Journal
FRONTIERS IN NEUROSCIENCE
Volume 13, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fnins.2019.00685
Keywords
community clustering; signed networks; modularity; temporal changes; resting state functional magnetic resonance image
Categories
Funding
- China Postdoctoral Science Foundation [2016M591590]
- key project of the Shanghai Science and Technology Innovation Plan [15JC1400101, 16JC1420402]
- National Natural Science Foundation of China [71661167002, 91630314]
- 111 Project [B18015]
- National Key Research and Development Program of China [2018YFC0910503]
- Young Scientists Fund of the National Natural Science Foundation of China [81801774]
- Natural Science Foundation of Shanghai [18ZR1403700]
- Shanghai AI Platform for Diagnosis and Treatment of Brain Diseases
- Projects of Zhangjiang Hi-Tech District Management Committee, Shanghai [2016-17]
- key project of Shanghai Science and Technology [16JC1420402]
- Base for the Introducing Talents of Discipline to Universities [B18015]
- Shanghai Municipal Science and Technology Major Project [2018SHZDZX01]
- ZJLab
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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.
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