A Generalized Framework of Pathlength Associated Community Estimation for Brain Structural Network

Diffusion MRI-derived brain structural network has been widely used in brain research and community or modular structure is one of popular network features, which can be extracted from network edge-derived pathlengths. Conceptually, brain structural network edges represent the connecting strength between pair of nodes, thus non-negative. The pathlength. Many studies have demonstrated that each brain network edge can be affected by many confounding factors (e.g. age, sex, etc.) and this influence varies on each edge. However, after applying generalized linear regression to remove those confounding's effects, some network edges may become negative, which leads to barriers in extracting the community structure. In this study, we propose a novel generalized framework to solve this negative edge issue in extracting the modular structure from brain structural network. We have compared our framework with traditional Q method. The results clearly demonstrated that our framework has significant advantages in both stability and sensitivity.

[1]  Paul Thompson,et al.  Integrating Heterogeneous Brain Networks for Predicting Brain Disease Conditions , 2019, MICCAI.

[2]  Alex D. Leow,et al.  From Default Mode Network to the Basal Configuration: Sex Differences in the Resting-State Brain Connectivity as a Function of Age and Their Clinical Correlates , 2018, Front. Psychiatry.

[3]  Liang Zhan,et al.  The oblique effect: The relationship between profiles of visuospatial preference, cognition, and brain connectomics in older adults , 2019, Neuropsychologia.

[4]  M. Chun,et al.  Functional connectome fingerprinting: Identifying individuals based on patterns of brain connectivity , 2015, Nature Neuroscience.

[5]  Robert Kurzban,et al.  Modularity and the Social Mind , 2007, Personality and Social Psychology Review.

[6]  Xavier Font,et al.  Community structure informs species geographic distributions , 2018, PloS one.

[7]  Y. Benjamini.  Discovering the false discovery rate , 2010 .

[8]  David J. C. MacKay,et al.  Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.

[9]  Liang Zhan,et al.  Connectome signatures of neurocognitive abnormalities in euthymic bipolar I disorder. , 2015, Journal of psychiatric research.

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

[11]  Bader H Alhajeri,et al.  Community structure in ecological assemblages of desert rodents , 2018, Biological Journal of the Linnean Society.

[12]  Aifeng Zhang,et al.  White Matter Tract Integrity of Anterior Limb of Internal Capsule in Major Depression and Type 2 Diabetes , 2013, Neuropsychopharmacology.

[13]  Dan Schonfeld,et al.  Investigating brain community structure abnormalities in bipolar disorder using path length associated community estimation , 2014, Human brain mapping.

[14]  Alex R. Smith,et al.  Sex differences in the structural connectome of the human brain , 2013, Proceedings of the National Academy of Sciences.

[15]  Á. Dias,et al.  Reconsidering the Modularity of Social Cognition in the Human Brain , 2009, Front. Behav. Neurosci..

[16]  Lei Guo,et al.  Classifying Stages of Mild Cognitive Impairment via Augmented Graph Embedding , 2019, MBIA/MFCA@MICCAI.

[17]  A Díaz-Guilera,et al.  Self-similar community structure in a network of human interactions. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[18]  Adam J. Schwarz,et al.  Negative edges and soft thresholding in complex network analysis of resting state functional connectivity data , 2011, NeuroImage.

[19]  P. Matthews,et al.  A common brain network links development, aging, and vulnerability to disease , 2014, Proceedings of the National Academy of Sciences.

[20]  Lei Guo,et al.  Brain Dynamics Through the Lens of Statistical Mechanics by Unifying Structure and Function , 2019, MICCAI.

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

[22]  M. Newman,et al.  Finding community structure in very large networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[23]  Cassandra D. Leonardo,et al.  Comparison of nine tractography algorithms for detecting abnormal structural brain networks in Alzheimer’s disease , 2015, Front. Aging Neurosci..

[24]  Ken Wakita,et al.  Finding community structure in mega-scale social networks: [extended abstract] , 2007, WWW '07.

[25]  J. Langlois,et al.  The Epidemiology and Impact of Traumatic Brain Injury: A Brief Overview , 2006, The Journal of head trauma rehabilitation.

[26]  Liang Zhan,et al.  Altered structural brain connectome in young adult fragile X premutation carriers , 2014, Human brain mapping.

[27]  Paul M. Thompson,et al.  Biological Knowledge Guided Deep Neural Network for Brain Genotype-Phenotype Association Study , 2019, MBIA/MFCA@MICCAI.

[28]  Liang Zhan,et al.  Cardiovascular disease risk factors, tract-based structural connectomics, and cognition in older adults , 2019, NeuroImage.

[29]  P. V. van Zijl,et al.  Three‐dimensional tracking of axonal projections in the brain by magnetic resonance imaging , 1999, Annals of neurology.

[30]  Liang Zhan,et al.  The significance of negative correlations in brain connectivity , 2017, The Journal of comparative neurology.