- \n
- “Detecting meditative states through meta-state matching with time-varying functional connectivity matrices”
\nS.HIWA; T.HIROYASU.<\/li>\n<\/ul>\n- \n
- “Mindful driving:Brain functional state of mind wandering in driving and PVT task”
\nY.FUJIWARA; S.HIWA; T.HIROYASU.<\/li>\n<\/ul>\n- \n
- “Discussions of brain activity and eye movement during driving”
\nS.NAKAMURA; S.HIWA; T.HIROYASU.<\/li>\n<\/ul>\n- \n
- “Brain activity and functional connectivity in attention and careless states by fNIRS”
\nM.NISHIZAWA; S.HIWA; T.HIROYASU.<\/li>\n<\/ul>\n- \n
- “Network analysis of brain activity during breath-counting meditation by fNIRS”
\nS.YAMAMOTO; S.HIWA; T.HIROYASU.<\/li>\n<\/ul>\n<\/div>\n
\n![\"\"](\"http:\/\/www.is.doshisha.ac.jp\/news\/wp-content\/uploads\/2017\/12\/2017-11-14_12-53-07-300x225.jpg\")
<\/a> ![\"\"](\"http:\/\/www.is.doshisha.ac.jp\/news\/wp-content\/uploads\/2017\/12\/DSC00704-300x225.jpg\")
<\/a>
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\n\u79c1\u306b\u3068\u3063\u3066\u521d\u3081\u3066\u306e\u56fd\u969b\u5b66\u4f1a\u3067\uff0c\u82f1\u8a9e\u3067\u306e\u767a\u8868\u3068\u3044\u3046\u3053\u3068\u3082\u3042\u308a\u4e0d\u5b89\u3060\u3063\u305f\u306e\u3067\u3059\u304c\uff0c\u591a\u304f\u306e\u65b9\u304c\u8208\u5473\u3092\u6301\u3063\u3066\u30dd\u30b9\u30bf\u30fc\u3092\u898b\u306b\u6765\u3066\u304f\u3060\u3055\u3044\u307e\u3057\u305f\uff0e\u7814\u7a76\u5ba4\u306e\u4eba\u4ee5\u5916\u306b\u82f1\u8a9e\u3067\u8aac\u660e\u3092\u3057\uff0c\u69d8\u3005\u306a\u8cea\u554f\u3084\u30a2\u30c9\u30d0\u30a4\u30b9\u3092\u3057\u3066\u3044\u3044\u305f\u3060\u3044\u305f\u3053\u3068\u306f\u4eca\u5f8c\u306e\u7814\u7a76\u306b\u3068\u3063\u3066\u975e\u5e38\u306b\u53c2\u8003\u306b\u306a\u308b\u3068\u601d\u3044\u307e\u3059\uff0e\u3057\u304b\u3057\uff0c\u81ea\u5206\u306e\u7814\u7a76\u306e\u7406\u89e3\u304c\u4e0d\u5341\u5206\u3067\u3042\u308b\u3053\u3068\u3084\u82f1\u8a9e\u3067\u306e\u8aac\u660e\u306e\u96e3\u3057\u3055\u3092\u611f\u3058\u307e\u3057\u305f\uff0e\u307e\u305f\uff0c\u81ea\u5206\u306e\u540c\u3058\u3088\u3046\u306a\u7814\u7a76\u5206\u91ce\u306b\u3064\u3044\u3066\u306e\u767a\u8868\u3082\u3042\u308a\uff0c\u4e16\u754c\u3067\u306f\u3069\u306e\u3088\u3046\u306a\u7814\u7a76\u304c\u884c\u308f\u308c\u3066\u3044\u308b\u306e\u304b\u3092\u77e5\u308b\u3053\u3068\u306e\u3067\u304d\u305f\u826f\u3044\u6a5f\u4f1a\u3068\u306a\u308a\u307e\u3057\u305f\uff0e\u3057\u304b\u3057\uff0c\u4ed6\u306e\u4eba\u306e\u767a\u8868\u3092\u7406\u89e3\u3059\u308b\u305f\u3081\u306b\u306f\u82f1\u8a9e\u529b\u304c\u5fc5\u8981\u3067\u3042\u308b\u3053\u3068\u3092\u75db\u611f\u3057\u307e\u3057\u305f\uff0e\u4eca\u56de\u306e\u5b66\u4f1a\u3067\u306f\uff0c\u82f1\u8a9e\u529b\u304c\u4e4f\u3057\u3044\u3053\u3068\u306b\u3088\u308a\u81ea\u5206\u304c\u767a\u8868\u3059\u308b\u6642\u306e\u8aac\u660e\u3084\u4ed6\u306e\u4eba\u306e\u8aac\u660e\u3092\u7406\u89e3\u3067\u304d\u306a\u304b\u3063\u305f\u5834\u9762\u304c\u591a\u304f\u3042\u308a\u307e\u3057\u305f\uff0e\u305d\u3053\u3067\u6765\u5e74\u306e\u56fd\u969b\u5b66\u4f1a\u306b\u5411\u3051\u3066\uff0c\u4eca\u56de\u5f97\u3089\u308c\u305f\u77e5\u8b58\u3084\u7d4c\u9a13\u3092\u6d3b\u304b\u3057\u7814\u7a76\u3092\u6df1\u3081\u3066\u3044\u304f\u3053\u3068\u306b\u52a0\u3048\uff0c\u82f1\u8a9e\u529b\u306e\u5411\u4e0a\u306b\u52aa\u3081\u3066\u3044\u304d\u305f\u3044\u3068\u601d\u3044\u307e\u3059\uff0e
\n
\n![\"\"](\"http:\/\/www.is.doshisha.ac.jp\/news\/wp-content\/uploads\/2017\/12\/20171111_091834-1024x584.jpg\")
<\/a>
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\n\u3010\u6587\u8cac\uff1aM1 \u85e4\u539f\u3011
\n
\n
\n\u5b66\u4f1a\u53c2\u52a0\u5831\u544a\u66f8<\/strong><\/p>\n\n\n
\n \u00a0<\/strong>
\n\u5831\u544a\u8005\u6c0f\u540d<\/strong><\/td>\n
\n\u897f\u6fa4 \u7f8e\u7d50<\/td>\n<\/tr>\n\n \u767a\u8868\u8ad6\u6587\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n fNIRS\u3092\u7528\u3044\u305f\u6ce8\u610f\u72b6\u614b\u3068\u4e0d\u6ce8\u610f\u72b6\u614b\u306b\u304a\u3051\u308b\u8133\u6d3b\u52d5\u3068\u6a5f\u80fd\u7684\u7d50\u5408<\/td>\n<\/tr>\n \n \u767a\u8868\u8ad6\u6587\u82f1\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n Brain activity and functional connectivity in attention and careless states by fNIRS<\/td>\n<\/tr>\n \n \u8457\u8005<\/strong><\/td>\n \u897f\u6fa4\u7f8e\u7d50, \u65e5\u548c\u609f, \u5ee3\u5b89\u77e5\u4e4b<\/td>\n<\/tr>\n \n \u4e3b\u50ac<\/strong><\/td>\n SOCIETY for NEUROSCIENCE<\/td>\n<\/tr>\n \n \u8b1b\u6f14\u4f1a\u540d<\/strong><\/td>\n NEUROSCIENCE2017<\/td>\n<\/tr>\n \n \u4f1a\u5834<\/strong><\/td>\n Washington Convention Center<\/td>\n<\/tr>\n \n \u958b\u50ac\u65e5\u7a0b<\/strong><\/td>\n 2017\/11\/10-2017\/11\/15<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\n <\/p>\n- \n
- \u8b1b\u6f14\u4f1a\u306e\u8a73\u7d30<\/li>\n<\/ol>\n
2017\/11\/10\u304b\u30892017\/11\/15\u306b\u304b\u3051\u3066\uff0cWashington Convention Center\u306b\u3066\u958b\u50ac\u3055\u308c\u307e\u3057\u305fNEUROSCIENCE2017(https:\/\/www.sfn.org\/annual-meeting\/neuroscience-2017)\u306b\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u3053\u306e\u5b66\u4f1a\u306f\uff0c30,300\u4eba\u4ee5\u4e0a\uff0c80\u304b\u56fd\u4ee5\u4e0a\u306e\u795e\u7d4c\u5de5\u5b66\u306b\u95a2\u308f\u308b\u5148\u751f\u65b9\u3084\u5b66\u751f\u304c\u53c2\u52a0\u3057\uff0c\u5404\u3005\u306e\u7814\u7a76\u5185\u5bb9\u306b\u95a2\u3059\u308b\u60c5\u5831\u3092\u5171\u6709\u3067\u304d\u308b\u5834\u3067\u3059\uff0e
\n\u79c1\u306f\u5168\u65e5\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u672c\u7814\u7a76\u5ba4\u304b\u3089\u306f\u4ed6\u306b\u65e5\u548c\u5148\u751f\uff0c\u85e4\u539f\uff0c\u4e2d\u6751\uff0c\u5c71\u672c\u304c\u53c2\u52a0\u3057\u307e\u3057\u305f\uff0e
\n <\/p>\n- \n
- \u7814\u7a76\u767a\u8868\n
- \n
- \u767a\u8868\u6982\u8981<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n
\u79c1\u306f15\u65e5\u306e13\u6642-17\u6642\u306e\u30dd\u30b9\u30bf\u30fc\u30bb\u30c3\u30b7\u30e7\u30f3\u300cAttention Circuits\u300d\u306b\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u767a\u8868\u5f62\u5f0f\u306f\u30dd\u30b9\u30bf\u30fc\u767a\u8868\u30674\u6642\u9593\u81ea\u7531\u306b\u53c2\u52a0\u8005\u306e\u65b9\u3068\u8b70\u8ad6\u3092\u884c\u3044\u307e\u3057\u305f\uff0e
\n\u4eca\u56de\u306e\u767a\u8868\u306f\u300cBrain activity and functional connectivity in attention and careless states by fNIRS\u300d\u3068\u3044\u3046\u984c\u76ee\u3067\uff0c\u767a\u8868\u3092\u884c\u3044\u307e\u3057\u305f\uff0e\u4ee5\u4e0b\u306b\u6284\u9332\u3092\u8a18\u8f09\u81f4\u3057\u307e\u3059\uff0e<\/p>\n\n\n
\n \u3010Introduction\u3011
\nWhen sustained attention is lost, humans become distracted and an accident may occur. An accident caused by carelessness is prevented when the status of attentive and carelessness is defined. The goal is to define two states based on brain activity. Research to examine the attentive state from the brain function information has been conducted so far, and most of them used fMRI, in this paper, we attempt to investigate brain activation and functional network of two states using functional Infrared Spectroscopy(fNIRS).
\n\u3010Method\u3011
\nBrain blood flow change during the task of Psychomotor Vigilance Task (PVT) was measured. Two types of stimulation were used auditory and visual. Attentive state was defined using Reaction Time (RT) which is the time from stimulus presentation to reaction\uff0eThe brain activation was examined in the following way. Hemodynamic function (HRF) and boxcar function are convolved, and a model was constructed. By GLM operation, the stimulation vector was optimized to reduced the differences between the observed time series data and convolved model. The integral value of the obtained model was derived, and the top 10 brain regions with high integral values were defined as active regions. Also, the functional network is described as follows. A correlation coefficient matrix which shows the coupling among each region the attentive state and the inattentive state was calculated. The degree which is the total number of links in each region was calculated\uff0e
\n\u3010Result\uff06Discussion\u3011
\nAmong the active regions in auditory stimulation task, the degree of the left dorsolateral prefrontal cortex (DLPFC) was lower of attentive states then inattentive states\uff0eThe left DLPFC was highly coupled with the left and right frontal pole. It is reported that the left DLPFC is suppressed at the dorsolateral side and frontal pole works for the future prediction\uff0e Thus, the results suggest that brain predicted against stimulation and suppressed unintended touch at the time of attention. Among the active regions in visual stimulus, the degree of the left secondary visual cortex(V2) was lower of attentive states then inattentive states\uff0eAt the same time, the left V2 had a high binding with the left DLPFC. It is reported that the left V2 detects subjective contours, and the left DLPFC has suppression function. At the time of attention, results illustrate that subjects observed carefully and suppressed stimulation which is occurred by the unintended touch.
\n\u3010Conclusion\u3011
\nThis paper mentioned that the left V2 is essential for auditory stimulation and the left DLPFC is necessary for visual stimulation due to the difference in the state.
\n <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/p>\n
- \n
- \u8cea\u7591\u5fdc\u7b54<\/li>\n<\/ul>\n
\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
\n
\n\u30fb\u8cea\u554f\u5185\u5bb9<\/strong>1<\/strong>
\n\u8cea\u554f\u8005\u306e\u6c0f\u540d\u3092\u63a7\u3048\u640d\u306d\u3066\u3057\u307e\u3044\u307e\u3057\u305f\uff0e\u72b6\u614b\u306e\u6642\u7cfb\u5217\u5909\u5316\u306e\u56f3\u3092\u898b\u3066\u8cea\u554f\u3057\u3066\u304f\u3060\u3055\u3044\u307e\u3057\u305f\uff0e\u72b6\u614b\u5909\u5316\u306e\u6fc0\u3057\u3044\uff0c\u4e00\u5b9a\u306e\u88ab\u9a13\u8005\u306b\u3064\u3044\u3066\u306e\u8003\u5bdf\u306f\u306a\u3044\u306e\u304b\u3068\u3044\u3046\u8cea\u554f\u3092\u53d7\u3051\u307e\u3057\u305f\uff0e\u3053\u308c\u306b\u5bfe\u3057\u3066\u72b6\u614b\u5909\u5316\u306e\u6fc0\u3057\u3044\u88ab\u9a13\u8005\u306f\u72b6\u614b\u304c\u4e00\u5b9a\u306b\u306a\u3063\u3066\u3044\u306a\u3044\u3053\u3068\u304b\u3089\u4e0d\u6ce8\u610f\uff0c\u4e00\u5b9a\u306e\u88ab\u9a13\u8005\u306f\u6ce8\u610f\u3068\u8003\u5bdf\u3059\u308b\u3053\u3068\u3082\u53ef\u80fd\u3067\u3042\u308b\u3068\u7b54\u3048\u307e\u3057\u305f\uff0e\u307e\u305f\uff0c\u3053\u306e\u56de\u7b54\u306b\u5bfe\u3057\u3066\uff0c\u5468\u6ce2\u6570\u89e3\u6790\u3092\u884c\u3063\u3066\u3082\u3044\u3044\u304b\u3082\u3057\u308c\u306a\u3044\u3068\u30a2\u30c9\u30d0\u30a4\u30b9\u3092\u3044\u305f\u3060\u304d\u307e\u3057\u305f\uff0e
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\n\u30fb\u8cea\u554f\u5185\u5bb9<\/strong>2<\/strong>
\n\u8cea\u554f\u8005\u306e\u6c0f\u540d\u3092\u63a7\u3048\u640d\u306d\u3066\u3057\u307e\u3044\u307e\u3057\u305f\uff0e\u306a\u305c\u7a93\u5e45\u306f10\u79d2\u3067\u3042\u308b\u306e\u304b\u3068\u3044\u3046\u8cea\u554f\u3092\u53d7\u3051\u307e\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3059\u308b\u79c1\u306e\u56de\u7b54\u306f\uff0c\u6642\u9593\u5909\u5316\u3092\u691c\u51fa\u3059\u308b\u305f\u3081\u306b\u306f\u3088\u308a\u7d30\u304b\u3044\u5e45\u3067\u691c\u8a0e\u3059\u308b\u5fc5\u8981\u304c\u3042\u308b\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e\u305d\u306e\u305f\u3081\u4eca\u56de\u306e\u89e3\u6790\u3067\u306f\uff0c\u5148\u884c\u7814\u7a76\u3067\u4f7f\u7528\u3055\u308c\u3066\u3044\u308b\u6700\u3082\u77ed\u3044\u7a93\u5e45\u3067\u3042\u308b10\u79d2\u3092\u63a1\u7528\u3057\u691c\u8a0e\u3092\u884c\u3063\u305f\u3068\u7b54\u3048\u307e\u3057\u305f\uff0e\u307e\u305f\uff0c\u8cea\u554f\u3092\u3057\u3066\u304f\u3060\u3055\u3063\u305f\u65b9\u306f\u30de\u30a6\u30b9\u3092\u4f7f\u3063\u3066\u540c\u3058\u3088\u3046\u306a\u89e3\u6790\u3092\u884c\u3063\u3066\u304a\u308a\uff0c\u305d\u306e\u969b\u306b\u7a93\u5e45\u3092\u3044\u304f\u3064\u306b\u3059\u308b\u306e\u304b\u8ff7\u3063\u3066\u3044\u308b\u3068\u304a\u3063\u3057\u3083\u3063\u3066\u3044\u307e\u3057\u305f\uff0e
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\n\u30fb\u8cea\u554f\u5185\u5bb9<\/strong>3<\/strong>
\n\u8cea\u554f\u8005\u306e\u6c0f\u540d\u3092\u63a7\u3048\u640d\u306d\u3066\u3057\u307e\u3044\u307e\u3057\u305f\uff0eNIRS\u3067\u306f\u3069\u308c\u304f\u3089\u3044\u306e\u8133\u90e8\u4f4d\u304c\u30ec\u30b8\u30b9\u30c8\u30ec\u30fc\u30b7\u30e7\u30f3\u3055\u308c\u308b\u306e\u304b\u3068\u3044\u3046\u8cea\u554f\u3092\u53d7\u3051\u307e\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3059\u308b\u79c1\u306e\u56de\u7b54\u306f\uff0c\u88ab\u9a13\u8005\u306e6\u5272\u4ee5\u4e0a\u304c\u5171\u901a\u3057\u3066\u30ec\u30b8\u30b9\u30c8\u30ec\u30fc\u30b7\u30e7\u30f3\u3055\u308c\u305f\u90e8\u4f4d\u306f48\u9818\u57df\u3067\u3042\u308b\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e
\n
\n\u30fb\u8cea\u554f\u5185\u5bb9<\/strong>4<\/strong>
\n\u8cea\u554f\u8005\u306e\u6c0f\u540d\u3092\u63a7\u3048\u640d\u306d\u3066\u3057\u307e\u3044\u307e\u3057\u305f\uff0eDMN\u306b\u306f\u3069\u306e\u9818\u57df\u304c\u542b\u307e\u308c\u3066\u3044\u308b\u306e\u304b\u3068\u3044\u3046\u8cea\u554f\u3092\u53d7\u3051\u307e\u3057\u305f\uff0e\u4eca\u56de\u5b9a\u7fa9\u3057\u305f\u8133\u90e8\u4f4d\u306f\u4e2d\u5fc3\u5f8c\u56de\uff0c\u7dd1\u4e0a\u56de\uff0c\u89d2\u56de\uff0c\u6954\u524d\u90e8\u3067\u3042\u308b\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0eDMN\u306b\u9650\u3089\u305a\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306b\u3064\u3044\u3066\u306f\u304d\u3061\u3093\u3068\u8abf\u67fb\u3059\u308b\u3079\u304d\u3060\u3068\u611f\u3058\u307e\u3057\u305f\uff0e
\n
\n\u30fb\u8cea\u554f\u5185\u5bb9<\/strong>5<\/strong>
\n\u8cea\u554f\u8005\u306e\u6c0f\u540d\u3092\u63a7\u3048\u640d\u306d\u3066\u3057\u307e\u3044\u307e\u3057\u305f\uff0ePVT\u3068\u306f\u4f55\u3092\u3057\u3066\u3044\u308b\u8ab2\u984c\u306a\u306e\u304b\u3068\u3044\u3046\u8cea\u554f\u3092\u53d7\u3051\u307e\u3057\u305f\uff0e \u3053\u306e\u8cea\u554f\u306b\u5bfe\u3059\u308b\u79c1\u306e\u56de\u7b54\u306f\uff0c\u6301\u7d9a\u7684\u6ce8\u610f\u3092\u8a08\u6e2c\u3059\u308b\u8ab2\u984c\u3060\u3068\u7b54\u3048\u307e\u3057\u305f\uff0e\u3057\u304b\u3057\uff0c\u3053\u306e\u56de\u7b54\u306b\u30d4\u30f3\u3068\u304d\u3066\u3044\u306a\u3055\u305d\u3046\u3060\u3063\u305f\u306e\u3067\uff0c\u5177\u4f53\u7684\u306b\u306f\u30e9\u30f3\u30c0\u30e0\u306a\u523a\u6fc0\u306b\u5bfe\u3057\u3066\u7d20\u65e9\u304f\u53cd\u5fdc\u3059\u308b\u8ab2\u984c\u3067\u3042\u308b\u3068\u7b54\u3048\u307e\u3057\u305f\uff0e
\n
\n\u30fb\u8cea\u554f\u5185\u5bb9<\/strong>6<\/strong>
\n\u8cea\u554f\u8005\u306e\u6c0f\u540d\u3092\u63a7\u3048\u640d\u306d\u3066\u3057\u307e\u3044\u307e\u3057\u305f\uff0e\u3053\u3046\u3044\u3063\u305f\u8133\u6a5f\u80fd\u7814\u7a76\u306ffMRI\u3092\u4f7f\u7528\u3057\u305f\u7814\u7a76\u304c\u591a\u3044\u306e\u304b\u3068\u3044\u3046\u8cea\u554f\u3092\u53d7\u3051\u307e\u3057\u305f\uff0e \u3053\u306e\u8cea\u554f\u306b\u5bfe\u3059\u308b\u79c1\u306e\u56de\u7b54\u306f\uff0c\u3053\u306e\u89e3\u6790\u3092\u59cb\u3081\uff0cPVT\u7814\u7a76\u306b\u304a\u3044\u3066\u3082fMRI\u3092\u4f7f\u7528\u3057\u305f\u7814\u7a76\u304c\u591a\u3044\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e\u3053\u306e\u56de\u7b54\u306b\u5bfe\u3057\u3066\uff0c\u4f55\u6545fNIRS\u3092\u4f7f\u7528\u3059\u308b\u306e\u304b?\u3068\u3044\u3046\u8cea\u554f\u3092\u53d7\u3051\u307e\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\uff0cfNIRS\u306f\u4ed6\u306e\u8133\u6a5f\u80fd\u30a4\u30e1\u30fc\u30b8\u30f3\u30b0\u88c5\u7f6e\u3068\u6bd4\u8f03\u3057\u3066\u6642\u9593\u5206\u89e3\u80fd\u304c\u9ad8\u3044\u9577\u6240\u3092\u3082\u3064\uff0e\u79c1\u306e\u7814\u7a76\u3067\u306f\u6642\u9593\u5909\u5316\u3059\u308b\u8133\u72b6\u614b\u3092\u691c\u8a0e\u3059\u308b\u5fc5\u8981\u304c\u3042\u308b\u305f\u3081\u3088\u308a\u9ad8\u3044\u6642\u9593\u5206\u89e3\u80fd\u3092\u6301\u3064fNIRS\u3092\u4f7f\u7528\u3057\u305f\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e
\n\u00a0<\/strong>
\n\u30fb\u8cea\u554f\u5185\u5bb9<\/strong>7<\/strong>
\n\u8cea\u554f\u8005\u306e\u6c0f\u540d\u3092\u63a7\u3048\u640d\u306d\u3066\u3057\u307e\u3044\u307e\u3057\u305f\uff0e\u8a08\u6e2cCH\u306f\u3044\u304f\u3064\u304b\u3068\u3044\u3046\u8cea\u554f\u3092\u53d7\u3051\u307e\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3059\u308b\u79c1\u306e\u56de\u7b54\u306f\uff0c116CH\u3067\u3059\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e
\n\u00a0<\/strong>
\n\u30fb\u8cea\u554f\u5185\u5bb9<\/strong>8<\/strong>
\n\u8cea\u554f\u8005\u306e\u6c0f\u540d\u3092\u63a7\u3048\u640d\u306d\u3066\u3057\u307e\u3044\u307e\u3057\u305f\uff0eDegree\u3068\u306f\u4f55\u304b\u3068\u3044\u3046\u8cea\u554f\u3092\u53d7\u3051\u307e\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3059\u308b\u79c1\u306e\u56de\u7b54\u306f\uff0c \u4ed6\u9818\u57df\u3068\u7d50\u5408\u3057\u3066\u3044\u308b\u672c\u6570\u3067\u3042\u308b\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e
\n\u00a0<\/strong>
\n\u30fb\u8cea\u554f\u5185\u5bb9<\/strong>9<\/strong>
\n\u8cea\u554f\u8005\u306e\u6c0f\u540d\u3092\u63a7\u3048\u640d\u306d\u3066\u3057\u307e\u3044\u307e\u3057\u305f\uff0e\u72b6\u614b\u306e\u6642\u7cfb\u5217\u5909\u5316\u304c\u5168\u54e1\u5206\u306a\u3044\u306e\u306f\u4f55\u6545\u304b\u3068\u3044\u3046\u8cea\u554f\u3092\u53d7\u3051\u307e\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3059\u308b\u79c1\u306e\u56de\u7b54\u306f\u4eca\u56de\u306e\u767a\u8868\u3067\u306f\u4ee3\u8868\u30674\u4eba\u5206\u63b2\u8f09\u3057\u307e\u3057\u305f\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e\u30dd\u30b9\u30bf\u30fc\u306b\u8f09\u305b\u3066\u306f\u3044\u306a\u3044\u304c\u5168\u54e1\u304c2\u3064\u306e\u72b6\u614b\u304c\u7e70\u308a\u8fd4\u3057\u3042\u3089\u308f\u308c\u3066\u3044\u308b\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e
\n <\/p>\n- \n
- \u611f\u60f3<\/li>\n<\/ul>\n
\u79c1\u306f\u4eca\u56de\u521d\u3081\u3066\u306e\u56fd\u969b\u5b66\u4f1a\u3067\u3057\u305f\u304c\u843d\u3061\u7740\u3044\u3066\u767a\u8868\u3059\u308b\u3053\u3068\u304c\u51fa\u6765\u305f\u3068\u601d\u3044\u307e\u3059\uff0e\u82f1\u8a9e\u3067\u306e\u3084\u308a\u53d6\u308a\u306b\u306f\u5c11\u3057\u7dca\u5f35\u3057\u307e\u3057\u305f\u304c\uff0c\u8272\u3093\u306a\u8cea\u554f\u3092\u53d7\u3051\uff0c\u89e3\u6790\u3092\u3059\u308b\u4e0a\u3067\u518d\u691c\u8a0e\u3057\u306a\u3051\u308c\u3070\u306a\u3089\u306a\u3044\u3053\u3068\u306f\u305f\u304f\u3055\u3093\u3042\u308b\u3068\u611f\u3058\u307e\u3057\u305f\uff0e
\n\u89e3\u6790\u306b\u3064\u3044\u3066\u3082\u3046\u4e00\u5ea6\u8aac\u660e\u3057\u3066\u307b\u3057\u3044\u3068\u8a00\u308f\u308c\u308b\u3053\u3068\u304c\u591a\u304f\uff0c\u767a\u8868\u306e\u969b\u306e\u308f\u304b\u308a\u3084\u3059\u3055\u3082\u8003\u3048\u306a\u3051\u308c\u3070\u3044\u3051\u306a\u3044\u3068\u611f\u3058\u307e\u3057\u305f\uff0e
\n <\/p>\n- \n
- \u8074\u8b1b<\/li>\n<\/ol>\n
\u4eca\u56de\u306e\u8b1b\u6f14\u4f1a\u3067\u306f\uff0c\u4e0b\u8a18\u306e5\u4ef6\u306e\u767a\u8868\u3092\u8074\u8b1b\u3057\u307e\u3057\u305f\uff0e<\/p>\n
\n\n
\n \u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Dorsal attention network activation and diminished attention in cerebral visual impairment
\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aC.M.BAUER, E>S>BAILIN,P.J.BEX,L.B.MERABET
\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Attention Networks
\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Visual attention is a complex process, encompassing projections between the thalamus and frontal, parietal, and occipital cortices. Damage to any of the specific brain regions or to the white matter connections between them can cause functional deficits in visual attention. The dorsal attention network (DAN) is responsible for the top-down conscious control of attention and may be particularly affected in children with cortical\/cerebral visual impairment (CVI), who demonstrate grey and white matter damage throughout occipital, parietal, and subcortical regions. Visual attention is often limited in children with CVI, but it is not known whether these deficits are due to structural or functional abnormalities within the DAN. To this end, the current study examined functional connectivity of the DAN using resting state functional MRI (rsfMRI) in a cohort of individuals with CVI compared to controls. Resting state fMRI was run on a cohort of 7 individuals with CVI (Ages 14-24, mean 18.4 years) and normally sighted and developed controls (Ages 15-24, mean 19.75 years). A structural T1W and field map were also acquired on a 3T Philips Achieva System. To correct for motion, ICA-AROMA was performed on each subject\u2019s rsfMRI data, which were then processed in FreeSurfer. A 6mm diameter spherical seed was placed in the frontal eye fields (MNI 26, -6, 48). The average time course between the seed and the rest of the cortex was calculated for each subject and a GLM was run to compare between CVI and control groups. Visual attention was assessed using computer-based psychometric tests of functional vision, namely a conjunction search and go-no-go sustained attention paradigm. Compared to controls, individuals with CVI demonstrated significant increases in functional activation between the frontal eye fields, superior frontal, caudal middle frontal, par orbitalis, inferior parietal, and pericalcarine regions (p < 0.05). Clusters of significant decreases in activation were observed between the frontal eye fields and the precuneus\/rostral anterior cingulate and inferior parietal areas (p < 0.05). The CVI group also demonstrated poor performance on both psychophysical tests of visual attention, as indexed by increased error rates and reaction times (p < 0.05). Our results indicate that functional correlations between the frontal eye fields and portions of the dorsal and ventral attention networks are increased in individuals with CVI compared to controls. These functional changes likely relate to the visual attention difficulties observed in CVI, whereby individuals with poorer performance must recruit more cortical resources in order to complete the task.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\u3053\u306e\u767a\u8868\u306f\uff0c\u6ce8\u610f\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u6d3b\u6027\u5316\u3068\u8996\u899a\u969c\u5bb3\u306e\u6ce8\u610f\u529b\u306e\u4f4e\u4e0b\u306b\u3064\u3044\u3066\u306e\u767a\u8868\u3067\u3057\u305f\uff0e\u3053\u306e\u7814\u7a76\u3067\u306f\uff0c\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u3068\u6a5f\u80fd\u7684\u7d50\u5408\u3068\u3044\u3063\u305f\u90e8\u5206\u3067\u79c1\u306e\u7814\u7a76\u3068\u4f3c\u305f\u3088\u3046\u306a\u3082\u306e\u3092\u611f\u3058\u307e\u3057\u305f\uff0e\u3053\u3046\u3044\u3063\u305f\u7814\u7a76\u304c\u8996\u899a\u969c\u5bb3\u3068\u3044\u3063\u305f\u3088\u3046\u306a\u969c\u5bb3\u304c\u306a\u3044\u88ab\u9a13\u8005\u3067\u3082\u5bfe\u5fdc\u3057\u3066\u89e3\u6790\u53ef\u80fd\u3060\u3068\u3082\u3063\u3068\u304a\u3082\u3057\u308d\u304f\u306a\u308b\u306e\u3060\u308d\u3046\u3068\u611f\u3058\u307e\u3057\u305f\uff0e
\n <\/p>\n\n\n
\n \u00a0\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0 \uff1aLow trait mind wandering is associated with optimized intrinsic functional connectivity
\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a J. Z. LIM, S. A. A. MASSAR, J. TENG, Z. HASSIRIM, K. WONG, C. WANG, M. W. CHEE
\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Attention Networks
\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Abstract: Objective Mind wandering and low meta-awareness are associated with poor cognitive performance and unhappiness in daily life. Furthermore, the tendency to mind wander is trait-like, yet amenable to change through training. Here, we conducted a resting-state fMRI to investigate the individual differences in functional connectivity associated with trait-mind wandering, We hypothesized that lower levels of mind wandering would be associated with greater optimization of the intrinsic functional connectome (i.e. connectivity patterns with higher similarity to that seen during task engagement). Methods 100 healthy young participants were recruited to perform a breath-counting task, a covert measure of meta-awareness and mind wandering. Participants kept track of their breath over an 18-minute period by pressing a button with every 1st to 8th breath, and a separate button for every 9th breath. From this sample, good (accuracy > 81%; N=15) and poor (accuracy < 63%; N=11) performers were invited for an imaging session, which consisted of a second run of the breath-counting task (behavioral), and an ~8 minute resting state (rs)fMRI scan. Whole-brain data were segmented based on the Yeo parcellation, and connectivity was computed using the multiplication of temporal derivatives (MTD) method. Static connectivity maps were calculated as a time-series average, and dynamic functional connectivity analysis was performed using k-means clustering after averaging within a 7-TR sliding window across the MTD time series. Connectivity was compared between the good and poor groups. Results Inter-session reliability of breath counting accuracy was high (ICC = .57; p < .001), and good and poor performers continued to differ significantly in their second test (p = .01). Static rsfMRI connectivity maps showed greater anti-correlation between the dorsal attention network and the default mode network, and greater connectivity strength within the salience network in good performers. Dynamic functional connectivity analysis revealed two reproducible patterns of connectivity, corresponding to optimized (high arousal) and non-optimized (low arousal) brain states. Good performers had significantly more dwell time in the optimized state compared to poor performers. Conclusions Our data demonstrate that breath-counting accuracy is trait-like and reproducible, and indicate that intrinsic functional connectivity is more optimized in individuals with low trait mind wandering. Shifts towards this pattern of optimization may represent a useful biomarker of the gains from training meta-awareness, such as those obtained from mindfulness-based interventions.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\u3053\u306e\u767a\u8868\u306f\u7791\u60f3\u7814\u7a76\u3067\u52d5\u7684\u6a5f\u80fd\u63a5\u7d9a\u5206\u6790\u3092\u7528\u3044\u305f\u767a\u8868\u3067\u3057\u305f\uff0ePVT\u306b\u3064\u3044\u3066\u3082\u8a08\u6e2c\u3092\u3057\u3066\u304a\u308a\uff0c\u81ea\u5206\u306e\u7814\u7a76\u306b\u3061\u304b\u3044\u3082\u306e\u3092\u611f\u3058\u307e\u3057\u305f\uff0eState\u306e\u51fa\u73fe\u7387\u3068FFMQ\u306e\u9593\u306b\u76f8\u95a2\u304c\u307f\u3089\u308c\u305f\u3068\u3044\u3046\uff0c\u8133\u72b6\u614b\u3068\u8cea\u554f\u7d19\u9593\u306e\u76f8\u95a2\u3067\u3082\u7d50\u679c\u3092\u51fa\u3057\u3066\u304a\u308a\uff0c\u9a5a\u304d\u307e\u3057\u305f\uff0e\u7a93\u5e45\u306e\u6700\u9069\u5316\u3084k\u6570\u306e\u6c7a\u5b9a\u306a\u3069\u3069\u3046\u3057\u3066\u3044\u308b\u306e\u304b\u6c17\u306b\u306a\u308a\u307e\u3057\u305f\uff0e
\n <\/p>\n\n\n
\n \u00a0\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0 \uff1aReal-time neurofeedback of functional connectivity in large-scale brain networks that predict attention
\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a M. D. ROSENBERG, D. SCHEINOST, W.-T. HSU, R. T. CONSTABLE, M. M. CHUN
\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Attention Networks
\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Abstract: Recent work has demonstrated that real-time neurofeedback based on patterns of fMRI activity may be used to train attention (deBettencourt et al., 2015; Zilverstand et al., 2017). Given evidence that attention relies on coordinated activity across the brain, we explored the feasibility of using connectome-based feedback to train focus. Specifically, we used fMRI neurofeedback to modulate functional connectivity in two networks \u2014 a \u201chigh-attention\u201d network with 757 connections and a \u201clow-attention\u201d network with 630 connections \u2014 that predict individuals\u2019 attentional abilities across several independent datasets (Rosenberg et al., 2016a, 2016b). To this end, 10 participants performed the gradual-onset continuous performance task (Esterman et al., 2013) during 3 fMRI runs. Each run included four 3-min task blocks each followed by a 30-s block of feedback, visualized as a gas gauge. Participants were told that a \u201cfull\u201d gauge indicated optimal attention whereas an \u201cempty\u201d gauge indicated suboptimal focus, and were instructed to keep the gauge as close to full as possible. For neurofeedback participants (n = 6), the position of the gauge reflected high-attention relative to low-attention network strength during the preceding task block. Stronger high-attention and weaker low-attention networks resulted in better feedback. Sham feedback participants (n = 4) saw a yoked participant\u2019s feedback. During neurofeedback sessions, a 268-node brain atlas (Shen et al., 2013) was warped into subject space. Motion correction and nuisance variable regression were performed during data collection (Scheinost et al., 2013). After each task block, timecourses in each pair of nodes were correlated to generate a 268 \u00d7 268 connectivity matrix. High- and low-attention network strength values were calculated as the dot product of the connectivity matrix and the attention network masks defined previously. Demonstrating the feasibility of connectome-based feedback, network strength values calculated in real-time and after data collection using published methods were significantly correlated (mean within-subject r-value = .80; range = .58\u2013.94; p < .05 in all participants). As expected, the relationship between feedback and network strength calculated off-line was lower in the sham feedback group (mean within-subject r-value = .44; p < .05 in one participant). Furthermore, mean feedback was more positively correlated with mean task performance (d\u2019) in the neurofeedback than the sham feedback group (r = .48 vs. r = \u2013.61). Thus, these results provide preliminary evidence that whole-brain connectivity-based neurofeedback is feasible and may be useful for attention training.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\u3053\u306e\u767a\u8868\u306f\u6ce8\u610f\u529b\u306e\u4e88\u6e2c\u306b\u95a2\u3059\u308b\u767a\u8868\u3067\u3057\u305f\uff0e\u9ad8\u6ce8\u610f\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3068\u4f4e\u6ce8\u610f\u306e2\u3064\u306b\u308f\u3051\u3067\u305d\u308c\u304b\u3089\u4e88\u6e2c\u30e2\u30c7\u30eb\u3092\u4f5c\u6210\u3057\u3066\u3044\u307e\u3057\u305f\uff0e\u884c\u52d5\u4e88\u6e2c\u3068\u3044\u3046\u90e8\u5206\u3067\u79c1\u306e\u7814\u7a76\u306b\u3042\u3066\u306f\u307e\u308b\u3082\u306e\u304c\u3042\u308a\u5927\u5909\u8208\u5473\u6df1\u304b\u3063\u305f\u3067\u3059\uff0e\u4eca\u5f8c\u53c2\u8003\u306b\u3057\u3066\u3044\u304d\u305f\u3044\u3068\u601d\u3044\u307e\u3057\u305f\uff0e
\n <\/p>\n\n\n
\n \u00a0\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0 \uff1aVisual-verbal working memory training versus visual search training have overlapping and distinct transfer effects on tasks of spatial working memory and cognitive control: An event-related potential study
\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a T. J. COVEY, J. L. SHUCARD, X. WANG, K. SHERWOOD, J. NAKUCI, L. GOH, D. W. SHUCARD
\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Attention Networks
\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Abstract: <\/strong>Cognitive training may improve aspects of cognitive performance. However, the findings in this literature have been mixed, and the unique impact of different forms of training on distinct cognitive abilities is still not fully understood. We examined the effects of two different forms of cognitive training on brain function and performance. Young adult participants were randomly assigned to one of two different training groups. Both groups underwent 20sessions of adaptive cognitive training (30 minutes per session) over the course of approximately four weeks. One group trained on an n-back task of working memory (WM) with visual letter stimuli (n = 20); the other group trained on a visual search task of selective attention\/perceptual discrimination, also with letter stimuli (n = 20). The two tasks were well-matched in terms of difficulty and participant engagement. Participants were administered a battery of tests before and after training (pre- and posttest), which included a Spatial 3-back task and a Go\/Nogo Flanker task. The Spatial 3-back task measured transfer of training gains to spatial WM (note, a different domain than the visual-verbal n-back training task); the Go\/Nogo Flanker task measured transfer of training gains to cognitive control processes such as response inhibition. Electroencephalographic (EEG) data were obtained during these tasks at pre- and posttest, and event-related potentials (ERPs) were derived for each task. The results indicated that both groups improved on their respective training tasks at a similar rate over the course of training. Only the n-back training group showed improved accuracy (and a greater decrease in RT than the visual search group) from pretest to posttest on the Spatial 3-back task. The n-back training group also exhibited enhancement of the N1 ERP component (within 150 msec after stimulus onset) and reduced latency of the N2 component at posttest on the spatial 3-back task, effects that were not observed for the visual search training group. For the Go\/Nogo Flanker task, there was a significant reduction in RT at post- compared to pretest, regardless of group. ERP findings for this task indicated some overlap in training-related changes between the two groups. For example, both groups had reduced P3 latency for trials of the task that required response inhibition. The findings provide evidence that (1) training on a verbal-visual n-back WM task resulted in changes in brain function and cognitive gains on a spatial WM task, and (2) training on tasks that target aspects of attention, regardless of whether they explicitly engage WM, may result in performance gains and changes in brain function on tasks of cognitive control.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\u3053\u306e\u767a\u8868\u306f2\u3064\u306e\u7570\u306a\u308b\u5f62\u614b\u306e\u8a8d\u77e5\u8a13\u7df4\u304c\u8133\u306e\u6a5f\u80fd\u304a\u3088\u3073\u80fd\u529b\u306b\u53ca\u307c\u3059\u5f71\u97ff\u306b\u95a2\u3059\u308b\u767a\u8868\u3067\u3057\u305f\uff0e\u5177\u4f53\u7684\u306b\u306fGo\/NoGo\u8ab2\u984c\u3068nback\u8ab2\u984c\u304c\u4f7f\u7528\u3055\u308c\u3066\u304a\u308a\uff0c\u305d\u306e\u524d\u5f8c\u306e\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u5909\u5316\u306b\u95a2\u3059\u308b\u7814\u7a76\u3067\u3057\u305f\uff0e\u985e\u4f3c\u3057\u305f\u8ad6\u6587\u3092\u80c3\u708e\u8aad\u3093\u3060\u3053\u3068\u304c\u3042\u308a\uff0c\u3053\u3046\u3057\u305f2\u8ab2\u984c\u9593\u306e\u89e3\u6790\u3068\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u306e\u95a2\u4fc2\u6027\u306f\uff0c\u79c1\u306e\u7814\u7a76\u3067\u3082\u4f7f\u7528\u3057\u3088\u3046\u3068\u601d\u3063\u3066\u3044\u308b\u306e\u3067\u5927\u5909\u53c2\u8003\u306b\u306a\u308a\u307e\u3057\u305f\uff0e
\n <\/p>\n\n\n
\n \u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aNeural correlates of an associative memory of elapsed time
\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a V. G. VAN DE VEN, J. LIFANOV, O. IOSIF, S. KOCHS, F. SMULDERS, P. DE WEERD
\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Attention Networks
\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Abstract: The extent to which time duration is represented in associate memory remains under-investigated. We designed a time paired associate task (TPAT) in which participants implicitly learnt cue-time-target associations between cue-target pairs and specific cue-target intervals ranging from 500 to 2000 msec (van de Ven et al. 2017). Importantly, participants only judged whether a cue and probe item were part of the same pair, while making no explicit judgment about time. During learning, some cue-target pairs became associated to a short interval while others became associated to a long interval. During subsequent memory testing, cue-target pairs were shown with both the short and long intervals. Participants showed increased accuracy of identifying matching cue-target pairs if the time interval during testing matched the implicitly learnt interval. A control experiment showed that participants had no explicit knowledge about the time associations. In subsequent neuroimaging experiments we investigated the neural correlates of TPAT memory performance. Using ultra-high field magnetic resonance imaging (UHF-MRI) study at 7 Tesla we found less hippocampal activity (in left Dentate Gyrus and CA1) when time intervals during test trials did not match the learnt interval, compared to when they did match. Further, in an electroencephalography (EEG) study we found decreased Theta oscillation power (centered at 6 Hz) at occipital\/parietal scalp locations for the same comparison of trial types. These findings are in line with the role of hippocampus and Theta oscillations in associate memory (Buzs\u00e1ki 2006) and suggest that the same mechanisms also play a role in representing time in memory (Ranganath and Hsieh 2016). Mismatch between presented and expected associate memory of time may change hippocampal activity and cortical Theta oscillations, possibly through a common neural source. We suggest that cue-dependent retrieval of time in associate memory could perhaps serve as a mechanism for prospective coding of expected visual spatiotemporal events.References: Buzs\u00e1ki G. 2006. Rhythms of the brain.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\u3053\u306e\u767a\u8868\u306f\u95a2\u9023\u3059\u308b\u8a18\u61b6\u306b\u304a\u3051\u308b\u6642\u9593\u306e\u30ad\u30e5\u30fc\u4f9d\u5b58\u691c\u7d22\u306b\u95a2\u3059\u308b\u767a\u8868\u3067\u3057\u305f\uff0e\u6642\u9593\u9593\u9694\u306b\u95a2\u3059\u308b\u7814\u7a76\u3067\uff0c\u79c1\u306e\u7814\u7a76\u3067ISI\u306a\u3069\u306b\u95a2\u3057\u3066\u8003\u3048\u305f\u3053\u3068\u304c\u3042\u3063\u305f\u306e\u3067\u53c2\u8003\u306b\u306a\u308b\u7814\u7a76\u3067\u3057\u305f\uff0e\u8a18\u61b6\uff0c\u5b66\u7fd2\u3055\u308c\u3066\u3044\u305f\u6642\u9593\u3068\uff0c\u51fa\u73fe\u523a\u6fc0\u304c\u5408\u308f\u306a\u304b\u3063\u305f\u6642\u306b\u6d77\u99ac\u6d3b\u52d5\u304c\u95a2\u9023\u3057\u3066\u3044\u308b\u3068\u3044\u3046\u767a\u8868\u3067\u3057\u305f\uff0e\u4f7f\u7528\u3055\u308c\u3066\u3044\u305fTPAT\u3068\u3044\u3046\u30bf\u30b9\u30af\u3092\u4eca\u5f8c\u8abf\u3079\u3066\u898b\u3088\u3046\u3068\u601d\u3044\u307e\u3057\u305f\uff0e
\n\u53c2\u8003\u6587\u732e<\/p>\n- \n
- NeuroScience2017, https:\/\/www.sfn.org\/annual-meeting\/neuroscience-2017<\/li>\n<\/ul>\n
\u5b66\u4f1a\u53c2\u52a0\u5831\u544a\u66f8<\/strong><\/p>\n
\n\n
\n \u00a0<\/strong>
\n\u5831\u544a\u8005\u6c0f\u540d<\/strong><\/td>\n
\n\u85e4\u539f\u4f91\u4eae<\/td>\n<\/tr>\n\n \u767a\u8868\u8ad6\u6587\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n \u30de\u30a4\u30f3\u30c9\u30d5\u30eb\u30c9\u30e9\u30a4\u30d3\u30f3\u30b0\uff1a\u904b\u8ee2\u30bf\u30b9\u30af\u3068PVT\u6642\u306b\u304a\u3051\u308b\u30de\u30a4\u30f3\u30c9\u30ef\u30f3\u30c0\u30ea\u30f3\u30b0\u306e\u8133\u6a5f\u80fd\u72b6\u614b\u306e\u691c\u8a0e<\/td>\n<\/tr>\n \n \u767a\u8868\u8ad6\u6587\u82f1\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n Mindful Driving: Brain functional state of mind wandering in driving and PVT task<\/td>\n<\/tr>\n \n \u8457\u8005<\/strong><\/td>\n \u85e4\u539f\u4f91\u4eae, \u65e5\u548c\u609f\uff0c\u5ee3\u5b89\u77e5\u4e4b<\/td>\n<\/tr>\n \n \u4e3b\u50ac<\/strong><\/td>\n Society of Neuroscience<\/td>\n<\/tr>\n \n \u8b1b\u6f14\u4f1a\u540d<\/strong><\/td>\n Neuroscience2017\uff08https:\/\/www.sfn.org\/annual-meeting\/neuroscience-2017\uff09<\/td>\n<\/tr>\n \n \u4f1a\u5834<\/strong><\/td>\n Washington Convention Center Hall A-C<\/td>\n<\/tr>\n \n \u958b\u50ac\u65e5\u7a0b<\/strong><\/td>\n 2017\/11\/11-2017\/11\/15<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\n <\/p>\n- \n
- \u8b1b\u6f14\u4f1a\u306e\u8a73\u7d30<\/li>\n<\/ol>\n
2017\/11\/11\u304b\u30892017\/11\/15\u306b\u304b\u3051\u3066\uff0cWashington Convention Center Hall A-C\u306b\u3066\u958b\u50ac\u3055\u308c\u307e\u3057\u305fNeuroscience2017\u306b\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u3053\u306eNeuroscience2017\u306f\uff0cSociety of Neuroscience\u306b\u3088\u3063\u3066\u4e3b\u50ac\u3055\u308c\u305f\u5b66\u4f1a\u3067\uff0c\u8133\u3084\u795e\u7d4c\u7cfb\u306e\u7406\u89e3\u306b\u5c02\u5ff5\u3059\u308b\u79d1\u5b66\u8005\u3084\u533b\u5e2b\u306e\u305f\u3081\u306e\u4e16\u754c\u6700\u5927\u306e\u795e\u7d4c\u79d1\u5b66\u306b\u95a2\u3059\u308b\u5b66\u4f1a\u3067\u3042\u308b\uff0e\u795e\u7d4c\u7cfb\u3084\u8133\u306b\u3064\u3044\u3066\u306e\u7814\u7a76\u3092\u884c\u3063\u3066\u3044\u308b\u7814\u7a76\u8005\u305f\u3061\u306e\u4ea4\u6d41\u3084\uff0c\u795e\u7d4c\u79d1\u5b66\u306b\u95a2\u3059\u308b\u5b66\u8853\u7684\u767a\u5c55\u306b\u5bc4\u4e0e\u3059\u308b\u3053\u3068\u3092\u76ee\u7684\u306b\u958b\u50ac\u3055\u308c\u3066\u3044\u307e\u3059\uff0e
\n\u79c1\u306f11\u65e5\u304b\u308915\u65e5\u306b\u304b\u3051\u3066\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u672c\u7814\u7a76\u5ba4\u304b\u3089\u306f\u4ed6\u306b\u65e5\u548c\u5148\u751f\uff0cM1\u897f\u6fa4\u3055\u3093\uff0c\u4e2d\u6751\u6e05\u5fd7\u90ce\uff0cB4\u5c71\u672c\u3055\u3093\u304c\u53c2\u52a0\u3057\u307e\u3057\u305f\uff0e
\n <\/p>\n- \n
- \u7814\u7a76\u767a\u8868\n
- \n
- \u767a\u8868\u6982\u8981<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n
\u79c1\u306f14\u65e5\u306e\u5348\u5f8c\u306e\u30dd\u30b9\u30bf\u30fc\u30bb\u30c3\u30b7\u30e7\u30f3\uff0813:00~17:00\uff09\u306b\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u767a\u8868\u306e\u5f62\u5f0f\u306f\u30dd\u30b9\u30bf\u30fc\u767a\u8868\u3067\uff0c4\u6642\u9593\u306e\u30dd\u30b9\u30bf\u30fc\u767a\u8868\u3068\u306a\u3063\u3066\u304a\u308a\u307e\u3057\u305f\uff0e
\n\u4eca\u56de\u306e\u767a\u8868\u306f\uff0cMindful Driving: Brain functional state of mind wandering in driving and PVT task\u3068\u3044\u3046\u984c\u76ee\u3067\u884c\u3044\u307e\u3057\u305f\uff0e\u4ee5\u4e0b\u306b\u6284\u9332\u3092\u8a18\u8f09\u81f4\u3057\u307e\u3059\uff0e
\n
\n <\/p>\n\n\n
\n \u6284\u9332\u4e2d\u8eab
\n\u672c\u7814\u7a76\u306e\u76ee\u7684\u306f\uff0c\u8133\u6d3b\u52d5\u3092\u7528\u3044\u305f\u904b\u8ee2\u6642\u306b\u304a\u3051\u308b\u30de\u30a4\u30f3\u30c9\u30ef\u30f3\u30c0\u30ea\u30f3\u30b0\u306e\u691c\u51fa\u3067\u3042\u308b\uff0e\u305d\u3053\u3067\uff0c\u672c\u5b9f\u9a13\u3067\u306f\u30de\u30a4\u30f3\u30c9\u30ef\u30f3\u30c0\u30ea\u30f3\u30b0\u72b6\u614b\u306b\u9665\u3089\u305b\u308b\u305f\u3081\u306bdual task\u3092\u7528\u3044\u305f\uff0e\u30e1\u30a4\u30f3\u30bf\u30b9\u30af\u306f\u30b7\u30df\u30e5\u30ec\u30fc\u30bf\u3092\u7528\u3044\u3066\u306e\u904b\u8ee2\u8ab2\u984c\uff0c\u30b5\u30d6\u30bf\u30b9\u30af\u306f\u6301\u7d9a\u7684\u6ce8\u610f\u3092\u6e2c\u5b9a\u3059\u308b\u8ab2\u984c\u3067\u3042\u308bPVT\u3067\u3042\u308b\uff0e\u305d\u306e\u6642\u306e\u8133\u6d3b\u52d5\u3092fNIRS\u3067\u8a08\u6e2c\u3057\u305f\uff0e\u53cd\u5fdc\u6642\u9593\u304b\u3089\u6ce8\u610f\u306e\u5bfe\u8c61\u3068\u533a\u9593\u3092\u5b9a\u7fa9\u3057\uff0c\u305d\u306e\u533a\u9593\u306e\u904b\u8ee2\u8a55\u4fa1\u6307\u6a19\u3068\u3057\u3066\u7528\u3044\u305f\u30b9\u30c6\u30a2\u30ea\u30f3\u30b0\u306e\u8235\u89d2\u5909\u5316\u3068\u8133\u8840\u6d41\u30c7\u30fc\u30bf\u3092\u7528\u3044\u3066\u904b\u8ee2\u6642\u306b\u304a\u3051\u308b\u30de\u30a4\u30f3\u30c9\u30ef\u30f3\u30c0\u30ea\u30f3\u30b0\u72b6\u614b\u306e\u691c\u8a0e\u3092\u884c\u3063\u305f\uff0e<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/p>\n
- \n
- \u8cea\u7591\u5fdc\u7b54<\/li>\n<\/ul>\n
\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
\n\u30fb\u8cea\u554f\u5185\u5bb9<\/strong>1<\/strong>
\n\u5cf6\u6839\u5927\u5b66\u306e\u5ddd\u8d8a\u3055\u3093\u304b\u3089\u306e\u8cea\u554f\u3067\u3059\uff0e\u3053\u3061\u3089\u306e\u8cea\u554f\u306f\u524d\u982d\u90e8\u306e\u307f\u3067\u30ef\u30f3\u30c0\u30ea\u30f3\u30b0\u72b6\u614b\u3092\u8a55\u4fa1\u3067\u304d\u308b\u306e\u304b\uff0c\u4ed6\u306e\u533a\u9593\uff08RT10%\u533a\u9593\uff09\u3067\u3082\u540c\u3058\u50be\u5411\u304c\u898b\u3089\u308c\u308b\u306e\u304b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3059\u308b\u79c1\u306e\u56de\u7b54\u306f\uff0cDMN\u306f\u5168\u8133\u3092\u7528\u3044\u3066\u8a55\u4fa1\u3057\u3066\u3044\u308b\u7814\u7a76\u304c\u591a\u3044\u304c\uff0c\u4eca\u56de\u306f\u524d\u982d\u90e8\u306b\u52a0\u3048\uff0c\u8eca\u4e21\u60c5\u5831\u3092\u7528\u3044\u308b\u3053\u3068\u3067\u8a55\u4fa1\u3067\u304d\u308b\u306e\u3067\u306f\u306a\u3044\u304b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u307e\u305f\uff0c\u4ed6\u306e\u533a\u9593\u3067\u306e\u691c\u8a0e\u306f\u4eca\u56de\u884c\u3063\u3066\u3044\u306a\u3044\u304c\u4eca\u5f8c\u691c\u8a0e\u3057\u3066\u3044\u304d\u305f\u3044\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e
\n
\n\u30fb\u8cea\u554f\u5185\u5bb9<\/strong>2<\/strong>
\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\u30de\u30a4\u30f3\u30c9\u30d5\u30eb\u30c9\u30e9\u30a4\u30d3\u30f3\u30b0\u3068\u306f\u4f55\u304b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\uff0c\u30de\u30a4\u30f3\u30c9\u30d5\u30eb\u30c9\u30e9\u30a4\u30d3\u30f3\u30b0\u306f\u30de\u30a4\u30f3\u30c9\u30d5\u30eb\u30cd\u30b9\u306e\u6982\u5ff5\u3092\u30c9\u30e9\u30a4\u30d3\u30f3\u30b0\u306b\u5fdc\u7528\u3059\u308b\u65b0\u3057\u3044\u6982\u5ff5\u3067\u3042\u308b\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e
\n
\n\u30fb\u8cea\u554f\u5185\u5bb9<\/strong>3<\/strong>
\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\u306fDMN\u306b\u3042\u305f\u308b\u30c1\u30e3\u30f3\u30cd\u30eb\u3067\u306f\u6709\u610f\u5dee\u306f\u898b\u3089\u308c\u306a\u304b\u3063\u305f\u306e\u304b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\uff0c\u6bd4\u8f03\u3057\u305f\u7d50\u679c\u6709\u610f\u5dee\u306f\u898b\u3089\u308c\u306a\u304b\u3063\u305f\u304c\uff0c\u6ce8\u610f\u306b\u95a2\u3059\u308b\u8133\u90e8\u4f4d\u3067\u6709\u610f\u5dee\u304c\u898b\u3089\u308c\u305f\u3068\u4f1d\u3048\u307e\u3057\u305f\uff0e
\n
\n\u30fb\u8cea\u554f\u5185\u5bb9<\/strong>4<\/strong>
\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\u306a\u305c\u524d\u982d\u90e8\u3057\u304b\u8a08\u6e2c\u3057\u3066\u3044\u306a\u3044\u306e\u304b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\uff0c\u5168\u8133\u8a08\u6e2c\u3059\u308b\u74b0\u5883\u304c\u6574\u3063\u3066\u3044\u306a\u3044\u3053\u3068\u306b\u52a0\u3048\uff0c\u5b9f\u9a13\u6642\u9593\u304c\u9577\u3044\u3053\u3068\u304b\u3089\u88ab\u9a13\u8005\u306b\u8ca0\u62c5\u306b\u306a\u308b\u53ef\u80fd\u6027\u304c\u3042\u308b\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e
\n
\n\u30fb\u8cea\u554f\u5185\u5bb9<\/strong>5<\/strong>
\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\uff0cDMN\u306b\u3042\u305f\u308b\u8133\u90e8\u4f4d\u306e\u8a08\u6e2c\u306f\u3067\u304d\u3066\u3044\u308b\u306e\u304b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\uff0c\u8a08\u6e2c\u3067\u304d\u3066\u3044\u308b\u88ab\u9a13\u8005\u3082\u3044\u308b\u304c\uff0c\u904e\u534a\u6570\u306f\u8a08\u6e2c\u3067\u304d\u306a\u304b\u3063\u305f\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e
\n\u30fb\u8cea\u554f\u5185\u5bb9<\/strong>6<\/strong>
\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\uff0cAttention\u8a55\u4fa1\u306b\u306a\u3063\u3066\u3044\u308b\u306e\u3067\u306f\u306a\u3044\u304b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\uff0c\u4eca\u56de\u6709\u610f\u5dee\u306e\u898b\u3089\u308c\u305f\u8133\u90e8\u4f4d\u306f\u6ce8\u610f\u306b\u95a2\u3059\u308b\u90e8\u4f4d\u3067\u3042\u308b\u304c\uff0c\u30ef\u30f3\u30c0\u30ea\u30f3\u30b0\u3057\u3066\u3044\u308b\u6642\u306b\u306f\u6ce8\u610f\u3092\u5411\u3051\u308b\u3079\u304d\u5bfe\u8c61\u306b\u6ce8\u610f\u304c\u5411\u3051\u3089\u308c\u3066\u3044\u306a\u3044\u3068\u8003\u3048\u3066\u3044\u308b\u3053\u3068\u304b\u3089\u30ef\u30f3\u30c0\u30ea\u30f3\u30b0\u72b6\u614b\u3067\u3042\u308b\u53ef\u80fd\u6027\u3082\u8003\u3048\u3089\u308c\u308b\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e
\n <\/p>\n- \n
- \u611f\u60f3<\/li>\n<\/ul>\n
\u30fb\u4eca\u56de\u521d\u3081\u3066\u56fd\u969b\u5b66\u4f1a\u306b\u53c2\u52a0\u3057\u3066\uff0c\u4e16\u754c\u3067\u306f\u81ea\u5206\u306e\u7814\u7a76\u5206\u91ce\u3067\u3069\u306e\u3088\u3046\u306a\u3053\u3068\u304c\u884c\u308f\u308c\u3066\u3044\u308b\u306e\u304b\u306b\u3064\u3044\u3066\u77e5\u308c\u305f\u826f\u3044\u7d4c\u9a13\u3068\u306a\u308a\u307e\u3057\u305f\uff0e\u81ea\u5206\u306e\u7814\u7a76\u306b\u3082\u751f\u304b\u305b\u308b\u3088\u3046\u306a\u65b0\u3057\u3044\u767a\u898b\u3082\u3042\u308a\uff0c\u4eca\u5f8c\u306e\u65b9\u91dd\u304c\u898b\u3048\u305f\u3068\u601d\u3044\u307e\u3057\u305f\uff0e\u307e\u305f\uff0c\u307e\u3060\u63d0\u5531\u3055\u308c\u3066\u3044\u306a\u3044\u30de\u30a4\u30f3\u30c9\u30d5\u30eb\u30c9\u30e9\u30a4\u30d3\u30f3\u30b0\u306b\u8208\u5473\u3092\u6301\u3063\u3066\u3082\u3089\u3048\u305f\u3053\u3068\u306f\uff0c\u4eca\u5f8c\u306e\u7814\u7a76\u306e\u30e2\u30c1\u30d9\u30fc\u30b7\u30e7\u30f3\u306b\u3064\u306a\u304c\u308b\u3068\u611f\u3058\u3066\u3044\u307e\u3059\uff0e
\n\u307e\u305f\uff0c\u53cd\u7701\u70b9\u3068\u3057\u3066\uff0c\u4e8b\u524d\u6e96\u5099\u304c\u4e0d\u5341\u5206\u3067\u3042\u3063\u305f\u3053\u3068\uff0c\u82f1\u8a9e\u306e\u554f\u984c\u304c\u3042\u308a\u307e\u3057\u305f\uff0e\u5b66\u4f1a\u6e96\u5099\u304c\u8a08\u753b\u901a\u308a\u306b\u9032\u307e\u306a\u304b\u3063\u305f\u3053\u3068\u3067\uff0c\u4eca\u56de\u63a8\u3057\u305f\u304b\u3063\u305f\u30de\u30a4\u30f3\u30c9\u30d5\u30eb\u30c9\u30e9\u30a4\u30d3\u30f3\u30b0\u306b\u95a2\u3057\u3066\u306e\u898b\u305b\u65b9\u304c\u30a4\u30de\u30a4\u30c1\u3060\u3063\u305f\u3068\u611f\u3058\u3066\u3044\u307e\u3059\uff0e\u307e\u305f\uff0c\u7d50\u679c\u306b\u3064\u3044\u3066\u3082\uff0c\u3082\u3063\u3068\u6df1\u3081\u308b\u5fc5\u8981\u304c\u3042\u3063\u305f\u3068\u611f\u3058\u3066\u3044\u307e\u3059\uff0e\u82f1\u8a9e\u306b\u95a2\u3057\u3066\u306f\uff0c\u81ea\u5206\u306e\u767a\u8868\u3084\u4ed6\u306e\u4eba\u306e\u767a\u8868\u306b\u304a\u3044\u3066\uff0c\u4f1d\u3048\u308b\u90e8\u5206\uff0c\u805e\u304f\u90e8\u5206\u4e21\u65b9\u3067\u652f\u969c\u304c\u51fa\u307e\u3057\u305f\uff0e\u3082\u3063\u3068\u82f1\u8a9e\u306b\u95a2\u3059\u308b\u6e96\u5099\u3092\u3057\u3066\u3044\u308c\u3070\uff0c\u3082\u3063\u3068\u5f97\u308b\u3082\u306e\u304c\u591a\u3044\u5b66\u4f1a\u306b\u306a\u3063\u305f\u306e\u3067\u306f\u3068\u601d\u3044\u307e\u3059\uff0e\u4eca\u56de\u306e\u53cd\u7701\u3092\u751f\u304b\u3057\u3066\uff0c\u6b21\u56de\u306e\u5b66\u4f1a\u306b\u5411\u3051\u3066\u7814\u7a76\u3092\u9032\u3081\u3066\u3044\u304d\u305f\u3044\u3068\u601d\u3044\u307e\u3059\uff0e
\n <\/p>\n- \n
- \u8074\u8b1b<\/li>\n<\/ol>\n
\u4eca\u56de\u306e\u8b1b\u6f14\u4f1a\u3067\u306f\uff0c\u4e0b\u8a18\u306e5\u4ef6\u306e\u767a\u8868\u3092\u8074\u8b1b\u3057\u307e\u3057\u305f\uff0e<\/p>\n
\n\n
\n \u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000The relationship between cognitive workload and attentional reserve: An empirical investigation
\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a K. JAQUESS, R. J. GENTILI, L.-C. LO, H. OH, J. ZHANG, J. C. RIETSCHEL, M. W. MILLER, Y. Y. TAN, B. D. HATFIELD
\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Nanosymposium, Attention Networks
\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a \u00a0It has long been considered, on the conceptual level, that cognitive workload and attentional reserve have an inverse relationship. However, to our knowledge, this relationship has never been tested empirically. The purpose of this study was to investigate the relationship between cognitive workload and attentional reserve using objective measures derived from the electroencephalogram (EEG). To assess cognitive workload, we utilized spectral power measures of cortical activation (theta, alpha, beta, and the ratio of theta\/alpha). To assess attentional reserve, we utilized components of the event-related potential (ERP) from the presentation of unattended \u201cnovel\u201d sounds (N1, P2, and P3a amplitudes). The relationship between these two families of measures was assessed using a canonical correlation methodology. Twenty-seven participants undergoing flight training performed a flight simulator task under three levels of difficulty. Results revealed a strong, negative relationship between measures of cognitive workload and attentional reserve (all canonical correlation coefficients > 0.9). This finding provides empirical support for the theoretical and intuitive notion that cognitive workload and attentional reserve are inversely related. While cognitive workload and attentional reserve are broad concepts and may consist of many elements, these results inform further work investigating the more specific aspects of these constructs and their relationships.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\u3053\u306e\u767a\u8868\u306f\uff0cEEG\u3092\u7528\u3044\u3066\u306e\u8a8d\u77e5\u4f5c\u696d\u8ca0\u8377\u3068\u6ce8\u610f\u306b\u95a2\u3057\u3066\u306e\u767a\u8868\u3067\u3057\u305f\uff0e\u5b9f\u9a13\u30bf\u30b9\u30af\u306b\u98db\u884c\u6a5f\u64cd\u7e26\u30bf\u30b9\u30af\u3092\u884c\u3044\uff0c\u8133\u6ce2\u306e\u5468\u6ce2\u6570\u5e2f\u5225\u306e\u30d1\u30ef\u30fc\u30b9\u30da\u30af\u30c8\u30eb\u3092\u6307\u6a19\u3068\u3057\u3066\u7528\u3044\u3066\u3044\u307e\u3057\u305f\uff0e\u307e\u305f\uff0c\u540c\u6642\u306bERP\u3082\u898b\u3066\u3044\u307e\u3057\u305f\uff0e\u4ee5\u524dEEG\u3092\u4f7f\u3063\u3066\u3044\u305f\u306e\u3067\uff0cEEG\u306e\u89e3\u6790\u306b\u306f\u4e21\u65b9\u306e\u89e3\u6790\u3092\u3059\u308b\u3053\u3068\u304c\u5fc5\u8981\u3060\u3063\u305f\u3068\u611f\u3058\u307e\u3057\u305f\uff0e \u307e\u305f\uff0c\u30bf\u30b9\u30af\u306e\u4f5c\u696d\u8ca0\u8377\u306e\u8a55\u4fa1\u306bNASA-TLX\u3092\u7528\u3044\u3066\u3044\u3066\uff0c\u305d\u306e\u30a2\u30f3\u30b1\u30fc\u30c8\u7d50\u679c\u3068EEG\u89e3\u6790\u7d50\u679c\u3082\u898b\u3066\u3044\u305f\u306e\u3067\uff0c\u79c1\u306e\u7814\u7a76\u3067\u3082\u884c\u3046\u5fc5\u8981\u304c\u3042\u308b\u3068\u611f\u3058\u307e\u3057\u305f\uff0e
\n <\/p>\n\n\n
\n \u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aReal-time neurofeedback of functional connectivity in large-scale brain networks that predict attention
\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a M. D. ROSENBERG, D. SCHEINOST, W.-T. HSU, R. T. CONSTABLE, M. M. CHUN
\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Nanosymposium, Attention Networks
\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Recent work has demonstrated that real-time neurofeedback based on patterns of fMRI activity may be used to train attention (deBettencourt et al., 2015; Zilverstand et al., 2017). Given evidence that attention relies on coordinated activity across the brain, we explored the feasibility of using connectome-based feedback to train focus. Specifically, we used fMRI neurofeedback to modulate functional connectivity in two networks \u2014 a \u201chigh-attention\u201d network with 757 connections and a \u201clow-attention\u201d network with 630 connections \u2014 that predict individuals\u2019 attentional abilities across several independent datasets (Rosenberg et al., 2016a, 2016b). To this end, 10 participants performed the gradual-onset continuous performance task (Esterman et al., 2013) during 3 fMRI runs. Each run included four 3-min task blocks each followed by a 30-s block of feedback, visualized as a gas gauge. Participants were told that a \u201cfull\u201d gauge indicated optimal attention whereas an \u201cempty\u201d gauge indicated suboptimal focus, and were instructed to keep the gauge as close to full as possible. For neurofeedback participants (n = 6), the position of the gauge reflected high-attention relative to low-attention network strength during the preceding task block. Stronger high-attention and weaker low-attention networks resulted in better feedback. Sham feedback participants (n = 4) saw a yoked participant\u2019s feedback. During neurofeedback sessions, a 268-node brain atlas (Shen et al., 2013) was warped into subject space. Motion correction and nuisance variable regression were performed during data collection (Scheinost et al., 2013). After each task block, timecourses in each pair of nodes were correlated to generate a 268 \u00d7 268 connectivity matrix. High- and low- attention network strength values were calculated as the dot product of the connectivity matrix and the attention network masks defined previously. Demonstrating the feasibility of connectome-based feedback, network strength values calculated in real-time and after data collection using published methods were significantly correlated (mean within-subject r-value = .80; range = .58\u2013.94; p < .05 in all participants). As expected, the relationship between feedback and network strength calculated off-line was lower in the sham feedback group (mean within-subject r-value = .44; p < .05 in one participant). Furthermore, mean feedback was more positively correlated with mean task performance (d\u2019) in the neurofeedback than the sham feedback group (r = .48 vs. r = \u2013.61). Thus, these results provide preliminary evidence that whole-brain connectivity-based neurofeedback is feasible and may be useful for attention training.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\u3053\u306e\u767a\u8868\u306f\uff0cconnectome\u306b\u57fa\u3065\u3044\u305f\u30d5\u30a3\u30fc\u30c9\u30d0\u30c3\u30af\u3092\u4f7f\u7528\u3057\u3066\u7126\u70b9\u3092\u935b\u3048\u308b\u7814\u7a76\u306b\u3064\u3044\u3066\u3067\u3057\u305f\uff0e\u624b\u6cd5\u3068\u3057\u3066\uff0clow-network strength\u3068high-network strength \u306ematrix\u306b\u5206\u89e3\u3057\u3066\u3053\u308c\u3092\u8aac\u660e\u5909\u6570\u3068\u3057\u3066\u76ee\u7684\u5909\u6570 behavioral data\u3092\u4e88\u6e2c\u3059\u308b\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3059\u308b\u3082\u306e\u3067\u3057\u305f\uff0e\u79c1\u304c\u6ce8\u76ee\u3057\u305f\u70b9\u306f\uff0c\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3068\u30d5\u30a3\u30fc\u30c9\u30d0\u30c3\u30af\u65b9\u6cd5\u3067\u3059\uff0e\u306a\u305c\u306a\u3089\uff0c\u9ad8\u6ce8\u610f\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3068\u4f4e\u6ce8\u610f\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u304c\u3042\u308b\u3053\u3068\u3092\u77e5\u308b\u3053\u3068\u304c\u51fa\u6765\u305f\u3053\u3068\uff0c\u30b2\u30fc\u30b8\u306b\u3088\u3063\u3066\u30d5\u30a3\u30fc\u30c9\u30d0\u30c3\u30af\u3059\u308b\u3068\u3044\u3046\u65b9\u6cd5\u3092\u7528\u3044\u3066\u3044\u305f\u304b\u3089\u3067\u3059\uff0e\u306a\u305c\u3053\u306e\u30d5\u30a3\u30fc\u30c9\u30d0\u30c3\u30af\u65b9\u6cd5\u3092\u7528\u3044\u305f\u306e\u304b\u306f\u8abf\u67fb\u304c\u5fc5\u8981\u3060\u3068\u611f\u3058\u307e\u3057\u305f\uff0e
\n <\/p>\n\n\n
\n \u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Connectome-based predictive modeling (CPM) of sustained attention: Comparing different methods for feature selection and prediction
\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a K. R. YOO, K, M. D. ROSENBERG, W.-T. HSU, S. ZHANG, C.-S. R. LI, D. SCHEINOST, R. T. CONSTABLE, M. M. CHUN
\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Nanosymposium, Attention Networks
\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Connectome-based predictive modeling (CPM; Shen et al., 2017) was recently developed to predict individual differences in traits and behaviors, including fluid intelligence (Finn et al., 2015) and sustained attention (Rosenberg et al., 2016a). CPM is a data-driven approach to construct a model predicting individual behaviors from brain connectome data. Here, we compared the predictive power of three different connectivity features (Pearson\u2019s correlation, accordance and discordance) and two different prediction algorithms (linear regression and partial least square regression; PLSR) in CPM for attention function. Accordance and discordance are recently proposed connectivity measures that separately track in-phase synchronization and out-of-phase anti-correlation, respectively (Meskaldji et al., 2016). We defined models over task or rest fMRI data, and tested 1) whether accordance and discordance are more reliable measures than Pearson\u2019s correlation for functional connectivity and 2) whether PLSR or linear regression better relates connectivity features to behavioral traits. Connectome-based predictive models of sustained attention were developed from fMRI data collected while participants performed a sustained attention task (gradCPT), and while at rest (N=25; Rosenberg et al., 2016a). The three other independent fMRI datasets included: 1) data collected during stop-signal task performance and rest (N=83, including 19 participants administered methylphenidate prior to scanning; Rosenberg et al., 2016b), 2) data collected during Attention Network Task performance and rest (N=41), and 3) resting data and ADHD symptom severity from the ADHD-200 Consortium (N=113; Rosenberg et al., 2016a). All models significantly predicted individual performance with correlations between predicted and observed measures of attention as high as 0.9 for internal, and 0.6 for external validation; all p\u2019s < 0.05). Models trained on task-data outperformed models based on rest data. Accordance features generally showed a small numerical advantage over correlation features, while PLSR models were usually better than linear regression models. Overall, in addition to correlation features combined with linear models (Rosenberg et al., 2016a), it is useful to consider accordance features and PLSR for CPM.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\u3053\u306e\u767a\u8868\u306fMRI\u3092\u7528\u3044\u305fConnectome\u30d9\u30fc\u30b9\u306e\u4e88\u6e2c\u30e2\u30c7\u30ea\u30f3\u30b0\u306b\u3064\u3044\u3066\u306e\u7814\u7a76\u3067\u3057\u305f\uff0e\u8133\u7d50\u5408\u30c7\u30fc\u30bf\u304b\u3089\u500b\u3005\u306e\u884c\u52d5\u3092\u4e88\u6e2c\u3059\u308b\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3059\u308b\u305f\u3081\u306e\u30c7\u30fc\u30bf\u99c6\u52d5\u30a2\u30d7\u30ed\u30fc\u30c1\u3067\u3042\u308bCPM\u3092\u7528\u3044\u3066\u6301\u7d9a\u7684\u6ce8\u610f\u3092\u8a55\u4fa1\u3059\u308b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u307e\u305f\uff0cAccordance\u3068discordance\u3092connectivity\u306e\u6307\u6a19\u306b\u3057\u3066\u3044\u307e\u3057\u305f\uff0e\u3053\u306e\u624b\u6cd5\u306f\uff0c\u81ea\u5206\u306e\u7814\u7a76\u3067\u3082\u7d50\u5408\u304c\u53d6\u308b\u3053\u3068\u304c\u51fa\u6765\u308c\u3070\uff0c\u5fdc\u7528\u3059\u308b\u3053\u3068\u304c\u53ef\u80fd\u3060\u3068\u601d\u3044\u307e\u3057\u305f\uff0e
\n <\/p>\n\n\n
\n \u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Visual-verbal working memory training versus visual search training have overlapping and distinct transfer effects on tasks of spatial working memory and cognitive control: An event-related potential study
\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a T. J. COVEY, J. L. SHUCARD, X. WANG, K. SHERWOOD, J. NAKUCI, L. GOH, D. W. SHUCARD
\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Nanosymposium: Mechanisms of Working Memory
\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aCognitive training may improve aspects of cognitive performance. However, the findings in this literature have been mixed, and the unique impact of different forms of training on distinct cognitive abilities is still not fully understood. We examined the effects of two different forms of cognitive training on brain function and performance. Young adult participants were randomly assigned to one of two different training groups. Both groups underwent 20 sessions of adaptive cognitive training (30 minutes per session) over the course of approximately four weeks. One group trained on an n-back task of working memory (WM) with visual letter stimuli (n = 20); the other group trained on a visual search task of selective attention\/perceptual discrimination, also with letter stimuli (n = 20). The two tasks were well-matched in terms of difficulty and participant engagement. Participants were administered a battery of tests before and after training (pre- and posttest), which included a Spatial 3-back task and a Go\/Nogo Flanker task. The Spatial 3-back task measured transfer of training gains to spatial WM (note, a different domain than the visual-verbal n-back training task); the Go\/Nogo Flanker task measured transfer of training gains to cognitive control processes such as response inhibition. Electroencephalographic (EEG) data were obtained during these tasks at pre- and posttest, and event-related potentials (ERPs) were derived for each task. The results indicated that both groups improved on their respective training tasks at a similar rate over the course of training. Only the n-back training group showed improved accuracy (and a greater decrease in RT than the visual search group) from pretest to posttest on the Spatial 3-back task. The n-back training group also exhibited enhancement of the N1 ERP component (within 150 msec after stimulus onset) and reduced latency of the N2 component at posttest on the spatial 3-back task, effects that were not observed for the visual search training group. For the Go\/Nogo Flanker task, there was a significant reduction in RT at post- compared to pretest, regardless of group. ERP findings for this task indicated some overlap in training-related changes between the two groups. For example, both groups had reduced P3 latency for trials of the task that required response inhibition. The findings provide evidence that (1) training on a verbal-visual n-back WM task resulted in changes in brain function and cognitive gains on a spatial WM task, and (2) training on tasks that target aspects of attention, regardless of whether they explicitly engage WM, may result in performance gains and changes in brain function on tasks of cognitive control.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\u3053\u306e\u767a\u8868\u306f\uff0c\u30ef\u30fc\u30ad\u30f3\u30b0\u30e1\u30e2\u30ea\u306e\u8a13\u7df4\u3068\u3057\u3066\uff0cn-back\u3092\u884c\u3063\u3066\u3044\u308b\u7fa4\u3068\u884c\u3063\u3066\u3044\u306a\u3044\u7fa4\u3067selective attention task\u306b\u304a\u3044\u3066\u6bd4\u8f03\u3092\u884c\u3063\u3066\u3044\u308b\u7814\u7a76\u3067\u3057\u305f\uff0eEEG\u3067\u8a08\u6e2c\u3092\u884c\u3063\u3066\u304a\u308a\uff0cP1\uff0cN1\uff0cP2\uff0cN2\uff0cP3\u306a\u3069\u306eERP\u3092\u89e3\u6790\u5bfe\u8c61\u3068\u3057\u3066\u3044\u307e\u3057\u305f\uff0e\u8a13\u7df4\u3092\u3057\u3066\u3044\u308b\u7fa4\u306e\u65b9\u304cN2\u306e\u6f5c\u6642\u3092\u6e1b\u5c11\u3055\u305b\u305f\u3068\u3044\u3046\u7d50\u679c\u304c\u5831\u544a\u3055\u308c\u307e\u3057\u305f\uff0e\u7591\u554f\u306b\u601d\u3063\u305f\u70b9\u306f\uff0cERP\u306e\u89e3\u6790\u306b\u3064\u3044\u3066\u306f\uff0c\u591a\u304f\u306e\u52a0\u7b97\u5e73\u5747\u304c\u5fc5\u8981\u3068\u306a\u308b\u304c\uff0c\u30bf\u30b9\u30af\u6642\u9593\u304c\u9577\u304f\u306a\u3044\u306e\u306b\u8a66\u884c\u6570\u306b\u95a2\u3057\u3066\u306f\u3069\u3046\u3057\u3066\u3044\u308b\u306e\u304b\u3068\u3044\u3046\u70b9\u3067\u3057\u305f\uff0e
\n <\/p>\n\n\n
\n \u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Neural prediction of community support providers
\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Y. LEONG, S. MORELLI, R. CARLSON, M. KULLAR, J. ZAKI
\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Nanosymposium: Social Decision-Making
\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a The receipt of high-quality social support bolsters individuals\u2019 mental and physical health. It is often beneficial for members of a community to keep in mind the people likely to provide social support, such that one knows whom to turn to in times of distress. Here, we test the hypothesis that people passively keep track of the individuals who are likely to provide social support within their community. We recruited 97 students from two freshman dormitories and had them nominate individuals in their dorm who provide them with eight different types of social support (e.g., companionship, social advice, emotional support). We computed a sociometric index of social support by taking a weighted sum of the number of nominations received by a given individual. Individuals who scored in the top, middle and bottom tercile on this metric were designated as high, medium and low support providers respectively. In a separate session, we scanned a subset of the participants (N=50) as they passively viewed photos of their dormmates. Participants were told to attend to the photos, but were not otherwise instructed on what to think about. We trained a Lasso-PCR algorithm on the BOLD data of participants from one of the dorms (N = 26) to identify a pattern of BOLD activity that monotonically increased when participants viewed dormmates with increasing levels of<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\u3053\u306e\u767a\u8868\u306f\uff0cMeasuring social value\u3068\u3044\u3046\u8cea\u554f\u7d19\u3067Social value\u304c\u9ad8\u3044\u4eba\uff0c\u4f4e\u3044\u4eba\u3092\u898b\u3064\u3051\u308b\u3053\u3068\u3067\uff0c\u3053\u308c\u3092\u8133\u6d3b\u52d5\u304b\u3089\u4e88\u6e2c\u3059\u308b\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3059\u308b\u7814\u7a76\u3067\u3057\u305f\uff0e\u89e3\u6790\u624b\u6cd5\u304c\u8208\u5473\u6df1\u3044\u3068\u601d\u3044\u307e\u3057\u305f\uff0e\u306a\u305c\u306a\u3089\uff0c\u8133\u6d3b\u52d5\u3092\u307f\u3066\uff0c\u30cf\u30d6\u3067\u3042\u308b\u4eba\u3092\u898b\u3064\u3051\u308b\u3088\u3044\u3046\u65b9\u6cd5\u306f\u3088\u304f\u3042\u308b\u3068\u601d\u3046\u304c\uff0c\u30a2\u30f3\u30b1\u30fc\u30c8\u304b\u3089\u4e88\u6e2c\u3057\u3066\u3044\u308b\u3068\u3044\u3046\u70b9\u304c\u9762\u767d\u3044\u3068\u611f\u3058\u307e\u3057\u305f\uff0e\u3053\u306e\u7814\u7a76\u304c\u9032\u3080\u3068\uff0c\u30a2\u30f3\u30b1\u30fc\u30c8\u306e\u307f\u3067\u306e\u8a55\u4fa1\u3082\u3067\u304d\u308b\u306e\u3067\u306f\u306a\u3044\u304b\u3068\u601d\u3044\u307e\u3057\u305f\u3002\u81ea\u5206\u306e\u7814\u7a76\u306b\u306f\uff0c\u6ce8\u610f\u306e\u4e88\u6e2c\u306b\u7f6e\u304d\u63db\u3048\u308b\u3068\u7528\u3044\u308b\u3053\u3068\u304c\u3067\u304d\u308b\u306e\u3067\u306f\u306a\u3044\u304b\u3068\u8003\u3048\u307e\u3057\u305f\uff0e
\n
\n\u53c2\u8003\u6587\u732e<\/p>\n- \n
- Neuroscience2017, https:\/\/www.sfn.org\/annual-meeting\/neuroscience-2017<\/li>\n<\/ul>\n
\u5b66\u4f1a\u53c2\u52a0\u5831\u544a\u66f8<\/strong><\/p>\n
\n\n
\n \u00a0<\/strong>
\n\u5831\u544a\u8005\u6c0f\u540d<\/strong><\/td>\n
\n\u5c71\u672c\u6e09\u5b50<\/td>\n<\/tr>\n\n \u767a\u8868\u8ad6\u6587\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n <\/td>\n<\/tr>\n \n \u767a\u8868\u8ad6\u6587\u82f1\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n Network analysis of brain activity during breath-counting meditation by fNIRS<\/td>\n<\/tr>\n \n \u8457\u8005<\/strong><\/td>\n \u5c71\u672c\u6e09\u5b50, \u65e5\u548c\u609f\uff0c\u5ee3\u5b89\u77e5\u4e4b,<\/td>\n<\/tr>\n \n \u4e3b\u50ac<\/strong><\/td>\n Society for Neuroscience<\/td>\n<\/tr>\n \n \u8b1b\u6f14\u4f1a\u540d<\/strong><\/td>\n Neuroscience2017<\/td>\n<\/tr>\n \n \u4f1a\u5834<\/strong><\/td>\n Walter E. Washington Convention Center<\/td>\n<\/tr>\n \n \u958b\u50ac\u65e5\u7a0b\u3000<\/strong><\/td>\n 2017\/11\/11-2017\/11\/15<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\n <\/p>\n- \n
- \u8b1b\u6f14\u4f1a\u306e\u8a73\u7d30<\/li>\n<\/ol>\n
2017\/11\/11\u304b\u30892017\/11\/15\u306b\u304b\u3051\u3066\uff0cWalter E. Washington Convention Center\u306b\u3066\u958b\u50ac\u3055\u308c\u307e\u3057\u305fNeuroscience2017\u306b\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u3053\u306eNeuroscience2017\u306f\uff0cSociety for Neuroscience\u306b\u3088\u3063\u3066\u4e3b\u50ac\u3055\u308c\u305f\u7814\u7a76\u4f1a\u3067\uff0c\u5b66\u751f\u3084\u6559\u54e1\u304c\u53c2\u52a0\u3057\u3066\uff0c\u795e\u7d4c\u79d1\u5b66\u306b\u95a2\u3059\u308b\u5b66\u8853\u7684\u306a\u767a\u898b\u3092\u76ee\u7684\u306b\u958b\u50ac\u3055\u308c\uff0c80\u30ab\u56fd\u304b\u30893\u4e07\u4eba\u4ee5\u4e0a\u53c2\u52a0\u304c\u3057\u305f\u5927\u304d\u306a\u5b66\u4f1a\u3067\u3059\uff0e
\n\u79c1\u306f\u5168\u3066\u306e\u65e5\u7a0b\u306b\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u672c\u7814\u7a76\u5ba4\u304b\u3089\u306f\u4ed6\u306b\u65e5\u548c\u5148\u751f\uff0c\u85e4\u539f\u3055\u3093\uff0e\u4e2d\u6751\uff08\u6e05\uff09\u3055\u3093\uff0c\u897f\u6fa4\u3055\u3093\u304c\u53c2\u52a0\u3057\u307e\u3057\u305f\uff0e
\n <\/p>\n- \n
- \u7814\u7a76\u767a\u8868\n
- \n
- \u767a\u8868\u6982\u8981<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n
\u79c1\u306f11\u65e5\u306e13\uff0d17\u6642\u306e\u30bb\u30c3\u30b7\u30e7\u30f3\u300cPoster\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
\n\u4eca\u56de\u306f\uff0cNetwork analysis of brain activity during breath-counting meditation by fNIRS\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<\/p>\n\n\n
\n Introduction<\/u>: Attention deviates from the focused task, and the state that is not related to the task is called mind wandering. About 50% in daily life, people are in a state of mind wandering. Mind wandering is said to lead to a decrease in happiness and work efficiency. Mindfulness meditation is drawing attention. Mindfulness is to pay attention to what is happening now, and effects of stress reduction and concentration are expected by mindfulness meditation. In this study, brain states during breath-counting meditation were compared with meditation novices, and the degree of functional brain network was compared. In the experiments, brain
\nMethods<\/u>: The meditation task is breath-counting which counts entrance and exit of breathing. Six male meditation beginners participated in the experiment. Brain activity at rest and breath-counting was measured using fNIRS (ETG – 7100). The measurement site was 116 channels in the forehead, crown and occipital area. The measured data was processed by a band pass filter with a frequency of 0.008 Hz to 0.09 Hz. All the channels were correlated by stochastic registration with the sites divided by Automated Anatomical Labeling (AAL). The correlation coefficient matrix between all channels with the correlation of the channel’s blood flow change was calculated at rest and breath-counting, respectively. The order of degree corresponding to an edge density of 15% was compared to rest and breath-counting.
\nResults, Discussion<\/u>: At breath – counting time compared to rest before pre-task, it was found that the order of the coupling of the middle prefrontal cortex increased in many subjects. The middle prefrontal cortex is reported to be a brain part that acts during meditation and controls attention. The results suggest that the brain part of attention control emphasizes with other regions during meditation. Also, at the time of rest after breath-counting, the degree of coupling of the middle frontal round remained high. This result indicates that the brain state during meditation was preserved even after meditation. It is a new result that brain states at the time of rest and pre-task rest were different.
\nConclusion<\/u>: A comparison of the order of the degree of resting time and meditating in a novice was performed. Brain activity was measured by fNIRS. When breath-counting was performed, the order of the degree of the medial frontal gyrus increased compared to that at rest. Therefore, even if a meditate novice, there is a possibility that the brain part of attention control exaggerates with other regions more than at rest.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/p>\n
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- \u8cea\u7591\u5fdc\u7b54<\/li>\n<\/ul>\n
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\n\u30fb\u8cea\u554f\u5185\u5bb9<\/strong>1<\/strong>
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\n\u00a0<\/strong>
\n\u00a0<\/strong>
\n\u30fb\u8cea\u554f\u5185\u5bb9<\/strong>2<\/strong>
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\n
\n\u30fb\u8cea\u554f\u5185\u5bb9<\/strong>3<\/strong>
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\n
\n\u30fb\u8cea\u554f\u5185\u5bb9<\/strong>4<\/strong>
\n\u53f0\u6e7e\u304b\u3089\u3044\u3089\u3057\u305fYu Chen\u3055\u3093\u304b\u3089\u8cea\u554f\u3044\u305f\u3060\u304d\u307e\u3057\u305f\uff0e\u3000\u3053\u3061\u3089\u306e\u8cea\u554f\u306f\uff0cfNIRS\u3092\u306a\u305c\u4f7f\u3063\u3066\u3044\u308b\u306e\u304b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\uff0cfMRI\u3068\u6bd4\u3079\u3066\u65e5\u5e38\u306e\u751f\u6d3b\u306b\u8fd1\u3044\u72b6\u614b\u3067\u6e2c\u5b9a\u3067\u304d\u308b\u304b\u3089\u3067\u3042\u308b\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e
\n\u00a0<\/strong>
\n\u30fb\u8cea\u554f\u5185\u5bb9<\/strong>5<\/strong>
\n\u540c\u3058\u304f\uff0cYu Chen\u3055\u3093\u304b\u3089\u8cea\u554f\u3044\u305f\u3060\u304d\u307e\u3057\u305f\uff0e\u3053\u3061\u3089\u306e\u8cea\u554f\u306f\uff0c\u306a\u305cdegree\u3092\u4f7f\u3046\u306e\u304b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3059\u308b\u56de\u7b54\u3067\u3059\u304c\uff0cdegree\u3068\u306f\u5404\u30ce\u30fc\u30c9\u306e\u7d50\u5408\u672c\u6570\u306e\u548c\u3067\u3042\u308b\u3068\u56de\u7b54\u3057\uff0c\u30dd\u30b9\u30bf\u30fc\u306e\u56f3\u3092\u7528\u3044\u3066\uff0c\u5404\u30ce\u30fc\u30c9\u306edegree\u304c\u3044\u304f\u3064\u306b\u306a\u308b\u304b\u8aac\u660e\u3057\u307e\u3057\u305f\uff0e
\ndegree\u3092\u7528\u3044\u308b\u610f\u5473\u306f\uff0c\u3046\u307e\u304f\u7b54\u3048\u3089\u308c\u305a\u65e5\u548c\u5148\u751f\u306b\u52a9\u3051\u3066\u3044\u305f\u3060\u304d\u307e\u3057\u305f\uff0edegree\u306f\u7d50\u5408\u672c\u6570\u3092\u8868\u3059\u306e\u3067\uff0cdegree\u304c\u9ad8\u3044\u307b\u3069\u4ed6\u306e\u9818\u57df\u3068\u591a\u304f\u5f37\u8abf\u3057\u3066\u3044\u308b\u306e\u3067\u91cd\u8981\u306a\u9818\u57df\u3067\u3042\u308b\u3068\u8003\u3048\u3066\u3044\u308b\u3068\u56de\u7b54\u3057\u3066\u3044\u305f\u3060\u304d\u307e\u3057\u305f\uff0e
\n
\n\u30fb\u8cea\u554f\u5185\u5bb9<\/strong>6<\/strong>
\n\u8907\u6570\u306e\u65b9\u304b\u3089\u8cea\u554f\u3044\u305f\u3060\u304d\u307e\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306f\uff0c\u521d\u5fc3\u8005\u3068\u719f\u7df4\u8005\u306e\u5e74\u9f62\u5dee\u306f\u91cd\u8981\u306a\u610f\u5473\u306f\u306a\u3044\u306e\u304b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\uff0c\u554f\u984c\u306a\u3044\u3068\u8003\u3048\u3066\u3044\u308b\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e\u3057\u304b\u3057\u4eca\u5f8c\u691c\u8a0e\u3059\u308b\u5fc5\u8981\u304c\u3042\u308b\u3068\u8003\u3048\u3066\u3044\u307e\u3059\uff0e
\n
\n\u30fb\u8cea\u554f\u5185\u5bb9<\/strong>7<\/strong>
\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\uff0cPC1\uff0c2\u3092\u4f7f\u308f\u305a\u306a\u305cPC4\uff0c7\uff0c8\u3092\u4f7f\u3063\u305f\u306e\u304b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\uff0c\u4eca\u56de\u306f\u521d\u5fc3\u8005\u3068\u719f\u7df4\u8005\u306e\u9055\u3044\u3092\u8996\u899a\u5316\u3067\u304d\u308b\u8ef8\u3092\u898b\u3064\u3051\u305f\u304b\u3063\u305f\u304b\u3089\u3067\u3042\u308b\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e
\n
\n\u30fb\u8cea\u554f\u5185\u5bb9<\/strong>8<\/strong>
\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\u77e2\u5370\u306eincrease\u304c\u8868\u3059\u306e\u306f\u76f8\u95a2\u304b\uff0cactivity\u304b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3059\u308b\u56de\u7b54\u3067\u3059\u304c\uff0c\u76f8\u95a2\u3084activity\u3067\u306f\u306a\u304fdegree\u304c\u5897\u3048\u305f\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e
\n
\n\u30fb\u8cea\u554f\u5185\u5bb9<\/strong>9<\/strong>
\n\u540c\u5fd7\u793e\u5927\u5b66\u8133\u795e\u7d4c\u884c\u52d5\u5de5\u5b66\u7814\u7a76\u5ba4\u306e\u6881\u3055\u3093\u304b\u3089\u8cea\u554f\u3044\u305f\u3060\u304d\u307e\u3057\u305f\uff0e\u3053\u3061\u3089\u306e\u8cea\u554f\u306f\uff0c\u30ec\u30b8\u30b9\u30c8\u30ec\u30fc\u30b7\u30e7\u30f3\u306f\u3069\u306e\u3088\u3046\u306b\u884c\u3063\u3066\u3044\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\uff0c3D\u30c7\u30b8\u30bf\u30a4\u30b6\u30fc\u3092\u4f7f\u3063\u3066\u5ea7\u6a19\u3092\u6e2c\u5b9a\u3057\uff0cNIRS-SPM\u3092\u4f7f\u3063\u3066\u884c\u3063\u305f\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e
\n
\n\u30fb\u8cea\u554f\u5185\u5bb9<\/strong>10<\/strong>
\n\u540c\u5fd7\u793e\u5927\u5b66\u8133\u795e\u7d4c\u884c\u52d5\u5de5\u5b66\u7814\u7a76\u5ba4\u306e\u677f\u57a3\u3055\u3093\u304b\u3089\u8cea\u554f\u3044\u305f\u3060\u304d\u307e\u3057\u305f\uff0e\u3053\u3061\u3089\u306e\u8cea\u554f\u306f\uff0ccloser\u3068\u306f\u76f8\u95a2\u4fc2\u6570\u3092\u8a08\u7b97\u3057\u305f\u7d50\u679c\u304b\u3089\u8a00\u3063\u3066\u3044\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\u8a08\u7b97\u3057\u305f\u308f\u3051\u3067\u306f\u306a\u304f\u76ee\u8996\u3067\u5224\u65ad\u3057\u305f\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e
\n\u8a08\u7b97\u3057\u306a\u3044\u3068\u6839\u62e0\u304c\u306a\u3044\u3068\u8a00\u308f\u308c\u308b\u306e\u3067\u306f\u306a\u3044\u304b\u3068\u3054\u610f\u898b\u3044\u305f\u3060\u3044\u305f\u306e\u3067\uff0c\u4eca\u5f8c\u691c\u8a0e\u3059\u308b\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e
\n
\n\u30fb\u8cea\u554f\u5185\u5bb9<\/strong>11<\/strong>
\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\uff0cfNIRS\u3067\u306fcortex\u3057\u304b\u6e2c\u5b9a\u3067\u304d\u306a\u3044\u306e\u304b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3059\u308b\u56de\u7b54\u3067\u3059\u304c\uff0ccortex\u306e\u307f\u3057\u304b\u6e2c\u5b9a\u3067\u304d\u305a\uff0c\u6df1\u90e8\u306f\u6e2c\u5b9a\u3067\u304d\u306a\u3044\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e
\n
\n\u30fb\u8cea\u554f\u5185\u5bb9<\/strong>
- \u8cea\u7591\u5fdc\u7b54<\/li>\n<\/ul>\n
- \u767a\u8868\u6982\u8981<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n
- \u7814\u7a76\u767a\u8868\n
- \u8b1b\u6f14\u4f1a\u306e\u8a73\u7d30<\/li>\n<\/ol>\n
- Neuroscience2017, https:\/\/www.sfn.org\/annual-meeting\/neuroscience-2017<\/li>\n<\/ul>\n
- \u8074\u8b1b<\/li>\n<\/ol>\n
- \u611f\u60f3<\/li>\n<\/ul>\n
- \u8cea\u7591\u5fdc\u7b54<\/li>\n<\/ul>\n
- \u767a\u8868\u6982\u8981<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n
- \u7814\u7a76\u767a\u8868\n
- \u8b1b\u6f14\u4f1a\u306e\u8a73\u7d30<\/li>\n<\/ol>\n
- NeuroScience2017, https:\/\/www.sfn.org\/annual-meeting\/neuroscience-2017<\/li>\n<\/ul>\n
- \u8074\u8b1b<\/li>\n<\/ol>\n
- \u611f\u60f3<\/li>\n<\/ul>\n
- \u8cea\u7591\u5fdc\u7b54<\/li>\n<\/ul>\n
- \u767a\u8868\u6982\u8981<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n
- \u7814\u7a76\u767a\u8868\n
- \u8b1b\u6f14\u4f1a\u306e\u8a73\u7d30<\/li>\n<\/ol>\n
- “Network analysis of brain activity during breath-counting meditation by fNIRS”
- “Brain activity and functional connectivity in attention and careless states by fNIRS”
- “Discussions of brain activity and eye movement during driving”
- “Mindful driving:Brain functional state of mind wandering in driving and PVT task”
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