Hierarchical functional modularity in the resting‐state human brain

Functional magnetic resonance imaging (fMRI) studies have shown that anatomically distinct brain regions are functionally connected during the resting state. Basic topological properties in the brain functional connectivity (BFC) map have highlighted the BFC's small‐world topology. Modularity, a more advanced topological property, has been hypothesized to be evolutionary advantageous, contributing to adaptive aspects of anatomical and functional brain connectivity. However, current definitions of modularity for complex networks focus on nonoverlapping clusters, and are seriously limited by disregarding inclusive relationships. Therefore, BFC's modularity has been mainly qualitatively investigated. Here, we introduce a new definition of modularity, based on a recently improved clustering measurement, which overcomes limitations of previous definitions, and apply it to the study of BFC in resting state fMRI of 53 healthy subjects. Results show hierarchical functional modularity in the brain. Hum Brain Mapp, 2009. © 2008 Wiley‐Liss, Inc.

[1]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[2]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[3]  Jon H. Kaas,et al.  Why is Brain Size so Important:Design Problems and Solutions as Neocortex Gets Biggeror Smaller , 2000 .

[4]  Karl J. Friston,et al.  Variability in fMRI: An Examination of Intersession Differences , 2000, NeuroImage.

[5]  Albert-László Barabási,et al.  Error and attack tolerance of complex networks , 2000, Nature.

[6]  Lada A. Adamic,et al.  Power-Law Distribution of the World Wide Web , 2000, Science.

[7]  Stephen M. Smith,et al.  A global optimisation method for robust affine registration of brain images , 2001, Medical Image Anal..

[8]  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.

[9]  Michael Brady,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[10]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[11]  A. Barabasi,et al.  Hierarchical Organization of Modularity in Metabolic Networks , 2002, Science.

[12]  Jennifer Hallinan.  Self-Organization Leads to Hierarchical Modularity in an Internet Community , 2003, KES.

[13]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[14]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[15]  O. Sporns,et al.  Motifs in Brain Networks , 2004, PLoS biology.

[16]  Claudio Castellano,et al.  Defining and identifying communities in networks. , 2003, Proceedings of the National Academy of Sciences of the United States of America.

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

[18]  Stephen M Smith,et al.  Variability in fMRI: A re‐examination of inter‐session differences , 2005, Human brain mapping.

[19]  G. Cecchi,et al.  Scale-free brain functional networks. , 2003, Physical review letters.

[20]  Olaf Sporns,et al.  The Human Connectome: A Structural Description of the Human Brain , 2005, PLoS Comput. Biol..

[21]  D. Drachman.  Do we have brain to spare? , 2005, Neurology.

[22]  A. Vázquez,et al.  Network clustering coefficient without degree-correlation biases. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[23]  G. Rees,et al.  Predicting the orientation of invisible stimuli from activity in human primary visual cortex , 2005, Nature Neuroscience.

[24]  T. Vicsek,et al.  Uncovering the overlapping community structure of complex networks in nature and society , 2005, Nature.

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

[26]  Leon Danon,et al.  Comparing community structure identification , 2005, cond-mat/0505245.

[27]  A. Barabasi,et al.  Taming complexity , 2005 .

[28]  S. Rombouts,et al.  Consistent resting-state networks across healthy subjects , 2006, Proceedings of the National Academy of Sciences.

[29]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[30]  E. Bullmore,et al.  A Resilient, Low-Frequency, Small-World Human Brain Functional Network with Highly Connected Association Cortical Hubs , 2006, The Journal of Neuroscience.

[31]  Olaf Sporns,et al.  Small worlds inside big brains , 2006, Proceedings of the National Academy of Sciences.

[32]  Stephen M. Smith,et al.  fMRI resting state networks define distinct modes of long-distance interactions in the human brain , 2006, NeuroImage.

[33]  Rainer Goebel,et al.  Classification of fMRI independent components using IC-fingerprints and support vector machine classifiers , 2007, NeuroImage.

[34]  John A. Bullinaria,et al.  Understanding the Emergence of Modularity in Neural Systems , 2007, Cogn. Sci..

[35]  H. Berendse,et al.  The application of graph theoretical analysis to complex networks in the brain , 2007, Clinical Neurophysiology.

[36]  Cornelis J Stam,et al.  Graph theoretical analysis of complex networks in the brain , 2007, Nonlinear biomedical physics.

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

[38]  Olaf Sporns,et al.  Network structure of cerebral cortex shapes functional connectivity on multiple time scales , 2007, Proceedings of the National Academy of Sciences.

[39]  O. Sporns,et al.  Identification and Classification of Hubs in Brain Networks , 2007, PloS one.

[40]  Edward T. Bullmore,et al.  Frequency based mutual information measures between clusters of brain regions in functional magnetic resonance imaging , 2007, NeuroImage.

[41]  C. Stam,et al.  Heritability of “small‐world” networks in the brain: A graph theoretical analysis of resting‐state EEG functional connectivity , 2008, Human brain mapping.

[42]  P. Cuijpers,et al.  The Netherlands Study of Depression and Anxiety (NESDA): rationale, objectives and methods , 2008, International journal of methods in psychiatric research.

[43]  Edward T. Bullmore,et al.  A simple view of the brain through a frequency-specific functional connectivity measure , 2008, NeuroImage.

[44]  Daniel L. Rubin,et al.  Network Analysis of Intrinsic Functional Brain Connectivity in Alzheimer's Disease , 2008, PLoS Comput. Biol..

[45]  Alan C. Evans,et al.  Structural Insights into Aberrant Topological Patterns of Large-Scale Cortical Networks in Alzheimer's Disease , 2008, The Journal of Neuroscience.

[46]  C. Stam,et al.  Small‐world properties of nonlinear brain activity in schizophrenia , 2009, Human brain mapping.

[47]  Olaf Sporns,et al.  What Is the Human Connectome , 2009 .

[48]  J. Haynes.  Brain Reading: Decoding Mental States From Brain Activity In Humans , 2011 .