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Assessment of resting-state functional connectivity (FC) has become an important tool in studying brain disease mechanisms. Conclusions from previous resting-state investigations were based upon the hypothesis which assumed that the FC was constant throughout a period of task-free time. However, emerging evidence suggests that it may change over time. Here we investigate the dynamic FC based on the 64 electrodes EEG (electroencephalogram) of 25 healthy subjects in eyes closed (EC) and eyes open (EO) resting-state. A data-driven approach based on independent component analysis, standardized low-resolution tomography analysis, sliding time window, and graph theory are employed. Dynamic changes of FC over time with EC and EO in the visual network, the default mode network etc. are discovered. And the principal component analysis is used to the concatenated dynamic FC matrixes for finding meaningful FC patterns. Our results have complemental the traditional stationary analyses, and revealed novel insights in choosing the type of resting condition in experimental design and EEG clinical research.
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Keywords:
- electroencephalogram /
- resting-state /
- source localization /
- dynamical functional connectivity
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[1] Gusnard D A, Raichle M E 2001 Nat. Rev. Neurosci. 2 685
[2] Morcom A M, Fletcher P C 2007 NeuroImage 37 1073
[3] Castellanos F X, Margulies D S, Kelly A M C, Uddin L Q, Ghaffari M, Kirsch A, Shaw D, Shehzad Z, Martino A D, Biswal B, Sonuga-Barke E J S, Rotrosen J, Adler L A, Milham M P 2008 Biol. Psychiatry 63 332
[4] Tian L X, Jiang T Z, Liang M, Zang Y F, He Y, Sui M Q, Wang Y F 2008 Brain & Dev. 30 342
[5] Boytsova Y A, Danko S G 2010 Human Physiology 36 367
[6] Marx E, Deutschländer A, Stephan T, Dieterich M, Wiesmann M, Brandt T 2004 NeuroImage 21 1818
[7] Liu T B, Yao W P, Ning X B, Ni H J, Wang J 2013 Acta Phys. Sin. 62 218704 (in Chinese) [刘铁兵, 姚文坡, 宁新宝, 倪黄晶, 王俊 2013 62 218704]
[8] Bai F, Zhang Z J, Yu H, Shi Y M, Yuan Y G, Zhu W L, Zhang X R, Qian Y 2008 Neuroscience Letters 438 111
[9] Raichle M E, MacLeod A M, Snyder A Z, Powers W J, Gusnard D A, Shulman G L 2001 Proc. Natl. Acad. Sci. U. S. A. 98 676
[10] Zeng L L, Shen H, Liu L, Wang L B, Li B J, Fang P, Zhou Z T, Li Y M, Hu D W 2012 Brain 135 1498
[11] Leonardi N, Richiardi J, Gschwind M, Simioni S, Annoni J M, Schluep M, Vuilleumier P, Ville D V D 2013 NeuroImage 83 937
[12] Greicius M D, Supekar K, Menon V, Dougherty R F 2009 Cereb. Cortex 19 72
[13] Li L, Jin Z L, Li B 2011 Chin. Phys. B 20 038701
[14] Yi G S, Wang J, Hang C X, Deng B, Wei X L, Li N 2013 Chin. Phys. B 22 028702
[15] Fang X L, Jiang Z L 2007 Acta Phys. Sin. 56 7330 (in Chinese) [方小玲, 姜宗来 2007 56 7330]
[16] Hou F Z, Dai J F, Liu X F, Huang X L 2014 Acta Phys. Sin. 63 040506 (in Chinese) [侯凤贞, 戴加飞, 刘新峰, 黄晓林 2014 63 040506]
[17] Yin N, Xu G Z, Zhou Q 2013 Acta Phys. Sin. 62 118704 (in Chinese) [尹宁, 徐桂芝, 周茜 2013 62 118704]
[18] Calhoun V D, Adali T, Pekar J J 2004 Magn. Reson. Imaging 22 1181
[19] Chen J L, Ros T, Gruzelier J H 2013 Hum. Brain Mapp. 34 852
[20] Roth J K, Johnson M K, Tokoglu F, Murphy I, Constable R T 2014 PLoS ONE 9 90672
[21] Hutchison R M, Womelsdorf T, Allen E A, Bandettini P A, Calhoun V D, Corbetta M, Penna S D, Duyn J H, Glover G H, Gonzalez-Castillo J, Handwerker D A, Keilholz S, Kiviniemi V, Leopold D A, Pasquale F, Sporns O, Walter M, Chang C 2013 NeuroImage 80 360
[22] Sakoglu , Pearlson G D, Kiehl K A, Wang Y M, Michael A M, Calhoun V D 2010 Magn. Reson. Mater. Phy. 23 351
[23] Allen E A, Damaraju E, Plis S M, Erhardt E B, Eichele T, Calhoun V D 2014 Cereb. Cortex 24 663
[24] Bell A J, Sejnowski T J 1995 Neural Comput. 7 1129
[25] Jones D T, Vemuri P, Murphy M C, Gunter J L, Senjem M L, Machulda M M, Przybelski S A, Gregg B E, Kantarci K, Knopman D S, Boeve B F, Petersen R C, Jack Jr C R 2012 PLoS ONE 7 39731
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