFC matrix\u306e\u8ddd\u96e2\u304b\u3089\u3070\u3089\u3064\u304d\u3092\u691c\u8a0e\u3057\u3066\u3044\u307e\u3059\u3068\u7b54\u3048\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>3<\/strong><br \/>\n\u8cea\u554f\u306f\uff0c\u3069\u3046\u3084\u3063\u3066\u4f4e\u6b21\u5143\u306b\u30de\u30c3\u30d4\u30f3\u30b0\u3057\u3066\u3044\u308b\u306e\u304b\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\u306f\uff0c\u8ddd\u96e2\u884c\u5217\u3092\u57fa\u306b\u3057\u305fMDS\u3092\u4f7f\u3063\u3066\u30de\u30c3\u30d4\u30f3\u30b0\u3057\u305f\u3068\u7b54\u3048\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>4<\/strong><br \/>\n\u8cea\u554f\u306f\uff0crest\u3068meditation\u306f\u9055\u3046\u306e\u304b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\u306f\uff0c\u8ddd\u96e2\u884c\u5217\u884c\u5217\u304b\u3089\u985e\u4f3c\u5ea6\u304c\u9ad8\u3044\u3068\u7b54\u3048\u307e\u3057\u305f\uff0e<br \/>\n<strong>\u00a0<\/strong><br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>5<\/strong><br \/>\n\u8cea\u554f\u306f\uff0c\u8ddd\u96e2\u884c\u5217\u3092\u6c42\u3081\u308b\u624b\u6cd5\u306f\u4f55\u304b\u6587\u732e\u3092\u53c2\u8003\u306b\u3057\u305f\u306e\u304b\uff0c\u305d\u308c\u3068\u3082\uff0c\u30aa\u30ea\u30b8\u30ca\u30eb\u306a\u306e\u304b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\u306f\uff0cFunctional Brain Networks Are Dominated by Stable Group and Individual Factors, Not Cognitive or Daily Variation\u306e\u8ad6\u6587\u3092\u53c2\u8003\u306b\u3057\u305f\u3068\u7b54\u3048\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>6<\/strong><br \/>\n\u8cea\u554f\u306f\uff0c\u7791\u60f3\u306f\u4f55\u3092\u884c\u306a\u3063\u3066\u3044\u308b\u306e\u304b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\u306f\uff0c\u6570\u606f\u89b3\u3092\u884c\u306a\u3063\u3066\u3044\u308b\u3068\u7b54\u3048\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>7<\/strong><br \/>\n\u8cea\u554f\u306f\uff0c MDS\u306e\u30d7\u30ed\u30c3\u30c8\u3092\u3069\u3046\u3084\u3063\u3066\u5b9a\u91cf\u5316\u3057\u305f\u306e\u304b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\u306f\uff0c\u4f4e\u6b21\u5143\u306b\u843d\u3068\u3057\u8fbc\u3080\u524d\u306e\u8ddd\u96e2\u884c\u5217\u3092\u57fa\u306b\u5b9a\u91cf\u5316\u3092\u3057\u305f\u3068\u7b54\u3048\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>8<\/strong><br \/>\n\u8cea\u554f\u306f\uff0c\u8cea\u554f\u7d19\u3084behavior\u306f\u53d6\u3063\u3066\u3044\u308b\u306e\u304b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\u306f\uff0c\u8cea\u554f\u7d19\u306f\u53d6\u3063\u3066\u3044\u308b\u304cbehavior\u306f\u53d6\u3063\u3066\u3044\u306a\u3044\u3068\u7b54\u3048\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>9<\/strong><br \/>\n\u8cea\u554f\u306f\uff0c\u500b\u4eba\u306e\u7279\u5fb4\u3068\u306f\u4f55\u304b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\u306f\uff0c\u6027\u5225\u3084\u5e74\u9f62\u306a\u3069\u3068\u7b54\u3048\u307e\u3057\u305f\uff0e<\/p>\n<ul>\n<li>\u611f\u60f3<\/li>\n<\/ul>\n<p>\u30fb\u53bb\u5e74\u306b\u5f15\u304d\u7d9a\u304d\uff0c2\u5ea6\u76ee\u306eOHBM\u3067\u306e\u767a\u8868\u3067\u3057\u305f\uff0e\u53bb\u5e74\u3068\u306f\u7570\u306a\u308a\uff0c1\u65e5\u306e\u307f\u306e\u767a\u8868\u3067\u3057\u305f\u304c\uff0c\u53bb\u5e74\u3088\u308a\u3082\u591a\u304f\u306e\u65b9\u304c\u8074\u304d\u306b\u6765\u3066\u304f\u3060\u3055\u308a\uff0c\u305f\u304f\u3055\u3093\u306e\u65b9\u3068\u8b70\u8ad6\u3092\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3057\u305f\uff0e\u3070\u3089\u3064\u304d\u304c\u30c6\u30fc\u30de\u306e\u7814\u7a76\u304c\u591a\u304f\u3042\u308a\uff0c\u81ea\u8eab\u306e\u7814\u7a76\u30c6\u30fc\u30de\u304c\u4e16\u754c\u304b\u3089\u6ce8\u76ee\u3092\u96c6\u3081\u3066\u3044\u308b\u3053\u3068\u3092\u5b9f\u611f\u3057\u307e\u3057\u305f\uff0e\u4eca\u56de\u306f\uff0c\u767a\u8868\u307e\u3067\u306e\u6e96\u5099\u304c\u4e0d\u5341\u5206\u3067\u3042\u308a\u53cd\u7701\u3059\u308b\u5fc5\u8981\u304c\u3042\u308b\u3068\u601d\u3044\u307e\u3057\u305f\uff0e\u8a08\u753b\u3092\u7acb\u3066\u308b\u3053\u3068\u306e\u5fc5\u8981\u6027\u3092\u611f\u3058\u307e\u3057\u305f\uff0e\u4eca\u5f8c\u306f\uff0c\u4eca\u307e\u3067\u4ee5\u4e0a\u306bwbs\u3092\u6d3b\u7528\u3057\u306a\u304c\u3089\u7814\u7a76\u3092\u9032\u3081\u3066\u3044\u304d\u305f\u3044\u3068\u601d\u3044\u307e\u3059\uff0e<br \/>\n&nbsp;<\/p>\n<ol start=\"3\">\n<li>\u8074\u8b1b<\/li>\n<\/ol>\n<p>\u4eca\u56de\u306e\u8b1b\u6f14\u4f1a\u3067\u306f\uff0c\u4e0b\u8a18\u306e2\u4ef6\u306e\u767a\u8868\u3092\u8074\u8b1b\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Individual Variability of Functional Connectivity in Resting-State and Naturalistic fMRI Paradigms<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Mark O&#8217;Reilly<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Poster session<br \/>\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Introduction\uff1a\u3000Resting state fMRI studies are often criticized due to their lack of control over the cognitive states of individuals during observation, which may lead to increased intersubject variability (ISV) in estimates of functional connectivity. Mueller and colleagues (2013) estimated the ISV of the functional connectivity architecture in the human brain at rest, finding higher variability in heteromodal association cortex and lower variability in unimodal areas such as primary sensory or motor cortex. Engaging movies have been shown to increase intersubject correlations (ISC) of neural activity in sensory and other regions (Hasson 2010), suggesting an alignment of cognitive states across individuals based on the events of the movie, potentially reducing intersubject variability in connectivity estimates. The objective of this study is to investigate the differences in ISV of functional connectivity between rest and movie conditions.<br \/>\nMethods: Minimally preprocessed 7T data of 25 subjects (25 F, Age 31-35) from the Human Connectome Project (Van Essen 2013) were analyzed, with each subject providing four 15-minute resting state sessions and four 15-minute movie stimulation sessions. The Glasser parcellation (Glasser 2016) was used to identify 180 regions of interest (ROI) per hemisphere in each subject for further analysis. A functional correlation matrix was computed for each subject and each session (100 total), where each row or column represents the connectivity fingerprint, a 1 x 360 vector of correlations, for a particular ROI. For each ROI, the variability across the 25 subjects was quantified by averaging the correlation values between each pairing of subject fingerprints and inverting the averaged value to attain a measure of dissimilarity. This was performed for each session and averaged across sessions to get a single measure of dissimilarity. To remove effects of measurement noise, intrasubject variability was calculated using a similar method but using dissimilarity across sessions, instead of subjects. The intrasubject variability was regressed from the intersubject variability to obtain a final variability value. This process was performed in both conditions to obtain two variability maps. The movie variability was subtracted from the rest variability to highlight differences between the conditions. Finally, we speculated that ISC in the movie might explain reduced ISV, so ISC values of the movie BOLD timeseries were calculated by region to determine the overlap between ISC and differences in variability of functional connectivity between conditions.<br \/>\nResults: To test if ISV differences between conditions were associated with ISC values in the movie condition, we computed the correlation between the two variables and found them to be moderately correlated across the brain (r=0.554, p&lt;0.0001). The variability difference map is presented in Fig. 1. Regions with notable differences between the conditions include prefrontal and superior temporal cortex. Differences observed in the superior temporal cortex may suggest that the movie induces more stable connectivity in structures involved in speech processing and auditory sensation across participants than the rest condition. The differences observed in the prefrontal cortex may suggest a similar conclusion for structures involved in high level cognitive processes. When we compare the difference map to the ISC map shown in Fig.2, we observe that high levels of ISC are present in the superior temporal, but not in prefrontal cortex.<br \/>\nWe validate MN-SRM (a hyperalignment method) on real data: our ECM algorithm estimates far fewer parameters than SRM by integrating the projection matrices instead of the shared time series. As a result, we show modest improvements in out-of-sample reconstruction relative to SRM on the sherlock and raider datasets (Chen et al., 2016; Haxby et al., 2011).<br \/>\nConclusions: We analyzed the spatial distribution of differences in intersubject variability between the rest and movie conditions. We also found that variability differences exist in superior temporal and prefrontal cortex. We also found an association between these differences and ISC values in the movie condition. This suggest that the high ISC in the movie may result in more stable estimates of functional connectivity across subjects.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u3053\u306e\u767a\u8868\u306f\uff0c\u5b89\u9759\u6642\u3068\u6620\u753b\u3092\u898b\u3066\u3044\u308b\u6642\u306e\u500b\u4eba\u9593\u3068\u500b\u4eba\u5185\u306e\u3070\u3089\u3064\u304d\u3092\u691c\u8a0e\u3057\u3066\u3044\u307e\u3057\u305f\uff0e\u89e3\u6790\u624b\u6cd5\u304c\u81ea\u5206\u306e\u3082\u306e\u3068\u4f3c\u3066\u304a\u308a\uff0c\u81ea\u5206\u306e\u7814\u7a76\u3067\u306f\uff0cFC matrix\u9593\u306e\u76f8\u95a2\u3092\u6c42\u3081\u3066\u3044\u308b\u304c\uff0c\u3053\u306e\u7814\u7a76\u3067\u306f\uff0c\u9818\u57df\u3054\u3068\u306b\u88ab\u9a13\u8005\u9593\u3068\u88ab\u9a13\u8005\u5185\u306e\u30bb\u30c3\u30b7\u30e7\u30f3\u9593\u306e\u76f8\u95a2\u3092\u6c42\u3081\u3066\u3044\u307e\u3057\u305f\uff0e\u81ea\u5206\u306e\u7814\u7a76\u3067\u3082\u8a66\u3057\u3066\u307f\u308b\u4fa1\u5024\u304c\u3042\u308b\u3068\u8003\u3048\u3066\u3044\u307e\u3059\uff0e<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aHemispheric Difference in Group, Task and Individual-dependent Variation of Functional Networks<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Chenxi Zhao, Yaya Jiang, Xinhu Jin, Gaolang Gong<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Poster session<br \/>\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Introduction\uff1a As a topic of general interest, individual difference of human brain has been intensively studied[1,2]. By comparing human with monkeys, a previous study has shown human-specific left-lateralized anatomical variations[3], suggesting a hemisphere-dependent variation in particular brain phenotypes. Recently, the contribution of the group, task, and individual factors to the variation in whole-brain functional networks has been detangled[2]. Following this, the present study aims to examine the magnitudes of group, task and individual-dependent variations in the two hemispheric functional networks, and further evaluate the hemispheric differences. In addition, the heritability of the hemispheric differences in each type of variation was assessed.<br \/>\nMethods: In total, 933 human connectome project (HCP) subjects (508 females,age: 22-37,212 monozygotic twins) with resting-state and task fMRI scans(emotion, language, motor, working memory) were included. All images were preprocessed by the HCP pipeline and then feed into the GRETNA toolbox[4] to do linear detrending(only resting-state fMRI), nuisance signals regression and temporally filtering (resting-state:0.01\u20130.1 Hz, tasks:&gt;0.01 Hz). The AICHA atlas[5] was used to define the nodes of network(186 in each hemisphere). Functional connectivity between each within-hemispheric node pair was defined by the Pearson correlation of mean time series (z transformed). For each hemisphere, a network similarity matrix was calculated by correlating among the linearized upper triangles of hemispheric network matrices(Fig1A). As did by Gratton et al.[2], the group, task, and individual-dependent variations were calculated per subject as following: 1) the average similarity from different individuals and tasks(group, baseline), 2) the added similarity from the same task but different individuals relative to group(task), and 3) the added similarity from the same subject but different tasks relative to group(individual). The task and individual-dependent variations were compared with the group-dependent variation using paired t-tests. To test the hemispheric differences, two hemispheric variations attributable to each factor were compared using a paired t-test. For each significantly lateralized effect, the asymmetry index (AI=(L-R)\/(L+R)) was calculated and its heritability (h2) was estimated using the SOLAR software[6]. Multiple comparisons were corrected by the Bonferroni method (p&lt;0.05).<br \/>\nResults: As shown in Fig1, both left and right hemispheric (LH and RH) functional networks showed substantial similarity across group (LH\/RH:0.46\u00b10.03\/0.44\u00b10.03) and added similarity of networks from the same individual (LH\/RH:0.18\u00b10.08\/0.17\u00b10.07), whereas subtle but significant added similarity due to task (LH\/RH:0.09\u00b10.04\/0.1\u00b10.04). Paired t-tests showed significant left-lateralized contributions of the group (t=26.8,p=0) and individual (t=4.9,p=10-6) factors to network variation, but a right-lateralized contribution of the task factor (t=20.1,p=0) (Fig2). As listed in Table1, the h2 was significant for the AI of group (h2=0.18,p=0.002) and individual-dependent variation (h2=0.24,p=7\u00d710-5) but non-significant for the task (h2=0.18,p=0.027, not surviving the multiple-comparison correction).<br \/>\nConclusions: Our results demonstrated a strong hemispheric functional network stability (group-shared organization and individual features) and moderate state-dependence. Intriguingly, the variation attributable to either the group, task, or individual factors markedly differed between the two hemispheres: shared group-level factor and individual-specific features had stronger influences on the LH network organization, while state-changes had a greater impact on the RH network. Furthermore, our heritability results indicated a significant genetic role in hemispheric differences in group and individual-dependent variations, though tenuously. These findings together provide novel insight into the hemispheric functional network organization and its lateralization.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u3053\u306e\u767a\u8868\u3067\u306f\uff0cHCP\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f7f\u7528\u3057\u3066\u304a\u308a\u88ab\u9a13\u8005\u6570\u304c933\u4eba\u3067\u30bf\u30b9\u30af\u3068\u500b\u4eba\u7279\u6027\u306b\u304a\u3051\u308b\u8133\u6a5f\u80fd\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u3070\u3089\u3064\u304d\u306b\u3064\u3044\u3066\u691c\u8a0e\u3057\u3066\u3044\u307e\u3057\u305f\uff0eTask\u306f\uff0cemotion, language, motor, working memory\u3092\u4f7f\u7528\u3057\u3066\u3044\u307e\u3057\u305f\uff0e\u7d50\u679c\u306f\uff0c\u8133\u6a5f\u80fd\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306f\u30bf\u30b9\u30af\u3088\u308a\u3082\u500b\u4eba\u7279\u6027\u306e\u5f71\u97ff\u3092\u53d7\u3051\u308b\u3068\u3055\u308c\u3066\u3044\u307e\u3057\u305f\uff0e933\u4eba\u3068\u3044\u3046\u591a\u304f\u306e\u88ab\u9a13\u8005\u3067\u305d\u306e\u3053\u3068\u304c\u793a\u3055\u308c\u3066\u3044\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Individual-Specific Areal-Level Parcellations Improve Functional Connectivity Prediction of Behavior<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Ru Kong, Qing Yang, Evan Gordon, Xinian Zuo, Avram Holmes, Simon B. Eickhoff, B. T. Thomas Yeo<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Poster session<br \/>\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Introduction\uff1a\u3000A The human cerebral cortex comprises hundreds of functionally distinct areas, which are in turn organized into at least ten to twenty large-scale networks. Most resting-state fMRI (rs-fMRI) parcellations have relied on group-averaged data[1\u20133], which might obscure individual-specific topographic features[4, 5]. Here, we propose an approach to generate individual-specific areal-level parcellations and show that the resulting parcellations can improve individual predictions of behavioral phenotypes based on functional connectivity (FC).<br \/>\nMethods: We have previously proposed and validated a multi-session hierarchical Bayesian model (MS-HBM) for estimating individual-specific cortical networks[6]. The multiple layers of the MS-HBM explicitly differentiate intra-subject (within-subject) from inter-subject (between-subject) network variability. In contrast to MS-HBM, previous network mappings ignore intra-subject variability, so might confuse intra-subject variability for inter-subject differences.<br \/>\n&nbsp;<br \/>\nWe have previously utilized MS-HBM for network parcellations[6].To estimate areal-level parcellations, the MS-HBM could be re-trained by initializing with a group-level areal-level parcellation (e.g., Schaefer2018[7]). Furthermore, we constrained the individual-specific parcels to be within 30mm of the group-level parcels, since previous studies suggest that individual variation in cortical areal location can go up to 30mm[8].<br \/>\n&nbsp;<br \/>\nWe compared MS-HBM with a well-known individual-specific parcellation approach (Gordon2017[5]). We considered rs-fMRI from 10 subjects (10 sessions each) in the MSC dataset[5]. Each subject was parcellated using all rs-fMRI sessions. Task inhomogeneity[5, 7] (standard deviation of task activation within each parcel) was then evaluated using task-fMRI data from the same subjects. A lower task inhomogeneity indicates better parcellation quality. For fair comparison, the number of MS-HBM parcels were constrained to be the same as Gordon2017.<br \/>\n&nbsp;<br \/>\nSecond, we considered ICA-FIX denoised rs-fMRI data from the HCP S1200 release[9]. We selected 58 behavioral measures across cognition, personality and emotion[6]. Individual-specific MS-HBM parcellations were estimated for subjects with four runs and no missing behavior (N = 752). For each subject, we obtained 400\u00d7400 FC matrices using the 400-area group-level parcellation (Schaefer2018) or the 400-area individual-specific MS-HBM parcellations. The FC matrices were then used for predicting the 58 behavioral measures using kernel ridge regression[10]. We performed 20-fold cross-validation: kernel ridge regression was trained on 19 folds and used to predict behavior in the test fold. The regularization parameter was determined using inner-loop cross-validation. Furthermore, the 20-fold cross-validation was repeated 100 times[6].<br \/>\nResults: Individual-specific MS-HBM parcellations achieved better task inhomogeneity than Gordon2017, suggesting better generalization to task data (Fig. 1A). Fig. 1B shows the parcellations of two representative MSC subjects estimated from 5 rs-fMRI sessions. We observed significant topological differences between the two subjects, which were highly replicable across sessions.<br \/>\n&nbsp;<br \/>\nCompared with Schaefer2018, the FC of MS-HBM parcels achieved a higher average prediction accuracy with a relative improvement of 9.44%. We note that we could not compare with Gordon2017 because the Gordon2017 approach estimated different number of parcels in each participant, so the resulting FC matrices were not comparable across participants.<br \/>\nConclusions: Compared with other approaches, MS-HBM individual-specific cortical parcellations generalized better to new rs-fMRI (not shown due to space constraints) and task-fMRI data from the same subjects. MS-HBM parcellations were highly reproducible within individuals, while capturing unique individual features. Individual-specific parcellations yield better FC-based behavioral prediction compared with group-level parcellations.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u3053\u306e\u767a\u8868\u306f\uff0cMSC\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f7f\u3063\u3066\u81ea\u8eab\u304c\u63d0\u6848\u3057\u305f\u30d1\u30fc\u30bb\u30ec\u30fc\u30b7\u30e7\u30f3\u306e\u624b\u6cd5\u3092\u9069\u5fdc\u3057\u3066\u3044\u307e\u3057\u305f\uff0e\u500b\u4eba\u5185\u306b\u304a\u3044\u3066\uff0c\u65e5\u306b\u3088\u308b\u5909\u52d5\u304c\u5c0f\u3055\u3044\u3053\u3068\u304c\u30d1\u30fc\u30bb\u30ec\u30fc\u30b7\u30e7\u30f3\u3055\u308c\u305f\u8133\u306b\u304a\u3044\u3066\u3082\u793a\u3055\u308c\u3066\u3044\u305f\uff0e<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aIndividual parcellation of structural connectivity with machine learning<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Hantian Zhang, Lingzhong Fan, Luqi Cheng, Tianzi Jiang<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Poster session<br \/>\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Introduction\uff1a\u3000Individual parcellation is important for clinic and necessary for understanding brain. However, the performance of single-subject parcellation is limited (Wang D et al.2015;Chong M et al.2017). Structural connectivity is widely used to parcellate cortex according to different connectivity profiles. Typical pipelinex(Fan L et al.2016) employed volume-based diffusion tractography and used spectral clustering to parcellate each subject separately. The limited data for single-subject may obtain instable result(Chong M et al.2017). In order to improve tracking performance and acquire a model collecting information of group-average atlas and existing dataset. We apply surface-based tractography and performed a supervised machine learning method(Glasser, M et al.2016; Ganepola, T et al.2018) based on structural connectivity.<br \/>\n&nbsp;<br \/>\nMethods:<br \/>\nIn the current study, we parcellate right cortex into 105 regions using surface-based probabilistic tracking and supervised learning. We choose Brainnetome Atlas(Fan L et al.2016) as our template.180 subjects for training were obtained from Human Connectome Project(HCP) database. Another 30 subjects were used from HCP test-retest data to evaluate the results. There is no overlap between the two datasets and 40 HCP subjects selected by Brainnetome atlas doesn&#8217;t overlap with these two datasets.MRI data were preprocessed by Freesurfer Pipeline HCP script to generate surface mesh. Each cortex region&#8217;s probability map(PM) was projected into corresponding position on group-average surface mesh. Then all PMs were merged on surface. Merged PM was employed as a measurement of inter-subject variability. Probabilistic tracking was performed and white matter surface was selected as seed. Connectivity profile was represented the distribution of fibers connecting brain regions labelled with Brainnetome Maximum Probability Map(MPM).Firstly, 180 HCP subjects were divided into training (N=150) and validation(N=30).Labels from Brainnetome MPM were (Fig. 1A)mapped into individual surface mesh as training labels. Secondly, the vertices with high inter-subject measured by merged PM(Fig. 1B) were filtered with a threshold and remaining vertices were used for training. The step aims to avoid vertices-level misalignment of label across template and single-subject.Thirdly, Areal classifiers(Fig.1C) of all regions in cortex were trained as region(blue) against neighbors(yellow) using multi-label Random Forest(max_depth=10, n_estimators=150). Finally, when a new individual comes, subject-specific results were predicted with trained areal classifiers(Fig.1D). Dice coefficient was computed for test-retest dataset to quantitatively analysis reproducibility and variability.<br \/>\nResults:<br \/>\n105 areal classifiers were trained for right cortex. Mean accuracy was 92% obtained in validation set and the lowest accuracy still got 88%.Inter-subject similarity was measured as mean pairwise dice from HCP-test dataset and intra-subject similarity was calculated in test-retest dataset. Inter-subject dice(0.51) is lower than intra-subject dice(0.76) revealed in Fig.2B. 4 subject-specific parcellation results (without post processing) from HCP test-retest dataset are displayed in Fig.2A.Two arrows pointing two representative parcels(A44rd,A40d) which are variable across subjects while relatively robust within subjects.<br \/>\nConclusions:<br \/>\nThe proposed method obtained discriminant classifiers for each region. The parcellation method achieved intuitive result about inter-subject and intra-subject variability. We need more caution to choose the threshold to filter vertices while remaining enough correct vertices for learning. In the future, we will test distinct quality-level dataset and train different areal classifiers with specific parameters and strategy.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u3053\u306e\u767a\u8868\u306f\uff0c\u6a5f\u68b0\u5b66\u7fd2\u3092\u7528\u3044\u3066\u8133\u306e\u69cb\u9020\u7684\u63a5\u7d9a\u6027\u306b\u304a\u3051\u308b\u500b\u4eba\u8133\u30d1\u30fc\u30bb\u30ec\u30fc\u30b7\u30e7\u30f3\u3092\u884c\u306a\u3063\u3066\u3044\u307e\u3057\u305f\uff0e\u88ab\u9a13\u8005\u9593\u306b\u304a\u3051\u308b\u985e\u4f3c\u5ea6\u306f\u88ab\u9a13\u8005\u5185\u306e\u985e\u4f3c\u5ea6\u3088\u308a\u3082\u4f4e\u3044\u3053\u3068\u304b\u3089\u8133\u69cb\u9020\u306f\u88ab\u9a13\u8005\u306b\u3088\u3063\u3066\u7570\u306a\u308b\u3053\u3068\u304c\u660e\u3089\u304b\u306b\u306a\u3063\u3066\u3044\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u5b66\u4f1a\u53c2\u52a0\u5831\u544a\u66f8<\/strong><\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"147\"><strong>\u00a0<\/strong><br \/>\n<strong>\u5831\u544a\u8005\u6c0f\u540d<\/strong><\/td>\n<td width=\"373\">&nbsp;<br \/>\n\u5c71\u672c\u6e09\u5b50<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u767a\u8868\u8ad6\u6587\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n<td width=\"373\"><\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u767a\u8868\u8ad6\u6587\u82f1\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n<td width=\"373\">An fMRI study on the attentional state induced by breath-counting meditation<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u8457\u8005<\/strong><\/td>\n<td width=\"373\">\u5c71\u672c\u6e09\u5b50, \u65e5\u548c\u609f\uff0c\u5ee3\u5b89\u77e5\u4e4b,<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u4e3b\u50ac<\/strong><\/td>\n<td width=\"373\">Organization for Human Brain Mapping<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u8b1b\u6f14\u4f1a\u540d<\/strong><\/td>\n<td width=\"373\">OHBM2019<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u4f1a\u5834<\/strong><\/td>\n<td width=\"373\">Auditorium Parco Della Musica<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u958b\u50ac\u65e5\u7a0b\u3000<\/strong><\/td>\n<td width=\"373\">2019\/06\/09-2019\/06\/13<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<br \/>\n&nbsp;<\/p>\n<ol>\n<li>\u8b1b\u6f14\u4f1a\u306e\u8a73\u7d30<\/li>\n<\/ol>\n<p>2019\/06\/09\u304b\u308906\/13\u306b\u304b\u3051\u3066\uff0c\u30ed\u30fc\u30de\u306eAuditorium Parco Della Musica\u306b\u3066\u958b\u50ac\u3055\u308c\u307e\u3057\u305fOHBM2019\u306b\u53c2\u52a0\u81f4\u3057\u307e\u3057\u305f\uff0eOHBM2019\u306f\uff0cOrganization for Human Brain Mapping\u306b\u3088\u3063\u3066\u4e3b\u50ac\u3055\u308c\u305f\u5b66\u4f1a\u3067\uff0c\u4eba\u9593\u306e\u8133\u306e\u30de\u30c3\u30d4\u30f3\u30b0\u306b\u304a\u3051\u308b\u88c5\u7f6e\u3092\u6a2a\u65ad\u3059\u308b\u6700\u65b0\u306e\u56fd\u969b\u7684\u306a\u7814\u7a76\u306b\u3064\u3044\u3066\u5b66\u3076\u305f\u3081\u306e\u5834\u6240\u3067\u3059\uff0e\u307e\u305f\uff0c\u3053\u306e\u5206\u91ce\u306e\u5c02\u9580\u5bb6\u3068\u8b70\u8ad6\u3057\uff0c\u4e16\u754c\u4e2d\u3068\u3064\u306a\u304c\u308b\u3053\u3068\u304c\u3067\u304d\u308b\u6a5f\u4f1a\u3067\u3059\uff0e\u6559\u80b2\u30bb\u30c3\u30b7\u30e7\u30f3\u3067\u306f\uff0c\u69d8\u3005\u306a\u7d4c\u6b74\u3092\u6301\u3064\u82e5\u624b\u304a\u3088\u3073\u4e0a\u7d1a\u306e\u79d1\u5b66\u8005\u304c\uff0c\u6a5f\u68b0\u5b66\u7fd2\u6280\u8853\uff0c\u9ad8\u89e3\u50cf\u5ea6\u753b\u50cf\u51e6\u7406\uff0c\u305d\u3057\u3066\u6700\u8fd1\u3067\u306f\u30aa\u30fc\u30d7\u30f3\u30b5\u30a4\u30a8\u30f3\u30b9\u624b\u6cd5\u3092\u542b\u3080\uff0c\u3053\u306e\u5206\u91ce\u306e\u6700\u65b0\uff0c\u304b\u3064\u753b\u671f\u7684\u306a\u958b\u767a\u306b\u3064\u3044\u3066\u6559\u3048\u3066\u3044\u307e\u3059\uff0e<br \/>\n\u79c1\u306f\u5168\u3066\u306e\u65e5\u7a0b\u306b\u53c2\u52a0\u81f4\u3057\u307e\u3057\u305f\uff0e\u672c\u7814\u7a76\u5ba4\u304b\u3089\u306f\u4ed6\u306b\u5ee3\u5b89\u5148\u751f\uff0c\u65e5\u548c\u5148\u751f\uff0cM2\u53e4\u5bb6\u304f\u3093\uff0c\u5927\u585a\u304f\u3093\uff0c\u5965\u6751(\u99ff)\u304f\u3093\uff0c\u6749\u91ce\u3055\u3093\uff0c\u5409\u7530\u3055\u3093\uff0cM1\u98a8\u5442\u8c37\u304f\u3093\uff0c\u4e39\u3055\u3093\u304c\u53c2\u52a0\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<ol start=\"2\">\n<li>\u7814\u7a76\u767a\u8868\n<ul>\n<li>\u767a\u8868\u6982\u8981<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<p>\u79c1\u306f6\/11\u306e12\u664245\u5206\u304b\u308914\u664245\u5206\u306b\u884c\u308f\u308c\u305f\u300cPoster Session\u300d\uff0c17\u6642\u304b\u308918\u6642\u306b\u884c\u308f\u308c\u305f\u300cPoster Reception\u300d\u306b\u3066\u767a\u8868\u81f4\u3057\u307e\u3057\u305f\uff0e\u767a\u8868\u306e\u5f62\u5f0f\u306f\u30dd\u30b9\u30bf\u30fc\u767a\u8868\u3067\uff0c\u8a082\u6642\u9593\u3067\u3057\u305f\uff0e<br \/>\n\u4eca\u56de\u306f\uff0cAn fMRI study on the attentional state induced by breath-counting meditation\u3068\u3044\u3046\u30bf\u30a4\u30c8\u30eb\u3067\u767a\u8868\u81f4\u3057\u307e\u3057\u305f\uff0e\u4ee5\u4e0b\u306b\u6284\u9332\u3092\u8a18\u8f09\u81f4\u3057\u307e\u3059\uff0e<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">[Introduction]<br \/>\nMindfulness refers to paying attention to the present moment, nonjudgmentally, and is expected to promote individuals\u2019 mental well-being. Considering that attention is one of the main components of meditation, practitioners try to sustain their attention on their physical sensations during meditation to improve their interoceptive attention. In this study, we aim to investigate whether meditation induces individuals\u2019 interoceptive attention through the fMRI scanning of brain activity during breath-counting meditation. Furthermore, the exteroceptive state of practitioners observed while responding to and counting the external cues was assessed to emphasize the characteristics of the interoceptive state. The brain activity seen in such states was investigated using activation analysis.<br \/>\n[Methods]<br \/>\nA total of 12 male meditation novices (aged 23.1 \u00b1 1.2 years), who never experienced meditation practices, participated in the experiment. As reported in Fig. 1, they were asked to perform two tasks, namely a breath-counting task (BCT) and an auditory counting task (ACT). The BCT was first proposed by Levinson et al. and was shown to be effective as a behavioral measure of mindfulness meditation (Levinson et al. 2014). In the BCT, participants repeatedly counted their breaths mentally from 1 to 9 and pressed two buttons at breaths 1\u20138 and at the ninth breath, respectively. In contrast, they were requested to quickly press the button responding to the auditory cues in the ACT and to count them similarly to the BCT. Furthermore, they restarted counting from 1 in both tasks when they got distracted and pressed a third button. An fMRI scanner was used to measure the brain activity during such tasks. The whole brain was first divided into 116 regions based on the automated anatomical labeling atlas. Successively, an activation analysis was performed on the measured data after the preprocessing, which included realignment, slice timing, coregistration, normalization, and smoothing by SPM12. Finally, a group analysis was performed on the data of all subjects.<br \/>\n[Results]<br \/>\nFrontal_Mid_R (MFG.R) was significantly activated in the ACT (uncorrected, p&lt;0.001). Since the MFG is included in the central executive network and is known to be involved in external-oriented tasks (Manoliu et al. 2013), attention was suggested to be directed to the outside of the body in the ACT. Moreover, as described in Table 1, brain activation was increased in the Insula_L\/R (INS.L\/R), Temporal_Sup_L\/R (STG.L\/R) during the ACT compared to the BCT (uncorrected, p&lt;0.001). While the INS is part of the salience network (SN) and is related to the sensing of external stimuli, the STG is associated with the recognition of auditory information. Therefore, contrary to the BCT, attention was indicated to be directed to the outside as an auditory stimulus in the ACT. Based on these results, the characteristics of the brain activity of the BCT were investigated by comparing them with those of the ACT. In contrast, brain activation was increased in the Cingulum_Ant_L (ACG.L) in the BCT compared to the ACT (uncorrected, p&lt;0.001), as illustrated in Table 2. Since the ACG is included in the SN and is related to both self-recognition and attentional control, attention was suggested to be directed to the bodily sensations in the BCT as opposed to the ACT. Overall, we assumed that meditation induced participants\u2019 interoceptive attention.<br \/>\n[Conclusions]<br \/>\nIn this study, the brain activity of meditation novices during a task that focused on the attention to the self was investigated by comparing it with that observed during a task in which attention was directed to the outside the body. As a result, brain activation was increased in the ACG (i.e., the region related to self-recognition, attentional control) during meditation (i.e., attention to the physical sensations) compared to the task in which the attention was directed to external stimuli. These results suggest that meditation induced participants\u2019 interoceptive attention.<br \/>\n\u3010References\u3011<br \/>\n[1] D.B. Levinson, E.L. Stoll, S.D. Kindy, H.L. Merry and R.J. Davidson (2014), &#8216;A mind you can count on: validating breath counting as a behavioral measure of mindfulness&#8217;, Frontiers in psychology, vol. 5, p.1202.<br \/>\n[2] Manoliu, Andrei, et al (2013), &#8216;Aberrant dependence of default mode\/central executive network interactions on anterior insular salience network activity in schizophrenia&#8217;, Schizophrenia bulletin, vol. 40, no.2, pp. 428-437.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<ul>\n<li>\u8cea\u7591\u5fdc\u7b54<\/li>\n<\/ul>\n<p>\u4eca\u56de\u306e\u8b1b\u6f14\u767a\u8868\u3067\u306f\uff0c\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u8cea\u7591\u3092\u53d7\u3051\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>1<\/strong><br \/>\n\u8cea\u554f\u8005\u306e\u6c0f\u540d\u3092\u63a7\u3048\u640d\u306d\u3066\u3057\u307e\u3044\u307e\u3057\u305f\uff0e\u3053\u3061\u3089\u306e\u8cea\u554f\u306f\uff0cfeature values\u3068\u306f\u4f55\u304b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\uff0c\u4eca\u56de\u306f\u30b0\u30e9\u30d5\u7406\u8ad6\u306e\u6307\u6a19\u3067\u3042\u308bdegree centlarity\u3068betweenness centrality\u3092\u4f7f\u7528\u3057\u305f\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e\u307e\u305f\uff0c\u3053\u306e\u7279\u5fb4\u91cf\u306f\u306a\u306b\u304b\u3068\u3044\u3046\u8cea\u554f\u3082\u3044\u305f\u3060\u3044\u305f\u306e\u3067\uff0cdegree centlarity\u306f\uff0c\u4ed6\u306e\u8133\u9818\u57df\u3068\u306e\u63a5\u7d9a\u6570\u3067\u3042\u308a\uff0cbetweenness centrality\u306f\uff0c\u4ed6\u306e2\u3064\u306e\u9818\u57df\u3092\u7d50\u3076\u6700\u77ed\u7d4c\u8def\u306b\u305d\u306e\u8133\u9818\u57df\u304c\u542b\u307e\u308c\u308b\u5272\u5408\u3067\u3042\u308b\u3068\u8aac\u660e\u81f4\u3057\u307e\u3057\u305f\uff0e<br \/>\n<strong>\u00a0<\/strong><br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>2<\/strong><br \/>\n\u8cea\u554f\u8005\u306e\u6c0f\u540d\u3092\u63a7\u3048\u640d\u306d\u3066\u3057\u307e\u3044\u307e\u3057\u305f\uff0e\u3053\u3061\u3089\u306f\uff0cBCT\uff0cACT\u305d\u308c\u305e\u308c\u306e\u5b9f\u9a13\u65b9\u6cd5\u306f\u81ea\u5206\u306e\u30aa\u30ea\u30b8\u30ca\u30eb\u306e\u65b9\u6cd5\u306a\u306e\u304b\u3068\u3044\u3046\u8cea\u554f\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\uff0cBCT\u306f\u65e2\u5b58\u306e\u624b\u6cd5\u3092\u7528\u3044\uff0cACT\u306f\u81ea\u5206\u3067\u8003\u3048\u305f\u3082\u306e\u3067\u3042\u308b\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>3<\/strong><br \/>\n\u8cea\u554f\u8005\u306e\u6c0f\u540d\u3092\u63a7\u3048\u640d\u306d\u3066\u3057\u307e\u3044\u307e\u3057\u305f\uff0e\u3053\u3061\u3089\u306e\u8cea\u554f\u306f\uff0cCounting accuracy\u3068\u306f\u306a\u306b\u304b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\uff0c\u5b9f\u9a13\u4e2d\u3069\u306e\u3088\u3046\u306b\u30dc\u30bf\u30f3\u30d7\u30ec\u30b9\u3092\u884c\u3063\u3066\u3044\u308b\u304b\u3092\u8aac\u660e\u3057\uff0c\u6b63\u3057\u3044\u30dc\u30bf\u30f3\u30d7\u30ec\u30b9\u306e\u30bb\u30c3\u30c8\u6570\u3092\u7dcf\u30bb\u30c3\u30c8\u6570\u3067\u5272\u308b\u3053\u3068\u3067\u7b97\u51fa\u3057\u3066\u3044\u308b\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>4<\/strong><br \/>\n\u8cea\u554f\u8005\u306e\u6c0f\u540d\u3092\u63a7\u3048\u640d\u306d\u3066\u3057\u307e\u3044\u307e\u3057\u305f\uff0e\u3053\u3061\u3089\u306e\u8cea\u554f\u306f\uff0cMW\u3068\u306f\u4f55\u3092\u8868\u3057\u3066\u3044\u308b\u306e\u304b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\uff0c\u30de\u30a4\u30f3\u30c9\u30ef\u30f3\u30c0\u30ea\u30f3\u30b0\u306e\u7565\u79f0\u3067\u3042\u308b\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>5<\/strong><br \/>\n\u8cea\u554f\u8005\u306e\u6c0f\u540d\u3092\u63a7\u3048\u640d\u306d\u3066\u3057\u307e\u3044\u307e\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306f\uff0c\u7d50\u679c\u306e\u8133\u306e\u56f3\u306b\u5bfe\u3057\u3066\u9818\u57df\u540d\u3092\u8907\u6570\u8a18\u8f09\u3057\u3066\u3044\u305f\u305f\u3081\uff0c\uff11\u3064\u306enode\u306e\u9818\u57df\u540d\u306f\u4f55\u304b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\uff0cTemporal Inf R\uff08ITG.R\uff09\u3067\u3042\u308b\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e\u9818\u57df\u540d\u306e\u7565\u79f0\u306b\u3064\u3044\u3066\u306f\u4ed6\u306e\u65b9\u304b\u3089\u3082\u3054\u8cea\u554f\u3044\u305f\u3060\u3044\u305f\u306e\u3067\uff0c\u6b63\u5f0f\u540d\u79f0\u3092\u304a\u4f1d\u3048\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>6<\/strong><br \/>\n\u8cea\u554f\u8005\u306e\u6c0f\u540d\u3092\u63a7\u3048\u640d\u306d\u3066\u3057\u307e\u3044\u307e\u3057\u305f\uff0e\u3053\u3061\u3089\u306e\u8cea\u554f\u306f\uff0c\u3053\u306e\u30bf\u30b9\u30af\u306f\u4eba\u306b\u3088\u3063\u3066\u6570\u3048\u308b\u5bfe\u8c61\u306e\u7dcf\u6570\u306f\u7570\u306a\u308b\u306e\u304b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3059\u308b\u56de\u7b54\u3067\u3059\u304c\uff0c\u4e21\u30bf\u30b9\u30af\u3067\u500b\u4eba\u306b\u3088\u3063\u3066\u56de\u6570\u304c\u7570\u306a\u308a\uff0cBCT\u306f\u4eba\u306b\u3088\u3063\u3066\u547c\u5438\u306e\u30b9\u30d4\u30fc\u30c9\u304c\u9055\u3046\u305f\u3081\u6570\u306f\u7570\u306a\u308b\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e\u307e\u305f\uff0c\u547c\u5438\u306e\u30b9\u30d4\u30fc\u30c9\u306f\u6e2c\u5b9a\u3057\u3066\u3044\u308b\u306e\u304b\u3068\u3044\u3046\u8cea\u554f\u3082\u3044\u305f\u3060\u3044\u305f\u306e\u3067\uff0c\u884c\u52d5\u30c7\u30fc\u30bf\u306b\u3064\u3044\u3066\u306f\u30dc\u30bf\u30f3\u30d7\u30ec\u30b9\u306e\u307f\u8a18\u9332\u3057\u3066\u3044\u308b\u3068\u304a\u7b54\u3048\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>7<\/strong><br \/>\n\u8cea\u554f\u8005\u306e\u6c0f\u540d\u3092\u63a7\u3048\u640d\u306d\u3066\u3057\u307e\u3044\u307e\u3057\u305f\uff0e\u3053\u3061\u3089\u306e\u8cea\u554f\u306f\uff0c\u30dc\u30bf\u30f3\u30d7\u30ec\u30b9\u306e\u969b\uff0c\u7dd1\u306e\u30dc\u30bf\u30f3\u306fACT\u3067\u3082\u62bc\u3059\u306e\u304b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3059\u308b\u56de\u7b54\u3067\u3059\u304c\uff0cACT\u3067\u3082\u30ab\u30a6\u30f3\u30c8\u3092\u9593\u9055\u3048\u305f\u308a\uff0c\u6c17\u304c\u9038\u308c\u305f\u3089\u62bc\u3059\u3088\u3046\u6307\u793a\u3057\u3066\u3044\u308b\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>8<\/strong><br \/>\n\u8cea\u554f\u8005\u306e\u6c0f\u540d\u3092\u63a7\u3048\u640d\u306d\u3066\u3057\u307e\u3044\u307e\u3057\u305f\uff0e\u3053\u3061\u3089\u306f\uff0cBCT\u3067\u306f\u7791\u60f3\u3092\u884c\u3063\u3066\u3044\u308b\u306e\u306b\u30de\u30a4\u30f3\u30c9\u30ef\u30f3\u30c0\u30ea\u30f3\u30b0\u306b\u306a\u3063\u3066\u3044\u3044\u306e\u304b\u3068\u3044\u3046\u8cea\u554f\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\uff0c\u5148\u884c\u7814\u7a76\u306b\u304a\u3044\u3066\u96c6\u4e2d\u7791\u60f3\u6642\u306b\u306f\u8a8d\u77e5\u30e2\u30c7\u30eb\u306e\u30b5\u30a4\u30af\u30eb\u304c\u5b58\u5728\u3059\u308b\u3068\u8a00\u308f\u308c\u3066\u304a\u308a\uff0c\u30de\u30a4\u30f3\u30c9\u30ef\u30f3\u30c0\u30ea\u30f3\u30b0\u306b\u306a\u3063\u3066\u3044\u308b\u72b6\u614b\u3068\u96c6\u4e2d\u3067\u304d\u3066\u3044\u308b\u72b6\u614b\u304c\u4e21\u65b9\u5b58\u5728\u3059\u308b\u3053\u3068\u3092\u8aac\u660e\u3057\u307e\u3057\u305f\uff0e\u307e\u305f\uff0c\u4eca\u56de\u306e\u5b9f\u9a13\u3067\u306f\uff0c\u88ab\u9a13\u8005\u304c\u5168\u54e1\u7791\u60f3\u521d\u5fc3\u8005\u3067\u3042\u3063\u305f\u305f\u3081\uff0c\u4eca\u5f8c\u306f\u7791\u60f3\u5b9f\u8df5\u8005\u3082\u6e2c\u5b9a\u3057\uff0c\u521d\u5fc3\u8005\u3068\u6bd4\u8f03\u3057\u3066\u3044\u304d\u305f\u3044\u3068\u304a\u4f1d\u3048\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n&nbsp;<\/p>\n<ul>\n<li>\u611f\u60f3<\/li>\n<\/ul>\n<p>OHBM\u306b\u306f\uff0c\u79c1\u305f\u3061\u306e\u7814\u7a76\u5ba4\u304b\u3089\u6bce\u5e74\u53c2\u52a0\u3057\u3066\u3044\u307e\u3059\u304c\uff0c\u79c1\u306f\u521d\u3081\u3066\u306e\u53c2\u52a0\u3060\u3063\u305f\u306e\u3067\u8133\u7814\u7a76\u306b\u95a2\u3059\u308b\u5b66\u4f1a\u3068\u3044\u3046\u3053\u3068\u3067\u3068\u3066\u3082\u697d\u3057\u307f\u306b\u3057\u3066\u3044\u307e\u3057\u305f\uff0e\u56fd\u969b\u5b66\u4f1a\u3068\u3057\u3066\u306f3\u5ea6\u76ee\u306e\u53c2\u52a0\u3067\u3042\u3063\u305f\u3053\u3068\u3082\u3042\u308a\uff0c\u4eca\u307e\u3067\u3088\u308a\u7dca\u5f35\u305b\u305a\u767a\u8868\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3057\u305f\uff0e\u521d\u65e5\u306b\u306feducational course\u306b\u53c2\u52a0\u3055\u305b\u3066\u3044\u305f\u3060\u304d\uff0c\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u89e3\u6790\u306b\u3064\u3044\u3066\u8a73\u3057\u304f\u5b66\u3076\u3053\u3068\u304c\u3067\u304d\u307e\u3057\u305f\uff0e\u81ea\u5206\u304c\u4eca\u307e\u3067\u77e5\u3089\u306a\u304b\u3063\u305f\u65b9\u6cd5\u3082\u305f\u304f\u3055\u3093\u3042\u308a\uff0c\u307e\u305f\uff0c\u81ea\u5206\u306e\u82f1\u8a9e\u80fd\u529b\u4e0d\u8db3\u306b\u3088\u308a\uff0c\u7406\u89e3\u3057\u304d\u308c\u306a\u304b\u3063\u305f\u3068\u3053\u308d\u3082\u3042\u308b\u306e\u3067\uff0c\u8cc7\u6599\u3092\u8aad\u307f\u8fd4\u3057\uff0c\u4eca\u5f8c\u306e\u7814\u7a76\u306b\u6d3b\u304b\u3057\u3066\u3044\u304d\u305f\u3044\u3068\u601d\u3044\u307e\u3059\uff0e<br \/>\n\u8133\u7814\u7a76\u306e\u5b66\u4f1a\u3068\u3044\u3046\u3053\u3068\u3067\uff0c\u4eca\u307e\u3067\u53c2\u52a0\u3055\u305b\u3066\u3044\u305f\u3060\u3044\u305f\u5b66\u4f1a\u4ee5\u4e0a\u306b\uff0c\u81ea\u5206\u306e\u77e5\u3063\u3066\u3044\u308b\u5358\u8a9e\u3084\u624b\u6cd5\u304c\u305f\u304f\u3055\u3093\u51fa\u3066\u304d\u3066\uff0c\u81ea\u5206\u3082\u8133\u7814\u7a76\u304c\u3067\u304d\u3066\u3044\u308b\u3053\u3068\u3092\u3059\u3054\u304f\u5149\u6804\u306b\u601d\u3044\u307e\u3057\u305f\uff0e\u4e16\u754c\u4e2d\u3067\uff0c\u3053\u3093\u306a\u306b\u3082\u591a\u304f\u306e\u8133\u7814\u7a76\u304c\u884c\u308f\u308c\u3066\u3044\u308b\u3068\u3044\u3046\u3053\u3068\u3092\u5b9f\u611f\u3059\u308b\u3053\u3068\u304c\u3067\u304d\uff0c\u3082\u3063\u3068\u3082\u3063\u3068\u81ea\u5206\u306e\u7814\u7a76\u3092\u6df1\u3081\u3066\u3044\u304d\u305f\u3044\u3068\uff0c\u81ea\u5206\u306e\u7814\u7a76\u306b\u5bfe\u3059\u308b\u30e2\u30c1\u30d9\u30fc\u30b7\u30e7\u30f3\u3082\u304b\u306a\u308a\u4e0a\u304c\u308a\u307e\u3057\u305f\uff0e\u4eca\u56de\u306e\u767a\u8868\u3067\u306f\uff0c\u7d50\u679c\u3092\u51fa\u3057\u7d42\u308f\u3063\u3066\u304b\u3089\u81ea\u5206\u306e\u30df\u30b9\u304c\u767a\u899a\u3059\u308b\u306a\u3069\uff0c\u6e96\u5099\u304c\u304e\u308a\u304e\u308a\u306b\u306a\u3063\u3066\u3057\u307e\u3063\u305f\u306e\u3067\uff0c\u4eca\u5f8c\u3082\u3046\u5c11\u3057\u4f59\u88d5\u3092\u3082\u3063\u3066\u6e96\u5099\u3067\u304d\u308b\u3088\u3046\u306b\u3057\u3088\u3046\u3068\u5f37\u304f\u601d\u3044\u307e\u3059\uff0e\u767a\u8868\u7df4\u7fd2\u306a\u3069\u3082\u3082\u3063\u3068\u5ff5\u5165\u308a\u306b\u884c\u3044\uff0c\u81ea\u5206\u306e\u7814\u7a76\u306e\u3059\u3054\u3055\u3092\u30a2\u30d4\u30fc\u30eb\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u308a\u305f\u3044\u3068\u601d\u3044\u307e\u3059\uff0e\u307e\u305f\uff0c\u3084\u306f\u308a\u82f1\u8a9e\u529b\u304c\u4e4f\u3057\u304b\u3063\u305f\u3053\u3068\u3082\u53cd\u7701\u70b9\u306e\u4e00\u3064\u3067\u3059\uff0e\u305b\u3063\u304b\u304f\u306e\u6a5f\u4f1a\u3092\u3044\u305f\u3060\u3044\u3066\u3044\u308b\u306e\u306b\uff0c\u82f1\u8a9e\u529b\u4e0d\u8db3\u306b\u3088\u308a\u8b70\u8ad6\u304c\u6df1\u3081\u3089\u308c\u306a\u3044\u306e\u306f\u3082\u3063\u305f\u3044\u306a\u3044\u306e\u3067\uff0c\u6b21\u56de\u53c2\u52a0\u3059\u308b\u3068\u304d\u306b\u306f\uff0c\u81ea\u5206\u306e\u82f1\u8a9e\u80fd\u529b\u304c\u5411\u4e0a\u3057\u305f\u3053\u3068\u304c\u611f\u3058\u3089\u308c\u308b\u3088\u3046\u306b\uff0c\u65e5\u5e38\u304b\u3089\u82f1\u8a9e\u306e\u5b66\u7fd2\u306b\u53d6\u308a\u7d44\u3093\u3067\u304d\u305f\u3044\u3068\u601d\u3044\u307e\u3059\uff0e\u4eca\u56de\u306e\u53cd\u7701\u70b9\u3092\u4eca\u5f8c\u5fc5\u305a\u6d3b\u304b\u3057\u3066\uff0c\u3088\u308a\u3088\u3044\u767a\u8868\uff0c\u7814\u7a76\u306b\u3057\u3066\u3044\u304d\u307e\u3059\uff0e<br \/>\n\u4eca\u56de\u306e\u5b66\u4f1a\u3092\u901a\u3057\u3066\u8cb4\u91cd\u306a\u7d4c\u9a13\u3092\u3055\u305b\u3066\u3044\u305f\u3060\u304f\u3053\u3068\u3067\uff0c\u4e16\u754c\u4e2d\u306e\u7814\u7a76\u306b\u305f\u304f\u3055\u3093\u89e6\u308c\uff0c\u81ea\u5206\u306e\u7814\u7a76\uff0c\u82f1\u8a9e\u529b\u5411\u4e0a\u3078\u306e\u30e2\u30c1\u30d9\u30fc\u30b7\u30e7\u30f3\u3092\u9ad8\u3081\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3057\u305f\uff0e\u3053\u306e\u7d4c\u9a13\u3092\u6d3b\u304b\u3057\u3066\u3044\u3051\u308b\u3088\u3046\u65e5\u3005\u52aa\u529b\u3092\u3057\u3066\u3044\u304d\u305f\u3044\u3068\u601d\u3044\u307e\u3059\uff0e<br \/>\n&nbsp;<\/p>\n<ol start=\"3\">\n<li>\u8074\u8b1b<\/li>\n<\/ol>\n<p>\u4eca\u56de\u306e\u8b1b\u6f14\u4f1a\u3067\u306f\uff0c\u4e0b\u8a18\u306e6\u4ef6\u306e\u767a\u8868\u3092\u8074\u8b1b\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Basic Concepts of Network Neuroscience<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Alex Fornito<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a An Introduction to Network Neuroscience: How to build, model, and analyse connectomes<br \/>\nThe science of complex networks, which is grounded in the mathematics of graph theory, offers a remarkably flexible and general framework for understanding brain network organization. The emerging field of network neuroscience involves the application of methods and concepts from network science to neuroscientific data. The core assumption of this approach is that the brain, like any other networked system, can be represented as a graph of nodes (representing, for example, individual neurons or neuronal populations) connected by edges (representing some measure of structural or functional interaction). Modelling nervous systems in this way allows investigators to use the rich repertoire of concepts and techniques developed in network science to understand neural organization and dynamics, providing a common language for characterizing data acquired in diverse species with different tools. Critical first steps in brain network analysis include adequately preparing data for network analysis, framing scientific questions appropriately, and understanding the strengths and limitations of different approaches for constructing brain networks. In this talk, I will discuss these issues and other core concepts of the field to provide a general introduction that covers the promise and pitfalls of network neuroscience. An understanding of these issues provides a necessary foundation for the use of more advanced topics covered throughout the workshop.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u30ce\u30fc\u30c9\u3068\u30a8\u30c3\u30b8\u306e\u8aac\u660e\u304b\u3089\uff0c\u3069\u306e\u30b0\u30e9\u30d5\u304c\u8133\u3092\u8868\u3059\u306e\u306b\u6700\u3082\u9069\u3057\u3066\u3044\u308b\u306e\u304b\u307e\u3067\uff0c\u5e45\u5e83\u304f\u8133\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306b\u95a2\u3057\u3066\u306e\u8aac\u660e\u304c\u3042\u308a\u307e\u3057\u305f\uff0e\u7279\u306b\u79c1\u305f\u3061\u306e\u7814\u7a76\u3067\u306f\u7121\u5411\u30b0\u30e9\u30d5\u3092\u4e3b\u306b\u7528\u3044\u3066\u3044\u308b\u305f\u3081\uff0c\u6709\u5411\u30b0\u30e9\u30d5\u306b\u3064\u3044\u3066\u3082\u4eca\u5f8c\u5b66\u3093\u3067\u3044\u304d\u305f\u3044\u3068\u601d\u3044\u307e\u3057\u305f\uff0e\u307e\u305f\uff0c\u30a8\u30c3\u30b8\u3092\u5168\u3066\u540c\u69d8\u306b\u6271\u3046\u306e\u3067\u306f\u306a\u3044\uff0cHeterogeneous edges\u3068\u3044\u3046\u8a00\u8449\u3082\u51fa\u3055\u308c\u3066\u3044\u307e\u3057\u305f\uff0e\u4eca\u307e\u3067\u305d\u306e\u3088\u3046\u306a\u6271\u3044\u65b9\u306f\u77e5\u3089\u306a\u304b\u3063\u305f\u306e\u3067\uff0c\u3069\u3093\u306a\u65b9\u6cd5\u304c\u3042\u308b\u306e\u304b\u81ea\u5206\u3067\u8abf\u3079\u3066\u307f\u3088\u3046\u3068\u601d\u3063\u3066\u3044\u307e\u3059\uff0e<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Defining Network Nodes and Extracting Timeseries<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Janine Bijsterbosch<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a An Introduction to Network Neuroscience: How to build, model, and analyse connectomes<br \/>\nThis talk will introduce two crucial stages in any network neuroscience analysis, namely to define nodes and extract node timeseries. Node definition is achieved by parcellating the brain into a set of spatial brain regions that can be considered homogeneous functional areas (which are described by a single functional timeseries). Nodes do not typically change in their spatial layout once they are defined, so these steps are crucial for any network analysis. Concepts such as hard and soft parcellations, dimensionality, and anatomical and functional atlases will be introduced. In addition, important challenges such as between-subject differences in alignment and functional organisation will be discussed. A key focus of this talk will be to provide a critical overview of advantages and disadvantages of various alternative approaches. The aim is to provide the delegates with a comprehensive understanding of concepts and trade-offs, both from the technical methods perspective and from the applied neuroscience perspective, so that they are well equipped to make informed decisions in their future work.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u30ce\u30fc\u30c9\u306e\u5b9a\u7fa9\u306f\uff0c\u69d8\u3005\u306a\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u89e3\u6790\u306b\u304a\u3044\u3066\u91cd\u8981\u3067\u3042\u308a\uff0c\u79c1\u305f\u3061\u306e\u7814\u7a76\u3067\u306f\u4e3b\u306b\u7528\u3044\u3066\u304d\u305fmautomated anatomical labeling\uff08AAL\uff09\u306f\u7528\u3044\u3066\u306f\u306a\u3089\u306a\u3044\u3068\u3044\u3046\u8aac\u660e\u304c\u3042\u308a\u307e\u3057\u305f\uff0e\u4eca\u307e\u3067\u306fAAL\u4ee5\u5916\u3092\u7528\u3044\u308b\u3053\u3068\u307e\u3067\u8003\u3048\u305f\u3053\u3068\u304c\u7121\u304b\u3063\u305f\u305f\u3081\uff0c\u91cd\u8981\u6027\u304c\u5206\u304b\u3063\u3066\u304a\u3089\u305a\uff0c\u4ed6\u306e\u624b\u6cd5\u3082\u3042\u307e\u308a\u77e5\u308a\u307e\u305b\u3093\u3067\u3057\u305f\uff0e\u4ed6\u306e\u65b9\u6cd5\u3082\u8abf\u3079\uff0c\u8cc7\u6599\u3082\u8aad\u307f\u8fd4\u3057\uff0c\u4eca\u5f8c\u306e\u5bfe\u5fdc\u3092\u8003\u3048\u3066\u3044\u304d\u305f\u3044\u3068\u601d\u3044\u307e\u3059\uff0e\u307e\u305f\uff0cPROFUMO\u3068\u3044\u3046\u521d\u3081\u3066\u805e\u304f\u30a2\u30d7\u30ed\u30fc\u30c1\u3082\u51fa\u3066\u304d\u305f\u306e\u3067\uff0c\u3069\u3093\u306a\u624b\u6cd5\u306a\u306e\u304b\u8abf\u3079\uff0c\u4eca\u5f8c\u306e\u5fdc\u7528\u3082\u8003\u3048\u3066\u3044\u304d\u305f\u3044\u3068\u601d\u3044\u307e\u3059\uff0e<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Motivated Performance While Sleep Deprived: Reduced ACC and insula recruitment and effort-preference<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Stijn Massar<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a ORAL SESSION: Emotion, Motivation and Social Neuroscience<br \/>\nSleep deprivation has profound negative impact on attentional performance (Lim 2010) and associated brain mechanisms (Ma 2015). Recent studies have begun to explore to what extent such attentional decline could be explained by loss of motivation during sleep deprivation (Liu 2016; Massar 2018). In this study we approached this issue in two ways. First, participants performed a motivated attention task once after a night of sleep deprivation and once after a night of normal sleep. Subsequently, participants performed an effort-based decision task, indicating their preference for further task performance in return for additional reward.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u3053\u306e\u767a\u8868\u306f\uff0c\u7761\u7720\u4e0d\u8db3\u3068\u6ce8\u610f\u3068\u306e\u95a2\u9023\u306b\u3064\u3044\u3066\u8abf\u3079\u305f\u7814\u7a76\u3067\uff0c\u88ab\u9a13\u8005\u306f\u305d\u308c\u305e\u308c\u5341\u5206\u306a\u7761\u7720\u5f8c\u3068\uff0c\u4e00\u6669\u4e2d\u8d77\u304d\u3066\u3044\u305f\u5f8c\u3069\u3061\u3089\u306e\u30bb\u30c3\u30b7\u30e7\u30f3\u306b\u3082\u53c2\u52a0\u3057\uff0c\u5404\u6761\u4ef6\u5f8c\u306b\u540c\u3058\u30bf\u30b9\u30af\u3092\u884c\u3063\u3066\u3044\u307e\u3057\u305f\uff0e\u4e00\u3064\u6c17\u306b\u306a\u3063\u305f\u306e\u306f\uff0c\u540c\u3058\u88ab\u9a13\u8005\u304c\u4e21\u30bb\u30c3\u30b7\u30e7\u30f3\u3068\u3082\u884c\u3046\u3068\uff0c\u7761\u7720\u3068\u306e\u95a2\u4fc2\u3092\u8abf\u3079\u3066\u3044\u308b\u3053\u3068\u304c\u88ab\u9a13\u8005\u306b\u3068\u3063\u3066\u660e\u78ba\u306b\u306a\u3063\u3066\u3057\u307e\u3046\u3053\u3068\u3067\u3059\uff0e\u3057\u304b\u3057\uff0c\u7761\u7720\u306e\u6761\u4ef6\u3092\u4ed8\u3051\u308b\u6642\u70b9\u3067\u5206\u304b\u308b\u53ef\u80fd\u6027\u3082\u3042\u308b\u3053\u3068\uff0c\u307e\u305f\uff0c\u30bb\u30c3\u30b7\u30e7\u30f3\u6bce\u306b\u7570\u306a\u308b\u88ab\u9a13\u8005\u3092\u3068\u308b\u3068\uff0c\u30bb\u30c3\u30b7\u30e7\u30f3\u306e\u5dee\u306b\u52a0\u3048\u3066\u500b\u4eba\u5dee\u306e\u5f71\u97ff\u304c\u5b9f\u9a13\u7d50\u679c\u306b\u5f71\u97ff\u3092\u53ca\u307c\u3059\u53ef\u80fd\u6027\u304c\u3042\u308b\u3053\u3068\u3092\u8003\u3048\u308b\u3068\uff0c\u3053\u306e\u6761\u4ef6\u304c\u3044\u3044\u306e\u304b\u3082\u3057\u308c\u306a\u3044\u3068\u3082\u601d\u3044\u307e\u3057\u305f\uff0e\u3053\u306e\u3088\u3046\u306b\u8272\u3005\u306a\u6761\u4ef6\u3092\u8003\u3048\u308b\u3053\u3068\u3082\u4eca\u5f8c\u5927\u5207\u306b\u3057\u3066\u3044\u304d\u305f\u3044\u3068\u601d\u3044\u307e\u3059\uff0e<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a A New Attention Node in Macaque and Human Temporal Cortex Connects to Fronto-parietal Areas<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a IIaria Sani<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a ORAL SESSION: Mapping Sensation, Perception, and Attention<br \/>\nThe cerebral cortex comprises multiple areas involved in attentional processing. Classical studies identify the parietal and frontal cortices of human and macaques as major sources of attentional signals (Corbetta et al., 2008; Kastner &amp; Ungerleider, 2000). Recent results highlighted the existence of an area in the temporal cortex of the macaque brain that also plays a crucial role in the guidance of spatial visual attention (Stemmann et al., 2016). The discovery of this ventral attentional-control area posits new important questions about the network organization of the primate attention system and about the degree of homology between human and monkeys in the cognitive domain. We asked whether a comparative ventral attention node exists in macaques and humans, and whether it communicates via direct white matter pathways with frontal and dorsal attention areas.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u79c1\u306e\u7814\u7a76\u3067\u3082\u95a2\u4fc2\u306e\u3042\u308b\uff0c\u30c8\u30c3\u30d7\u30c0\u30a6\u30f3\u6ce8\u610f\u306a\u3069\u306e\u8a00\u8449\u3082\u51fa\u3066\u304d\u305f\uff0c\u6ce8\u610f\u306b\u95a2\u3059\u308b\u30bf\u30b9\u30af\u3092\u7528\u3044\u305f\u7814\u7a76\u3067\uff0c\u30d2\u30c8\u3068\u30de\u30ab\u30af\u30b6\u30eb\u306e\u6bd4\u8f03\u3092\u884c\u3063\u3066\u3044\u307e\u3057\u305f\uff0e\u3069\u3046\u3057\u3066\u3082\u6ce8\u76ee\u3059\u308b\u8ad6\u6587\u306f\u30d2\u30c8\u306e\u8133\u306e\u3082\u306e\u3070\u304b\u308a\u306a\u306e\u3067\uff0c\u4ed6\u306e\u52d5\u7269\u3068\u6bd4\u8f03\u3057\u3066\u3044\u308b\u306e\u304c\u65b0\u9bae\u3067\u3057\u305f\uff0e\u305d\u3057\u3066\uff0c\u7d50\u679c\u3068\u3057\u3066\uff0c\u7a2e\u3092\u8d85\u3048\u3066\u6ce8\u610f\u306e\u9078\u629e\u306b\u91cd\u8981\u306a\u9818\u57df\u304c\u5b58\u5728\u3059\u308b\u3068\u3044\u3046\u306e\u304c\u8208\u5473\u6df1\u304b\u3063\u305f\u3067\u3059\uff0e\u307e\u305f\uff0c\u3053\u306e\u7814\u7a76\u3060\u3051\u3067\u306f\u3042\u308a\u307e\u305b\u3093\u304c\uff0c3T\u306eMRI\u3092\u7528\u3044\u3066\u3044\u308b\u7814\u7a76\u304c\u591a\u304f\uff0c\u4e16\u754c\u306e\u6d41\u308c\u3092\u77e5\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3057\u305f\uff0e\u3055\u3089\u306b\u4ed6\u306b\uff0c7T\u306eMRI\u3092\u7528\u3044\u3066\u3044\u308b\u7814\u7a76\u307e\u3067\u5b58\u5728\u3057\uff0c\u3069\u3093\u3069\u3093\u4e16\u754c\u304c\u9032\u3093\u3067\u3044\u308b\u3053\u3068\u3082\u5b9f\u611f\u3067\u304d\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aShared brain connectivity patterns modulated by long-term meditation practice<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Roberto Guidotti, Cosimo Del Gratta, Mauro Gianni Perrucci, Antonino Raffone, Vittorio Pizzella, Laura Marzetti<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Poster Session<br \/>\nIntroduction:<br \/>\nIn the recent years meditation research has gained a great attention due to the beneficial effects on the physical and psychological status of the practitioners [1,2]. The effects of short- and long-term meditation practices on the functional and anatomical structure of the brain have also been addressed by several studies [2-4].<br \/>\nHowever, there is little knowledge on how different meditation styles can affect brain functional structure in short- and long-term meditators, more specifically on how different practices can modify the functional brain connectivity and if these modulations are shared across meditators and are depended by the meditation expertise. To address this issue, we tested whether the meditation style can be decoded using functional connectivity measured using Magnetic Resonance Imaging (fcMRI). Specifically, we investigated Focused Attention (FA) and Open Monitoring (OM) which are the main meditation styles in the Theravada practice [1], in a group of long-term Theravada monks and in a group of novice meditators.<br \/>\nMethods:<br \/>\nTwelve Theravada Buddhist monks (M, mean age 37.9, SD 9.4 yrs) from the Santacittarama Buddhist Monastery participated in our study. Participants practiced FA and OM meditation forms in a balanced way in this tradition. A group of ten novice meditators (M, mean age 33, SD 4 yrs) with no prior meditation experience were recruited from the local community. The participants gave their written informed consent according to the Declaration of Helsinki. The experimental procedure consisted of three blocks of the following sequence: 6 min FA and 6 min OM meditation blocks intermixed with 3 min of resting state block and cued by vocal instructions. The total duration of the experiment was 57 min. BOLD signal images were obtained using T2*-weighted echo planar (EPI) sequence (TR=4.087 s, 28 slices, voxel size 4\u00d74\u00d74 mm3, 860 volumes). fMRI images were temporal and motion corrected, then detrended and temporal filtered. Obtained images were further processed in order to remove white-matter, grey-matter and CSF signal, and motion confounds, finally a band-pass filter (0.01-0.1 Hz) and scrubbing was applied. Preprocessed images were divided in 14 resting state networks (RSNs) and 90 ROIs [5]. The signal within each ROI was averaged across voxels and then pairwise correlated to obtain the connectivity matrix. Connectivity matrices were calculated for each subject in each meditation block, and then the upper triangle of the matrix was extracted to fit the classifier. Linear Support Vector Machine was used to classify meditation style, separately for experts and novices. We used the 75% of the subjects for training and 25% for testing, using shuffled cross-validation to validate the model (n=200). The top 200 connections with the highest F-score were selected to reduce data dimensionality [6]. Permutation tests (PT) were used to assess for statistical significance of classification accuracy.<br \/>\nResults:<br \/>\nThe decoding accuracy for the expert group is of 65.6% (p=0.005; PT, n=200) while for the novice group is 54.4% (p=0.07; PT, n=200). Our analysis reveals that the connectivity matrix can be used to predict the meditation style in expert monks. In the novice group, despite the above-chance accuracy, we cannot state that meditation form can be predicted by fcMRI matrices. The analysis of the most important nodes used for the decoding in the expert group shows the contribution of nodes within the Language, both posterior and anterior Salience and dorsal Default mode networks (Fig. 1).<br \/>\nConclusions:<br \/>\nWe showed how meditation style can be predicted by the use of fcMRI patterns, our results showed that the prediction accuracy is far better in the expert group of meditators instead of the novice group. These results suggests that long-term meditation practices strongly impact the modulation of brain networks involved in different tasks, supporting the idea that connectivity profiles could predict cognitive behavior.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u3053\u306e\u767a\u8868\u306f\uff0c\u4eca\u56de\u306eOHBM\u3067\u6570\u4ef6\u3057\u304b\u898b\u3089\u308c\u306a\u304b\u3063\u305f\u7791\u60f3\u306b\u95a2\u3059\u308b\u7814\u7a76\u306e\u4e00\u3064\u3067\u3057\u305f\uff0e\u3053\u306e\u7814\u7a76\u3067\u306f\uff0c\u7791\u60f3\u5b9f\u8df5\u8005\u3068\u521d\u5fc3\u8005\u4e21\u65b9\u6e2c\u5b9a\u3057\uff0c\u3055\u3089\u306b\u7791\u60f3\u306e\u7a2e\u985e\u3067\u8133\u72b6\u614b\u304c\u3069\u3046\u7570\u306a\u308b\u306e\u304b\u3092MRI\u3092\u7528\u3044\u3066\u8abf\u3079\u305f\u7814\u7a76\u3067\u3057\u305f\uff0e\u307e\u305f\uff0c\u30b5\u30dd\u30fc\u30c8\u30d9\u30af\u30bf\u30fc\u30de\u30b7\u30fc\u30f3\u3092\u7528\u3044\u3066\u7791\u60f3\u306e\u7a2e\u985e\u3092\u4e88\u6e2c\u3057\u305f\u7d50\u679c\uff0c\u521d\u5fc3\u8005\u3068\u6bd4\u3079\u3066\u7791\u60f3\u5b9f\u8df5\u8005\u306e\u65b9\u304c\u9ad8\u3044\u4e88\u6e2c\u7cbe\u5ea6\u3067\u3042\u308a\uff0c\u7791\u60f3\u5b9f\u8df5\u304c\u8133\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u5909\u5316\u306b\u5f71\u97ff\u3057\u3066\u3044\u308b\u3068\u306e\u4e3b\u5f35\u3067\u3057\u305f\uff0e\u79c1\u3082\u4eca\u5f8c\uff0c\u4eca\u306e\u5b9f\u9a13\u8a2d\u8a08\u3067\u7791\u60f3\u5b9f\u8df5\u8005\u306e\u65b9\u3082\u6e2c\u5b9a\u3057\u3088\u3046\u3068\u601d\u3063\u3066\u3044\u308b\u3053\u3068\u3082\u3042\u308a\uff0c\u4eca\u8003\u3048\u308b\u3068\u7791\u60f3\u5b9f\u8df5\u8005\u306e\u7791\u60f3\u306e\u7a2e\u985e\u3084\uff0c\u5b9f\u8df5\u6642\u9593\u306a\u3069\uff0c\u3088\u308a\u8a73\u7d30\u306a\u60c5\u5831\u304c\u6c17\u306b\u306a\u308a\u307e\u3057\u305f\uff0e\u307e\u305f\uff0cPermutation test\u306a\u3069\uff0c\u79c1\u305f\u3061\u306e\u7814\u7a76\u5ba4\u3067\u3082\u4f7f\u3063\u3066\u3044\u308b\u4eba\u304c\u3044\u3066\uff0c\u8aac\u660e\u3092\u805e\u3044\u305f\u3053\u3068\u306f\u3042\u3063\u3066\u3082\u7406\u89e3\u3057\u304d\u308c\u3066\u3044\u306a\u3044\u51e6\u7406\u3082\u591a\u304b\u3063\u305f\u306e\u3067\uff0c\u3053\u308c\u304b\u3089\u5b66\u3093\u3067\u81ea\u5206\u306e\u7814\u7a76\u3078\u306e\u5fdc\u7528\u3082\u8003\u3048\u3066\u3044\u304d\u305f\u3044\u3068\u601d\u3044\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Dynamics of large-scale Brain Network Activity at High Spatial Resolution: Methods and applications<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Dimitri Van De Ville<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a The Pulsatile Integration at Multiple Time Scales in the Resting Brain<br \/>\nThe quest for better understanding of brain dynamics has triggered new ways to approach functional connectivity from fMRI data; i.e., using time-resolved rather than summarizing correlational measures that miss essential details of network interaction dynamics [1]. In this talk, I will highlight promising recent advances where fMRI data is analyzed at the voxel level, first, in terms of time-varying eigenvector centrality, and, second, in terms of transient activity from deconvolved BOLD signals. These new frameworks deal in different ways with spatial and temporal overlap of large-scale functional networks, and thus unravels their interdigitated and parallel organization. The first methodology captures both fine-scale and long-range interactions that can be summarized in a dynamic-FC driven parcellation, with many meaningful parcels and subdivisions [2]. The second methodology allows a frame-by-frame evaluation of transient activity and leads to innovation-driven coactivation patterns (iCAPs) [3]. The couplings (i.e., temporal overlap) between these networks provide promising new measures to build more mechanistic models of brain function at the systems level, with a large potential for interpretable disease diagnosis and prognosis [4].<br \/>\nReferences:<br \/>\n1) M. G. Preti, T. Bolton, D. Van De Ville. The Dynamic Functional Connectome: State-of-the-Art and Perspectives. NeuroImage, 2017, vol. 160, pp. 41-54 [DOI:10.1038\/s41598-017-12993-1]<br \/>\n2) M. G. Preti &amp; D. Van De Ville. Dynamics of Functional Connectivity at High Spatial Resolution Reveal Long-Range Interactions and Fine-Scale Organization. Scientific Reports, 2017, vol. 7, pp. 12773 [DOI:10.1038\/s41598-017-12993-1]<br \/>\n3) F. I. Karahanoglu, D. Van De Ville. Transient Brain Activity Disentangles fMRI Resting-State Dynamics in Terms of Spatially and Temporally Overlapping Networks. Nature Communications, 2015, vol. 6, pp. 7751 [DOI:10.1038\/NCOMMS8751]<br \/>\n4) F. I. Karahanoglu, D. Van De Ville. Dynamics of Large-Scale fMRI Networks: Deconstruct Brain Activity to Build Better Models of Brain Function. Current Opinion in Biomedical Engineering, 2017, vol. 3, pp. 28-36 [DOI:10.1016\/j.cobme.2017.09.008]<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>resting state\u306b\u3064\u3044\u3066\uff0c\u305d\u306e\u533a\u9593\u306e\u30c7\u30fc\u30bf\u5168\u3066\u3092\u4f7f\u3063\u3066\u3057\u307e\u3046\u3068\uff0c\u7d4c\u6642\u5909\u5316\u306e\u60c5\u5831\u304c\u6b20\u3051\u3066\u3057\u307e\u3046\u306e\u3067\uff0c\u52d5\u7684\u89e3\u6790\u3092\u7528\u3044\u3066\u89e3\u6790\u3092\u884c\u3063\u3066\u3044\u308b\u7814\u7a76\u3067\u3057\u305f\uff0e\u4eca\u5f8c\u81ea\u5206\u306e\u7814\u7a76\u3067\u3082\u52d5\u7684\u89e3\u6790\u3092\u884c\u3044\u305f\u3044\u3068\u8003\u3048\u3066\u3044\u308b\u305f\u3081\uff0c\u52d5\u7684\u89e3\u6790\u306e\u8a73\u7d30\u3068\u5171\u306b\uff0c\u4eca\u307e\u3067\u77e5\u3089\u306a\u304b\u3063\u305finnovation-driven coactivation patterns\uff08iCAP\uff09\u3068\u3044\u3046\u540c\u6642\u6d3b\u6027\u5316\u30d1\u30bf\u30fc\u30f3\u306e\u65b9\u6cd5\u306b\u3064\u3044\u3066\u3082\u4eca\u5f8c\u8abf\u3079\u3066\u307f\u3088\u3046\u3068\u601d\u3044\u307e\u3057\u305f\uff0eresting state\u3092\u5bfe\u8c61\u3068\u3057\u305f\u7814\u7a76\u304c\u591a\u304f\u898b\u3089\u308c\u307e\u3057\u305f\u304c\uff0c\u5b89\u9759\u72b6\u614b\u3068\u8a00\u3063\u3066\u3082\u3069\u3046\u3057\u3066\u3082\u7d71\u5236\u304c\u53d6\u308c\u305a\uff0c\u3069\u3093\u306a\u72b6\u614b\u306a\u306e\u304b\u306e\u500b\u4eba\u5dee\u304c\u5927\u304d\u3044\u3068\u601d\u3046\u306e\u3067\uff0c\u3069\u306e\u3088\u3046\u306b\u5b9f\u9a13\u3057\u3066\u3044\u308b\u306e\u304b\u77e5\u3063\u3066\u3044\u304d\u305f\u3044\u3068\u601d\u3044\u307e\u3059\uff0e<br \/>\n<strong>\u5b66\u4f1a\u53c2\u52a0\u5831\u544a\u66f8<\/strong><\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"147\"><strong>\u00a0<\/strong><br \/>\n<strong>\u5831\u544a\u8005\u6c0f\u540d<\/strong><\/td>\n<td width=\"373\">&nbsp;<br \/>\n\u5965\u6751\u99ff\u4ecb<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u767a\u8868\u8ad6\u6587\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n<td width=\"373\">\u500b\u4eba\u306e\u5168\u8133Parcellation\u304c\u6a5f\u80fd\u7684\u7d50\u5408\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306b\u304a\u3051\u308b\u500b\u4eba\u5dee\u3092\u660e\u3089\u304b\u306b\u3059\u308b<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u767a\u8868\u8ad6\u6587\u82f1\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n<td width=\"373\">Individual whole-brain parcellation reveals individual variability in functional connectivity<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u8457\u8005<\/strong><\/td>\n<td width=\"373\">\u5965\u6751\u99ff\u4ecb\uff0c\u4e2d\u6751\u572d\u4f51\uff0c\u65e5\u548c\u609f\uff0c\u5ee3\u5b89\u77e5\u4e4b<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u4e3b\u50ac<\/strong><\/td>\n<td width=\"373\">Organization for Human Brain Mapping<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u8b1b\u6f14\u4f1a\u540d<\/strong><\/td>\n<td width=\"373\">OHBM2019<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u4f1a\u5834<\/strong><\/td>\n<td width=\"373\">auditorium parco della musica<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u958b\u50ac\u65e5\u7a0b<\/strong><\/td>\n<td width=\"373\">2019\/06\/09-2019\/06\/13<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<br \/>\n&nbsp;<\/p>\n<ol>\n<li>\u8b1b\u6f14\u4f1a\u306e\u8a73\u7d30<\/li>\n<\/ol>\n<p>2019\u5e7406\u670809\u65e5\u304b\u30892019\u5e7406\u670813\u65e5\u306b\u304b\u3051\u3066\uff0cauditorium parco della musica(roma)\u306b\u3066\u958b\u50ac\u3055\u308c\u305fOrganization for Human Brain Mapping 2019\uff08OHBM2019\uff09\u306b\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u672c\u5b66\u4f1a\u306f\uff0c\u4eba\u306e\u8133\u6a5f\u80fd\u306b\u95a2\u5fc3\u3092\u5bc4\u305b\u308b\u795e\u7d4c\u79d1\u5b66\u8005\u30fb\u5fc3\u7406\u5b66\u8005\uff0c\u6280\u8853\u958b\u767a\u306b\u95a2\u308f\u308b\u5de5\u5b66\u30fb\u60c5\u5831\u5b66\u306e\u5c02\u9580\u5bb6\u304c\u96c6\u307e\u308a\uff0c\u767a\u5c55\u306e\u8457\u3057\u3044\u30cb\u30e5\u30fc\u30ed\u30a4\u30e1\u30fc\u30b8\u30f3\u30b0\u306e\u77e5\u898b\u3092\uff0c\u5e83\u304f\u793e\u4f1a\u3067\u6d3b\u7528\u3057\u3066\u3044\u304f\u65b9\u5411\u6027\u306b\u3064\u3044\u3066\u60c5\u5831\u4ea4\u63db\u3057\uff0c\u81ea\u5206\u306e\u7814\u7a76\u306b\u5bfe\u3059\u308b\u30d5\u30a3\u30fc\u30c9\u30d0\u30c3\u30af\u3092\u5f97\u308b\u3053\u3068\u3092\u76ee\u7684\u306b\u958b\u50ac\u3055\u308c\u3066\u3044\u307e\u3059\uff0e<br \/>\n&nbsp;<br \/>\n\u79c1\u306f\u5168\u65e5\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u672c\u7814\u7a76\u5ba4\u304b\u3089\u306f\u4ed6\u306b\uff0c\u5ee3\u5b89\u5148\u751f\uff0c\u65e5\u548c\u5148\u751f\uff0c\u5b66\u751f\u3068\u3057\u3066M2\u306e\u5c71\u672c\u3055\u3093\uff0c\u53e4\u5bb6\u541b\uff0c\u5927\u585a\u541b\uff0c\u6749\u91ce\u3055\u3093\uff0c\u5409\u7530\u3055\u3093\uff0cM1\u306e\u98a8\u5442\u8c37\u541b\uff0c\u4e39\u3055\u3093\u304c\u53c2\u52a0\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<ol start=\"2\">\n<li>\u7814\u7a76\u767a\u8868\n<ul>\n<li>\u767a\u8868\u6982\u8981<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<p>\u79c1\u306f11\u65e5\u306e\u30dd\u30b9\u30bf\u30fc\u30bb\u30c3\u30b7\u30e7\u30f3\u304a\u3088\u3073\u30dd\u30b9\u30bf\u30fc\u30ec\u30bb\u30d7\u30b7\u30e7\u30f3\u3067\u767a\u8868\u3092\u884c\u3044\u307e\u3057\u305f\uff0e\u767a\u8868\u306f\u30dd\u30b9\u30bf\u30fc\u5f62\u5f0f\u3067\uff0c\u8a082\u6642\u9593\u53c2\u52a0\u8005\u306e\u65b9\u3068\u8b70\u8ad6\u3092\u884c\u3044\u307e\u3057\u305f\uff0e<br \/>\n\u30bf\u30a4\u30c8\u30eb\u306f\uff0cIndividual whole-brain parcellation reveals individual variability in functional connectivity\u3067\uff0c\u63d0\u6848\u624b\u6cd5\u306b\u57fa\u3065\u304f\u500b\u4eba\u306e\u5168\u8133Parcellation\u306e\u7d50\u679c\u304c\u30bf\u30b9\u30af\u3088\u308a\u3082\u500b\u4eba\u306b\u3088\u308b\u5909\u52d5\u306e\u5f71\u97ff\u3092\u5927\u304d\u304f\u53d7\u3051\u3066\u3044\u308b\u3053\u3068\u3092\u660e\u3089\u304b\u306b\u3057\u305f\u3068\u3044\u3046\u5185\u5bb9\u3092\u5831\u544a\u3057\u307e\u3057\u305f\uff0e\u4ee5\u4e0b\u306b\u6284\u9332\u3092\u8a18\u8f09\u81f4\u3057\u307e\u3059\uff0e<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\"><strong>Introduction: <\/strong><br \/>\nInvestigation on both intra-individual and inter-individual variability in functional connectivity networks is some of the most remarkable research being conducted at present. For example, Gratton revealed that inter-individual variation in functional connectivity is greater than intra-individual variation [1]. However, conventional research uses parcellations based on existing atlases, and analysis results strongly depend on the method of parcellation. On the other hand, it is possible to generate an individualized brain atlas segmenting the whole brain at the individual level, and enabling the generation of highly reliable atlases maintaining individual characteristics [2]. Here, we aim to examine individual variability in functional connectivity by comparing the results of individual whole brain parcellation. In this study, an individual whole brain parcellation method based on functional connectivity was proposed, and inter-individual and intra-individual variation were quantified and compared.<br \/>\n<strong>Methods: <\/strong><br \/>\nIn the proposed parcellation, the whole brain of an individual was segmented into an arbitrary number of regions using both functional and structural MRI data. First, the structural image was segmented into an arbitrary number of regions using simple linear iterative clustering (SLIC). Subsequently, a functional connectivity matrix was generated based on the segmented regions. The network defined by this matrix was segmented into multiple user-determined communities using spectral clustering. Finally, the set of regions making up each community was labeled as one region. The framework of the proposed method is shown in Fig. 1. In this study, data from 8 tasks (Sem_coh-01, Sem_coh-02, Memory_faces, Memory_scenes, Memory_words, Motor 01, Motor 02, and Rest) in 10 healthy adults randomly selected from the Midnight Scan Club were used. The number of brain parcellations was set to 25. In this experiment, individual atomic atlases with 25 regions were generated using data from 10 participants who completed 8 different tasks. Similarity was calculated between the 10 atlases and all individual atlases for the similarity matrix was embedded in two-dimensional space using multi-dimensional scaling (MDS). Subsequently, the intra-individual and inter-individual distances between tasks were calculated and compared.<br \/>\n<strong>Results: <\/strong><br \/>\nA similarity matrix between individual atlases of 10 participants across 8 tasks (A), the distance between individual atlases visualized in two-dimensional space by MDS (B), and comparison between tasks and individual distances (C) are shown in Fig. 2. The MDS plot shows that the distribution of the atlases was small among tasks, and large among subjects. Furthermore, the average value of distances calculated between tasks and between subjects were significantly larger among subjects (p&lt;0.05). This result reveals that the phenomenon that functional relationships are strongly influenced by individual differences rather than differences in task conditions can be reflected at the individual level.<br \/>\n<strong>Conclusion: <\/strong><br \/>\nIn this study, an individual whole brain parcellation method based on functional connectivity was proposed, and inter-individual and intra-individual variation was quantified based on the similarity between them. The results showed that distances between subjects were significantly greater than those between tasks. Therefore, it can be concluded that the result of individual parcellation using the proposed method is significantly more influenced by individual variation than by task.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<ul>\n<li>\u8cea\u7591\u5fdc\u7b54<\/li>\n<\/ul>\n<p>\u4eca\u56de\u306e\u8b1b\u6f14\u767a\u8868\u3067\u306f\uff0c\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u8cea\u7591\u3092\u53d7\u3051\u307e\u3057\u305f\uff0e\u672c\u767a\u8868\u3067\u306f\u8cea\u554f\u8005\u306e\u540d\u524d\u306f\u805e\u3044\u3066\u304a\u308a\u307e\u305b\u3093\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>1<\/strong><br \/>\nParcellation\u306e\u9818\u57df\u6570\u306f25ROIs\uff0c50ROIs\uff0c100ROIs\u4ee5\u5916\u3082\u8a66\u3057\u3066\u3044\u308b\u306e\u304b\uff0c\u307e\u305fROI\u304c\u5897\u3048\u308b\u3068MDS\u306e\u5206\u5e03\u306f\u3069\u3046\u5909\u5316\u3057\u3066\u3044\u308b\u304b\u3068\u3044\u3046\u8cea\u554f\u3092\u9802\u304d\u307e\u3057\u305f\uff0e<br \/>\n\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\uff0c\u4eca\u56de\u306f\u3053\u306e3\u7a2e\u985e\u306e\u307f\u3092\u8a66\u3057\u305f\u3068\u7b54\u3048\u307e\u3057\u305f\uff0e\u307e\u305f\uff0c\u4eca\u56de\u306e\u63d0\u6848Parcellation\u3092\u7528\u3044\u305f\u691c\u8a0e\u3067\u306fROI\u304c\u5897\u52a0\u3059\u308b\u306b\u3064\u308c\u3066\u3088\u308a\u30bf\u30b9\u30af\u3088\u308a\u3082\u500b\u4eba\u306b\u5f71\u97ff\u3092\u53d7\u3051\u308b\u7d50\u679c\u306b\u306a\u3063\u305f\u3068\u7b54\u3048\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>2<\/strong><br \/>\nSLIC\u306e\u521d\u671f\u30af\u30e9\u30b9\u30bf2048\u5206\u5272\u306b\u610f\u5473\u306f\u3042\u308b\u306e\u304b\uff0c\u3068\u3044\u3046\u8cea\u554f\u3092\u9802\u304d\u307e\u3057\u305f\uff0e<br \/>\n\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\uff0c\u4eca\u56de\u306f\u30e6\u30fc\u30b6\u30fc\u5b9a\u7fa9\u306b\u3088\u308b\u6c7a\u3081\u6253\u3061\u3067\u6c7a\u5b9a\u3057\u3066\u3044\u308b\u3068\u7b54\u3048\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>3<\/strong><br \/>\nSLIC\u3067\u306e\u5206\u5272\u304b\uff0cSpectral Clustering\u3067\u306e\u5206\u5272\u304b\u3069\u3061\u3089\u304c\u4eca\u56de\u306e\u7d50\u679c\u306b\u5f71\u97ff\u3057\u3066\u3044\u308b\u3068\u8003\u3048\u3066\u3044\u308b\u304b\u3068\u3044\u3046\u8cea\u554f\u3092\u9802\u304d\u307e\u3057\u305f\uff0e<br \/>\n\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\uff0c\u69cb\u9020\u7684\u304b\u3064\u6a5f\u80fd\u7684\u7279\u5fb4\u3092\u8003\u616e\u3057\u3066\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u3092\u884c\u306a\u3063\u3066\u3044\u308bSpectral clustering\u306e\u65b9\u304c\u3088\u308a\u5f71\u97ff\u3057\u3066\u3044\u308b\u3068\u7b54\u3048\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>4<\/strong><br \/>\n\u30ec\u30b9\u30c8\u30c7\u30fc\u30bf\u306f\u4f7f\u7528\u3057\u3066\u3044\u306a\u3044\u306e\u304b\u3068\u3044\u3046\u8cea\u554f\u3092\u9802\u304d\u307e\u3057\u305f\uff0e<br \/>\n\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\uff0c\u4f7f\u7528\u3057\u305f8\u7a2e\u985e\u306e\u30bf\u30b9\u30af\u306e\u4e2d\u306b\u30ec\u30b9\u30c8\u3082\u542b\u307e\u308c\u3066\u3044\u308b\u3068\u7b54\u3048\u307e\u3057\u305f\uff0e\u52a0\u3048\u3066\uff0c\u540c\u3058Parcellation\u6cd5\u3092\u7528\u3044\u3066\u30ec\u30b9\u30c8\u6642\u306b\u304a\u3051\u308b\u65e5\u9593\u5909\u52d5\u306e\u691c\u8a0e\u306b\u3064\u3044\u3066\u6628\u5e74\u306eOHBM\u3067\u767a\u8868\u3057\u3066\u3044\u308b\u3068\u7b54\u3048\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>5<\/strong><br \/>\n\u4eca\u56de\u306e\u691c\u8a0e\u3067\u306f\uff0c\u5f97\u3089\u308c\u305fParcellation\u306e\u985e\u4f3c\u5ea6\u884c\u5217\u306b\u5bfe\u3057\u3066\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u306f\u8a66\u307f\u305f\u304b\u3068\u3044\u3046\u8cea\u554f\u3092\u9802\u304d\u307e\u3057\u305f\uff0e<br \/>\n\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\uff0c\u4eca\u56de\u306f\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u306f\u3057\u3066\u3044\u306a\u3044\uff0e\u540c\u4e00\u500b\u4eba\u306e\u30bf\u30b9\u30af\u9593\u8ddd\u96e2\uff0c\u540c\u4e00\u30bf\u30b9\u30af\u306e\u500b\u4eba\u9593\u8ddd\u96e2\u3092\u5b9a\u91cf\u5316\u3057\u3066Parcellation\u306e\u5206\u5e03\u3092\u5b9a\u91cf\u5316\u3057\u3066\u3044\u308b\u3068\u7b54\u3048\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<ul>\n<li>\u611f\u60f3<\/li>\n<\/ul>\n<p>\u8fd1\u5e74\u8133\u6a5f\u80fd\u30de\u30c3\u30d4\u30f3\u30b0\u306b\u304a\u3044\u3066\uff0cParcellation\u3084Individual variability\u306e\u7814\u7a76\u306b\u6ce8\u76ee\u304c\u96c6\u307e\u3063\u3066\u3044\u308b\u305f\u3081\u304b\uff0c\u30dd\u30b9\u30bf\u30fc\u767a\u8868\u3067\u306f\u975e\u5e38\u306b\u591a\u304f\u306e\u65b9\u304c\u805e\u304d\u306b\u304d\u3066\u304f\u3060\u3055\u308a\uff0c\u5bc6\u306e\u6fc3\u3044\u30c7\u30a3\u30b9\u30ab\u30c3\u30b7\u30e7\u30f3\u304c\u3067\u304d\u305f\u3088\u3046\u306b\u601d\u3044\u307e\u3059\uff0e\u4eca\u56de\u306f2\u5ea6\u76ee\u306e\u56fd\u969b\u5b66\u4f1a\u3067\u3042\u3063\u305f\u305f\u3081\uff0c\u6d77\u5916\u306e\u7814\u7a76\u8005\u3068\u82f1\u8a9e\u3067\u8b70\u8ad6\u3092\u884c\u3046\u3053\u3068\u306b\u5bfe\u3059\u308b\u62b5\u6297\u306f\u5c11\u306a\u304f\uff0c\u6bd4\u8f03\u7684\u30b9\u30e0\u30fc\u30ba\u306b\u767a\u8868\u3092\u9032\u3081\u3089\u308c\u305f\u3068\u601d\u3044\u307e\u3059\uff0e\u307e\u305f\uff0c\u5468\u308a\u306b\u306f\u79c1\u3068\u540c\u3058\u3088\u3046\u306bParcellation\u65b9\u6cd5\u306e\u63d0\u6848\u3084\u305d\u308c\u3092\u7528\u3044\u305f\u89e3\u6790\u3092\u884c\u3063\u3066\u3044\u308b\u7814\u7a76\u8005\u304c\u591a\u304f\u304a\u3089\u308c\uff0c\u8133\u6a5f\u80fd\u30de\u30c3\u30d4\u30f3\u30b0\u306b\u304a\u3051\u308b\u672c\u7814\u7a76\u306e\u7acb\u3061\u4f4d\u7f6e\u3092\u518d\u78ba\u8a8d\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3057\u305f\uff0e\u672c\u5b66\u4f1a\u3067\u5f97\u305f\u77e5\u898b\u3068\u30e2\u30c1\u30d9\u30fc\u30b7\u30e7\u30f3\u3092\u6d3b\u304b\u3057\u3066\uff0c\u4eca\u5f8c\u3082\u3088\u308a\u7814\u7a76\u306b\u52b1\u3093\u3067\u3044\u304d\u305f\u3044\u3068\u601d\u3044\u307e\u3059\uff0e<br \/>\n&nbsp;<\/p>\n<ol start=\"3\">\n<li>\u8074\u8b1b<\/li>\n<\/ol>\n<p>\u4eca\u56de\u306e\u8b1b\u6f14\u4f1a\u3067\u306f\uff0c\u4e0b\u8a18\u306e4\u4ef6\u306e\u767a\u8868\u3092\u8074\u8b1b\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aHemispheric Difference in Group, Task and Individual-dependent Variation of Functional Networks<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Chenxi Zhao, Yaya Jiang, Xinhu Jin, Gaolang Gong<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Poster session<br \/>\nAbstract \uff1a<br \/>\n<strong>Introduction:<\/strong><br \/>\nAs a topic of general interest, individual difference of human brain has been intensively studied[1,2]. By comparing human with monkeys, a previous study has shown human-specific left-lateralized anatomical variations[3], suggesting a hemisphere-dependent variation in particular brain phenotypes. Recently, the contribution of the group, task, and individual factors to the variation in whole-brain functional networks has been detangled[2]. Following this, the present study aims to examine the magnitudes of group, task and individual-dependent variations in the two hemispheric functional networks, and further evaluate the hemispheric differences. In addition, the heritability of the hemispheric differences in each type of variation was assessed.<br \/>\n<strong>Methods:<\/strong><br \/>\nIn total, 933 human connectome project (HCP) subjects (508 females,age: 22-37,212 monozygotic twins) with resting-state and task fMRI scans(emotion, language, motor, working memory) were included. All images were preprocessed by the HCP pipeline and then feed into the GRETNA toolbox[4] to do linear detrending(only resting-state fMRI), nuisance signals regression and temporally filtering (resting-state:0.01\u20130.1 Hz, tasks:&gt;0.01 Hz). The AICHA atlas[5] was used to define the nodes of network(186 in each hemisphere). Functional connectivity between each within-hemispheric node pair was defined by the Pearson correlation of mean time series (z transformed). For each hemisphere, a network similarity matrix was calculated by correlating among the linearized upper triangles of hemispheric network matrices(Fig1A). As did by Gratton et al.[2], the group, task, and individual-dependent variations were calculated per subject as following: 1) the average similarity from different individuals and tasks(group, baseline), 2) the added similarity from the same task but different individuals relative to group(task), and 3) the added similarity from the same subject but different tasks relative to group(individual). The task and individual-dependent variations were compared with the group-dependent variation using paired t-tests. To test the hemispheric differences, two hemispheric variations attributable to each factor were compared using a paired t-test. For each significantly lateralized effect, the asymmetry index (AI=(L-R)\/(L+R)) was calculated and its heritability (h2) was estimated using the SOLAR software[6]. Multiple comparisons were corrected by the Bonferroni method (p&lt;0.05).<br \/>\n<strong>Results:<\/strong><br \/>\nAs shown in Fig1, both left and right hemispheric (LH and RH) functional networks showed substantial similarity across group (LH\/RH:0.46\u00b10.03\/0.44\u00b10.03) and added similarity of networks from the same individual (LH\/RH:0.18\u00b10.08\/0.17\u00b10.07), whereas subtle but significant added similarity due to task (LH\/RH:0.09\u00b10.04\/0.1\u00b10.04). Paired t-tests showed significant left-lateralized contributions of the group (t=26.8,p=0) and individual (t=4.9,p=10-6) factors to network variation, but a right-lateralized contribution of the task factor (t=20.1,p=0) (Fig2). As listed in Table1, the h2 was significant for the AI of group (h2=0.18,p=0.002) and individual-dependent variation (h2=0.24,p=7\u00d710-5) but non-significant for the task (h2=0.18,p=0.027, not surviving the multiple-comparison correction).<br \/>\n<strong>Conclusions:<\/strong><br \/>\nOur results demonstrated a strong hemispheric functional network stability (group-shared organization and individual features) and moderate state-dependence. Intriguingly, the variation attributable to either the group, task, or individual factors markedly differed between the two hemispheres: shared group-level factor and individual-specific features had stronger influences on the LH network organization, while state-changes had a greater impact on the RH network. Furthermore, our heritability results indicated a significant genetic role in hemispheric differences in group and individual-dependent variations, though tenuously. These findings together provide novel insight into the hemispheric functional network organization and its lateralization.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u672c\u7814\u7a76\u306f\uff0cHuman Connectome Project 933\u4eba\u306e\u88ab\u9a13\u8005\uff0c\u305d\u308c\u305e\u308c5\u7a2e\u985e\u306e\u30bf\u30b9\u30af\u306e\u6a5f\u80fd\u7684\u7d50\u5408\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3092\u7528\u3044\u3066\u985e\u4f3c\u5ea6\u89e3\u6790\u3092\u884c\u3044\u30bf\u30b9\u30af\u9593\u3068\u500b\u4eba\u9593\u306e\u5909\u52d5\u6bd4\u8f03\u3092\u884c\u3063\u3066\u304a\u308a\u307e\u3057\u305f\uff0e\u7d50\u679c\uff0c\u500b\u4eba\u9593\u3067\u6700\u3082\u5909\u52d5\u304c\u5927\u304d\u304f\u306a\u3063\u3066\u304a\u308a\uff0cParcellation\u306e\u985e\u4f3c\u5ea6\u30d9\u30fc\u30b9\u3067\u540c\u3058\u691c\u8a0e\u3092\u884c\u3063\u3066\u3044\u308b\u79c1\u3068\u5168\u304f\u540c\u3058\u7d50\u679c\u304c\u5f97\u3089\u308c\u3066\u304a\u308a\u307e\u3057\u305f\uff0e\u4eca\u56de\u79c1\u306fMSC\u30c7\u30fc\u30bf\u3092\u7528\u3044\u3066\u89e3\u6790\u3092\u884c\u3063\u3066\u304a\u308a\u307e\u3057\u305f\u304c\uff0c\u672c\u691c\u8a0e\u3067\u7528\u3044\u3089\u308c\u3066\u3044\u308bHCP\u30c7\u30fc\u30bf\u3067\u3082\u3067\u3082\u540c\u3058\u7d50\u679c\u304c\u5f97\u3089\u308c\u308b\u304b\u8a66\u3057\u3066\u307f\u305f\u3044\u3068\u601d\u3044\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aInfluence of parcellation atlas on quality of classification of neurodegenerative diseases<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Michaela Montilla, Martin Gajdos<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Poster session<br \/>\nAbstract \uff1a<br \/>\n<strong>Introduction:<\/strong><br \/>\nAn fMRI is considered as one of potential sources providing metrics suitable for evaluation of neural changes inducted in early stages of neurodegenerative diseases and for monitoring their progress.<br \/>\n<strong>Methods:<\/strong><br \/>\nWe used graph-theory based analysis methods to classify patients within one of five groups, divided due to standard clinical diagnostic: 1. Health control (HC), 2. Alzheimer&#8217;s disease (AD), 3. Mild cognitive impairment (MCI), 4. Parkinson&#8217;s disease (PD), 5. Combination of PD and MCI. Using 7 different atlases in a segmentation step of connectivity analysis, we obtained 7 different sets of graph metrics for each subject. Overview of used atlases provides a Table 1a.<br \/>\nData was acquired on Siemens Prisma 3 T. A total number of 53 patients and 43 age-matched control participants with no neurological diagnosis were recruited. Demographic data on patients and healthy controls are summarized in Table 1b.<br \/>\nT1-weighted data (1 mm iso) and functional RS-fMRI data (3 mm iso) was acquired.<br \/>\nData was pre-processed using SPM12. Time series were realigned, normalized into MNI152 space and filtered applying high pass temporal filter with cut off frequency 1\/128 Hz. We regressed out the effect of nuisance covariates, particularly 24 movement regressors, CSF and WM regressors and spatial smoothing using a 6 mm FWHM. Overview of individual steps of processing pipeline are shown on Figure 1.<br \/>\nAfter parcellation we defined single nodes as atlas ROIs and extracted representative signal as first principal component of time series. We obtained weights of edges as Pearson&#8217;s correlation coefficient and these correlation matrices were input for Brain Connectivity Toolbox functions. We used 13 different metrics to describe each of seven networks.<br \/>\nLast step of the analysis was to classify 96 subject based on obtained metrics into one of 5 groups. We used hybrid PCA-LDA model of linear discriminative analysis and support vector machines method.<br \/>\n<strong>Results:<\/strong><br \/>\nClassifying each subject into one of five groups with 7 different sets of metrics allowed us statistically to compare quality of classification using different definitions of regions of interest (ROIs) so the different definitions of nodes in a networks respectively.<br \/>\nComparing with a priory known clinic classification we evaluated quality of classification using sensitivity, specificity, accuracy and Youden index.<br \/>\nUsing one-versus-one multi class classification model, we conclude that the choice of atlas affects the success rate and thus the quality of the classification.<br \/>\nWe found out the highest values of index when classifying patients with AD (compared to the HC). Using YEO211, Harvard-Oxford and Juelich atlas we obtained specificity &gt; 0.8 with higher values of sensitivity as well.<br \/>\nThe highest specificity value of 0.93 was achieved for the classification of PD, again compared to the control group, for the YEO211 atlas. For differentiation between patients with Alzheimer&#8217;s disease and Parkinson&#8217;s disease, the highest values of the Youden index were for atlas YEO211 and HCP. For HCP atlas, the LDA classification was achieved with both the specificity and the sensitivity values above the 0.8 limit, so the fMRI modality can be referred to as the biomarker of these diseases.<br \/>\n<strong>Conclusions:<\/strong><br \/>\nWith finding the most suitable option for brain volume segmentation, our aim was to propose the most appropriate way of determining graph properties for a given data set, so the specificity and sensitivity of classification subjects into one of neurodegenerative diseases group or health control respectively, could achieve biomarker&#8217;s values (Ritsner, 2009).<br \/>\nBy completing the classification according to the 7 atlases we found that AAL and Brodmann&#8217;s atlases used often in the neuroscience studies did not achieve the classification success at biomarker level and therefore these atlases are not as suitable for research and classification of neurodegenerative disease using the method used in this particular and similar studies as other atlases.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u672c\u7814\u7a76\u3067\u306f\uff0c7\u7a2e\u985e\u306e\u7570\u306a\u308bParcellation\u65b9\u6cd5\uff08AAL\uff0cYEO211\uff0cCraddock\uff0cHarvard-Oxford\uff0cBrodmann\uff0cJuelich\uff0cHCP MMP 1.0\uff09\u3092\u7528\u3044\u3066\uff0cPCA-LDA\u30e2\u30c7\u30eb\u3067\u75c7\u4f8b\u3054\u3068\u306b 1. \u5065\u5eb7\u5bfe\u7167\u8005 (HC)\uff0c2. \u30a2\u30eb\u30c4\u30cf\u30a4\u30de\u30fc\u75c5\u60a3\u8005 (AD) \uff0c3. \u8efd\u5ea6\u8a8d\u77e5\u969c\u5bb3(MCI)\uff0c4. \u30d1\u30fc\u30ad\u30f3\u30bd\u30f3\u75c5 (PD)\uff0c5. PD\u3068MCI\u306e\u6df7\u5408 \u306e5\u7a2e\u985e\u306b\u5206\u985e\u3059\u308b\u7814\u7a76\u3067\u3057\u305f\uff0e\u672c\u691c\u8a0e\u3067\u306fYEO211\u304c\u3082\u3063\u3068\u3082\u5206\u985e\u306b\u9069\u3057\u305fParcellation\u3068\u3044\u3046\u7d50\u679c\u306b\u306a\u3063\u3066\u304a\u308a\u307e\u3057\u305f\uff0e\u96c6\u56e3\u8133Parcellation\u304c\u81e8\u5e8a\u306b\u304a\u3044\u3066\u75c5\u5909\u306e\u5206\u985e\u306b\u9069\u5fdc\u3067\u304d\u308b\u3068\u3044\u3046\u30a2\u30a4\u30c7\u30a2\u304c\u975e\u5e38\u306b\u8208\u5473\u6df1\u304b\u3063\u305f\u3067\u3059\uff0e<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a A large open-source dataset of acute stroke MRIs and related automated lesion segmentation algorithm<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Chin-Fu Liu, Sandhya Ramachandran, Victor Wang, Xin Xu, John Hsu, Susumu Mori, Michael Miller, Andreia Faria<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Poster session<br \/>\nAbstract \uff1a<br \/>\n<strong>Introduction:<\/strong><br \/>\nStroke is one of the leading causes of death and the first cause of long-term disability. Research-wise, strokes provide a wealthy source of data for studying brain function. However, the power of a lesion study is limited by the resolution of the lesion sampling. This limit depends not only on variations in the frequency of damage across the brain but also on the multivariate pattern of damage (1). Large samples are therefore imperative for lesion-based studies, and consequently, fully automated technologies that permit large datasets to be generated and analyzed are needed.<br \/>\nCurrently, a few tools are efficient on acute stroke detection, but less accurate on lesion segmentation (2) . Others are accurate on quantification, but developed on high dimensional and relatively modest, well-characterized dataset, therefore lacking clinical generalization (3). There is a gap to be filled by large publicly available datasets associated to efficient technologies (i.e., accurate for lesion delineation, fully automated, fast, robust on clinical data, accessible to the general user) to perform stroke segmentation and quantification. We present 1) a large database of acute strokes, associated to structured radiological reports, and 2) an efficient algorithm for stroke segmentation.<br \/>\n<strong>Methods:<\/strong><br \/>\nLarge database of acute strokes: Acute stroke was defined in the MRI Diffusion Weighted Images (DWI\/B0) of patients diagnosed with (first episode of) acute stroke, at the Johns Hopkins Hospital, between 2007-2017. Although other sequences (e.g., perfusion-, low resolution T1- and T2-WIs) are archived, the data presented here concerns to DWI only, as this is the most universal and eloquent sequence performed in acute stroke. This clinical DWI dataset includes 1.5 and 3T scans, diverse protocols, axial oriented, of high in plane resolution (less than 1x1mm) and high slice thickness (4-6mm). Although the technical heterogeneity, low image resolution, and clinical standards for quality control introduce noise in the image collection, it guarantees generalization to clinical scenarios. Two trained evaluators performed the manual lesion segmentation, a neuroradiologist reviewed all the cases. The process was repeated four times, until each segmentation was declared successful by consensus. Two radiologists created consensual structured radiological reports with information about type of stroke, location according different criteria (e.g., 34 brain structures and 11 vascular territories), and associated finings.<br \/>\nAutomated stroke segmentation: The pipeline (Fig. 1) involves: a) &#8220;pre-processing&#8221; steps as brain registration, skull stripping, (whitestripe) normalization and DWI scaling ; b) abnormal voxel detection , based on the &#8220;radiological normal&#8221; dataset, and properties such as brain symmetry and complementary DWI\/B0 information, resulting in &#8220;ischemic&#8221; and &#8220;hemorrhagic&#8221; probabilistic maps (IS and HS); c) unsupervised deep learning (8,9) , with DWI\/B0\/IS\/HS as inputs, trained in a subset of manual stroke delineations, in a leave-one-out manner. The accuracy of this pipeline, and its variations, is measured by the agreement with the manual delineations, in an independent subset.<br \/>\n<strong>Results:<\/strong><br \/>\nWe created a large dataset of acute strokes, with manual lesion delineation and structured reports, containing 2312 cases (401 radiological &#8220;normal&#8221;, 1512 ischemic strokes, 206 hemorrhagic, 193 mixed). Using this dataset, we created and tested models for automated lesion delineation (Fig. 2). The most efficient model (accuracy 0.95, Dice 0.9) was the &#8220;hierarchical&#8221; UnetH, which cascades a CNN and minimizes the loss function on each resolution levels of Unet.<br \/>\n<strong>Conclusions:<\/strong><br \/>\nWe generated the largest dataset of acute strokes reported so far, and an efficient algorithm for automated lesion delineation. Both will be publicly available in the near future, as a &#8220;biobank&#8221; and a web-service (10) for stroke quantification, enabling lesion-based studies and related clinical tools to achieve full potential in practice.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u672c\u7814\u7a76\u306f\uff0c\u6025\u6027\u8133\u5352\u4e2d\u3068\u8a3a\u65ad\u3055\u308c\u305f2312\u75c7\u4f8b\u306eMRI\u62e1\u6563\u5f37\u8abf\u753b\u50cf\uff08DWI \/ B0\uff09\u3088\u308a\uff0c\u81ea\u52d5\u30bb\u30b0\u30e1\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3\u65b9\u6cd5\u3092\u7528\u3044\u3066\u75c5\u5909\u90e8\u4f4d\u3092\u691c\u51fa\u3059\u308b\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u63d0\u6848\u3057\u3066\u3044\u308b\u7814\u7a76\u3067\u3057\u305f\uff0e\u63d0\u6848\u624b\u6cd5\u3067\u306f\uff0c3\u6b21\u5143\u306eDWI\u306e\u753b\u50cf\u306b\u5bfe\u3057\u3066\uff0c\u524d\u51e6\u7406\u3092\u884c\u3063\u305f\u5f8c\uff0c\u6559\u5e2b\u306a\u3057\u5b66\u7fd2\u3092\u7528\u3044\u3066\u51fa\u8840\u6027\u8133\u5352\u4e2d\u90e8\u4f4d\uff08HS\uff09\u3068\u865a\u8840\u6027\u8133\u5352\u4e2d\u90e8\u4f4d\uff08IS\uff09\u3092\u691c\u51fa\u3057\uff0cDeep Learning\u3092\u7528\u3044\u3066\u6700\u7d42\u7684\u306a\u75c5\u5909\u90e8\u4f4d\u3092\u7279\u5b9a\u3057\u3066\u304a\u308a\u307e\u3057\u305f\uff0e\u69cb\u9020\u753b\u50cf\u3092\u7528\u3044\u3066\u3044\u308b\u70b9\u3084\u6559\u5e2b\u306a\u3057\u3067\u30bb\u30b0\u30e1\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3\u3092\u884c\u3046\u70b9\u304c\uff0c\u79c1\u306e\u7814\u7a76\u3068\u985e\u4f3c\u3057\u3066\u3044\u305f\u305f\u3081\uff0c\u8208\u5473\u6df1\u304b\u3063\u305f\u3067\u3059\uff0e\u6559\u5e2b\u306a\u3057\u5b66\u7fd2\u306e\u969b\u306b\u7528\u3044\u3066\u3044\u308b\u7279\u5fb4\u91cf\u306b\u95a2\u3057\u3066\u8a73\u3057\u304f\u7406\u89e3\u3067\u304d\u306a\u304b\u3063\u305f\u3068\u3053\u308d\u306f\u6b8b\u5ff5\u3067\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aIndividual-Specific Areal-Level Parcellations Improve Functional Connectivity Prediction of Behavior<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Ru Kong, Qing Yang, Evan Gordon, Xinian Zuo, Avram Holmes, Simon B. Eickhoff, B. T. Thomas Yeo<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Poster session<br \/>\nAbstract \uff1a<br \/>\n<strong>Introduction:<\/strong><br \/>\nThe human cerebral cortex comprises hundreds of functionally distinct areas, which are in turn organized into at least ten to twenty large-scale networks. Most resting-state fMRI (rs-fMRI) parcellations have relied on group-averaged data[1\u20133], which might obscure individual-specific topographic features[4, 5]. Here, we propose an approach to generate individual-specific areal-level parcellations and show that the resulting parcellations can improve individual predictions of behavioral phenotypes based on functional connectivity (FC).<br \/>\n<strong>Methods:<\/strong><br \/>\nWe have previously proposed and validated a multi-session hierarchical Bayesian model (MS-HBM) for estimating individual-specific cortical networks[6]. The multiple layers of the MS-HBM explicitly differentiate intra-subject (within-subject) from inter-subject (between-subject) network variability. In contrast to MS-HBM, previous network mappings ignore intra-subject variability, so might confuse intra-subject variability for inter-subject differences. We have previously utilized MS-HBM for network parcellations[6].To estimate areal-level parcellations, the MS-HBM could be re-trained by initializing with a group-level areal-level parcellation (e.g., Schaefer2018[7]). Furthermore, we constrained the individual-specific parcels to be within 30mm of the group-level parcels, since previous studies suggest that individual variation in cortical areal location can go up to 30mm[8].\u00a0We compared MS-HBM with a well-known individual-specific parcellation approach (Gordon2017[5]). We considered rs-fMRI from 10 subjects (10 sessions each) in the MSC dataset[5]. Each subject was parcellated using all rs-fMRI sessions. Task inhomogeneity[5, 7] (standard deviation of task activation within each parcel) was then evaluated using task-fMRI data from the same subjects. A lower task inhomogeneity indicates better parcellation quality. For fair comparison, the number of MS-HBM parcels were constrained to be the same as Gordon2017. Second, we considered ICA-FIX denoised rs-fMRI data from the HCP S1200 release[9]. We selected 58 behavioral measures across cognition, personality and emotion[6]. Individual-specific MS-HBM parcellations were estimated for subjects with four runs and no missing behavior (N = 752). For each subject, we obtained 400\u00d7400 FC matrices using the 400-area group-level parcellation (Schaefer2018) or the 400-area individual-specific MS-HBM parcellations. The FC matrices were then used for predicting the 58 behavioral measures using kernel ridge regression[10]. We performed 20-fold cross-validation: kernel ridge regression was trained on 19 folds and used to predict behavior in the test fold. The regularization parameter was determined using inner-loop cross-validation. Furthermore, the 20-fold cross-validation was repeated 100 times[6].<br \/>\n<strong>Results:<\/strong><br \/>\nIndividual-specific MS-HBM parcellations achieved better task inhomogeneity than Gordon2017, suggesting better generalization to task data (Fig. 1A). Fig. 1B shows the parcellations of two representative MSC subjects estimated from 5 rs-fMRI sessions. We observed significant topological differences between the two subjects, which were highly replicable across sessions. Compared with Schaefer2018, the FC of MS-HBM parcels achieved a higher average prediction accuracy with a relative improvement of 9.44%. We note that we could not compare with Gordon2017 because the Gordon2017 approach estimated different number of parcels in each participant, so the resulting FC matrices were not comparable across participants.<br \/>\n<strong>Conclusions:<\/strong><br \/>\nCompared with other approaches, MS-HBM individual-specific cortical parcellations generalized better to new rs-fMRI (not shown due to space constraints) and task-fMRI data from the same subjects. MS-HBM parcellations were highly reproducible within individuals, while capturing unique individual features. Individual-specific parcellations yield better FC-based behavioral prediction compared with group-level parcellations.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u672c\u7814\u7a76\u3067\u306f\uff0c\u500b\u4eba\u306e\u5168\u8133Parcellation\u65b9\u6cd5\u304c\u63d0\u6848\u3055\u308c\uff0cMSC\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u304a\u3051\u308b\u30bf\u30b9\u30af\u3068\u30ec\u30b9\u30c8\u306e\u30c7\u30fc\u30bf\u3092\u7528\u3044\u3066\u30bb\u30c3\u30b7\u30e7\u30f3\u3054\u3068\u306e\u5909\u52d5\u3092\u691c\u8a0e\u3057\u3066\u304a\u308a\u307e\u3057\u305f\uff0e\u7d50\u679c\u3068\u3057\u3066\u63d0\u6848Parcellation\u304c\u500b\u4eba\u306e\u518d\u73fe\u6027\u304c\u9ad8\u304f\u306a\u308b\u3053\u3068\u3092\u793a\u3057\u3066\u304a\u308a\u307e\u3057\u305f\uff0eParcellation\u65b9\u6cd5\u306e\u63d0\u6848\uff0c\u500b\u4eba\u5dee\u306e\u691c\u8a0e\uff0c\u307e\u305f\u7528\u3044\u3066\u3044\u308b\u30c7\u30fc\u30bf\u304c\u79c1\u306e\u7814\u7a76\u3068\u540c\u3058\u3067\u3042\u308a\uff0c\u9577\u6642\u9593\u7814\u7a76\u306e\u5171\u6709\u3092\u884c\u3044\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n\u53c2\u8003\u6587\u732e<br \/>\n<a href=\"https:\/\/ww5.aievolution.com\/hbm1901\/index.cfm?do=abs.pubSearchAbstracts\">https:\/\/ww5.aievolution.com\/hbm1901\/index.cfm?do=abs.pubSearchAbstracts<\/a><br \/>\n&nbsp;<br \/>\n<strong>\u5b66\u4f1a\u53c2\u52a0\u5831\u544a\u66f8<\/strong><\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"147\"><strong>\u00a0<\/strong><br \/>\n<strong>\u5831\u544a\u8005\u6c0f\u540d<\/strong><br \/>\n<strong>\u00a0<\/strong><\/td>\n<td width=\"373\">&nbsp;<br \/>\n\u4e39\u771f\u91cc\u5948<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u767a\u8868\u8ad6\u6587\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n<td width=\"373\">N-back\u8ab2\u984c\u6642\u306e\u30ef\u30fc\u30ad\u30f3\u30b0\u30e1\u30e2\u30ea\u8ca0\u8377\u91cf\u306b\u4f9d\u5b58\u3057\u305f\u8133\u6d3b\u52d5\u5909\u5316<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u767a\u8868\u8ad6\u6587\u82f1\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n<td width=\"373\">Working memory load-dependent changes in brain activity during the N-back task<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u8457\u8005<\/strong><\/td>\n<td width=\"373\">\u4e39\u771f\u91cc\u5948, \u65e5\u548c\u609f, \u5ee3\u5b89\u77e5\u4e4b<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u4e3b\u50ac<\/strong><\/td>\n<td width=\"373\">Organization for human brain mapping<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u8b1b\u6f14\u4f1a\u540d<\/strong><\/td>\n<td width=\"373\">25th Annual Meeting of the Organization of Human Brain Mapping<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u4f1a\u5834<\/strong><\/td>\n<td width=\"373\">Auditorium Parco Della Musica<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u958b\u50ac\u65e5\u7a0b<\/strong><\/td>\n<td width=\"373\">2019\/06\/09-2019\/06\/13<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<br \/>\n&nbsp;<\/p>\n<ol>\n<li>\u8b1b\u6f14\u4f1a\u306e\u8a73\u7d30<\/li>\n<\/ol>\n<p>2019\/6\/9\u304b\u30892019\/6\/13\u306b\u304b\u3051\u3066\uff0cAuditorium Parco Della Musica\u306b\u3066\u958b\u50ac\u3055\u308c\u307e\u3057\u305fHBM\u306b\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u3053\u306e\u5b66\u4f1a\u306f\uff0c\u8133\u753b\u50cf\u7814\u7a76\u3084\u305d\u306e\u5fdc\u7528\u306b\u95a2\u5fc3\u306e\u3042\u308b\u7814\u7a76\u8005\u3084\u533b\u5e2b\uff0c\u5b66\u751f\u304c\u53c2\u52a0\u3057\u3066\u304a\u308a\uff0c\u767a\u5c55\u306e\u8457\u3057\u3044\u30cb\u30e5\u30fc\u30ed\u30a4\u30e1\u30fc\u30b8\u30f3\u30b0\u3092\u7528\u3044\u305f\u8133\u306b\u95a2\u3059\u308b\u77e5\u898b\u3092\uff0c\u5e83\u304f\u793e\u4f1a\u3067\u6d3b\u7528\u3057\u3066\u3044\u304f\u53ef\u80fd\u6027\uff0c\u65b9\u5411\u6027\u306b\u3064\u3044\u3066\u306e\u60c5\u5831\u4ea4\u63db\uff0c\u8b70\u8ad6\u306e\u5834\u3068\u306a\u308b\u3053\u3068\u3092\u76ee\u6307\u3059\u5b66\u4f1a\u3067\u3059\uff0e<br \/>\n\u672c\u7814\u7a76\u5ba4\u304b\u3089\u306f\u79c1\u3068\u5ee3\u5b89\u5148\u751f\uff0c\u65e5\u548c\u5148\u751f\uff0c\u5c71\u672c\uff0c\u53e4\u5bb6\uff0c\u5927\u585a\uff0c\u5965\u6751(\u99ff)\uff0c\u6749\u91ce\uff0c\u5409\u7530\uff0c\u98a8\u5442\u8c37\u304c\u53c2\u52a0\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<ol start=\"2\">\n<li>\u7814\u7a76\u767a\u8868\n<ul>\n<li>\u767a\u8868\u6982\u8981<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<p>\u79c1\u306f12\u65e5\u306e12:45~14:45\u306e\u30bb\u30c3\u30b7\u30e7\u30f3\u300cPoster Session: Poster Numbers W001-W906\u300d\u306b\u3066\u767a\u8868\u3057\u307e\u3057\u305f\uff0e\u767a\u8868\u306e\u5f62\u5f0f\u306f\u30dd\u30b9\u30bf\u30fc\u767a\u8868\u3068\u306a\u3063\u3066\u304a\u308a\uff0c2\u6642\u9593\u306b\u308f\u305f\u308a\uff0c\u53c2\u52a0\u8005\u306e\u65b9\u3068\u8b70\u8ad6\u3092\u884c\u3044\u307e\u3057\u305f\uff0e<br \/>\n\u4eca\u56de\u306e\u767a\u8868\u3067\u306f\uff0c\u300cWorking memory load-dependent changes in brain activity during the N-back task\u300d\u3068\u984c\u3057\u3066\u767a\u8868\u3057\u307e\u3057\u305f\uff0e\u4ee5\u4e0b\u306b\u6284\u9332\u3092\u8a18\u8f09\u81f4\u3057\u307e\u3059\uff0e<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\"><strong>Introduction<\/strong>: Working memory (WM) is the ability to simultaneously hold and process information. Previous studies on the neural basis of WM examined its influence on the brain activity based on the differences observed in task performance. However, considering that the WM capacity varies among individuals, even with similar task scores, the influence of the task load on the brain activity is also considered to differ among people. Therefore, clarifying such an influence of the task load on the brain activity is of crucial importance. In the present study, the existence of a brain area which increases in activity with increasing task load was assumed. Specifically, the brain activity during the N-back task was measured by fMRI, while the brain region whose activity was altered by the changes in the WM load was extracted.<br \/>\n&nbsp;<br \/>\n<strong>Methods<\/strong>: Twenty healthy adults (age: 22.4\u00b10.17 years old, 10 females) participated in the current experiment and performed an N-back verbal identity WM task. Specifically, each participant completed three N-back fMRI runs (1, 2, 3-back) in a random order. As described in Fig. 1, each N-back run consisted of a 410-s block-design, which included both the N- and 0-back conditions in alternating 50-s blocks. Furthermore, activation analyses of the obtained fMR images were performed using SPM12 and the brain regions showing increases in activity with increasing loading amount were examined. Briefly, the activated voxels in the 3-back task with the highest load were assumed to be important for the WM load-dependent changes in brain activity. Additionally, the t-value of the 1,2,3-back task in the activated voxel at the 3-back task was extracted and the differences between the 3 groups were statistically tested. Finally, the pattern behind the WM load-dependent changes in brain activity was classified based on the differences between the 1-2-back, 2-3-back, and 1-3-back.<br \/>\n&nbsp;<br \/>\n<strong>Results<\/strong>: Fig. 2(a) shows the 10 voxels activated in the 3-back task. As the brain activity increased with increasing WM load in all voxels, the regions associated with memory and attention, including the inferior parietal lobule (IPL), the superior frontal gyrus (SFG), the right precuneus (PCUN.R) and the right insula (INS.R), were examined (van der Mark et al. 2011) (Courtney et al. 1998) (Cavanna, A. E et al. 2006). The more the increase in WM load, the more the brain attentional resources were consumed. Furthermore, the precuneus also correlated with performance (p &lt;0.001, uncorrected) and was considered to play a role in WM. Fig. 2(b) illustrates the activation levels of each of these 10 voxels at different WM loading. These areas and the changes in their activation levels were classified into three patterns; 1) pattern A: greatly increasing from the 1- to 2-back; 2) pattern B: gradually increasing as the WM load increased; and 3) pattern C: greatly increasing from the 2- to 3-back. Pattern A identified the left\/right IPL, the left SFG, PCUN.R, and the left caudate nucleus (2 voxels), which are regions associated mainly to WM. Pattern B represented the INS.R and the left cerebellum, suggesting the increases in attention and discomfort to be accompanied by an increase in task difficulty. Finally, the right SFG and the left supplementary motor area reported pattern C and were greatly activated in the 3-back, which indicates their involvement in the processing of high load tasks.<br \/>\n&nbsp;<br \/>\n<strong>Conclusions<\/strong>: In the current study, the WM load-dependent changes in brain activity were examined and the following brain regions, which are related to attention and memory, showed increases in activity with increases in WM load; IPL and SFG, PCUN.R, INS.R. These results suggest that the consumption of attention resources in the brain increases with increasing WM load. In addition, the existence of three patterns describing the changes in brain activity was identified in the extracted brain regions based on different activation levels seen at different WM loading amounts.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<ul>\n<li>\u8cea\u7591\u5fdc\u7b54<\/li>\n<\/ul>\n<p>\u4eca\u56de\u306e\u8b1b\u6f14\u767a\u8868\u3067\u306f\uff0c\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u8cea\u7591\u3092\u53d7\u3051\u307e\u3057\u305f\uff0e\u8cea\u554f\u8005\u306e\u6c0f\u540d\u306f\u63a7\u3048\u640d\u306d\u3066\u3057\u307e\u3044\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fbN-back<\/strong><strong>\u8ab2\u984c\u3068\u306f\u4f55\u304b\uff0e<\/strong><br \/>\n\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\uff0c\u79c1\u306f\u300c\u8ca0\u8377\u91cf\u3092\u5909\u5316\u3055\u305b\u308b\u3053\u3068\u304c\u3067\u304d\u308b\u8ab2\u984c\uff0e\u300d\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb<\/strong><strong>\u306a\u305c\u3053\u308c\u3089\u306e\u6587\u5b57\u3092\u4f7f\u3063\u305f\u306e\u304b\uff0c\u5927\u6587\u5b57\u3068\u5c0f\u6587\u5b57\u306f\u540c\u3058\u3082\u306e\u3068\u5224\u65ad\u3059\u308b\u306e\u304b\uff0e<\/strong><br \/>\n\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\uff0c\u79c1\u306f\u300c\u97f3\u97fb\u30b9\u30c8\u30a2\u306e\u6a5f\u80fd\u3092\u6392\u9664\u3059\u308b\u305f\u3081\uff0e\u5927\u6587\u5b57\u3068\u5c0f\u6587\u5b57\u306f\u9055\u3046\u3082\u306e\u3068\u5224\u65ad\u3059\u308b\uff0e\u300d\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb3-back<\/strong><strong>\u8ab2\u984c\u6642\u306e\u8ce6\u6d3b\u9818\u57df\u306b\u7740\u76ee\u3057\u3066\u3044\u308b\u304c\uff0c1,2-back<\/strong><strong>\u6642\u306b\u3082\u540c\u3058\u30dc\u30af\u30bb\u30eb\u304c\u8ce6\u6d3b\u3057\u305f\u306e\u304b\uff0e<\/strong><br \/>\n\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\uff0c\u79c1\u306f\u300c1,2-back\u6642\u306b\u306f\u5225\u306e\u9818\u57df\u3082\u8ce6\u6d3b\u3057\u305f\uff0e\u307e\u305f\uff0c\u540c\u3058\u9818\u57df\u3067\u306e\u8ce6\u6d3b\u3082\u78ba\u8a8d\u3057\u305f\uff0e\u300d\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb9<\/strong><strong>\u30dc\u30af\u30bb\u30eb\u3060\u3051\u3057\u304b\u8ce6\u6d3b\u3057\u306a\u304b\u3063\u305f\u306e\u304b\uff0e\u5468\u308a\u306e\u30dc\u30af\u30bb\u30eb\u306f\u3069\u3046\u3060\u3063\u305f\u306e\u304b\uff0e<\/strong><br \/>\n\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\uff0c\u79c1\u306f\u300cFWE-corrected\uff0cp&lt;.001\u3067\u7d5e\u3063\u305f\u305f\u3081\uff0c\u95be\u5024\u3092\u5909\u3048\u308b\u3068\u591a\u304f\u307f\u3089\u308c\u308b\uff0e\u300d\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fbGLM<\/strong><strong>\u3092\u7528\u3044\u3066\u3044\u308b\u306e\u304b\uff0e<\/strong><br \/>\n\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\uff0c\u79c1\u306f\u300c\u306f\u3044\uff0e\u300d\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e<br \/>\n<strong>\u00a0<\/strong><br \/>\n<strong>\u30fb<\/strong><strong>\u79c1\u306fEEG<\/strong><strong>\u3092\u4f7f\u3063\u3066\u3044\u308b\u304c\uff0c\u6a5f\u5668\u306f\u4f55\u3092\u4f7f\u7528\u3057\u3066\u3044\u308b\u306e\u304b\uff0e<\/strong><br \/>\n\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\uff0c\u79c1\u306f\u300cfMRI\uff0e\u300d\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fbMethod<\/strong><strong>\u306e1,2-back<\/strong><strong>\u8ab2\u984c\u6642\u306eT<\/strong><strong>\u5024\u62bd\u51fa\u304c\u3088\u304f\u5206\u304b\u3089\u306a\u3044\uff0e<\/strong><br \/>\n\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\uff0c\u79c1\u306f\u300c3-back\u8ab2\u984c\u6642\u306b\u8ce6\u6d3b\u3057\u305f\u9818\u57df\u306b\u304a\u3051\u308b\uff0c1,2,3-back\u8ab2\u984c\u6642\u306eT\u5024\u3092\u62bd\u51fa\u3057\u305f\uff0e\u300d\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb<\/strong><strong>\u6709\u610f\u5dee\u691c\u5b9a\u306fANOVA<\/strong><strong>\u3092\u7528\u3044\u305f\u306e\u304b\uff0e<\/strong><br \/>\n\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\uff0c\u79c1\u306f\u300c\u306f\u3044\uff0eOne-way ANOVA\u3092\u7528\u3044\u305f\uff0e\u300d\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb<\/strong><strong>\u306a\u305c\u5f90\u3005\u306b\u5897\u52a0\u3059\u308b\u3068\u3044\u3046\u3053\u3068\u304c\u3044\u3048\u308b\u306e\u304b\uff0e1,2-back<\/strong><strong>\u3067\u6709\u610f\u5dee\u306f\u3042\u3063\u305f\u306e\u304b\uff0e<\/strong><br \/>\n\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\uff0c\u79c1\u306f\u300c\u30d1\u30bf\u30fc\u30f3A\u3067\u306f1,2-back\u3067\u6709\u610f\u5dee\u306f\u306a\u304b\u3063\u305f\uff0e1,3-back\u3067\u306e\u307f\u6709\u610f\u5dee\u304c\u307f\u3089\u308c\u305f\u305f\u3081\uff0c\u5f90\u3005\u306b\u5897\u52a0\u3059\u308b\u3068\u8003\u3048\u3089\u308c\u308b\uff0e\u300d\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<ul>\n<li>\u611f\u60f3<\/li>\n<\/ul>\n<p>\u521d\u3081\u3066\u306e\u56fd\u969b\u5b66\u4f1a\u3067\u306e\u30dd\u30b9\u30bf\u30fc\u767a\u8868\u3060\u3063\u305f\u305f\u3081\u7dca\u5f35\u3057\u307e\u3057\u305f\u304c\uff0c\u4e8b\u524d\u306b\u6e96\u5099\u3057\u305f\u767a\u8868\u5185\u5bb9\u3092\u601d\u3063\u3066\u3044\u305f\u4ee5\u4e0a\u306b\u591a\u304f\u306e\u65b9\u3005\u306b\u8a71\u3059\u3053\u3068\u304c\u3067\u304d\u307e\u3057\u305f\uff0e\u540c\u3058\u7814\u7a76\u30c6\u30fc\u30de\u306e\u65b9\u306e\u3054\u610f\u898b\u3082\u3044\u305f\u3060\u304f\u3053\u3068\u304c\u3067\u304d\uff0c\u8cb4\u91cd\u306a\u7d4c\u9a13\u304c\u3067\u304d\u305f\u3068\u601d\u3044\u307e\u3059\uff0e\u4e00\u65b9\u3067\uff0c\u7814\u7a76\u30c6\u30fc\u30de\u306e\u65b9\u5411\u6027\u3084\u30dd\u30b9\u30bf\u30fc\u306e\u4f5c\u308a\u65b9\uff0c\u82f1\u8a9e\u80fd\u529b\u306e\u5411\u4e0a\u306a\u3069\uff0c\u81ea\u5206\u306e\u4eca\u5f8c\u306e\u76ee\u6a19\u3092\u660e\u78ba\u306b\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3057\u305f\uff0e\u307e\u305f\uff0c\u30dd\u30b9\u30bf\u30fc\u767a\u8868\u3084\u8b1b\u6f14\u3092\u805e\u304d\uff0c\u30ef\u30fc\u30ad\u30f3\u30b0\u30e1\u30e2\u30ea\u306b\u95a2\u3059\u308b\u65b0\u305f\u306a\u77e5\u898b\u3082\u5f97\u3089\u308c\u307e\u3057\u305f\uff0e\u69d8\u3005\u306a\u7814\u7a76\u5206\u91ce\u306e\u65b9\u304c\u53c2\u52a0\u3055\u308c\u3066\u3044\u305f\u305f\u3081\uff0c\u81ea\u5206\u306e\u7814\u7a76\u5185\u5bb9\u4ee5\u5916\u306b\u3082\u8208\u5473\u306e\u3042\u308b\u7814\u7a76\u304c\u5897\u3048\uff0c\u4eca\u5f8c\u306e\u7814\u7a76\u306b\u304a\u3044\u3066\u3068\u3066\u3082\u610f\u5473\u306e\u3042\u308b\u5b66\u4f1a\u53c2\u52a0\u3068\u306a\u308a\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<ol start=\"3\">\n<li>\u8074\u8b1b<\/li>\n<\/ol>\n<p>\u4eca\u56de\u306e\u5b66\u4f1a\u3067\u8074\u8b1b\u3057\u305f\u767a\u8868\u306e\u3046\u3061\uff0c\u4e0b\u8a184\u4ef6\u3092\u5831\u544a\u3044\u305f\u3057\u307e\u3059\uff0e<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Neurocognitive correlates of working memory in Postpartum Psychosis<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Olivia S. Kowalczyk, Astrid Pauls, Montserrat Fust\u00e9, Steven Williams, Katie Hazelgrove, Costanza Vecchio, Gertrude Seneviratne, Carmine Pariante, Paola Dazzan, Mitul Mehta<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a All posters M001-M897<br \/>\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a<br \/>\n&nbsp;<br \/>\n<strong>Introductions<\/strong>: Postpartum psychosis (PP) is a severe postpartum disorder. Working memory related brain activations are consistently impaired in disorders related to PP (e.g. bipolar disorder, non-puerperal psychosis), however, few studies have investigated this in PP. The aim of this study is to compare women at risk of PP and healthy postpartum women on measures of brain activation and functional connectivity related to working memory.<br \/>\n&nbsp;<br \/>\n<strong>Methods<\/strong>: Twenty-four women at risk of PP (11 developed an episode \u2013 PE; 13 remained well \u2013 NPE) and 20 healthy postpartum women completed a functional Magnetic Resonance Imaging (fMRI) scan within a year of delivery, including a classic working memory task (n-back). All fMRI data analysis was performed with FSL, following a standard preprocessing pipeline. General linear models were used to examine peak activations and psychophysiological interaction (PPI) analysis was performed to investigate task-related connectivity. For the PPI analysis the principal seeds were placed in the left and right dorsolateral prefrontal cortices (DLPFCs) based on previous studies (Owen et al., 2005). Two additional seeds (primary motor area \u2013 M1, and supplementary motor area \u2013 SMA) were used to capture connectivity associated with the motor response to the experimental conditions. Randomise (FSL) with threshold-free cluster enhancement was used for non-parametric permutations-based test of between-group differences (5000 permutations, corrected p&lt;0.05).<br \/>\n&nbsp;<br \/>\n<strong>Results<\/strong>: Hyperactivity of lateral visual areas during 0- (605 voxels), 1- (1705 voxels), and 3-back (1453 voxels) conditions was observed in the PE group compared to controls. PE and NPE women had increased connectivity with the right DLPFC and parietal, lateral visual, bilateral temporal, and cerebellar regions, during 1- (PE=1118 voxels, NPE=6847 voxels) and 2-back compared to controls (PE=48355 voxels, NPE=21826 voxels). Similarly, NPE and PE groups both had increased connectivity of bilateral parietal and visual regions with M1 compared to controls during 2-back (PE=91 voxels, NPE=16935 voxels). Additionally, the PE group had increased connectivity of the right middle temporal gyrus with the right DLPFC during 2-back compared to the NPE group (164 voxels). All p-values were &lt;0.05.<br \/>\n&nbsp;<br \/>\n<strong>Conclusions<\/strong>: This study reveals that while increased connectivity during the n-back task is evident in all women at risk of PP, there are specific increases in connectivity between the prefrontal cortex and temporal lobes in those who developed an episode. Importantly these changes differ from the reduced connectivity with the DLPFC usually observed in bipolar disorder (Cremaschi et al., 2013) and schizophrenia (Deserno et al., 2012; Quid\u00e9 et al., 2013), and provide initial evidence of the potentially differential nature of abnormalities in PP. These results require replication and extension into other cognitive domains and may contribute to the development of different treatment strategies for women at risk of PP compared to those with non-puerperal psychosis.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u672c\u767a\u8868\u306e\u7814\u7a76\u306f\uff0c \u7523\u5f8c\u7cbe\u795e\u75c5\u306e\u30ea\u30b9\u30af\u304c\u3042\u308b\u5973\u6027\u3068\u5065\u5eb7\u306a\u5973\u6027\u306b\u304a\u3044\u3066\uff0c\u30ef\u30fc\u30ad\u30f3\u30b0\u30e1\u30e2\u30ea\u306b\u95a2\u9023\u3059\u308b\u8133\u6d3b\u52d5\u3068\u6a5f\u80fd\u7684\u7d50\u5408\u6027\u306e\u6bd4\u8f03\u3092\u76ee\u7684\u3068\u3057\u3066\u3044\u307e\u3057\u305f\uff0e\u7d50\u679c\u3068\u3057\u3066\uff0c\u7523\u5f8c\u7cbe\u795e\u75c5\u306e\u30ea\u30b9\u30af\u306e\u3042\u308b\u5973\u6027\u306f\uff0cN-back\u8ab2\u984c\u6642\u306e\u53f3DLPFC\u306a\u3069\u306e\u7d50\u5408\u6027\u304c\u5897\u52a0\u3057\u307e\u3057\u305f\uff0e\u3053\u306e\u7814\u7a76\u3067\u306f\uff0c\u5065\u5e38\u7fa4\u3068\u75c5\u6c17\u306e\u30ea\u30b9\u30af\u304c\u3042\u308a\u7cbe\u795e\u75c5\u306b\u306a\u3063\u305f\u7fa4\u306b\u52a0\u3048\uff0c\u75c5\u6c17\u306e\u30ea\u30b9\u30af\u306f\u3042\u308b\u304c\u7cbe\u795e\u75c5\u306b\u306a\u3089\u306a\u304b\u3063\u305f\u7fa4\u306e\u691c\u8a0e\u3082\u884c\u3063\u3066\u3044\u308b\u70b9\u304c\u9762\u767d\u3044\u3068\u601d\u3044\u307e\u3057\u305f\uff0e\u307e\u305f\uff0c\u5148\u884c\u7814\u7a76\u306b\u304a\u3051\u308b\u7d71\u5408\u5931\u8abf\u75c7\u60a3\u8005\u306eDLPFC\u3068\u306e\u7d50\u5408\u6027\u306e\u4f4e\u4e0b\u3068\u306f\u7570\u306a\u308b\u7d50\u679c\u306b\u3064\u3044\u3066\u8a00\u53ca\u3055\u308c\u3066\u304a\u308a\uff0c\u5927\u5909\u8208\u5473\u6df1\u304b\u3063\u305f\u3067\u3059\uff0e<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Network differences during an n-back working memory task in adults with autism spectrum disorder<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Veronica Yuk, Benjamin Dunkley, Evdokia Anagnostou, Margot Taylor<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aAll posters M001-M897<br \/>\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a<br \/>\n&nbsp;<br \/>\n<strong>Introductions<\/strong>: Adults with autism spectrum disorder (ASD) exhibit a varying profile of working memory (WM) abilities, though a recent meta-analysis reported that overall, adults with ASD experience some WM difficulties (Demetriou et al., 2018). These differences may be grounded in atypical functional connectivity, as individuals with ASD have shown either decreased (Urbain et al., 2016) or alternative network recruitment (Koshino et al., 2005) during WM tasks. Although it is known that WM processes are frequency-specific (Roux and Uhlhaas, 2014), and while Urbain et al. (2016) found decreased alpha-band synchronization in children with ASD, it is unclear whether this decrease continues into adulthood, or whether this difference migrates to other frequency bands with development. Given the roles of theta, alpha, and gamma oscillations in recognition (Brookes et al., 2011; Klimesch et al., 2004), we predict that adults with ASD will show decreased synchronization between WM-related regions across all three frequency bands.<br \/>\n&nbsp;<br \/>\n<strong>Methods<\/strong>: We included 41 adults with ASD (27.16 \u00b1 6.19 years; 27 males) and 38 controls (27.42 \u00b1 6.06 years; 26 males). There were no group differences (all ps &gt; 0.2) in age (t(77) = 0.19), sex (\u03a7\u00b2(1) = 9.20&#215;10^-6), or IQ (t(74) = 1.09). Participants filled out the Behavioral Rating Inventory of Executive Function, Adult Version (BRIEF-A; Roth et al., 2005) as a measure of everyday executive function abilities; T scores on the WM subscale were compared between groups. To examine neural correlates of WM processes, participants performed a visual n-back task with abstract patterns with two loads (1 and 2) in the MEG scanner. MEG data were acquired on a CTF 151-channel system and preprocessed and analyzed in FieldTrip (Oostenveld et al., 2011). Data were epoched from -500 ms to 750 ms relative to the onset of the repeated stimulus. Source activity in the 90 AAL regions was estimated using a vector beamformer, and phase synchrony in each canonical frequency band was calculated using the weighted phase lag index. Between-group network differences were determined using Network Based Statistics (Zalesky et al., 2010), which accounts for multiple comparisons using family-wise error correction.<br \/>\n&nbsp;<br \/>\n<strong>Results<\/strong>: Adults with ASD reported having more WM difficulties on the BRIEF-A (t(40.6) = 4.76, p = 2.43&#215;10^-5), but both groups performed similarly on the n-back task (F(1,76) = 0.11, p = 0.74). Despite equivalent performance, preliminary between-group neuroimaging results in the 2-back condition showed that adults with ASD exhibit increased connectivity (t(52) = 2.5, p = 0.008) in a fronto-parietal network in the alpha band (8-14 Hz) involving the anterior cingulate cortex (ACC), the right inferior frontal gyrus (IFG), and the left superior parietal lobule (SPL). They also demonstrated decreased connectivity (t(52) = 3.0, p = 0.002) in a high-frequency gamma band (81-150 Hz) between the right superior frontal gyrus (SFG) and right IFG. An exploratory time-frequency analysis of source power in the left inferior parietal lobule (IPL) also revealed increased alpha event-related synchronization in the ASD group between 200-500 ms.<br \/>\n&nbsp;<br \/>\n<strong>Conclusions<\/strong>: Given that alpha band activity during WM tasks is thought to signify inhibition of task-irrelevant information through gating mechanisms (Palva and Palva, 2011), the increased alpha band connectivity and power we observed in adults with ASD may reflect higher inhibitory control demands, which is consistent with reports that adults with ASD often show inhibition difficulties (Demetriou et al., 2018). As alpha and gamma band activity is thought to index maintenance of information in WM (Palva and Palva, 2011), decreased gamma band connectivity and increased alpha band synchronization may indicate difficulties retaining previous visual stimuli in WM for those with ASD. While this atypical connectivity did not result in accuracy differences, it may interfere with more complex behaviours involving WM, such as those experienced in day-to-day life.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u672c\u767a\u8868\u306f\uff0c ASD\u60a3\u8005\u306e\u30ef\u30fc\u30ad\u30f3\u30b0\u30e1\u30e2\u30ea\u30d7\u30ed\u30bb\u30b9\u306e3\u3064\u306e\u5468\u6ce2\u6570\u5e2f\uff08\u30b7\u30fc\u30bf\uff0c\u30a2\u30eb\u30d5\u30a1\uff0c\u30d9\u30fc\u30bf\uff09\u306b\u304a\u3051\u308b\u6a5f\u80fd\u7684\u7d50\u5408\u6027\u304c\u4f4e\u4e0b\u3059\u308b\u3053\u3068\u3092\u4eee\u8aac\u3068\u3057\u3066\u3044\u307e\u3057\u305f\uff0e\u3053\u306e\u7814\u7a76\u3067\u306f1,2-back\u304c\u7528\u3044\u3089\u308c\u3066\u304a\u308a\uff0c\u305d\u308c\u306b\u3064\u3044\u3066\u8cea\u554f\u3057\u305f\u3068\u3053\u308d\uff0c2-back\u304c\u4e00\u822c\u7684\u3067\u3042\u308b\u3068\u3044\u3046\u3053\u3068\u3067\u3057\u305f\uff0e\u307e\u305f\uff0cN-back\u8ab2\u984c\u306b\u7528\u3044\u3066\u3044\u308b\u753b\u50cf\u306e\u7a2e\u985e\u3082\u898b\u305f\u3053\u3068\u304c\u306a\u304f\u3081\u305a\u3089\u3057\u304b\u3063\u305f\u305f\u3081\uff0c\u3068\u3066\u3082\u9762\u767d\u3044\u3068\u611f\u3058\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Task-dependent functional organizations of the visual ventral stream<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Han-Gue Jo, Thilo Kellermann, Junji Ito, Sonja Gr\u00fcn, Ute Habel<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a All posters T001-T898<br \/>\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a<br \/>\n&nbsp;<br \/>\n<strong>Introductions<\/strong>: The visual ventral stream is a series of hierarchical processing stages from the primary visual cortex V1 to inferior temporal cortex IT, in which neural interactions along this hierarchy enable us to recognize visual objects. However, its complex and diverse connectivity make it difficult to illustrate the functional organization, particularly when top-down cognition is involved. Depending on task-goal, the ventral stream may require different functional structure of the hierarchy to incorporate visual features of interest into object recognition [1,2]. Here we identified context-dependent functional structures of the ventral stream.<br \/>\n&nbsp;<br \/>\n<strong>Methods<\/strong>: Twenty-eight participants performed three types of visual cognition task during fMRI measurement. The three task conditions that required distinct cognitive processes for object recognition were used in order to drive the visual ventral stream: searching for a target object, memorizing objects in natural scenes, or free viewing of the same natural scenes. We identified a task-dependent connectivity network of the ventral stream, utilizing a hierarchical seed-based connectivity approach that explicitly compared task-specific BOLD time-series. Seed-based analysis was performed within the ventral stream, and the first cortical processing stage V1 was subjected as a seed region. Voxel clusters that revealed significant task effect were identified as regions of interest (ROIs) and these ROIs were further subjected as seeds for subsequent seed-based analyses. On the basis of the identified ROIs, we demonstrated task-dependent connectivity to which extent the connectivity increases or decreases during each of the visual search, memory, and free viewing conditions.<br \/>\n&nbsp;<br \/>\n<strong>Results<\/strong>: The hierarchical seed-based connectivity approach identified five ROIs in the visual ventral stream (Figure 1), representing a task-dependent functional network. The connections across the identified ROIs were organized into correlated and anti-correlated structures according to the context of visual cognition. Searching for a target object separated the visual area V1 and V4 from the high-order visual area PIT (the posterior part of the IT), while memorizing objects strengthened the coupling of V4 with PIT. Furthermore, task-dependent activation was found in V1 and V4, while the PIT showed deactivation.<br \/>\n&nbsp;<br \/>\n<strong>Conclusions<\/strong>: The present study demonstrated context-dependent functional structures of the visual ventral stream. In particular, while the ventral stream was organized into correlated and anti-correlated structures during searching for a target object, memorizing objects manifested a correlated structure. Our results further suggest a putative boundary between V4 and PIT, which divides the visual hierarchy into two subdivisions that interact competitively or cooperatively depending on task demand. These results highlight the context-dependent nature of the ventral stream and shed light on how the visual hierarchy is selectively mediated to bias object recognition toward features of interest.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u672c\u767a\u8868\u3067\u306f\uff0c\u8a18\u61b6\u30bf\u30b9\u30af\u3068\u7269\u4f53\u306e\u63a2\u7d22\u30bf\u30b9\u30af\u306b\u304a\u3051\u308b\u6a5f\u80fd\u7684\u7d50\u5408\u6027\u306e\u9055\u3044\u304c\u793a\u5506\u3055\u308c\u3066\u3044\u307e\u3057\u305f\uff0e\u7269\u4f53\u306e\u63a2\u7d22\u8ab2\u984c\u6642\u306b\u306f\uff0c\u5f8c\u982d\u90e8\u306e\u8996\u899a\u95a2\u9023\u9818\u57df\u306b\u304a\u3051\u308b\u6a5f\u80fd\u7684\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u5f37\u3044\u5897\u52a0\u304c\u793a\u3055\u308c\u3066\u3044\u307e\u3057\u305f\u304c\uff0c\u8a18\u61b6\u8ab2\u984c\u6642\u306b\u306f\u7570\u306a\u308b\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u69cb\u9020\u3092\u793a\u3057\u307e\u3057\u305f\uff0e\u305f\u3060\u306e\u8a18\u61b6\u306b\u6bd4\u3079\uff0c\u95a2\u5fc3\u306e\u3042\u308b\u7269\u4f53\u3078\u306e\u63a2\u7d22\u3067\u306f\uff0c\u305d\u306e\u7269\u4f53\u3078\u8a8d\u8b58\u3092\u504f\u3089\u305b\u308b\u305f\u3081\u306b\uff0c\u8996\u899a\u7684\u306a\u968e\u5c64\u304c\u3069\u306e\u3088\u3046\u306b\u4ef2\u4ecb\u3055\u308c\u308b\u304b\u3092\u691c\u8a0e\u3057\u3066\u3044\u308b\u3053\u3068\u304c\u5927\u5909\u8208\u5473\u6df1\u304b\u3063\u305f\u3067\u3059\uff0e<br \/>\n&nbsp;<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Cross-task Evidence for Language Recruiting an Episodic Buffer located in the Visual Word Form Area<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Lang Qin, Bingjiang Lyu, Su Shu, Yayan Yin, Wai-Ting Siok, Jia-Hong Gao<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a ORAL SESSION(Learning and Memory)<br \/>\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a<br \/>\n&nbsp;<br \/>\n<strong>Introductions<\/strong>: Language processing requires information from multidimensional perceptual and mnemonic sources to be temporarily held and collectively integrated towards intended representations, which is by definition in the charge of the episodic buffer, an important subsystem of working memory. However, neural substrates underlying the episodic buffer and its conceptual functions remain elusive. We measured functional MRI responses when subjects performing word-level spoken-\/written-language comprehension and production tasks. Intriguingly, cross-task conjunction analysis identified a shared buffer located in the &#8220;visual word form area&#8221; (VWFA), namely the middle portion of left ventral occipitotemporal area (mVOT). Accordant with the buffer&#8217;s putative role, Granger causality analysis and graph analysis conjointly suggested that mVOT maintains information from multiple sources and acts as an integrative hub, under the modulation of the central executive network. These findings initially ground the theoretic buffer in neurophysiological reality and contribute to resolving the long-established VWFA debate by reconciling working memory functions and language processes.<br \/>\n&nbsp;<br \/>\n<strong>Methods<\/strong>: To explore the buffer shared by multi-modal language tasks, 100 healthy adult right-handers were recruited in an fMRI experiment and instructed to participate two task sessions on separate days, where each session consisted of four single-word perception and production tasks (i.e., listening, reading, speaking and writing). Data were preprocessed with realignment, segmentation and normalization. To identify the shared buffers, conjunction analysis was conducted to locate the overlapping activatations across four tasks. Further, granger causality analysis (GCA) with model order one was applied to investigate the causal interactions between region-of-interests (ROI) for each task, with time-courses extracted from ROIs within significantly activated cortical loci. Differences between the total causal influence strength flowing into and out of a given ROI were calculated (in-degree). Functional connectivity (FC) was assessed with a brain atlas of 268 nodes covering the whole-brain (6). Having excluded the sub-cortical and cerebellar regions, the sum of weighted FC strength between a cortical locus and other loci was calculated to depict its connectivity degree, which indicates how likely it is an integrative hub (7).<br \/>\n&nbsp;<br \/>\n<strong>Results<\/strong>: Cross-task conjunction analysis revealed an overlap located in the middle portion of left ventral occipitotemporal area (LvOT) (Fig. 1), which resides not only in the lexical interface of dual-stream model (8), but also in the &#8220;visual word form area (VWFA)&#8221; (9); this area has also been reported to link to semantic dementia. GCA analysis revealed significant causal information flows from multiple sources directly correlated with LvOT in all tasks with significantly high in-degrees (Fig. 2,3), indicating its critical part in holding information. Further, FC analysis found LvOT as part of the regions with strongest FC out of whole brain cortices (Fig. 4) across tasks, implying that LvOT is highly possible to act as an integrative hub. This finding is consistent with a latest research which held that middle occipitotemporal sulcus is where integration between the visual system output and the language network happens (10).<br \/>\n&nbsp;<br \/>\n<strong>Conclusions<\/strong>: Firstly, our study provides compelling evidence for the existence of episodic buffer. We argue that LvOT the is an important node of the episodic buffer network, since it displayed hypothetical patterns that were generated from the putative functions of the buffer during both spoken- and written-language tasks. Also, VWFA may not be reading-specific, but rather a critical area recycled to support similar functions in reading for its special role in multi-dimensional integration (9). Lastly, this study can shed light on the &#8220;domain-general vs. domain-specific&#8221; debate by helping elucidate how language engages and specialize domain-general network.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u672c\u8b1b\u6f14\u3067\u306f\uff0c \u8a00\u8a9e\u51e6\u7406\u306b\u304a\u3044\u3066\u91cd\u8981\u306a\u5f79\u5272\u3092\u62c5\u3046\u30a8\u30d4\u30bd\u30fc\u30c9\u30d0\u30c3\u30d5\u30a1\u306e\u5b58\u5728\u3092\u660e\u3089\u304b\u306b\u3059\u308b\u3082\u306e\u3067\u3057\u305f\uff0e\u5b9f\u9a13\u3067\u306fspeaking\uff0cwriting\uff0creading\uff0clistening\u306e4\u3064\u306e\u30bf\u30b9\u30af\u304c\u7528\u3044\u3089\u308c\u3066\u3044\u307e\u3057\u305f\uff0e\u30ef\u30fc\u30ad\u30f3\u30b0\u30e1\u30e2\u30ea\u306e\u30b5\u30d6\u30b7\u30b9\u30c6\u30e0\u306e1\u3064\u3067\u3042\u308b\u30a8\u30d4\u30bd\u30fc\u30c9\u30d0\u30c3\u30d5\u30a1\u306e\u6a5f\u80fd\u7684\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3068\u3057\u3066\uff0c\u91cd\u8981\u306a\u30ce\u30fc\u30c9\u306e\u5b58\u5728\u3092\u4e3b\u5f35\u3057\u3066\u304a\u308a\uff0c\u30ef\u30fc\u30ad\u30f3\u30b0\u30e1\u30e2\u30ea\u306e\u7814\u7a76\u3092\u3059\u308b\u4e0a\u3067\u3068\u3066\u3082\u523a\u6fc0\u3092\u53d7\u3051\u307e\u3057\u305f\uff0e \u30ef\u30fc\u30ad\u30f3\u30b0\u30e1\u30e2\u30ea\u3092\u69cb\u6210\u3059\u308b\u5404\u8981\u7d20\u306e\u30e1\u30ab\u30cb\u30ba\u30e0\u3092\u7814\u7a76\u3059\u308b\u3053\u3068\u3082\u91cd\u8981\u3067\u3042\u308b\u3068\u611f\u3058\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n\u53c2\u8003\u6587\u732e<\/p>\n<ul>\n<li>OHBM 2019-Organization for Human Brain Mapping, https:\/\/www.humanbrainmapping.org\/i4a\/pages\/index.cfm?pageid=3882<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>OHBM2019\u306b\u3066\u767a\u8868\u3057\u307e\u3057\u305f\u3002<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[10],"tags":[],"class_list":["post-6087","post","type-post","status-publish","format-standard","hentry","category-10"],"_links":{"self":[{"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=\/wp\/v2\/posts\/6087","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=6087"}],"version-history":[{"count":1,"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=\/wp\/v2\/posts\/6087\/revisions"}],"predecessor-version":[{"id":7030,"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=\/wp\/v2\/posts\/6087\/revisions\/7030"}],"wp:attachment":[{"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6087"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6087"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6087"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}