Module dividing for brain functional networks by employing betweenness efficiency

Traditional researches assume that brain functional networks are static during the entire scanning process of functional magnetic resonance image (fMRI) in the resting state. However, it is not difficult to ignore the dynamic interaction patterns of brain regions that essentially change across time. In this study, we take the internal weight information of the brain functional network as a calculation condition of module dividing for brain functional networks. The concept of betweenness efficiency is firstly proposed to improve Girvan-Newman (GN) algorithm for a better module dividing result, and the maximum modularity is used as a criterion to classify the brain functional network modules of normal subjects. The effect of the improved method is verified by controlling subjects, parameters, environment and other conditions. Then, the improved method was utilized to separate the modules in brain functional networks of normal subjects, and the template was used to divide the functional network of Alzheimer’s disease (AD) patients and mild cognitive impairment (MCI) sufferers. The shortest path length of each module is calculated, and the experimental results are compared with the original GN and weighted GN algorithm improved by Newman. The experimental results demonstrate that, the maximum modularity of the improved method is higher while the dividing effect is better under the same conditions. Meanwhile, the conclusion is consistent with the existing research results when the proposed method is applicable to the analysis of the shortest path length. These results illustrate the viewpoint that the proposed method of module dividing is feasible in the analysis of modular structure of brain functional network.

[1]  Yudong Zhang,et al.  Color image enhancement based on HVS and PCNN , 2010, Science China Information Sciences.

[2]  Yudong Zhang,et al.  Multiple sclerosis identification by convolutional neural network with dropout and parametric ReLU , 2018, J. Comput. Sci..

[3]  Fan Ying.  Community Structure in Complex Networks , 2008 .

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

[5]  Harald Hampel,et al.  Diagnostic power of default mode network resting state fMRI in the detection of Alzheimer's disease , 2012, Neurobiology of Aging.

[6]  Yudong Zhang,et al.  Segment-based coding of color images , 2009, Science in China Series F: Information Sciences.

[7]  Yudong Zhang,et al.  Improved image filter based on SPCNN , 2008, Science in China Series F: Information Sciences.

[8]  Huan Wang,et al.  The connectivity measurement in complex directed networks by motif structure , 2016, Int. J. Sens. Networks.

[9]  Yudong Zhang,et al.  Preliminary study on angiosperm genus classification by weight decay and combination of most abundant color index with fractional Fourier entropy , 2017, Multimedia Tools and Applications.

[10]  Yudong Zhang,et al.  A Two-Level Iterative Reconstruction Method for Compressed Sensing MRI , 2011 .

[11]  Huan Wang,et al.  Effective Connectivity in the Default Network Using Granger Causal Analysis , 2017 .

[12]  Hui-Jia Li,et al.  Social significance of community structure: Statistical view , 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.

[13]  G. Sandini,et al.  Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer's disease. , 2009, Brain : a journal of neurology.

[14]  Maxym Myroshnychenko,et al.  Rich-Club Organization in Effective Connectivity among Cortical Neurons , 2016, The Journal of Neuroscience.

[15]  Hao Wang,et al.  Measuring robustness of community structure in complex networks , 2014, ArXiv.

[16]  Xi-Nian Zuo,et al.  Genetic and Environmental Contributions to Functional Connectivity Architecture of the Human Brain , 2016, Cerebral cortex.

[17]  Jie Cao,et al.  Evolving the attribute flow for dynamical clustering in signed networks , 2018 .

[18]  Zhu-Qing Jiao,et al.  Effective connectivity analysis of fMRI data based on network motifs , 2013, The Journal of Supercomputing.

[19]  Christian E. Waugh,et al.  Dynamic functional connectivity and individual differences in emotions during social stress , 2017, Human brain mapping.

[20]  Yudong Zhang,et al.  A Support-Based Reconstruction for SENSE MRI , 2013, Sensors.

[21]  Sidan Du,et al.  Application of stationary wavelet entropy in pathological brain detection , 2018, Multimedia Tools and Applications.

[22]  Yudong Zhang,et al.  WEIGHTS OPTIMIZATION OF NEURAL NETWORK VIA IMPROVED BCO APPROACH , 2008 .

[23]  Gustavo Deco,et al.  Understanding principles of integration and segregation using whole-brain computational connectomics: implications for neuropsychiatric disorders , 2017, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[24]  Yong He,et al.  Graph-based network analysis of resting-state functional MRI. , 2010 .

[25]  Yudong Zhang,et al.  Optimal Multi-Level Thresholding Based on Maximum Tsallis Entropy via an Artificial Bee Colony Approach , 2011, Entropy.

[26]  Yudong Zhang,et al.  Exponential Wavelet Iterative Shrinkage Thresholding Algorithm for compressed sensing magnetic resonance imaging , 2015, Inf. Sci..

[27]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[28]  M E J Newman,et al.  Fast algorithm for detecting community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[29]  H. Uylings,et al.  Atrophy in the parahippocampal gyrus as an early biomarker of Alzheimer’s disease , 2010, Brain Structure and Function.

[30]  Dinggang Shen,et al.  Hierarchical High-Order Functional Connectivity Networks and Selective Feature Fusion for MCI Classification , 2017, Neuroinformatics.

[31]  PeiPeng Liang,et al.  The human brain functional parcellation based on fMRI data , 2016 .

[32]  Yudong Zhang,et al.  Abnormal breast identification by nine-layer convolutional neural network with parametric rectified linear unit and rank-based stochastic pooling , 2018, J. Comput. Sci..

[33]  Marcus Kaiser,et al.  A tutorial in connectome analysis: Topological and spatial features of brain networks , 2011, NeuroImage.

[34]  Huan Wang,et al.  Research on node properties of resting-state brain functional networks by using node activity and ALFF , 2017, Multimedia Tools and Applications.

[35]  Dinggang Shen,et al.  Hybrid High-order Functional Connectivity Networks Using Resting-state Functional MRI for Mild Cognitive Impairment Diagnosis , 2017, Scientific Reports.

[36]  Dinggang Shen,et al.  Extraction of dynamic functional connectivity from brain grey matter and white matter for MCI classification , 2017, Human brain mapping.

[37]  Yudong Zhang,et al.  AN MR BRAIN IMAGES CLASSIFIER VIA PRINCIPAL COMPONENT ANALYSIS AND KERNEL SUPPORT , 2012 .

[38]  YuDong Zhang,et al.  Pattern Recognition via PCNN and Tsallis Entropy , 2008, Sensors.

[39]  N. Tzourio-Mazoyer,et al.  Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.

[40]  E. Bullmore,et al.  Neurophysiological architecture of functional magnetic resonance images of human brain. , 2005, Cerebral cortex.

[41]  Hua-Wei Shen,et al.  Community Structure of Complex Networks , 2013, Springer Theses.

[42]  Huan Wang,et al.  Directed connectivity of brain default networks in resting state using GCA and motif. , 2017, Frontiers in bioscience.

[43]  Huiying Li,et al.  Scalably revealing the dynamics of soft community structure in complex networks , 2015, Journal of Systems Science and Complexity.

[44]  Zhuqing Jiao,et al.  Functional Connectivity Analysis of Brain Default Mode Networks Using Hamiltonian Path. , 2017, CNS & neurological disorders drug targets.

[45]  Ravi S. Menon,et al.  Resting‐state networks show dynamic functional connectivity in awake humans and anesthetized macaques , 2013, Human brain mapping.

[46]  C. Stam,et al.  Small-world networks and functional connectivity in Alzheimer's disease. , 2006, Cerebral cortex.

[47]  Yudong Zhang,et al.  Exploring a smart pathological brain detection method on pseudo Zernike moment , 2017, Multimedia Tools and Applications.

[48]  Aihua Li,et al.  Fast and Accurate Mining the Community Structure: Integrating Center Locating and Membership Optimization , 2016, IEEE Transactions on Knowledge and Data Engineering.

[49]  Catie Chang,et al.  Time–frequency dynamics of resting-state brain connectivity measured with fMRI , 2010, NeuroImage.

[50]  Paul J. Laurienti,et al.  Modularity maps reveal community structure in the resting human brain , 2009 .

[51]  Lenan Wu,et al.  A New Classifier for Polarimetric SAR Images , 2009 .

[52]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[53]  Min Cai,et al.  Hub recognition for brain functional networks by using multiple-feature combination , 2018, Comput. Electr. Eng..

[54]  Yudong Zhang,et al.  Twelve-layer deep convolutional neural network with stochastic pooling for tea category classification on GPU platform , 2018, Multimedia Tools and Applications.

[55]  Huan Wang,et al.  Adaptive synchronisation of small-world networks with Lorenz chaotic oscillators , 2017, Int. J. Sens. Networks.