Combinatorial Structural Clustering (CSC): A Novel Structural Clustering Approach for Large Scale Networks

With the development of the brain cognition science and big data technologies, effective graph clustering is a key technique to uncover the brain mechanism, especially in resting state functional connectivity analysis. In this paper, a combinatorial structural clustering (CSC) algorithm is proposed for large scale networks. A structural similarity feature from adjacency structures of outliers and hubs is introduced to brain functional connectivity networks. Experimental results illustrate that our approach has some advantages compared with SCAN.

[1]  Yufeng Zang,et al.  DPARSF: A MATLAB Toolbox for “Pipeline” Data Analysis of Resting-State fMRI , 2010 .

[2]  J. Ford,et al.  Default mode network activity and connectivity in psychopathology. , 2012, Annual review of clinical psychology.

[3]  Karl J. Friston,et al.  Statistical parametric maps in functional imaging: A general linear approach , 1994 .

[4]  M. Nitsche,et al.  Modulating functional connectivity patterns and topological functional organization of the human brain with transcranial direct current stimulation , 2011, Human brain mapping.

[5]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[6]  Yasuhiro Fujiwara,et al.  SCAN++: Efficient Algorithm for Finding Clusters, Hubs and Outliers on Large-scale Graphs , 2015, Proc. VLDB Endow..

[7]  Archana Venkataraman,et al.  Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization. , 2010, Journal of neurophysiology.

[8]  Olaf Sporns,et al.  Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.

[9]  André Carlos Ponce de Leon Ferreira de Carvalho,et al.  Spectral methods for graph clustering - A survey , 2011, Eur. J. Oper. Res..

[10]  Xiaowei Xu,et al.  SCAN: a structural clustering algorithm for networks , 2007, KDD '07.

[11]  Mark S. Granovetter The Strength of Weak Ties , 1973, American Journal of Sociology.

[12]  M. Greicius,et al.  Resting-state functional connectivity reflects structural connectivity in the default mode network. , 2009, Cerebral cortex.

[13]  Chaogan Yan,et al.  DPARSF: A MATLAB Toolbox for “Pipeline” Data Analysis of Resting-State fMRI , 2010, Front. Syst. Neurosci..

[14]  M. Fukunaga,et al.  Low frequency BOLD fluctuations during resting wakefulness and light sleep: A simultaneous EEG‐fMRI study , 2008, Human brain mapping.

[15]  Allen W. Song,et al.  Measurement of spontaneous signal fluctuations in fMRI: adult age differences in intrinsic functional connectivity , 2009, Brain Structure and Function.

[16]  Vinod Menon,et al.  Functional connectivity in the resting brain: A network analysis of the default mode hypothesis , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[17]  Ferath Kherif,et al.  Distributed cell assemblies for general lexical and category‐specific semantic processing as revealed by fMRI cluster analysis , 2009, Human brain mapping.

[18]  William M. Rand,et al.  Objective Criteria for the Evaluation of Clustering Methods , 1971 .

[19]  Junjing Wang,et al.  Graph theoretical analysis reveals disrupted topological properties of whole brain functional networks in temporal lobe epilepsy , 2014, Clinical Neurophysiology.

[20]  E. Bullmore,et al.  Functional Connectivity and Brain Networks in Schizophrenia , 2010, The Journal of Neuroscience.

[21]  Camille Roth,et al.  Natural Scales in Geographical Patterns , 2017, Scientific Reports.