- \n
- “Characterizing the meditative state based on functional connectivity and low-frequency fluctuation”
\nS.HIWA; M.IZUKA; T.HIROYASU.<\/li>\n<\/ul>\n<\/ul>\n<\/p>\n
- \n
- “Functional connectivity analysis during breath-counting meditation using multichannel fNIRS”
\nT.KATAYAMA; S.HIWA; T.HIROYASU.<\/li>\n<\/ul>\n<\/ul>\n<\/p>\n
- \n
- “Frontal lobe activity during breath-counting meditation: fNIRS study”
\nS.FUJII; S.HIWA; T.HIROYASU.<\/li>\n<\/ul>\n<\/ul>\n<\/p>\n
- \n
- “Brain region segmentation method using SLIC and Normalized Cut”
\nK.NAKAMURA; S.HIWA; T.HIROYASU.<\/li>\n<\/ul>\n<\/ul>\n<\/p>\n
- \n
- “Optimizing electrode placement and frequency bands in EEG-based motor imagery BCIs”
\nT.ISHAHARA; S.HIWA; T.HIROYASU.<\/li>\n<\/ul>\n<\/ul>\n<\/p>\n
- \n
- “Intra-individual variations in functional connectivity during resting and meditative states”
\nT.AIMOTO; S.HIWA; T.HIROYASU.<\/li>\n<\/ul>\n<\/ul>\n<\/p>\n
- \n
- “Effects of breath-counting meditation on functional brain connectivity and salivary hormones”
\nT.MIYOSHI; S.HIWA; T.HIROYASU.<\/li>\n<\/ul>\n<\/ul>\n<\/p>\n
- \n
- “Functional connectivity analysis of pleasant and unpleasant states using fMRI”
\nS.ISHIDA; S.HIWA; T.HIROYASU.<\/li>\n<\/ul>\n<\/ul>\n<\/p>\n
- \n
- “Human state estimation from cerebral blood flow data using CNN and LSTM”
\nT.TAMAKI; S.HIWA; T.HIROYASU.<\/li>\n<\/ul>\n<\/ul>\n<\/p>\n
- \n
- “Functional connectivity analysis of brain activity during cooperative behavior using fNIRS”
\nM.MIZUNO; S.HIWA; T.HIROYASU.<\/li>\n<\/ul>\n<\/ul>\n<\/p>\n
- \n
- “Examination of the relationship between brain activity and eye movement during emotional stimulation”
\nH.WADA; S.HIWA; T.HIROYASU.<\/li>\n<\/ul>\n<\/ul>\n<\/p>\n
- \n
- “Adaptive HRF analysis of fNIRS data”
\nS.YOSHITAKE; S.HIWA; T.HIROYASU.<\/li>\n<\/ul>\n<\/ul>\n<\/p>\n
- \n
- “Classification of brain states using functional data obtained during a mental arithmetic task”
\nR.HAGIWARA; S.HIWA; T.HIROYASU.<\/li>\n<\/ul>\n<\/ul>\n<\/p>\n
- \n
- “Evaluation of a GLM analysis with adaptive hemodynamic response function on a visual stimulus task”
\nS.IKEDA; S.HIWA; T.HIROYASU.<\/li>\n<\/ul>\n<\/div>\n
\n![\"\"](\"http:\/\/www.is.doshisha.ac.jp\/news\/wp-content\/uploads\/2017\/07\/2017-06-28_15-27-20-300x224.jpg\")
<\/a> ![\"\"](\"http:\/\/www.is.doshisha.ac.jp\/news\/wp-content\/uploads\/2017\/06\/DSCF2910-300x225.jpg\")
<\/a>
\n<!>
\n\u79c1\u306b\u3068\u3063\u3066\u306f2\u5ea6\u76ee\u306e\u56fd\u969b\u5b66\u4f1a\u3067\u3042\u308a\uff0c\u524d\u56de\u3068\u306f\u5b66\u4f1a\u306e\u898f\u6a21\uff0c\u7814\u7a76\u5ba4\u304b\u3089\u306e\u53c2\u52a0\u6570\u306a\u3069\u304c\u7570\u306a\u308a\uff0c\u524d\u56de\u3068\u306f\u9055\u3063\u305f\u7d4c\u9a13\u3092\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u305f\u3068\u611f\u3058\u3066\u3044\u307e\u3059\uff0e\u79c1\u306f\u30dd\u30b9\u30bf\u30fc\u767a\u8868\u304c\u6700\u7d42\u65e5\u3067\u3042\u3063\u305f\u305f\u3081\uff0c\u524d\u65e5\u307e\u3067\u306e\u7814\u7a76\u5ba4\u30e1\u30f3\u30d0\u30fc\u306e\u767a\u8868\u59ff\u3092\u307f\u308b\u3053\u3068\u304c\u3067\u304d\uff0c\u305d\u308c\u306b\u3088\u308a\u30e2\u30c1\u30d9\u30fc\u30b7\u30e7\u30f3\u3092\u3088\u308a\u4e0a\u3052\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3057\u305f\uff0e\u767a\u8868\u3067\u306f\u7df4\u7fd2\u3057\u3066\u304d\u305f\u8aac\u660e\u3067\u304d\u307e\u3057\u305f\u304c\uff0c\u8cea\u554f\u3092\u8074\u304d\uff0c\u305d\u306e\u8cea\u554f\u306b\u7b54\u3048\u308b\u3053\u3068\u306f\u96e3\u3057\u304f\uff0c\u3068\u306b\u304b\u304f\u9ed9\u308b\u3053\u3068\u306a\u304f\u8cea\u554f\u3092\u8074\u304d\u8fd4\u3057\u305f\u308a\uff0c\u81ea\u5206\u304c\u8003\u3048\u305f\u3053\u3068\u3092\u8a71\u3059\u52aa\u529b\u3092\u3057\u3066\u30b3\u30df\u30e5\u30cb\u30b1\u30fc\u30b7\u30e7\u30f3\u3092\u9014\u5207\u308c\u305a\u306b\u5fc3\u304c\u3051\u307e\u3057\u305f\uff0e\u53bb\u5e74\u53c2\u52a0\u3057\u305f\u56fd\u969b\u5b66\u4f1a\u3088\u308a\uff0c\u591a\u304f\u306e\u7814\u7a76\u3092\u77e5\u308a\uff0c\u591a\u304f\u306e\u65b9\u3068\u304a\u8a71\u3057\u3067\u304d\u305f\u306e\u3067\u826f\u3044\u4f53\u9a13\u304c\u3067\u304d\u307e\u3057\u305f\uff0e\u524d\u56de\u306e\u56fd\u969b\u5b66\u4f1a\u306b\u6bd4\u3079\u3066\uff0c\u30dd\u30b9\u30bf\u30fc\u767a\u8868\u3092\u7a4d\u6975\u7684\u306b\u8074\u304d\u8cea\u554f\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u305f\u70b9\u304c\u6210\u9577\u3067\u304d\u305f\u3068\u611f\u3058\u3066\u3044\u307e\u3059\uff0e\u3057\u304b\u3057\uff0c\u81ea\u5206\u306e\u7814\u7a76\u3068\u95a2\u4fc2\u3059\u308b\u5c11\u3057\u306e\u30ad\u30fc\u30ef\u30fc\u30c9\u306b\u7d5e\u3063\u3066\u3057\u304b\u30dd\u30b9\u30bf\u30fc\u767a\u8868\u3092\u8074\u3044\u3066\u3044\u306a\u304b\u3063\u305f\u306e\u3067\uff0c\u591a\u304f\u306e\u767a\u8868\u304c\u3042\u308b\u5b66\u4f1a\u3067\u3042\u3063\u305f\u305f\u3081\u3088\u308a\u5e83\u7bc4\u306a\u7814\u7a76\u306b\u95a2\u3057\u3066\u767a\u8868\u3092\u8074\u3051\u305f\u3089\u3088\u304b\u3063\u305f\u3068\u601d\u3044\u307e\u3057\u305f\uff0e OHBM\u306b\u53c2\u52a0\u3059\u308b\u3053\u3068\u3067\uff0c\u7814\u7a76\u5ba4\u306e\u4e2d\u3067\u306f\u77e5\u308b\u6a5f\u4f1a\u304c\u5c11\u306a\u3044\uff0c\u30d2\u30c8\u306e\u8133\u306b\u95a2\u3059\u308b\u7814\u7a76\u306e\u5168\u4f53\u7684\u306a\u52d5\u5411\u3092\u77e5\u308b\u3053\u3068\u304c\u3067\u304d\u305f\u306e\u304c\u3088\u304b\u3063\u305f\u3068\u601d\u3063\u3066\u3044\u307e\u3059\uff0e\u4eca\u56de\u5f97\u305f\u77e5\u8b58\u3084\u4e0d\u8db3\u3057\u3066\u3044\u308b\u3068\u611f\u3058\u3066\u3044\u308b\u90e8\u5206\u3092\u8e0f\u307e\u3048\uff0c\u66f4\u306b\u7814\u7a76\u306b\u52b1\u3093\u3067\u3044\u304d\u305f\u3044\u3068\u601d\u3044\u307e\u3059.
\n
\n![\"\"](\"http:\/\/www.is.doshisha.ac.jp\/news\/wp-content\/uploads\/2017\/07\/GOPR0159-300x225.jpg\")
<\/a>
\n\u3010\u6587\u8cac\uff1aM2 \u8429\u539f\u3011
\n\u5b66\u4f1a\u53c2\u52a0\u5831\u544a\u66f8<\/strong><\/p>\n\n\n
\n \u5831\u544a\u8005\u6c0f\u540d<\/strong><\/td>\n \u6c34\u91ce\u3081\u3050\u307f<\/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 Functional connectivity analysis of brain activity during cooperative behavior using fNIRS<\/td>\n<\/tr>\n \n \u8457\u8005<\/strong><\/td>\n \u6c34\u91ce\u3081\u3050\u307f,\u65e5\u548c\u609f\uff0c\u5ee3\u5b89\u77e5\u4e4b<\/td>\n<\/tr>\n \n \u4e3b\u50ac<\/strong><\/td>\n \u533b\u7642\u60c5\u5831\u30b7\u30b9\u30c6\u30e0\u7814\u7a76\u5ba4<\/td>\n<\/tr>\n \n \u8b1b\u6f14\u4f1a\u540d<\/strong><\/td>\n 23nd Annual Meeting of the Organization of Human Brain Mapping<\/td>\n<\/tr>\n \n \u4f1a\u5834<\/strong><\/td>\n Vancouver Convention Centre<\/strong><\/td>\n<\/tr>\n \n \u958b\u50ac\u65e5\u7a0b<\/strong><\/td>\n 2017\/06\/25-2017\/06\/29<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\n <\/p>\n- \n
- \u8b1b\u6f14\u4f1a\u306e\u8a73\u7d30<\/li>\n<\/ol>\n
2017\/06\/25\u304b\u30892017\/06\/29\u306b\u304b\u3051\u3066\uff0cVancouver Convention Centre<\/strong>\u306b\u3066\u958b\u50ac\u3055\u308c\u307e\u3057\u305f23nd 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\u4eba\u9593\u306e\u8133\u306e\u89e3\u5256\u5b66\u7684\u30fb\u6a5f\u80fd\u7684\u7d44\u7e54\u3001\u304a\u3088\u3073\u5065\u5eb7\u3084\u75c5\u6c17\u306b\u3064\u3044\u3066\u7406\u89e3\u3092\u9032\u3081\u308b\u305f\u3081\uff0c\u4eba\u9593\u306e\u8133\u7d44\u7e54\u306b\u95a2\u3059\u308b\u7814\u7a76\u306b\u643a\u308f\u3063\u3066\u3044\u308b\u69d8\u3005\u306a\u80cc\u666f\u306e\u7814\u7a76\u8005\u304c\u96c6\u3044\uff0c\u7814\u7a76\u8005\u9593\u306e\u30b3\u30df\u30e5\u30cb\u30b1\u30fc\u30b7\u30e7\u30f3\u3092\u4fc3\u9032\u3057\u3001\u4eba\u9593\u306e\u8133\u7d44\u7e54\u306b\u304a\u3051\u308b\u6559\u80b2\u3092\u4fc3\u9032\u3059\u308b\u3053\u3068\u3092\u76ee\u7684\u3068\u3057\u3066\u3044\u308b\u3002
\n25\u65e5-29\u65e5\u306e\u5168\u65e5\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\u77f3\u539f\u3055\u3093\uff0c\u548c\u7530\u3055\u3093\uff0c\u7389\u57ce\u3055\u3093\uff0c\u7247\u5c71\u3055\u3093\uff0c\u8429\u539f\u3055\u3093\uff0c\u5409\u6b66\u3055\u3093\uff0c\u6c60\u7530\uff0c\u85e4\u4e95\uff0c\u4e09\u597d\uff0c\u76f8\u672c\uff0c\u77f3\u7530\uff08\u7fd4\uff09\uff0c\u4e2d\u6751\uff08\u572d\uff09\u304c\u53c2\u52a0\u3057\u307e\u3057\u305f\uff0e<\/p>\n- \n
- \u7814\u7a76\u767a\u8868\n
- \n
- \u767a\u8868\u6982\u8981<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n
\u79c1\u306f28\u65e5\u306e\u5348\u5f8c\u306ePoster Secssion\u300cImaging Methods\uff1afNIRS\u300d\u306b\u3066\u767a\u8868\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u767a\u8868\u306e\u5f62\u5f0f\u306f\u30dd\u30b9\u30bf\u30fc\u767a\u8868\u3067\uff0c2\u6642\u9593\u81ea\u7531\u306b\u767a\u8868\u304a\u3088\u3073\u8cea\u7591\u5fdc\u7b54\u3092\u884c\u3046\u6642\u9593\u3068\u306a\u3063\u3066\u304a\u308a\u307e\u3057\u305f\uff0e
\n\u4eca\u56de\u306e\u767a\u8868\u306f\uff0c\u300cFunctional connectivity analysis of brain activity during cooperative behavior using fNIRS\u300d\u3067\u3059\uff0e\u4ee5\u4e0b\u306b\u6284\u9332\u3092\u8a18\u8f09\u81f4\u3057\u307e\u3059\uff0e<\/p>\n\n\n
\n \u3010Introduction\u3011
\nHumans typically belong to the organizations, such as schools or companies, and often live with others. Cooperation is essential for a group to work well together; understanding the intention of others and acting accordingly is also required. Important brain functions for social life can be clarified by examining the brain during cooperative behaviors. Cooperative behavior is related to imitating and synchronizing behavior among individuals [1]. In this study, we investigated brain functions that synchronize behaviors with a stimulus whose presentation interval changes proportionally over time.
\n\u3010Methods\u3011
\nTwenty healthy subjects (10 females, aged 21-23 years) participated in this experiment. The synchronized tapping task was used to investigate timing control functions [2]. Brain activity during tapping synchronized to a sound stimulus with a presentation interval that increases proportionally over time was measured using fNIRS. Brain functional connectivity using temporal correlations in cerebral blood flow changes was analyzed using a graph theoretical analysis. The network threshold was set to preserve an edge density of 15%, and the degree centrality, which is the total number of links in each region, was calculated as the network characteristic. Subjects were divided into two groups (A = 12 subjects, B = 8 subjects) by hierarchical clustering using the Ward method after 116-channel degree centralities of all subjects were decomposed into 11-dimensional values using a principal component analysis. The tendency of each group was examined from two points: the regions with high degree and the difference in response time to the stimulus.
\n\u3010Results\u3011
\nFig. 1 shows the brain regions associated with high degree centrality for each group. In group A, the degree centrality of the left middle frontal gyrus (LMFG) and the triangular part of inferior frontal gyrus (TrIFG) was high. The average response time in group A was slower than the stimulus presentation. In group B, the degree centrality was high in the left middle frontal gyrus (LMFG), left middle occipital gyrus (LMOG), left postcentral gyrus (LPoG), left supramarginal gyrus (LSMG), and right middle temporal gyrus (RMTG). The average response time in group B was faster than the stimulus presentation. The dorsolateral prefrontal cortex (DLPFC), including the middle frontal gyrus (MFG), is related to sustained attention [3]. Moreover, the middle temporal gyrus (RMTG) was reported to be involved in the information processing of motion [4]. Predicted action involves a top-down process [5]; therefore, the results for group A suggest that the left frontal region plays a central role in controlling the attention to the stimulus, which facilitates rapid responses for synchronization. In group B, this network includes not only the left frontal region but also the parietal and temporal regions. We speculate that these regions adjust timing by predicting the next stimulus in addition to attention control.
\n\u3010Conclusions\u3011
\nWe examined brain activity during cooperative behavior. Functional brain networks during a synchronized tapping task were investigated based on the temporal correlation of cerebral blood flow measured by fNIRS. These data suggest that the important brain regions of the functional network for cooperative behavior are the left middle frontal gyrus (LMFG), left middle occipital gyrus (LMOG), left postcentral gyrus (LPoG), left supramarginal gyrus (LSMG), and right middle temporal gyrus (RMTG).<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\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\u5bb91<\/strong>
\n\u660e\u6cbb\u5927\u5b66\u306e\u90fd\u5730\u3055\u3093\u304b\u3089\u306e\u8cea\u554f\u3067\u3059\uff0e \u306a\u305c\u30b0\u30e9\u30d5\u7406\u8ad6\u3092\u7528\u3044\u3066\u3044\u308b\u306e\u304b\u3002\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3001\u8a08\u6e2cCH\u304c116CH\u3042\u308a\u3001\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u304c\u3068\u3066\u3082\u8907\u96d1\u3067\u3042\u308b\u305f\u3081\u3001\u305d\u306e\u8907\u96d1\u306a\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u69cb\u9020\u3092\u7406\u89e3\u3059\u308b\u305f\u3081\u306b\u3001\u30b0\u30e9\u30d5\u7406\u8ad6\u3092\u7528\u3044\u308b\u3053\u3068\u304c\u5fc5\u8981\u3067\u3042\u308b\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\u3002
\n
\n\u30fb\u8cea\u554f\u5185\u5bb92<\/strong>
\n\u8cea\u554f\u8005\u306e\u6c0f\u540d\u3092\u63a7\u3048\u640d\u306d\u3066\u3057\u307e\u3044\u307e\u3057\u305f\uff0e
\nBetweenness centrality \u306a\u3069\u3044\u308d\u3044\u308d\u4e2d\u5fc3\u6027\u306e\u6307\u6a19\u304c\u3042\u308b\u4e2d\u3067\u306a\u305cDegree centrality\u3092\u691c\u8a0e\u3057\u305f\u306e\u304b\u3002\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\u3001\u591a\u304f\u306e\u90e8\u4f4d\u3068\u5354\u8abf\u3057\u3066\u3044\u308b\u90e8\u4f4d\u540c\u58eb\u3092\u7e4b\u3050\u4e2d\u5fc3\u7684\u306a\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\uff08\u4e0a\u5c64\uff09\u3092\u691c\u8a0e\u3059\u308b\u306e\u3067\u306f\u306a\u304f\u3001\u591a\u304f\u306e\u90e8\u4f4d\u3068\u5354\u8abf\u3057\u3066\u3044\u308b\u90e8\u4f4d\u306e\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\uff08\u4e0b\u5c64\uff09\u3092\u898b\u308b\u3053\u3068\u304c\u91cd\u8981\u3067\u3042\u308b\u3068\u8003\u3048\u3066\u3044\u308b\u304b\u3089\u3067\u3059\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\u3002\u3053\u306e\u5b66\u4f1a\u3092\u901a\u3057\u3066\u3001\u4eca\u5f8c\u306frichclub\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u968e\u5c64\u69cb\u9020\u3092\u691c\u8a0e\u3059\u308b\u5fc5\u8981\u304c\u3042\u308b\u3053\u3068\u3092\u5b66\u3073\u307e\u3057\u305f\u3002
\n
\n\u30fb\u8cea\u554f\u5185\u5bb93<\/strong>
\n\u8cea\u554f\u8005\u306e\u6c0f\u540d\u3092\u63a7\u3048\u640d\u306d\u3066\u3057\u307e\u3044\u307e\u3057\u305f\uff0e
\n\u30d0\u30f3\u30c9\u30d1\u30b9\u30d5\u30a3\u30eb\u30bf\u306e\u7bc4\u56f2\u306b\u95a2\u3057\u3066\u30010.33Hz\u306f\u691c\u8a0e\u3057\u76f4\u3059\u5fc5\u8981\u304c\u3042\u308b\u3068\u3054\u6307\u6458\u9802\u304d\u307e\u3057\u305f\u3002\u6587\u732e\u8abf\u67fb\u3092\u3057\u3066\u3044\u308b\u4e2d\u3067\u30820.1Hz\u306e\u3082\u306e\u304c\u591a\u304f\u691c\u8a0e\u3057\u76f4\u3059\u5fc5\u8981\u304c\u3042\u308b\u3068\u8003\u3048\u3066\u3044\u307e\u3059\u3002
\n\u00a0<\/strong>
\n\u30fb\u8cea\u554f\u5185\u5bb94<\/strong>
\n\u8cea\u554f\u8005\u306e\u6c0f\u540d\u3092\u63a7\u3048\u640d\u306d\u3066\u3057\u307e\u3044\u307e\u3057\u305f\uff0e
\n\u884c\u52d5\u30c7\u30fc\u30bf\u306e\u7d50\u679c\u306b\u3064\u3044\u3066\u3001\u30af\u30e9\u30b9\u30bf\u5185\u306e\u88ab\u9a13\u8005\u306f\u5fdc\u7b54\u306e\u30d7\u30e9\u30b9\u30de\u30a4\u30ca\u30b9\u306e\u50be\u5411\u304c\u5171\u901a\u3057\u3066\u3044\u308b\u306e\u304b\u3002\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\u3001\u5e73\u5747\u306e\u5fdc\u7b54\u306a\u306e\u3067\u30af\u30e9\u30b9\u30bf\u5185\u3067\u3082\u30de\u30a4\u30ca\u30b9\u306e\u5834\u5408\u3068\u30d7\u30e9\u30b9\u306e\u5834\u5408\u304c\u3042\u308a\u307e\u3059\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\u3002\u4eca\u56de\u306e\u767a\u8868\u3067\u306f\u884c\u52d5\u30c7\u30fc\u30bf\u306b\u5bfe\u3057\u3066\u8cea\u554f\u3055\u308c\u308b\u3053\u3068\u304c\u4f55\u5ea6\u304b\u3042\u308a\u307e\u3057\u305f\u3002
\n <\/p>\n- \n
- \u611f\u60f3<\/li>\n<\/ul>\n
\u521d\u3081\u3066\u306e\u56fd\u969b\u5b66\u4f1a\u53c2\u52a0\u3067\u3057\u305f\u3002\u56fd\u5185\u5b66\u4f1a\u3068\u306f\u9055\u3044\u82f1\u8a9e\u529b\u304c\u4e4f\u3057\u304f\u3001\u7814\u7a76\u306e\u8b70\u8ad6\u3092\u6df1\u3081\u308b\u3053\u3068\u306f\u3067\u304d\u307e\u305b\u3093\u3067\u3057\u305f\u3002\u3057\u304b\u3057\u3001\u3053\u308c\u307e\u3067\u82f1\u8a9e\u3067\u5bfe\u8a71\u3059\u308b\u3068\u3044\u3046\u7d4c\u9a13\u304c\u306a\u304b\u3063\u305f\u306e\u3067\u3001\u3088\u3044\u7d4c\u9a13\u306b\u306a\u308a\u307e\u3057\u305f\u3002\u6b21\u56de\u3001\u56fd\u969b\u5b66\u4f1a\u306b\u53c2\u52a0\u3059\u308b\u3068\u304d\u306b\u306f\u3001\u3082\u3063\u3068\u82f1\u8a9e\u3067\u610f\u898b\u4ea4\u63db\u304c\u3067\u304d\u308b\u3088\u3046\u306b\u3001\u6e96\u5099\u3092\u3057\u3063\u304b\u308a\u3057\u3001\u82f1\u8a9e\u529b\u3092\u3064\u3051\u3066\u5bfe\u8a71\u304c\u3067\u304d\u308b\u3088\u3046\u306b\u601d\u3044\u307e\u3057\u305f\u3002<\/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\u3002<\/p>\n
\n\n
\n \u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Differential contributions of transient and sustained channels across the visual hierarchy
\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Catie Chang, NIH, Bethesda, MD,
\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Perception and Attention
\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a
\n\u3010Introduction\u3011
\nPrevailing psychophysical models propose that the human visual system contains separate temporal channels for processing transient and sustained visual stimuli (Kulikowski & Tolhurst, 1973; McKee & Taylor, 1984; Watson, 1985). While neural responses in primary visual cortex (V1) are consistent with a two temporal channel model (Horiguchi et al., 2009), the relative contribution of the two channels and their functional significance at later stages of the visual hierarchy is unknown.
\n\u3010Methods\u3011
\nTo address this gap in knowledge, we scanned 12 subjects in a 3T scanner using a novel fMRI paradigm that estimates independent transient and sustained contributions to blood oxygen level dependent (BOLD) responses across visual cortex using three experiments. All experiments used the same phase-scrambled stimuli, trial durations, and fixation task, and only varied in the temporal presentation of stimuli. Experiment 1 was designed to strongly activate the sustained channel by presenting a single static image continuously for trials lasting 2, 4, 8, 15 or 30 s. Experiment 2 was designed to strongly activate the transient channel by presenting in each 2, 4, 8, 15 or 30 s trial 30 different images, each shown for 33 ms and followed by a blank screen lasting 33-967 ms. Experiment 3 was designed to drive responses in both sustained and transient channels by presenting in each 2, 4, 8, 15 or 30 s trial 30 images for 67-1000 ms in a continuous fashion without intervening blanks. To model BOLD responses we implemented a neural model with two temporal channels: (1) a sustained channel of ongoing neural responses over the duration of a stimulus, and (2) a transient channel that generates a brief neural response at the onset and offset of an image. Then, we convolved the neural response of each channel with a HRF to predict BOLD responses. We estimated the contributions (\u03b2 weights) of the sustained and transient channels using data from Experiments 1 and 2 and cross-validated the model by quantifying how well it predicts responses for independent data from Experiment 3.
\n\u3010Results\u3011
\nThe standard model of BOLD responses depends only on stimulus duration, consequently predicting higher responses in Experiment 1 than in Experiment 2, and similar responses in trials of the same duration across Experiments 1 and 3. Different from these predictions: (1) in short trials (<8 s) responses in Experiment 2 were higher than Experiment 1 and (2) across all trial durations responses in Experiment 3 were higher than Experiment 1. These data suggest that transient responses that are not considered by the standard model contribute to BOLD responses. Indeed, a neural model with two temporal channels (sustained and transient) predicts BOLD responses significantly better than the standard model (Figure 1a, main effect of model, F(2, 22) = 56.49, p < 0.001). This improvement is larger at later stages of the visual hierarchy than V1 (Figure 1b, model-by-area interaction, F(2, 22) = 6.45, p < 0.01). Additionally, we find differential contributions of the sustained and transient neural channels across the visual hierarchy. Early visual areas (V1, V2, V3) have significant contributions from both channels, ventral regions (hV4, VO1, VO2) have twice as much contribution from the transient than sustained channel, and lateral regions (LO1, LO2, hMT+) are driven primarily by the transient channel with minimal sustained contributions (Figure 1c).
\n\u3010Conclusions\u3011
\nThese results demonstrate that the two temporal channel model accurately predicts responses to stimuli with a broad range of temporal characteristics and furthermore reveals functional differences across stages of the visual hierarchy. Critically, these data suggest that any model of BOLD responses in any cortical system needs to consider the temporal dynamics of the experimental paradigm to accurately predict brain responses.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\nMISL\u3067\u3082\u3001\u523a\u6fc0\u306b\u5bfe\u3057\u3066\u30e2\u30c7\u30eb\u3092\u4f5c\u3063\u3066\u3044\u308b\u306e\u3067\u3001\u7740\u76ee\u3057\u305f\u3002\u6301\u7d9a\u7684\u306a\u795e\u7d4c\u5fdc\u7b54\u3068\u4e00\u6642\u7684\u306a\u795e\u7d4c\u5fdc\u7b54\u306e\uff12\u3064\u306e\u523a\u6fc0\u3092\u8003\u616e\u3057\u3066\u30e2\u30c7\u30eb\u304c\u8003\u3048\u3089\u308c\u3066\u304a\u308a\u3001\u8208\u5473\u6df1\u3044\u5185\u5bb9\u3060\u3063\u305f\u3002
\n <\/p>\n\n\n
\n \u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Meta-analysis of heterogeneous EEG studies using hierarchical event descriptor (HED) tags
\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Nima Bigdely Shamlo, Alejandro Ojeda1, Tim Mullen, Kay Robbins
\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Poster Session
\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a
\n\u3010Introduction\u3011
\nAnalyzing EEG trials across studies with different experimental paradigms requires a formally-defined common conceptual space. Hierarchical event descriptor (HED) tags (Bigdely-Shamlo, Cockfield, et al., 2016) provides such a common space by providing an extensible controlled vocabulary to describe study trial (event) and state details. Here we present results of the first EEG (meta)analysis on over half a million trials from studies with different paradigms and investigate the statistical validity of using HED tags across these studies.
\n\u3010Methods\u3011
\nWe used data from six studies in ESS Level 2 (Bigdely-Shamlo, Makeig, & Robbins, 2016) containers. The containers included HED strings associated with study events along with other information needed for automated analysis of the data such as channel labels, montage, and tasks assigned to each data recording. Data in ESS Level 2 containers is also preprocessed using PREP pipeline (Bigdely-Shamlo, Mullen, Kothe, Su, & Robbins, 2015). EEG data was then band-passed 0.5-20 Hz. Single-trial EEG activity epochs from all scalp channels were then extracted for all events in each session of each study (total of 634,359 epochs) along with HED strings associated with each trial.
\nWe then computed two types of similarity matrices to be used in RSA analysis. (a) between single trial pairs of each two event types. (b) for each predefined HED tag group, between the HED strings associated with each two event types. For (a), data from all channels in each trial epoch was vectorized and the average Pearson correlation of all pairs of events across the two event types was computed (see Fig. 1, Left). The values in the resulting event similarity matrix g(Ei, Ej) were then normalized as described in the figure.
\nTo form HED-based event similarity matrices (b), we first formed tag groups for the validation analysis by creating a list of all HED tags from all 118 event types. For each tag, all parent levels were also included in the list, e.g. for the tag Sensory presentation\/Visual\/Rendering type\/Screen\/2d, the parent tags (1) Sensory presentation\/Visual\/Rendering type\/Screen (2) Sensory presentation\/Visual were also included. Tags that contained only a single child were removed from the list. This was to prevent unfounded generalization, i.e. if only instances of Sensory Item\/Symbolic\/Character\/Letter are present in our data (but not any instances of the parent tags), there is not enough data supporting the analysis of the more general parent tags, e.g. Item\/Symbolic\/. Analysis tag groups were then formed by applying connected-component labeling (Dillencourt, Samet, & Tamminen, 1992) to thresholded (>0.9) Pearson correlation of tag matches and event types.
\nFinally, we performed Representational Similarity Analysis (RSA) (Kriegeskorte, Mur, & Bandettini, 2008) between EEG-based event similarity matrix (a) and HED-based event similarity matrices, each associated with an analysis tag group.
\n\u3010Results\u3011
\nTable 1 shows the RSA results. Tag groups that are found to be associated with statistically significant similarities (after correcting for multiple comparisons with FDR (Benjamini & Hochberg, 1995), p<0.05) across EEG dynamics in our data are marked with *.
\n\u3010Conclusions\u3011
\nOur results show that at least for a subset of HED tags, events tagged similarly across studies are more similar to each other. This is a necessary condition for HED tags to be useful in EEG meta-analysis and contributes to the validation of HED tagging. More detailed validation of HED hierarchy (or any other ontology for EEG events) can be conducted by scaling up our analysis method on a larger number of diverse, tagged EEG studies.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\nEEG\u30c7\u30fc\u30bf\u3092\u7528\u3044\u305f\u30e1\u30bf\u30a2\u30ca\u30ea\u30b7\u30b9\u89e3\u6790\u306b\u3088\u308b\u7814\u7a76\u3002\u6708\u4f8b\u767a\u8868\u4f1a\u306b\u3066\u3001\u30e1\u30bf\u30a2\u30ca\u30ea\u30b7\u30b9\u3068\u3044\u3046\u3082\u306e\u3092\u521d\u3081\u3066\u77e5\u3063\u305f\u304c\u4e16\u754c\u3067\u3082\u6ce8\u76ee\u3055\u308c\u3066\u3044\u308b\u3053\u3068\u3092\u5b9f\u611f\u3057\u305f\u3002
\n
\n <\/p>\n\n\n
\n \u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Neural Responses to Dynamic Pain Expression of Same-Race and Other-Race Faces
\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Wenxin Li, Shihui Han
\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Poster session
\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a
\n\u3010Introduction\u3011
\nPrevious neuroimaging studies of empathy employed faces with static pain and neutral expressions and have shown stronger frontal activity as early as 120 ms after sensory stimulation to pain (vs. neutral) expression of same-race than other-race faces (Sheng and Han, 2012). It remains unknown how the brain responds to dynamic pain expression of same-race and other-race faces. Here we tested the hypothesis that brain activity is more sensitive to dynamic changes of facial expression from neutral to pain of same-race than other-race faces by recording event-related brain potentials (ERPs) from Chinese female adults while they performed judgments of Asian and Caucasian faces.
\n\u3010Methods\u3011
\nWe recruited 32 Chinese female adults (mean = 21.3 years, SD = 2.5 years). Participants performed a pain judgment task on two sets of face stimuli that were created by morphing a neutral face (0%) and a painful face (100%) of the same model (16 Asian and 16 Caucasian models were used) with a step of 10%. Each face was presented for 80 ms and followed by a fixation cross with a duration varying randomly between 600ms and 1200ms. EEG was recorded during 10 blocks of 352 trials (each face was presented once in a random order). For each participant, two Asian models (1 male) and two Caucasian models (1 male) were assigned as a target by adding a white frame for pain judgments (painful vs. neutral). Participants responded to each target by pressing one of two keys. Behavioral responses were fit by a Weibull function, p=1-e^(-(x\/\u03b1)^\u03b2 ), where p is the percentages of perceived pain expression, x is the percentage of pain pixels in the morphed faces, \u03b1 is the point of subjective equality (PSE), and \u03b2 is the slope of the sigmoidal response function. EEG data analysis focused on the P2 amplitude at 140-190 ms to non-target stimuli over frontocentral electrodes. ANOVAs were conducted on P2 amplitudes with Race (Asian vs. Caucasian) and Percentage of pain pixels (0% to 100%) as within-subjects variables.
\n\u3010Results\u3011
\nThe analysis of behavioral data did not show significant difference in the PSE between same-race and other-race faces. However, ANOVAs of the P2 amplitudes showed significant main effects of race (Fz: F(1,31)=99.29,p< .001; FCz: F(1,31)=113.49,p< .001; Cz: F(1,31)=124.93,p< .001) and percentage of pain-pixel (Fz: F(10,310)=9.58,p< .001; FCz: F(10,310)=10.40,p<.001; Cz: F(10,310)=11.45,p<.001). The P2 amplitudes were greater to other-race than same-race faces and increased as a function of the percentage of pain-pixels in photos. To estimate the sensitivity of brain activity to dynamic pain expression of same-race and other-race faces, we constructed a classical linear regression model (P2=ax+b) of the relationship between the P2 amplitudes and the percentage of pain-pixel for each participant. A paired t-test revealed a significantly greater slope of the linear function (i.e., a) for same-race than other-race faces (t(33)=2.22, p=0.034), further supporting that the P2 amplitudes were more sensitive to variation of painful information in same-race compared with other-race faces.
\n\u3010Conclusions\u3011
\nEven the behavioral data failed to uncover any difference in subjective sensitivity to variation of painful information in same-race and other-race faces, our ERP results provide evidence that the brain activity in an early time window is more sensitive to dynamic changes of facial expression from neutral to pain of same-race than other-race faces.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\u5171\u611f\u306e\u7814\u7a76\u3002\u753b\u50cf\u5448\u793a\u5b9f\u9a13\u306f\u3088\u304f\u3042\u308b\u304c\u3001\u5448\u793a\u753b\u50cf\u304c\u52d5\u7684\u3067\u3042\u308b\u3068\u3053\u308d\u304c\u9762\u767d\u3044\u3068\u611f\u3058\u305f\u3002\u52d5\u753b\u3067\u306f\u5b9a\u91cf\u7684\u306b\u305d\u306e\u3068\u304d\u306e\u523a\u6fc0\u72b6\u614b\u3092\u5b9a\u7fa9\u3059\u308b\u3053\u3068\u306f\u3067\u304d\u306a\u3044\u304c\u3001\u5448\u793a\u753b\u50cf\u3092\u52d5\u7684\u306b\u3059\u308b\u3053\u3068\u3067\u3001\u8a55\u4fa1\u3082\u3057\u3084\u3059\u304f\u304b\u3064\u52d5\u7684\u306a\u300c\u8868\u60c5\u300d\u306b\u5bfe\u3059\u308b\u795e\u7d4c\u5fdc\u7b54\u3092\u898b\u308b\u3053\u3068\u304c\u3067\u304d\u308b\u3002
\n <\/p>\n\n\n
\n \u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Neural underpinnings of mutual gaze and joint attention using hyperscanning functional MRI
\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Hiroki\u3000Tanabe
\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Brain-to-brain synchrony early in life: What can we learn from different hyperscanning techniques?
\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Mutual gaze provides a communicative link between humans, prompting joint attention, which is the ability to coordinate attention between interactive social partners with respect to objects or events to share an awareness of them. Joint attention is of particular importance during early social development representing the prerequisite of theory-of mind and social communication. To elucidate their neural underpinnings, we conducted several experiments employing hyperscanning functional MRI combined with online video cameras and voice exchange system. I will show the results of these studies and discuss core neural mechanisms of mutual gaze and joint attention.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\nMRI\u30922\u53f0\u4f7f\u7528\u3057\u3001\u30ab\u30e1\u30e9\u8d8a\u3057\u306b\u76f8\u624b\u304c\u898b\u3048\u308b\u3088\u3046\u306b\u74b0\u5883\u304c\u69cb\u7bc9\u3055\u308c\u3066\u304a\u308a\u3001\u5b9f\u9a13\u8a2d\u5099\u304c\u3068\u3066\u3082\u6574\u3063\u3066\u3044\u305f\u3002\u307e\u305f\u3001\u5b9f\u9a13\u8ab2\u984c\u306f3\u3064\u306e\u30b3\u30f3\u30c7\u30a3\u30b7\u30e7\u30f3\u304c\u3042\u308a\u3001\u5b9f\u9a13\u81ea\u4f53\u306e\u69cb\u6210\u3082\u8907\u96d1\u3060\u3063\u305f\u3002\u4eca\u5f8c\u5b9f\u9a13\u8a2d\u8a08\u3092\u8003\u3048\u308b\u53c2\u8003\u306b\u3057\u3066\u3044\u3053\u3046\u3068\u601d\u3046\u3002Hyperscannig\u306e\u7814\u7a76\u306e\u8b1b\u6f14\u3092\u805e\u304f\u3053\u3068\u304c\u3067\u304d\u3066\u3001\u4eca\u5f8c\u306e\u7814\u7a76\u306e\u30e2\u30c1\u30d9\u30fc\u30b7\u30e7\u30f3\u304c\u4e0a\u304c\u3063\u305f\u3002\u8074\u8b1b\u3060\u3051\u3067\u306f\u7406\u89e3\u3067\u304d\u306a\u304b\u3063\u305f\u3053\u3068\u3082\u591a\u304b\u3063\u305f\u304c\u3001\u6587\u732e\u3092\u8abf\u67fb\u3057\u7814\u7a76\u306b\u6d3b\u304b\u3057\u3066\u3044\u304d\u305f\u3044\u3002
\n <\/p>\n\n\n
\n \u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Exploring the neural evidence of mother-infant entrainment: Inter-brain synchronized hemodynamic activity
\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Yasuyo Minagawa
\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Brain-to-brain synchrony early in life: What can we learn from different hyperscanning techniques?
\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a In the present study, we performed functional Near-Infrared Spectroscopy (fNIRS) based hyperscanning to examine synchronized brain activity in mothers and 3- to 4-month-old infants. Cerebral hemodynamic changes in mother-infant dyads were measured under three conditions: i.e. (1) breast feeding, (2) resting state during mother holding her infant and (3) resting state during separation (control). The results showed inter-brain synchronized hemodynamic activity in the prefrontal cortex which was significantly larger in the holding condition than in the control condition. This may reflect a neural correlate of mother-infant bonding.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\u8d64\u3061\u3083\u3093\u306e\u8a08\u6e2c\u3092\u5b9f\u969b\u306b\u884c\u3063\u3066\u3044\u308b\u7814\u7a76\u306e\u8a71\u3092\u805e\u304f\u3053\u3068\u304c\u3067\u304d\u3066\u9762\u767d\u304b\u3063\u305f\u3002\u5354\u8abf\u6027\u3092\u8003\u3048\u308b\u4e0a\u3067\u5354\u8abf\u6027\u3092\u7372\u5f97\u3059\u308b\u767a\u9054\u904e\u7a0b\u306e\u8d64\u3061\u3083\u3093\u306e\u7814\u7a76\u306f\u95a2\u4fc2\u304c\u6df1\u3044\u3068\u601d\u3046\u3002\u8d64\u3061\u3083\u3093\u3068\u5927\u4eba\u306fHRF\u304c\u7570\u306a\u308b\u3053\u3068\u306a\u3069\u8208\u5473\u6df1\u3044\u5185\u5bb9\u3060\u3063\u305f\u3002\u3057\u304b\u3057\u3001\u8d64\u3061\u3083\u3093\u3092\u8a08\u6e2c\u3059\u308b\u306e\u306f\u672c\u5f53\u306b\u554f\u984c\u304c\u306a\u3044\u306e\u304b\u3068\u5c11\u3057\u7591\u554f\u306b\u601d\u3063\u3066\u3057\u307e\u3046\u3002
\n
\n\u53c2\u8003\u6587\u732e<\/p>\n- \n
- 2017 ANNUAL MEETING, https:\/\/www.humanbrainmapping.org\/i4a\/pages\/index.cfm?pageID=3744<\/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\u8429\u539f\u91cc\u5948<\/td>\n<\/tr>\n\n \u767a\u8868\u8ad6\u6587\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n \u6697\u7b97\u8ab2\u984c\u4e2d\u306b\u5f97\u3089\u308c\u305f\u6a5f\u80fd\u30c7\u30fc\u30bf\u3092\u7528\u3044\u305f\u8133\u72b6\u614b\u306e\u5206\u985e<\/td>\n<\/tr>\n \n \u767a\u8868\u8ad6\u6587\u82f1\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n Classification of brain states using functional data obtained during a mental arithmetic task<\/td>\n<\/tr>\n \n \u8457\u8005<\/strong><\/td>\n \u8429\u539f\u91cc\u5948\uff0c\u65e5\u548c\u609f\uff0c\u5ee3\u5b89\u77e5\u4e4b<\/td>\n<\/tr>\n \n \u4e3b\u50ac<\/strong><\/td>\n Organization for Human Brain Mapping<\/td>\n<\/tr>\n \n \u8b1b\u6f14\u4f1a\u540d<\/strong><\/td>\n The 23rd Annual Meeting of the Organization for Human Brain Mapping<\/td>\n<\/tr>\n \n \u4f1a\u5834<\/strong><\/td>\n Vancouver Convention Centre<\/td>\n<\/tr>\n \n \u958b\u50ac\u65e5\u7a0b<\/strong><\/td>\n 2017\/6\/25-2017\/6\/29<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\n <\/p>\n- \n
- \u8b1b\u6f14\u4f1a\u306e\u8a73\u7d30<\/li>\n<\/ol>\n
2017\/6\/25-29\u306b\uff0cVancouver Convention Centre\u306b\u3066\u958b\u50ac\u3055\u308c\u307e\u3057\u305fThe 23rd Annual Meeting of the Organization for Human Brain Mapping\u306b\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u3053\u306e\u5927\u4f1a\u306f\uff0c\u30d2\u30c8\u306e\u8133\u30de\u30c3\u30d4\u30f3\u30b0\u306b\u95a2\u5fc3\u306e\u3042\u308b\u7814\u7a76\u8005\u30fb\u533b\u5e2b\u30fb\u5b66\u751f\u304c\u53c2\u52a0\u3057\u307e\u3059\uff0e\u795e\u7d4c\u30a4\u30e1\u30fc\u30b8\u30f3\u30b0\u306e\u77e5\u8b58\uff0c\u7d4c\u9a13\u3092\u5206\u304b\u3061\u5408\u3044\uff0c\u6700\u65b0\u306e\u7814\u7a76\u3068\u4eca\u5f8c\u306e\u5c55\u671b\u306b\u3064\u3044\u3066\u306e\u60c5\u5831\u4ea4\u63db\uff0c\u8b70\u8ad6\u306e\u5834\u3068\u306a\u308b\u3053\u3068\u3092\u76ee\u7684\u306b\u958b\u50ac\u3055\u308c\u3066\u3044\u307e\u3059\uff0e
\n\u672c\u7814\u7a76\u5ba4\u304b\u3089\u306f\u4ed6\u306b\u5ee3\u5b89\u5148\u751f\uff0c\u65e5\u548c\u5148\u751f\uff0cM2\u306e\u7247\u5c71\u3055\u3093\uff0c\u5409\u6b66\u3055\u3093\uff0c\u77f3\u539f\u3055\u3093\uff0c\u548c\u7530\u3055\u3093\uff0c\u7389\u57ce\u3055\u3093\uff0cM1\u306e\u6c34\u91ce\u3055\u3093\uff0c\u85e4\u4e95\u3055\u3093\uff0c\u6c60\u7530\u3055\u3093\uff0c\u77f3\u7530\u7fd4\u4e5f\u3055\u3093\uff0c\u4e09\u597d\u3055\u3093\uff0c\u76f8\u672c\u3055\u3093\uff0c\u4e2d\u6751\u572d\u4f51\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\u306f29\u65e5\u306e\u30dd\u30b9\u30bf\u30fc\u30bb\u30c3\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\uff0c2\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\uff0c\u300cClassification of brain states using functional data obtained during a mental arithmetic task\u300d\u3068\u984c\u3057\u3066\u767a\u8868\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u4ee5\u4e0b\u306b\u6284\u9332\u3092\u8a18\u8f09\u3057\u307e\u3059\uff0e<\/p>\n\n\n
\n \u3010Introduction\u3011
\nWorking memory (WM), which is a temporary storage system that processes information, is necessary for daily life [1,2]. A previous study on the functional connectivity (FC) revealed that WM is a complex system and each brain region cooperates by exchanging information [3]. However, there are a paucity of studies on individual brain states due to differences in the strategies for WM tasks. Therefore, it is necessary to assess brain states due to difference in performance of WM. In this study, we compared brain states that were classified using FC data.\u3010Materials & Methods\u3011
\nThirty-two healthy adults (average age: 22.0 \u00b1 1.2) while performing a mental arithmetic task was scanned using fMRI. The task consisted of mental arithmetic with a low WM load (Low-WM task) or a high WM load (High-WM task). Preprocessing was applied to acquired fMRI images using SPM8. The whole brain was partitioned into 116 regions of interest (ROI) based on an automated anatomical labeling atlas, and a correlation matrix was created by a ROI-to-ROI analysis using the Conn toolbox [4]. This matrix was adjusted to a connection density of 15% by thresholding. The Jansen-Shannon divergence, which indicates the similarity between subjects\u2019 matrix, was calculated between each matrix, and a hierarchical clustering analysis was performed [5,6]. The activated regions in each classified group were compared using t-tests. Moreover, each matrix was binarized, and the degree (Deg) and clustering coefficient (CC) were calculated using the Brain Connectivity Toolbox [7]. The Deg is the number of connections with other regions, and the CC indicates the ratio of neighboring regions to each other.
\n\u3010Results\u3011Subjects were classified into two groups (Fig. 1), and the average correct answer rate of the High-WM task was 56.0 \u00b1 1.23 for Group A (n = 13) and 50.2 \u00b1 19.8 for Group B (n = 19). The superior parietal lobule (SPL) in Group A and the precuneus, lingual gyrus, calcarine sulcus, precentral gyrus, middle occipital lobe, and middle frontal gyrus in Group B were extracted as regions whose activations were significantly higher for High-WM task when compared with the Low-WM task. These regions were responsible for WM functions in previous studies [3,8]. Group B appeared to require many active regions, whereas Group A needed fewer activated regions to complete the task. Moreover, the Deg of the superior temporal gyrus (STG) and cerebellar vermis as well as the CC of the cerebellum (CRBL) and cerebellar vermis were significantly higher for the High-WM task than the Low-WM task (p < .05). The CRBL supports the execution of WM by internal utterance, and the STG is involved in auditory short-term memory [8,9]. Thus, our result suggests that internal speech is repeated during the WM task, and that these regions act as hubs. On the other hand, the Deg of the cuneus, superior occipital lobe, SPL, CRBL, and cerebellar vermis as well as the CC of the middle occipital lobe, fusiform gyrus, precuneus, and CRBL in Group B were significantly higher during the High-WM task when compared with the Low-WM task (p < .05). The SPL is involved in updating information [10]. Thus, it appears that Group B strengthened the cooperation of the SPL with other regions to update information. Our connectivity analysis showed that Group A, which had a high average score, had few regions with a strong connection to other regions, whereas Group B, which had a low average performance, increased the number of regions with strengthened connectivity.
\n\u3010Conclusion\u3011Trends among the groups classified from the FC data during a WM task were examined in this study. Subjects underwent fMRI while performing mental arithmetic tasks with different degrees of difficulty. The participants were classified into two groups. One group had a low score with activity in multiple regions, whereas the other group had a high score with limited activated areas. Therefore, FC data can be used to identify distinct brain states during WM tasks.
\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\u3053\u3061\u3089\u306e\u8cea\u554f\u306f\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u304c\u4e0a\u624b\u304f\u3067\u304d\uff0c\u3053\u306e\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u65b9\u6cd5\u304c\u6709\u7528\u3067\u3042\u308b\u3053\u3068\u3092\u793a\u3059accuracy\u306a\u3069\u306e\u7d50\u679c\u306f\u3042\u308b\u306e\u304b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u4eca\u56de\u306f\u305d\u306e\u3088\u3046\u306a\u89e3\u6790\u306f\u884c\u3048\u3066\u304a\u3089\u305a\uff0c\u4eca\u5f8c\u306e\u8ab2\u984c\u3067\u3042\u308b\u3068\u56de\u7b54\u81f4\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\u306fJS\u30c0\u30a4\u30d0\u30fc\u30b8\u30a7\u30f3\u30b9\u3068\u306f\u4f55\u304b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u3061\u3089\u306e\u8cea\u554f\u306b\u5bfe\u3059\u308b\u56de\u7b54\u3067\u3059\u304c\uff0c\u76f8\u95a2\u884c\u5217\u9593\u306e\u985e\u4f3c\u5ea6\u3092\u5206\u5e03\u3068\u3057\u3066\u6349\u3048\u3066\u3044\u308b\u3068\u56de\u7b54\u81f4\u3057\u307e\u3057\u305f\uff0e
\n
\n\u30fb\u8cea\u554f\u5185\u5bb9<\/strong>3<\/strong>
\n\u3000<\/strong>\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\u8133\u9818\u57df\u306edifficult\u30bf\u30b9\u30af\u3068easy\u30bf\u30b9\u30af\u306edegree\u306e\u9055\u3044\u306e\u691c\u8a0e\u306b\u95a2\u3057\u3066\uff0c\u7d71\u8a08\u7684\u306a\u89e3\u6790\u3092\u884c\u3063\u3066\u3044\u308b\u306e\u304b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\uff0c\u5404\u7fa4\u306eN\u6570\u304c\u5c11\u306a\u3044\u305f\u3081\u7d71\u8a08\u7684\u306a\u89e3\u6790\u306f\u884c\u3063\u3066\u304a\u3089\u305a\uff0cdifficult\u30bf\u30b9\u30af\u3068easy\u30bf\u30b9\u30af\u306edegree\u5024\u306e\u5dee\u304c\u5927\u304d\u3044\u3082\u306e\u3092\u62bd\u51fa\u3057\u305f\u3068\u56de\u7b54\u81f4\u3057\u307e\u3057\u305f\uff0e
\n
\n\u30fb\u8cea\u554f\u5185\u5bb9<\/strong>4<\/strong>
\n\u3000<\/strong>\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\u56f3\u306b\u793a\u3055\u308c\u308b\u8133\u306e\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306f\u3069\u306e\u3088\u3046\u306b\u62bd\u51fa\u3057\u305f\u306e\u304b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\uff0c\u5404\u88ab\u9a13\u8005\u306e\u76f8\u95a2\u884c\u5217\u3092\u30a8\u30c3\u30b8\u5bc6\u5ea615%\u3067\u95be\u5024\u51e6\u7406\u3057\uff0c\u5404\u7fa4\u306b\u304a\u3044\u3066\u62bd\u51fa\u3055\u308c\u305fdegree\u306e\u30bf\u30b9\u30af\u9593\u306e\u5dee\u304c\u5927\u304d\u3044\u8133\u9818\u57df\u3068\u306e\u7d50\u5408\u3092\u88ab\u9a13\u8005\u3054\u3068\u306b\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3068\u3057\u3066\u62bd\u51fa\u3057\u305f\u3068\u56de\u7b54\u81f4\u3057\u307e\u3057\u305f\uff0e
\n
\n\u30fb\u8cea\u554f\u5185\u5bb9<\/strong>5<\/strong>
\n\u3000<\/strong>\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\u3053\u306e\u3088\u3046\u306b\u88ab\u9a13\u8005\u7fa4\u3092\u5206\u985e\u3057\u305f\u306e\u304b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u968e\u5c64\u7684\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u306e\u7d50\u679c\u304b\u3089\uff0c\u305d\u308c\u305e\u308c\u306e\u4e3b\u306a\u679d\u3054\u3068\u306b\u305d\u306e\u30b0\u30eb\u30fc\u30d7\u304c\u3069\u306e\u3088\u3046\u306a\u8133\u72b6\u614b\u306b\u306a\u3063\u3066\u3044\u308b\u306e\u304b\u3092\u691c\u8a0e\u3057\u3088\u3046\u3068\u601d\u3044\uff0c\u4eca\u56de\u306e\u88ab\u9a13\u8005\u5206\u985e\u3092\u884c\u3063\u305f\u3068\u56de\u7b54\u81f4\u3057\u307e\u3057\u305f\uff0e
\n <\/p>\n- \n
- \u611f\u60f3<\/li>\n<\/ul>\n
\u79c1\u306b\u3068\u3063\u3066\u306f2\u5ea6\u76ee\u306e\u56fd\u969b\u5b66\u4f1a\u3067\u3042\u308a\uff0c\u524d\u56de\u3068\u306f\u5b66\u4f1a\u306e\u898f\u6a21\uff0c\u7814\u7a76\u5ba4\u304b\u3089\u306e\u53c2\u52a0\u6570\u306a\u3069\u304c\u7570\u306a\u308a\uff0c\u524d\u56de\u3068\u306f\u9055\u3063\u305f\u7d4c\u9a13\u3092\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u305f\u3068\u611f\u3058\u3066\u3044\u307e\u3059\uff0e\u30dd\u30b9\u30bf\u30fc\u767a\u8868\u306f\u767a\u8868\u65e5\u304c\u305d\u308c\u305e\u308c\u7570\u306a\u308a\uff0c\u767a\u8868\u6642\u306b\u7814\u7a76\u5ba4\u306e\u30e1\u30f3\u30d0\u30fc\u304c\u767a\u8868\u306e\u5fdc\u63f4\u306b\u6765\u3066\u304f\u308c\u305f\u305f\u3081\u7dca\u5f35\u3092\u307b\u3050\u3057\u3066\u767a\u8868\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3057\u305f\uff0e\u307e\u305f\uff0c\u79c1\u306f\u30dd\u30b9\u30bf\u30fc\u767a\u8868\u304c\u6700\u7d42\u65e5\u3067\u3042\u3063\u305f\u305f\u3081\uff0c\u524d\u65e5\u307e\u3067\u306e\u7814\u7a76\u5ba4\u30e1\u30f3\u30d0\u30fc\u306e\u767a\u8868\u59ff\u3092\u307f\u308b\u3053\u3068\u304c\u3067\u304d\uff0c\u305d\u308c\u306b\u3088\u308a\u30e2\u30c1\u30d9\u30fc\u30b7\u30e7\u30f3\u3092\u3088\u308a\u4e0a\u3052\u308b\u3053\u3068\u304c\u3067\u304d\u305f\u3068\u611f\u3058\u3066\u3044\u307e\u3059\uff0e\u767a\u8868\u3067\u306f\u7df4\u7fd2\u3057\u3066\u304d\u305f\u8aac\u660e\u306f\u3067\u304d\u308b\u306e\u3067\u3059\u304c\uff0c\u8cea\u554f\u3092\u8074\u304d\uff0c\u305d\u306e\u8cea\u554f\u306b\u7b54\u3048\u308b\u3068\u3044\u3046\u3053\u3068\u306f\u3084\u306f\u308a\u96e3\u3057\u304f\uff0c\u3068\u306b\u304b\u304f\u9ed9\u308b\u3053\u3068\u306a\u304f\u8cea\u554f\u3092\u8074\u304d\u8fd4\u3057\u305f\u308a\uff0c\u81ea\u5206\u304c\u8003\u3048\u305f\u3053\u3068\u3092\u8a71\u3059\u52aa\u529b\u3092\u3057\u3066\u30b3\u30df\u30e5\u30cb\u30b1\u30fc\u30b7\u30e7\u30f3\u3092\u9014\u5207\u308c\u3055\u305b\u306a\u3044\u3088\u3046\u306b\u3067\u304d\u305f\u306e\u304c\u3088\u304b\u3063\u305f\u3068\u601d\u3044\u307e\u3059\uff0e\u307e\u305f\uff0c\u5b66\u4f1a\u898f\u6a21\u304c\u524d\u56de\u53c2\u52a0\u3057\u305f\u56fd\u969b\u5b66\u4f1a\u3088\u308a\u5927\u304d\u304b\u3063\u305f\u3053\u3068\u304b\u3089\uff0c\u591a\u304f\u306e\u7814\u7a76\u3092\u77e5\u308b\u3053\u3068\u304c\u3067\u304d\uff0c\u591a\u304f\u306e\u65b9\u3068\u304a\u8a71\u3057\u3067\u304d\u305f\u306e\u3067\u826f\u3044\u4f53\u9a13\u304c\u3067\u304d\u307e\u3057\u305f\uff0e\u524d\u56de\u306e\u56fd\u969b\u5b66\u4f1a\u306b\u6bd4\u3079\u3066\uff0c\u30dd\u30b9\u30bf\u30fc\u767a\u8868\u3092\u7a4d\u6975\u7684\u306b\u8074\u304d\u8cea\u554f\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u305f\u70b9\u304c\u6210\u9577\u3067\u304d\u305f\u3068\u611f\u3058\u3066\u3044\u307e\u3059\uff0e\u3057\u304b\u3057\uff0c\u81ea\u5206\u306e\u7814\u7a76\u3068\u95a2\u4fc2\u3059\u308b\u5c11\u3057\u306e\u30ad\u30fc\u30ef\u30fc\u30c9\u306b\u7d5e\u3063\u3066\u3057\u304b\u30dd\u30b9\u30bf\u30fc\u767a\u8868\u3092\u8074\u3044\u3066\u3044\u306a\u304b\u3063\u305f\u306e\u3067\uff0c\u591a\u304f\u306e\u767a\u8868\u304c\u3042\u308b\u5b66\u4f1a\u3067\u3042\u3063\u305f\u305f\u3081\u3088\u308a\u5e83\u7bc4\u306a\u7814\u7a76\u306b\u95a2\u3057\u3066\u767a\u8868\u3092\u8074\u3051\u305f\u3089\u3088\u304b\u3063\u305f\u3068\u601d\u3044\u307e\u3057\u305f\uff0e\u7814\u7a76\u5ba4\u306e\u4e2d\u3067\u306f\u77e5\u308b\u3053\u3068\u304c\u3067\u304d\u308b\u7814\u7a76\u306e\u7bc4\u56f2\u304c\u72ed\u304f\u306a\u308a\u304c\u3061\u3067\u3059\u304c\uff0cOHBM\u306b\u53c2\u52a0\u3059\u308b\u3053\u3068\u3067\u30d2\u30c8\u306e\u8133\u306b\u95a2\u3059\u308b\u7814\u7a76\u306e\u5168\u4f53\u7684\u306a\u52d5\u5411\u3092\u77e5\u308b\u3053\u3068\u304c\u3067\u304d\u305f\u306e\u304c\u3088\u304b\u3063\u305f\u3068\u601d\u3063\u3066\u3044\u307e\u3059\uff0e\u4eca\u56de\u5f97\u305f\u77e5\u8b58\u3084\u4e0d\u8db3\u3057\u3066\u3044\u308b\u3068\u611f\u3058\u3066\u3044\u308b\u90e8\u5206\u3092\u8e0f\u307e\u3048\uff0c\u66f4\u306b\u7814\u7a76\u306b\u52b1\u3093\u3067\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
\n <\/p>\n\n\n
\n \u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Cortical-striatal connectivity in obsessive compulsive disorder is hyper-modulated by working memory
\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Jane Harness
\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Poster Session
\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a
\nIntroduction:
\nObsessive compulsive disorder (OCD) is a disorder that causes obsessive and intrusive thoughts presumed to drive repetitive compulsions. As most behavior is sub served by synchrony in cortical, striatal and thalamic sub-networks, it is likely that repetitive behaviors in OCD arise from dysfunctional interactions between these regions (Nakao et al., 2009). These interactions are presumed to be hyper-functional in nature, either compensating for latent connective deficits or reflect a hyper-synchrony that resists the modulatory effects of cognitive control, a domain that is impaired in the disorder (Diwadkar et al., 2015). Here we evaluated the ability of basic working memory tasks to a) modulate functional connectivity (FC) between brain networks in OCD and healthy controls (HC) and b) to estimate significant differences between groups on sub-network pairs identified in a). We demonstrate that OCD are characterized by significantly increased FC in dACC-centric networks between 1) the right and left basal ganglia and 2) other frontal and cortical regions.
\nMethods:
\nThirty-five participants (13 male, 22 female, mean age=16.3 yrs.) with a diagnosis of OCD and 35 controls (18 male, 17 female, mean age=17 yrs.) participated in the fMRI study (Siemens Verio 3T) of the verbal n-back task (Casey et al.,1995) used to drive activity in frontal, cingulate, striatal, parietal and thalamic networks. Data were processed in SPM8 using typical methods. From first-level statistical models, effects of interest contrasts were used to identify task-related voxels for each participant and were forwarded for second level analyses in which the one-way analysis of variance used Group (OCD, HC) as the single factor of interest. In this second level model, our intent was to identify brain regions that were co-activated across groups, using conjunction analysis (Nichols et al., 2005). Significant peaks from each co-activated region of interest were identified (pvoxel<0.05). These peaks were submitted to subsequent undirected FC analyses, assessed using typical methods (Whitfield-Gabrieli & Nieto-Castanon, 2012; Silverstein et al., 2016). Analyses proceeded in a two-step process: First sub-networks that were significantly modulated by task (pFDR<0.05) in both OCD and HC were identified. Next, these co-modulated sub-network pairs were submitted for subsequent analyses of inter-group differences (OCD \u2260 HC).
\nResults:
\nFigure 1 shows sub-networks that were significantly modulated by task (pFDR<0.05) in both OCD and HC for 0 back (1a) and 1 back (1b). From these co-modulated sub-network pairs, Figure 2 depicts inter-group differences (OCD > HC; p<0.05). Figure 2a represents these pairs for 0 back and figure 2b for 1 back. There were no sub-network pairs with HC>OCD.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\u5f37\u8feb\u6027\u969c\u5bb3\uff08OCD\uff09\u90e1\u3068\u30b3\u30f3\u30c8\u30ed\u30fc\u30eb\u7fa4\u306e0-back\u30681-back\u306e\u6a5f\u80fd\u7684\u30b3\u30cd\u30af\u30c6\u30a3\u30d3\u30c6\u30a3\u306e\u6bd4\u8f03\u306b\u95a2\u3059\u308b\u767a\u8868\u3067\u3057\u305f\uff0e\u5404\u7fa4\u306e\u6a5f\u80fd\u7684\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3092\u4f5c\u6210\u3057\uff0c\u7fa4\u9593\u3092\u691c\u5b9a\u3059\u308b\u3053\u3068\u3067\u7fa4\u306b\u7279\u6709\u306e\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3092\u62bd\u51fa\u3057\u3066\u3044\u307e\u3057\u305f\uff0e0-back\u30681-back\u3067\u306f\u8ca0\u8377\u306b\u305d\u308c\u307b\u3069\u9055\u3044\u304c\u306a\u3044\u3088\u3046\u306b\u611f\u3058\u3066\u3044\u307e\u3057\u305f\u304c\uff0c1-back\u306e\u7d50\u5408\u304c\u591a\u3044\u7d50\u679c\u3068\u306a\u3063\u3066\u3044\u307e\u3057\u305f\uff0e2-back\u306b\u95a2\u3057\u3066\u3082\u30c7\u30fc\u30bf\u306f\u3042\u308b\u304c\u89e3\u6790\u3092\u3057\u3066\u3044\u306a\u3044\u3068\u8a00\u3063\u3066\u304a\u308a\uff0c2-back\u306b\u304a\u3051\u308b\u7d50\u679c\u3082\u6c17\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 \uff1a\u3000Working memory in childhood onset schizophrenia patients and their nonpsychotic siblings
\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Siyuan Liu
\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a \u00a0Poster Session
\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a
\nIntroduction:
\nChildhood onset schizophrenia (COS), defined as onset of psychosis before age 13, is a rare and severe form of the disorder (Nicolson and Rapoport 1999). Working memory (WM) deficits are consistently reported in the adult-onset schizophrenia (AOS) literature (Goldman-Rakic 1994), and abnormal brain activations (Minzenberg, et al. 2009) and functional connectivity (Dauvermann, et al. 2014) have been found in adult-onset patients. COS patients and their nonpsychotic siblings show WM impairments as well (Gochman, et al. 2004; Karatekin and Asarnow 1998), but it is unclear whether similar abnormal functional activations and connections underlie these processes to those seen in adult-onset peers. Here, we conducted an fMRI experiment of n-back paradigm to examine whether COS patients and their siblings show impairment in WM function compared with healthy controls, and examine whether their WM related brain activation and connectivity patterns are abnormal.
\nMethods:
\n32 COS patients (21.3+-6.1 years), 30 nonpsychotic siblings (19.4+-4.1), and 39 healthy controls (20.0+-4.5), matched for age and sex, were scanned at 3T and performed a block-designed paradigm. It included 4 permuted runs, one for each of 1-, 2-back letter (verbal) and location (visual) WM tasks, and 0-back was embedded in each run as the baseline. The total duration was 17.64 min. COS patients met DSM-IV criteria for schizophrenia with the onset of psychosis before 13. An ICA based pipeline was used to preprocess fMRI images (Xu, et al. 2014). DVARS, a measure of artifact-induced changes in image intensity, was matched across three groups (<0.87%, p=0.27). In addition to typical activation analyses, we also evaluated the averaged functional connectivity, the average Pearson correlation of each voxel with all others (Cole, et al. 2010). Random-effects ANOVA models were used to draw statistical inferences at the group level. A family-wise error of 0.05 was used to determine corrected significance.
\nResults:
\nCOS patients scored significantly lower in accuracy rate than controls in all tasks (Cohen’s d > 1, p < .0001). To be noted, only 40% COS patients were able to accomplish 2-back tasks, indicating that they suffer a severe loss of WM function. Unlike patients, siblings showed no significantly lower accuracy rates compared to controls. However, when switching from 1- to 2-back tasks, their averaged effect sizes increased from 0.1 to 0.4 and p value reached 0.16, indicating that siblings suffer a subthreshold WM functional loss. fMRI analyses revealed a similar brain activation pattern in controls to that of adult controls (Owen, et al. 2005), including robust activations in bilateral dorsolateral and ventrolateral prefrontal cortex (DLPFC and VLPFC); medial and lateral posterior parietal cortex; dorsal cingulate and medial premotor cortex (ACC and preSMA); cerebellum; and thalamus as well as caudate nuclei. Compared with controls, both patients and siblings showed hypo activations in the frontal parietal network, ACC and caudate. Furthermore, compared with controls, only COS patients showed significantly reduced functional connectivity from the above hypo-activated regions to other areas during tasks, and siblings did not.
\nConclusions:
\nOnly a low percentage of COS patients able to accomplish the 2-back tasks and their large effect sizes of all tasks reveal that COS patients suffer a severe loss of WM functions. Our imaging findings identified dysfunction of the frontal-parietal network in COS patients, resembling the pattern seen in most studies of adult-onset patients. Both behavioral and imaging results show siblings to be intermediate between the patients and controls, which is aligned with the previous findings in twins of adult-onset patients (Karlsgodt, et al. 2007), supporting that abnormal brain activations could be a trait marker.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\u672c\u767a\u8868\u306f\uff0c\u5c0f\u5150\u767a\u75c7\u7d71\u5408\u5931\u8abf\u75c7\uff08COS\uff09\u3068\u611f\u899a\u7d71\u5408\u6a5f\u80fd\u4e0d\u5168\uff08SID\uff09\u60a3\u8005\u3068\u30b3\u30f3\u30c8\u30ed\u30fc\u30eb\u7fa4\u306b\u304a\u3051\u308b\u6587\u5b57\u3068\u4f4d\u7f6e\u306e1back\uff0c2back\u3092\u6210\u7e3e\uff0c\u8133\u6d3b\u6027\uff0c\u6a5f\u80fd\u7684\u30b3\u30cd\u30af\u30c6\u30a3\u30d3\u30c6\u30a3\u3092\u6bd4\u8f03\u3057\u305f\u7814\u7a76\u3067\u3057\u305f\uff0eCOS\u7fa4\u3068\u30b3\u30f3\u30c8\u30ed\u30fc\u30eb\u7fa4\uff0cSID\u7fa4\u3068\u30b3\u30f3\u30c8\u30ed\u30fc\u30eb\u7fa4\u306e\u6210\u7e3e\u306e\u6709\u610f\u306a\u9055\u3044\u3092\u6bd4\u8f03\u3057\u30ef\u30fc\u30ad\u30f3\u30b0\u30e1\u30e2\u30ea\u80fd\u529b\u306e\u9055\u3044\u3092\u793a\u3057\uff0c\u305d\u306e\u4e0a\u3067\u8133\u6d3b\u6027\u306e\u9055\u3044\uff0c\u8133\u6d3b\u6027\u3092\u3057\u305f\u9818\u57df\u3092Seed\u3068\u3057\u305f\u7d50\u5408\u306e\u9055\u3044\u3092\u691c\u8a0e\u3057\u3066\u304a\u308a\uff0c\u5404\u7d50\u679c\u30921\u3064\u305a\u3064\u691c\u8a3c\u3057\u305f\u4e0a\u3067\u7d50\u8ad6\u306b\u5c0e\u3044\u3066\u3044\u308b\u3053\u3068\u3092\u611f\u3058\uff0c\u81ea\u5206\u306e\u7814\u7a76\u3092\u3059\u308b\u969b\u306b\u3082\u53c2\u8003\u306b\u3057\u305f\u3044\u3068\u611f\u3058\u307e\u3057\u305f\uff0e\u307e\u305f\uff0cn-back\u8ab2\u984c\u306b\u95a2\u3057\u3066\u3082\u7814\u7a76\u5ba4\u3067\u884c\u308f\u308c\u3066\u3044\u308b\u5358\u7d14\u306a\u3082\u306e\u3067\u306f\u306a\u304f\uff0c\u6761\u4ef6\u304c\u9055\u3046\u3082\u306e\u3092\u8907\u6570\u4f7f\u3063\u3066\u3044\u308b\u3053\u3068\u304c\u8208\u5473\u6df1\u3044\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\u3000Single-patient analysis of impaired RS-fMRI connectivity
\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Azzurra Invernizzi
\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Poster Session
\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a
\nIntroduction:
\nResting-state fMRI (rs-fMRI) has been largely used to investigate intrinsic functional connectivity in the human brain, occurring in the absence of any stimulus. Several studies have proven rs-fMRI useful in detecting functional connectivity alterations, even in earlier stage, of neurodevelopmental disorders, aging and psychiatric conditions [1]. Although a wide range of methods have been developed to analyze rs-fMRI data, a complete pipeline to perform a single-patient level analysis is still lacking. Based on CONN functional connectivity toolbox [2] and SPM12, we developed an optimized single-patient data analysis pipeline of rs-fMRI data.
\nMethods:
\nThe pipeline is essentially based on three main steps: optimized preprocessing of rs-fMRI data, ROI-to-ROI functional connectivity analysis and linear statistical approach (Fig.1). First, rs-fMRI data are segmented, then spatially realigned, coregistered, normalized to the standard MNI-152 template. Deformation fields are computed on white matter (WM), grey matter (GM) and cerebral spinal fluid (CSF) of the structural MR image, in order to obtain a total deformation field that maps the MR image to MNI space. This total deformation is applied to the bias corrected rs-fMRI data, which are then resampled to 3mm isotropic voxels. Next, whole-brain ROI-to-ROI functional connectivity analyses are performed using the 96 ROIs based on Harvard-Oxford Atlas. Head realignment parameters, WM and CSF signals are regressed from the data following the CompCor-strategy [3] implemented in the CONN toolbox. Residual time-series of the rs-fMRI images are then band-pass filtered (0,0009<f<0,08 Hz). No global signal regression is applied. For each subject, we extract the mean time series by averaging across all voxels in each ROI. We then compute the bivariate correlation coefficients for each pair of ROIs. The resultant ROI-to-ROI correlation values are Fisher z-transformed. Finally, single-case statistical analysis is run on the ROI-to-ROI correlation matrices. Specifically, a t-test [4] was computed between the complete control dataset and each single patient. To evaluate the performance of our pipeline, we used rs-fMRI data from right-handed male volunteers included in the Autism Brain Imaging Data Exchange I and II (ABIDE I and II) [5]. Inclusion criteria for control and ASD subjects were: (i) age between 5 and 36 years, which represents approximately two standard deviations from the overall mean age across all male participants (14.7 \u00b1 6.2 years); (ii) a mean frame-wise displacement (FD) lower than 0.5 mm [6] and (iii) with successful preprocessing. These criteria turned out in a rs-fMRI dataset composed of 390 controls and 95 ASD males.
\nResults:
\nWe performed a cluster analysis, based on the functional connectivity matrices obtained in the single-patient statistical analysis, to define 3 main patient phenotypes. For each of these groups of patients, we evaluated the following scores from the Autism Diagnostic Interview (ADI): reciprocal social interaction, verbal, non-verbal and restricted patterns of behavior. Accordingly, we identified three main different patient profiles that present similar behavioral impairments and functional connectivity data (Fig. 2).
\n <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\nCONN\u3068SPM12\u306b\u57fa\u3065\u304f\uff0c\u5358\u4e00\u60a3\u8005\u30c7\u30fc\u30bf\u89e3\u6790\u30d1\u30a4\u30d7\u30e9\u30a4\u30f3\u306e\u958b\u767a\u306b\u95a2\u3059\u308b\u767a\u8868\u3067\u3057\u305f\uff0e\u6a5f\u80fd\u7684\u30b3\u30cd\u30af\u30c6\u30a3\u30d3\u30c6\u30a3\u30de\u30c8\u30ea\u30c3\u30af\u30b9\u3092\u7528\u3044\u3066\u30af\u30e9\u30b9\u30bf\u5206\u6790\u3092\u884c\u3044\uff0c3\u3064\u306e\u60a3\u8005\u8868\u73fe\u3092\u5b9a\u7fa9\u3057\uff0c\u884c\u52d5\u30d1\u30bf\u30fc\u30f3\u3092\u7528\u3044\u3066\u305d\u308c\u3089\u3092\u8a55\u4fa1\u3057\u3066\u3044\u307e\u3057\u305f\uff0e\u89e3\u6790\u30bd\u30d5\u30c8\u3084\u89e3\u6790\u65b9\u6cd5\u304c\u4f3c\u3066\u3044\u308b\u90e8\u5206\u304b\u3089\u8208\u5473\u3092\u6301\u3061\u307e\u3057\u305f\u304c\uff0c\u30af\u30e9\u30b9\u30bf\u5206\u6790\u4ee5\u964d\u306e\u89e3\u6790\u306b\u95a2\u3057\u3066\u5341\u5206\u306b\u7406\u89e3\u3059\u308b\u3053\u3068\u304c\u51fa\u6765\u306a\u304b\u3063\u305f\u306e\u304c\u6b8b\u5ff5\u3067\u3057\u305f\uff0e
\n <\/p>\n\n\n
\n \u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Brain Network of Emotion Regulation in Soldiers with Trauma
\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a D Rangaprakash
\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a ORAL SESSION: Emotion and Motivation
\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a
\nIntroduction:
\nOur ability to shape our emotional experience is termed emotion regulation (ER) [1], involving voluntary modification of emotions elicited in response to exogenous stimuli. Several functional MRI activation studies have consistently identified the middle frontal gyrus (MFG), anterior cingulate and insula to be involved in it [1]. Their limitation lies in the inability to explain the interrelationship between these regions, i.e. connectivity. The brain network of ER either in healthy adults or in psychiatric disorders like posttraumatic stress disorder (PTSD) and mild-traumatic brain injury (mTBI) has been elusive. Emotion dysregulation (ED) is regarded a primary cause for many symptoms observed in PTSD and mTBI [2]. Using fMRI data collected during an ER task, we obtained the network of ER in healthy soldiers and ED in soldiers with comorbid PTSD and post-concussion syndrome (PCS, or chronic mTBI).
\nMethods:
\n59 male U.S. Army soldiers were recruited (comorbid PCS+PTSD=36, combat controls=37, matched in age, race and education). FMRI data was acquired in a Siemens Verio 3T scanner (EPI sequence, TR\/TE=600\/30ms, flip-angle=55o, voxel size=3.5\u00d73.5\u00d75mm3). The ER task (Fig.1) was similar to Urry et.al. [3]. Participants were presented images eliciting a negative emotional response, and were asked to either “maintain” their emotional response, or “suppress” it (reduce negative feelings, requiring ER). There were 4 task blocks, with 24 trials in each block.
\nStandard pre-processing was performed in SPM (realignment, smoothing [8mm kernel], normalization to MNI space). We first identified significantly activated regions during ER (see Fig.2 for region selection procedure). Hemodynamic deconvolution was performed [4] on mean time series extracted from identified regions, to minimize the non-neural intra-subject HRF variability [5]. We employed effective connectivity (EC) modeling using Granger causality (GC) [6] to assess directional causal relationships between identified regions, similar to recent works [7]. Subject-wise EC between all regions were obtained, using which the networks of ER in healthy soldiers (suppress>maintain) and its impairment in PCS+PTSD (control>PCS+PTSD for ‘suppress’ condition) were obtained (p<0.001, Bonferroni corrected) (Fig.3). We provide novel evidence for the brain networks of both ER and ED in a clinical population.
\nResults:
\nWe investigated brain networks of ER in healthy soldiers, and ED in PCS+PTSD. We defined our ROIs around the 9 regions activated during the ER task (Fig.4). With EC analysis, we found the ER network having a top-down structure with the MFG driving the rest of the network (insula, medial prefrontal, amygdala and lateral parietal regions) (Figs 5a,5b,5c). During ED this network was imbalanced with reduced prefrontal connectivity and elevated subcortical and lateral parietal connectivity (Figs 5d,5e,5f).
\nOur ER network fits well with prior findings [1, 8], which identified the pivotal role of MFG in the initiation of ER. MFG is implicated in executive functions like cognitive control [1], which are necessary for regulating emotions. Soldiers with PTSD exhibit impaired emotional processing [9] and impaired cognitive functions associated with the MFG [10],. All directional connections are traceable to the MFG, implying that MFG could be the source of ER [1].
\nAs for ED, the MFG emerged as the key source of disruption in PCS+PTSD. All connections from MFG had reduced connectivity, whose “ripple-effect” culminated in disinhibition of amygdala, which might translate to symptoms like flashbacks, trauma re-experiencing and hyperarousal. This fits well with behavioral manifestations of these conditions [2].
\nConclusions:
\nIn summary, we identified the MFG as pivotal to ER in healthy soldiers and ED in PCS+PTSD. Our findings are significant given that these regions are implicated in prior activation studies [1, 8], but a precise understanding of the underlying network structure and their causal relationships had not emerged so far.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\u5175\u58eb\u3092\u88ab\u9a13\u8005\u3068\u3057\u3066\uff0c\u611f\u60c5\u8abf\u6574\u306e\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3092\u691c\u8a0e\u3059\u308b\u767a\u8868\u3067\u3057\u305f\uff0e\u8ce6\u6d3b\u89e3\u6790\uff0c\u30e1\u30bf\u30a2\u30ca\u30ea\u30b7\u30b9\uff0c\u52d5\u7684\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u89e3\u6790\u306a\u3069\u69d8\u3005\u306a\u89e3\u6790\u3092\u884c\u3063\u3066\u304a\u308a\uff0c\u8208\u5473\u6df1\u3044\u7814\u7a76\u3067\u3057\u305f\uff0e\u307e\u305f\uff0c\u8ce6\u6d3b\u89e3\u6790\u3067\u62bd\u51fa\u3055\u308c\u305f\u8133\u9818\u57df\u3092\u4e2d\u5fc3\u3068\u3057\u305f\uff0c\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u69cb\u9020\u3092\u691c\u8a0e\u3057\u3066\u304a\u308a\uff0c\u4eca\u5f8c\u306e\u81ea\u8eab\u306e\u7814\u7a76\u306e\u53c2\u8003\u306b\u3057\u305f\u3044\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\u3000Towards mapping the neural substrates of the residual variance in human working memory
\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Christelle van Antwerpen
\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a ORAL SESSION: Higher Cognitive Functions
\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a
\nIntroduction:
\nTraditionally working memory has been viewed as having a limited storage capacity, able to hold only a small, fixed number of items {1,2}. More recently working memory function has been conceptualised as being constrained by both short-term memory storage and processing efficiency {3,4,5}. However, there is evidence that a residual variance in working memory performance that is not wholly explained by these storage and processing components exists. This residual variance is the least understood component of working memory and there is much debate over what this variance represents, particularly over whether or not it is executive in nature {6,7,8,9}. We aimed to examine the neural mechanisms that underlie working memory, and shed further light on each of the subcomponents of working memory performance. We used fMRI to address the conceptualisation of working memory, specifically related to the residual variance and what it represents.
\nMethods:
\nParticipants: 62 healthy volunteers (49 females) were recruited (mean age = 34.05 years, age range 16-66) Paradigm: We used five tasks modified from Bayliss et al., (2003)3. A simple processing task with no storage requirements. Two complex memory tasks required performance of a concurrent processing task: a complex verbal memory task and a complex spatial memory task. Two tasks were simple verbal and spatial storage tasks (without a concurrent processing task). Data acquisition: 3T Siemens Skyra scanner, 32 channel head coil. fMRI scans were T2*-weighted gradient EPI images, TR of 2.5s, 3mm \u00b3 voxels, 40 slices covering the whole brain. A T1-weighted inversion recovery MPRAGE giving 1mm\u00b3 voxels, 192 slices was also acquired. Analysis: performed in SPM8. A general linear model was used to estimate brain regions showing greater task specific activation versus rest. Five t-contrasts: processing, storage, verbal working memory, spatial working memory and complex working memory. The verbal working memory, spatial working memory and complex working memory contrasts represent the residual variance, however separating verbal and spatial working memory contrasts allows us to directly test the domain generality of working memory. Consistent effects were then tested with one-sample t-test for each contrast and a two-sample t-test to compare between verbal a spatial domains was conducted. Significant results threshold p<0.05 Family-wise error corrected.
\nResults:
\nAccuracy results showed no significant effects for both task domain and task difficulty. The reaction time results showed a significant increase in complex tasks than storage tasks. In complex task reaction times were significantly faster in the spatial compared to verbal domain. The fMRI results showed that during the processing component, the bilateral superior temporal gyrus shows strong increases in the BOLD signal. In the analysis of the storage component results indicate strong activation of the bilateral inferior parietal lobule as well as activations in cerebellum (culmen). The analysis of residual variance revealed consistent activations in the bilateral temporal parietal junction, anterior cingulate cortex and prefrontal cortex. A direct comparison between spatial and verbal complex working memory showed no differences in the pattern of activation, suggesting the same neural pathway underlies both verbal and spatial working memory domain.
\nConclusions:
\nOur study is the first to use fMRI to address the debate of the residual variance and what it may represent [6,7,8,9]. Our findings show that the residual variance is supported by neural networks known to support attention and executive function and strongly indicates that the residual variance represents an executive component in working memory. Furthermore our findings demonstrate a common neural pathway shared by both verbal and spatial domains of working memory. These findings indicates that the residual is domain-general. Together these findings strongly indicate that the residual variance is indeed executive.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\u30ef\u30fc\u30ad\u30f3\u30b0\u30e1\u30e2\u30ea\u306e\u795e\u7d4c\u30e1\u30ab\u30cb\u30ba\u30e0\u3092\u8abf\u3079\uff0c\u5404\u30b5\u30d6\u30b3\u30f3\u30dd\u30fc\u30cd\u30f3\u30c8\u3092\u660e\u3089\u304b\u306b\u3059\u308b\u767a\u8868\u3067\u3057\u305f\uff0e2\u7a2e\u985e\u306e\u30bf\u30b9\u30af\u3092\u7528\u3044\u3066\uff0c\u884c\u52d5\u30c7\u30fc\u30bf\u3068\u5171\u306b\u8133\u6d3b\u52d5\u306e\u89e3\u6790\u304c\u884c\u308f\u308c\uff0c\u5404\u30bf\u30b9\u30af\u3092\u6bd4\u8f03\u3059\u308b\u3053\u3068\u3067\u691c\u8a0e\u3057\u305f\u3044\u8133\u6d3b\u52d5\u3092\u660e\u3089\u304b\u306b\u3057\u3066\u3044\u307e\u3057\u305f\uff0e\u3053\u3061\u3089\u306e\u767a\u8868\u306b\u304a\u3044\u3066\u3082\uff0c1\u30641\u3064\u306e\u3053\u3068\u3092\u660e\u3089\u304b\u306b\u3057\uff0c\u305d\u308c\u305e\u308c\u306e\u7d50\u679c\u3092\u5408\u308f\u305b\u3066\u7d50\u8ad6\u306b\u5c0e\u3044\u3066\u3044\u308b\u3068\u611f\u3058\u307e\u3057\u305f\uff0e
\n
\n\u53c2\u8003\u6587\u732e<\/p>\n- \n
- The 23rd Annual Meeting of the Organization for Human Brain Mapping,<\/li>\n<\/ul>\n
https:\/\/www.humanbrainmapping.org\/i4a\/pages\/index.cfm?pageID=3734
\n\u5b66\u4f1a\u53c2\u52a0\u5831\u544a\u66f8<\/strong><\/p>\n\n\n
\n \u5831\u544a\u8005\u6c0f\u540d<\/strong><\/td>\n \u6c60\u7530\u5e78\u6a39<\/td>\n<\/tr>\n \n \u767a\u8868\u8ad6\u6587\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n \u8996\u899a\u523a\u6fc0\u8ab2\u984c\u306b\u304a\u3051\u308bHRF\u306b\u9069\u5fdc\u3055\u305b\u305fGLM\u89e3\u6790\u306e\u8a55\u4fa1<\/td>\n<\/tr>\n \n \u767a\u8868\u8ad6\u6587\u82f1\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n Evaluation of a GLM analysis with adaptive hemodynamic response function on a visual stimulus task<\/td>\n<\/tr>\n \n \u8457\u8005<\/strong><\/td>\n \u6c60\u7530\u5e78\u6a39\uff0c\u65e5\u548c\u609f\uff0c\u5ee3\u5b89\u77e5\u4e4b<\/td>\n<\/tr>\n \n \u4e3b\u50ac<\/strong><\/td>\n \u533b\u7642\u60c5\u5831\u30b7\u30b9\u30c6\u30e0\u7814\u7a76\u5ba4<\/td>\n<\/tr>\n \n \u8b1b\u6f14\u4f1a\u540d<\/strong><\/td>\n 23rd Annual Meeting of the Organization of
\nHuman Brain Mapping<\/td>\n<\/tr>\n\n \u4f1a\u5834<\/strong><\/td>\n Vancouver, Canada<\/td>\n<\/tr>\n \n \u958b\u50ac\u65e5\u7a0b<\/strong><\/td>\n 2017\/06\/25-2017\/06\/29<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\n <\/p>\n- \n
- \u8b1b\u6f14\u4f1a\u306e\u8a73\u7d30<\/li>\n<\/ol>\n
2017\/06\/25\u304b\u30892017\/06\/29\u306b\u304b\u3051\u3066\uff0cVancouver, Canada\u306b\u3066\u958b\u50ac\u3055\u308c\u307e\u3057\u305f23rd Annual Meeting of the Organization of Human Brain Mapping\uff08OHBM2017\uff09\u306b\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u3053\u306eOHBM\u306f\uff0c\u79d1\u5b66\u8005\u9593\u306e\u30b3\u30df\u30e5\u30cb\u30b1\u30fc\u30b7\u30e7\u30f3\u3092\u4fc3\u9032\u3057\uff0c\u5065\u5eb7\u30fb\u75c5\u6c17\u306e\u4eba\u9593\u306e\u8133\u306b\u304a\u3051\u308b\u89e3\u5256\u5b66\u7684\u30fb\u6a5f\u80fd\u7684\u7d44\u7e54\u306e\u7406\u89e3\u3092\u9032\u3081\u308b\u3053\u3068\u3092\u76ee\u7684\u3068\u3057\u3066\u958b\u50ac\u3055\u308c\u3066\u3044\u307e\u3059\uff0e
\n\u79c1\u306f\u5168\u65e5\u7a0b\u53c2\u52a0\u3057\uff0c\u672c\u7814\u7a76\u5ba4\u304b\u3089\u306f\u4ed6\u306b\u5ee3\u5b89\u5148\u751f\uff0c\u65e5\u548c\u5148\u751f\uff0c\u77f3\u539f\u3055\u3093\uff0c\u7389\u57ce\u3055\u3093\uff0c\u548c\u7530\u3055\u3093\uff0c\u7247\u5c71\u3055\u3093\uff0c\u8429\u539f\u3055\u3093\uff0c\u5409\u6b66\u3055\u3093\uff0c\u76f8\u672c\u3055\u3093\uff0c\u4e09\u597d\u3055\u3093\uff0c\u77f3\u7530(\u7fd4)\uff0c\u4e2d\u6751(\u572d)\u3055\u3093\uff0c\u85e4\u4e95(\u8056)\u3055\u3093\uff0c\u6c34\u91ce\u3055\u3093\u304c\u53c2\u52a0\u3055\u308c\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\u306f29\u65e5\u306e\u5348\u5f8c\u306e\u30dd\u30b9\u30bf\u30fc\u30bb\u30c3\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\u8cbc\u308a\u51fa\u3057\u671f\u9593\u306f2\u65e5\u9593\uff0c\u30bb\u30c3\u30b7\u30e7\u30f3\u6642\u9593\u306f2\u6642\u9593\u3068\u306a\u3063\u3066\u304a\u308a\u307e\u3057\u305f\uff0e
\n\u4eca\u56de\u306e\u767a\u8868\u306b\u3064\u3044\u3066\u4ee5\u4e0b\u306b\u6284\u9332\u3092\u8a18\u8f09\u81f4\u3057\u307e\u3059\uff0e<\/p>\n\n\n
\n \u524d\u66f8\u304d\uff1a\u4e00\u822c\u7dda\u5f62\u30e2\u30c7\u30eb\uff08GLM\uff09\u306b\u57fa\u3065\u304f\u8133\u6d3b\u52d5\u306e\u89e3\u6790\u6cd5\u306f\u3001fMRI\u306b\u304a\u3044\u3066\u4e00\u822c\u7684\u306b\u7528\u3044\u3089\u308c\u3066\u3044\u308b\u304c\u3001\u3053\u306e\u65b9\u6cd5\u306f\u6a5f\u80fd\u7684\u8fd1\u8d64\u5916\u5206\u5149\u6cd5\uff08fNIRS\uff09\u306b\u3082\u5bb9\u6613\u306b\u9069\u7528\u3067\u304d\u308b\u3002 GLM\u3067\u306f\u3001\u8840\u884c\u52d5\u614b\u95a2\u6570\uff08HRF\uff09\u3068\u30dc\u30af\u30bb\u30eb\u95a2\u6570\u3068\u306e\u7573\u307f\u8fbc\u307f\u306b\u3088\u3063\u3066\u30b7\u30df\u30e5\u30ec\u30fc\u30c8\u3055\u308c\u305f\u8133\u8840\u6d41\u5909\u5316\u304c\u751f\u6210\u3055\u308c\u3001\u6d3b\u6027\u5316\u306e\u7a0b\u5ea6\u306f\u3001\u6e2c\u5b9a\u3055\u308c\u305f\u8133\u6d3b\u52d5\u30c7\u30fc\u30bf\u306b\u9069\u5408\u3055\u305b\u308b\u3053\u3068\u306b\u3088\u3063\u3066\u63a8\u5b9a\u3055\u308c\u308b[1] [2]\u3002\u901a\u5e38\u3001HRF\u306f\u6240\u5b9a\u306e\u5f62\u72b6\u306b\u56fa\u5b9a\u3055\u308c\u3066\u304a\u308a\u3001boxcar\u95a2\u6570\u306e\u5024\u306f1\u306b\u56fa\u5b9a\u3055\u308c\u3066\u3044\u308b\u304c\u3001\u3053\u308c\u3089\u306f\u523a\u6fc0\u304a\u3088\u3073\u500b\u4eba\u306b\u3088\u3063\u3066\u7570\u306a\u308b\u3068\u4eee\u5b9a\u3059\u308b[3] [4]\u3002\u3053\u306e\u554f\u984c\u3092\u89e3\u6c7a\u3059\u308b\u305f\u3081\u306b\u3001\u8457\u8005\u3089\u306f\u3001\u5b9f\u969b\u306e\u30c7\u30fc\u30bf\u3068\u30b7\u30df\u30e5\u30ec\u30fc\u30c8\u3055\u308c\u305f\u30c7\u30fc\u30bf\u3068\u306e\u9593\u306e\u8aa4\u5dee\u3092\u6700\u5c0f\u306b\u3059\u308b\u305f\u3081\u306b\u3001HRF\u3068boxcar\u95a2\u6570\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u6700\u9069\u5316\u3059\u308b\u65b0\u3057\u3044GLM\u6cd5\u3092\u63d0\u6848\u3057\u305f\u3002\u672c\u8ad6\u6587\u3067\u306f\u3001\u63d0\u6848\u624b\u6cd5\u306e\u6709\u52b9\u6027\u3092\u8996\u899a\u523a\u6fc0\u8ab2\u984c\u306e\u5206\u6790\u30c7\u30fc\u30bf\u306b\u3088\u308a\u691c\u8a3c\u3057\u305f\u3002\u30e1\u30bd\u30c3\u30c9\uff1a\u63d0\u6848\u3055\u308c\u305f\u65b9\u6cd5\u3067\u306f\u3001HRF\u306e3\u3064\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u3001\u7b2c1\u306e\u30d4\u30fc\u30af\u9045\u5ef6tp\u3001\u7b2c2\u306e\u30d4\u30fc\u30af\u9045\u5ef6\u6642\u9593tu\u3001\u7b2c1\u306e\u30d4\u30fc\u30af\u3068\u7b2c2\u306e\u30d4\u30fc\u30af\u306e\u6bd4A\u3001\u304a\u3088\u3073\u523a\u6fc0\u63d0\u793a\u6642\u306eboxcar\u95a2\u6570\u306e\u5024\uff08\u3053\u3053\u3067\u306f\u3001\u523a\u6fc0\u30d9\u30af\u30c8\u30eb\u3068\u547c\u3076\uff09\u306f\u3001 HRF\u306e\u7573\u307f\u8fbc\u307f\u306b\u3088\u3063\u3066\u5f97\u3089\u308c\u305f\u30b7\u30df\u30e5\u30ec\u30fc\u30c8\u3055\u308c\u305f\u8840\u6d41\u5909\u5316\u3068\u30dc\u30c3\u30af\u30b9\u30ab\u30fc\u95a2\u6570\u3068\u6e2c\u5b9a\u30c7\u30fc\u30bf\u3068\u306e\u9593\u306e\u8aa4\u5dee\u306f\u6700\u5c0f\u9650\u306b\u3055\u308c\u308b\u3002\u3057\u304b\u3057\u3001\u672c\u8ad6\u6587\u3067\u306f\u3001HRF\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u5171\u901a\u306e\u8a2d\u5b9ap = 6\u3001u = 10\u3001A = 6\u306b\u56fa\u5b9a\u3057\u3001\u523a\u6fc0\u30d9\u30af\u30c8\u30eb\u306e\u307f\u3092\u6700\u9069\u5316\u3057\u305f\u3002\u63d0\u6848\u3057\u305f\u65b9\u6cd5\u306e\u6709\u52b9\u6027\u3092\u8a55\u4fa1\u3059\u308b\u305f\u3081\u306e\u5b9f\u9a13\u30c7\u30fc\u30bf\u3068\u3057\u3066\u3001\u5065\u5e38\u8005\uff08n = 20;\u5973\u602710\u540d;\u5e74\u9f6222 +\/- 0.1\u6b73\uff09\u306e\u8133\u8840\u6d41\u5909\u5316\u30c7\u30fc\u30bf\u3092\u3001116\u30c1\u30e3\u30f3\u30cd\u30eb\u3067\u6e2c\u5b9a\u3057\u305fNAPS\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8fNIRS\uff08\u65e5\u7acb\u3001ETG-7100\uff09\u3092\u7528\u3044\u305f\u3002 fNIRS\u306e\u5168\u3066\u306e\u6e2c\u5b9a\u30c1\u30e3\u30cd\u30eb\u306f\u3001\u81ea\u52d5\u5316\u89e3\u5256\u5b66\u7684\u6a19\u8b58\uff08ALL\uff09\u306b\u57fa\u3065\u304f\u8133\u9818\u57df\u3068\u95a2\u9023\u3057\u3066\u3044\u305f\u3002\u30bf\u30b9\u30af\u533a\u9593\u306e\u7a4d\u5206\u5024\u306f\u3001\u63d0\u6848\u624b\u6cd5\u306b\u3088\u308a\u5f97\u3089\u308c\u305f\u30b7\u30df\u30e5\u30ec\u30fc\u30c8\u3055\u308c\u305f\u8133\u8840\u6d41\u5909\u5316\u30c7\u30fc\u30bf\u304b\u3089\u7b97\u51fa\u3057\u305f\u3002\u7a4d\u5206\u5024\u306e\u6700\u5927\u5024\u3092\u6709\u3059\u308b\u4e0a\u4f4d5\u3064\u306e\u8133\u9818\u57df\u3092\u6d3b\u6027\u5316\u9818\u57df\u3068\u307f\u306a\u3057\u305f\u3002\u7d50\u679c\uff1a\u56f31\u306f\u63d0\u6848\u624b\u6cd5\u3067\u51e6\u7406\u3057\u305ffNIRS\u30c7\u30fc\u30bf\u304b\u3089\u62bd\u51fa\u3057\u305f\u6d3b\u6027\u5316\u9818\u57df\u3092\u793a\u3059\u3002\u5de6\u53f3\u306e\u80cc\u5074\u4e0a\u524d\u982d\u56de\u3001\u53f3\u524d\u982d\u56de\u3001\u5de6\u5f8c\u982d\u90e8\u56de\u306e4\u9818\u57df\u3092\u62bd\u51fa\u3057\u305f\u3002\u7279\u306b\u3001\u4e0d\u5feb\u306a\u611f\u60c5\u306e\u8a8d\u77e5\u5236\u5fa1\u306b\u95a2\u9023\u3057\u3066\u3044\u308b\u305f\u3081\u3001\u5de6\u80cc\u5074\u4e0a\u524d\u982d\u56de\u304c\u6d3b\u6027\u5316\u3055\u308c\u3066\u3044\u308b\u3068\u8003\u3048\u3089\u308c\u3066\u3044\u308b[5]\u3002\u3053\u308c\u3089\u306e\u7d50\u679c\u304b\u3089\u3001\u63d0\u6848\u624b\u6cd5\u306f\u30bf\u30b9\u30af\u3092\u6271\u3046\u9818\u57df\u3092\u62bd\u51fa\u3067\u304d\u308b\u3053\u3068\u304c\u793a\u3055\u308c\u305f\u3002\u00a0\u7d50\u8ad6\uff1a\u672c\u8ad6\u6587\u3067\u306f\u3001\u4e0d\u5feb\u306a\u753b\u50cf\u3092\u898b\u306a\u304c\u3089\u8133\u6d3b\u52d5\u306efNIRS\u30c7\u30fc\u30bf\u306bHRF\u3068boxcar\u95a2\u6570\u306e\u5024\u3092\u6700\u9069\u5316\u3059\u308b\u65b0\u3057\u3044GLM\u89e3\u6790\u3092\u9069\u7528\u3057\u3001\u305d\u306e\u6709\u52b9\u6027\u3092\u691c\u8a3c\u3057\u305f\u3002\u305d\u306e\u7d50\u679c\u3001\u4e0d\u5feb\u306a\u611f\u60c5\u306b\u95a2\u9023\u3059\u308b\u8133\u9818\u57df\u3092\u62bd\u51fa\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u3001\u672c\u624b\u6cd5\u306e\u6709\u52b9\u6027\u304c\u78ba\u8a8d\u3055\u308c\u305f\u3002<\/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\u5bb91<\/strong>
\n\u3053\u3061\u3089\u306e\u8cea\u554f\u306f\uff0c\u306a\u305c\u6700\u9069\u5316\u3059\u308b\u306e\u304b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\uff0c\u5f93\u6765\u306eGLM\u89e3\u6790\u3067\u306f\u30bf\u30b9\u30af\u3067\u4e00\u5b9a\u306e\u523a\u6fc0\u304c\u51fa\u3066\u3044\u308b\u3068\u4eee\u5b9a\u3057\u3066\u3044\u307e\u3057\u305f\u304c\uff0c\u5b9f\u969b\u306f\u30c1\u30e3\u30f3\u30cd\u30eb\u3084\u500b\u4eba\u306b\u3088\u3063\u3066\u7570\u306a\u308b\u305f\u3081\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e
\n
\n\u30fb\u8cea\u554f\u5185\u5bb92<\/strong>
\n\u3053\u3061\u3089\u306e\u8cea\u554f\u306f\uff0c\u4ed6\u306e\u30bf\u30b9\u30af\u3067\u6700\u9069\u5316\u3092\u884c\u3063\u305f\u3089\u3069\u3046\u306a\u308b\u306e\u304b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\uff0c\u4eca\u5f8c\u4ed6\u306e\u5b9f\u9a13\u306b\u3082\u9069\u5fdc\u3057\u3066\u3044\u304f\u4e88\u5b9a\u3067\u3042\u308b\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e
\n
\n\u30fb\u8cea\u554f\u5185\u5bb93<\/strong>
\n\u3053\u3061\u3089\u306e\u8cea\u554f\u306f\uff0c\u306a\u305c\u30ec\u30b9\u30c8\u3067\u3082\u523a\u6fc0\u304c\u51fa\u3066\u3044\u308b\u306e\u304b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\uff0c\u30ec\u30b9\u30c8\u3067\u3082\u5341\u6642\u56fa\u8996\u70b9\u3092\u63d0\u793a\u3057\u3066\u304a\u308a\u30bf\u30b9\u30af\u304b\u3089\u306e\u5f71\u97ff\u3092\u53d7\u3051\u523a\u6fc0\u304c\u51fa\u3066\u3044\u308b\u53ef\u80fd\u6027\u304c\u3042\u308b\u305f\u3081\u3060\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e
\n <\/p>\n- \n
- \u611f\u60f3<\/li>\n<\/ul>\n
\u672c\u5b66\u4f1a\u306b\u53c2\u52a0\u3057\uff0c\u4e00\u756a\u5370\u8c61\u306b\u6b8b\u3063\u3066\u3044\u308b\u3053\u3068\u306f\u79c1\u306e\u62d9\u3044\u82f1\u8a9e\u306e\u767a\u8868\u3092\u805e\u3044\u305f\u5f8c\uff0c\u591a\u304f\u306e\u4eba\u306b\u8208\u5473\u6df1\u3044\u9762\u767d\u3044\u7814\u7a76\u3060\u3068\u8a00\u3063\u3066\u3044\u305f\u3060\u3044\u305f\u3053\u3068\u3067\u3059\uff0e \u4eca\u307e\u3067fNIRS\u306e\u65b0\u3057\u3044GLM\u89e3\u6790\u306f\u672c\u5f53\u306b\u6b63\u3057\u3044\u306e\u304b\u306a\u3069\u69d8\u3005\u306a\u601d\u3044\u3092\u62b1\u3048\u3066\u7814\u7a76\u3092\u9032\u3081\u3066\u304d\u307e\u3057\u305f\u304c\uff0c\u521d\u3081\u3066\u306e\u5916\u90e8\u767a\u8868\u3067\u4eca\u307e\u3067\u9032\u3081\u3066\u304d\u305f\u7814\u7a76\u306b\u81ea\u4fe1\u3092\u6301\u3064\u3053\u3068\u304c\u3067\u304d\u307e\u3057\u305f\uff0e\u307e\u305f\uff0c\u82f1\u8a9e\u529b\u306e\u5411\u4e0a\u3068\u81ea\u5206\u306e\u7814\u7a76\u3078\u306e\u7406\u89e3\u3092\u6df1\u3081\u308b\u5fc5\u8981\u6027\u3092\u5b9f\u611f\u3057\u307e\u3057\u305f\uff0e\u7814\u7a76\u3078\u306e\u7406\u89e3\u304c\u4f4e\u3044\u305f\u3081\uff0c\u8cea\u554f\u3055\u308c\u3066\u308b\u3053\u3068\u304c\u7406\u89e3\u3067\u304d\u3066\u3082\u81ea\u4fe1\u3092\u6301\u3063\u3066\u7b54\u3048\u308b\u3053\u3068\u304c\u3067\u304d\u306a\u3044\u5834\u9762\u304c\u591a\u304f\u3042\u308a\u307e\u3057\u305f\uff0e\u3053\u306e\u7d4c\u9a13\u3092\u751f\u304b\u3057\uff0c\u4eca\u5f8c\u306f\u304d\u3061\u3093\u3068\u7406\u89e3\u3057\u3066\u7814\u7a76\u3092\u9032\u3081\u3066\u3044\u3053\u3046\u3068\u601d\u3044\u307e\u3057\u305f\uff0e
\n <\/p>\n- \n
- \u8074\u8b1b<\/li>\n<\/ol>\n
\u4eca\u56de\u306e\u5b66\u4f1a\u3067\u306f\uff0c\u4e0b\u8a18\u306e5\u4ef6\u306e\u767a\u8868\u3092\u8074\u8b1b\u3057\u307e\u3057\u305f\uff0e
\n <\/p>\n\n\n
\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
- The 23rd Annual Meeting of the Organization for Human Brain Mapping,<\/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
- 2017 ANNUAL MEETING, https:\/\/www.humanbrainmapping.org\/i4a\/pages\/index.cfm?pageID=3744<\/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
- “Evaluation of a GLM analysis with adaptive hemodynamic response function on a visual stimulus task”
- \n
- “Classification of brain states using functional data obtained during a mental arithmetic task”
- \n
- “Adaptive HRF analysis of fNIRS data”
- \n
- “Examination of the relationship between brain activity and eye movement during emotional stimulation”
- \n
- “Functional connectivity analysis of brain activity during cooperative behavior using fNIRS”
- \n
- “Human state estimation from cerebral blood flow data using CNN and LSTM”
- \n
- “Functional connectivity analysis of pleasant and unpleasant states using fMRI”
- \n
- “Effects of breath-counting meditation on functional brain connectivity and salivary hormones”
- \n
- “Intra-individual variations in functional connectivity during resting and meditative states”
- \n
- “Optimizing electrode placement and frequency bands in EEG-based motor imagery BCIs”
- \n
- “Brain region segmentation method using SLIC and Normalized Cut”
- \n
- “Frontal lobe activity during breath-counting meditation: fNIRS study”
- \n
- “Functional connectivity analysis during breath-counting meditation using multichannel fNIRS”
- \n
![\"\"](\"http:\/\/www.is.doshisha.ac.jp\/news\/wp-content\/uploads\/2017\/07\/2017-06-28_15-27-20.jpg\")
<\/a> ![\"\"](\"http:\/\/www.is.doshisha.ac.jp\/news\/wp-content\/uploads\/2017\/06\/DSCF2910.jpg\")
<\/a>![\"\"](\"http:\/\/www.is.doshisha.ac.jp\/news\/wp-content\/uploads\/2017\/07\/GOPR0159.jpg\")
<\/a>