10.3389/fnins.2019.00685.s001 Qunjie Zhou Qunjie Zhou Lu Zhang Lu Zhang Jianfeng Feng Jianfeng Feng Chun-Yi Zac Lo Chun-Yi Zac Lo Image_1_Tracking the Main States of Dynamic Functional Connectivity in Resting State.pdf Frontiers 2019 community clustering signed networks modularity temporal changes resting state functional magnetic resonance image 2019-07-09 04:41:16 Figure https://frontiersin.figshare.com/articles/figure/Image_1_Tracking_the_Main_States_of_Dynamic_Functional_Connectivity_in_Resting_State_pdf/8831015 <p>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-C<sub>1</sub>) mainly involved visual, somatomotor, attention and cerebellar (posterior lobe) modules. State 2 (WFC-C<sub>2</sub>) was similar to WFC-C<sub>1</sub>, 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-C<sub>1</sub> and WFC-C<sub>2</sub>. 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.</p>