Establishing the community structure of signed interconnected graph in data

Lu, Wenlian; Chen, Boyu; Jin, Zhongxiao; Waxman, David J.; Feng, Jianfeng

doi:10.23919/chicc.2017.8029133

Cited by 1 publication

(2 citation statements)

References 17 publications

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“…As declared above, it is natural to use the similarity matrix, calculated from Pearson correlation coefficient of all pairs of dWFC’s, as the adjacent matrix in community clustering. In this study, the adjacent matrix A is a signed weighted matrix, and we employ an approach based on an extended signed Q-modularity of the graph (Lu et al, 2017). The graph is divided into two graphs composed by positive edges and negative edges, respectively, represent by A + and A − , where Ai,j+ ≥ 0 and Ai,j- ≤ 0.…”

Section: Methodsmentioning

confidence: 99%

“…The graph is divided into two graphs composed by positive edges and negative edges, respectively, represent by A + and A − , where Ai,j+ ≥ 0 and Ai,j- ≤ 0. The extended signed Q-modularity equals (Lu et al, 2017): (i) the fraction of edge weights, of which both head and tail nodes fall within the same community, minus (ii) the expected value of the edge weights of a random graph that follows the same positive weight degree distribution of the intrinsic graph, plus (iii) the expected value of the edge weights of a random graph that follows the same negative weight degree distribution of this intrinsic graph. This can be formulated as…”

Section: Methodsmentioning

confidence: 99%

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Tracking the Main States of Dynamic Functional Connectivity in Resting State

et al. 2019

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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-C 1 ) mainly involved visual, somatomotor, attention and cerebellar (posterior lobe) modules. State 2 (WFC-C 2 ) was similar to WFC-C 1 , 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 1 and WFC-C 2 . 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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