%0 Figure %A Zhou, Qunjie %A Zhang, Lu %A Feng, Jianfeng %A Lo, Chun-Yi Zac %D 2019 %T Image_1_Tracking the Main States of Dynamic Functional Connectivity in Resting State.pdf %U https://frontiersin.figshare.com/articles/figure/Image_1_Tracking_the_Main_States_of_Dynamic_Functional_Connectivity_in_Resting_State_pdf/8831015 %R 10.3389/fnins.2019.00685.s001 %2 https://frontiersin.figshare.com/ndownloader/files/16171286 %K community clustering %K signed networks %K modularity %K temporal changes %K resting state functional magnetic resonance image %X

Dynamical changes have recently been tracked in functional connectivity (FC) calculated from resting-state functional magnetic resonance imaging (R-fMRI), when a person is conscious but not carrying out a directed task during scanning. Diverse dynamical FC states (dFC) are believed to represent different internal states of the brain, in terms of brain-regional interactions. In this paper, we propose a novel protocol, the signed community clustering with the optimized modularity by two-step procedures, to track dynamical whole brain functional connectivity (dWFC) states. This protocol is assumption free without a priori threshold for the number of clusters. By applying our method on sliding window based dWFC’s with automated anatomical labeling 2 (AAL2), three main dWFC states were extracted from R-fMRI datasets in Human Connectome Project, that are independent on window size. Through extracting the FC features of these states, we found the functional links in state 1 (WFC-C1) mainly involved visual, somatomotor, attention and cerebellar (posterior lobe) modules. State 2 (WFC-C2) was similar to WFC-C1, but more FC’s linking limbic, default mode, and frontoparietal modules and less linking the cerebellum, sensory and attention modules. State 3 had more FC’s linking default mode, limbic, and cerebellum, compared to WFC-C1 and WFC-C2. With tests of robustness and stability, our work provides a solid, hypothesis-free tool to detect dWFC states for the possibility of tracking rapid dynamical change in FCs among large data sets.

%I Frontiers