The (in)stability of functional brain network measures across thresholds

The large-scale organization of the brain has features of complex networks that can be quantified using network measures from graph theory. However, many network measures were designed to be calculated on binary graphs, whereas functional brain organization is typically inferred from a continuous measure of correlations in temporal signal between brain regions. Thresholding is a necessary step to use binary graphs derived from functional connectivity data. However, there is no current consensus on what threshold to use, and network measures and group contrasts may be unstable across thresholds. Nevertheless, whole-brain network analyses are being applied widely with findings typically reported at an arbitrary threshold or range of thresholds. This study sought to evaluate the stability of network measures across thresholds in a large resting state functional connectivity dataset. Network measures were evaluated across absolute (correlation-based) and proportional (sparsity-based) thresholds, and compared between sex and age groups. Overall, network measures were found to be unstable across absolute thresholds. For example, the direction of group differences in a given network measure may change depending on the threshold. Network measures were found to be more stable across proportional thresholds. These results demonstrate that caution should be used when applying thresholds to functional connectivity data and when interpreting results from binary graph models.

[1]  Marián Boguñá,et al.  Extracting the multiscale backbone of complex weighted networks , 2009, Proceedings of the National Academy of Sciences.

[2]  Dustin Scheinost,et al.  A preliminary investigation of Stroop-related intrinsic connectivity in cocaine dependence: associations with treatment outcomes , 2013, The American journal of drug and alcohol abuse.

[3]  Xiaoqi Huang,et al.  Disrupted Brain Connectivity Networks in Drug-Naive, First-Episode Major Depressive Disorder , 2011, Biological Psychiatry.

[4]  Adolf Pfefferbaum,et al.  Disruption of functional connectivity of the default-mode network in alcoholism. , 2011, Cerebral cortex.

[5]  Irene Tracey,et al.  Resting fluctuations in arterial carbon dioxide induce significant low frequency variations in BOLD signal , 2004, NeuroImage.

[6]  Dustin Scheinost,et al.  Unified Framework for Development, Deployment and Robust Testing of Neuroimaging Algorithms , 2011, Neuroinformatics.

[7]  Edward T. Bullmore,et al.  SYSTEMS NEUROSCIENCE Original Research Article , 2009 .

[8]  Yong He,et al.  Hemisphere- and gender-related differences in small-world brain networks: A resting-state functional MRI study , 2011, NeuroImage.

[9]  Xenophon Papademetris,et al.  Groupwise whole-brain parcellation from resting-state fMRI data for network node identification , 2013, NeuroImage.

[10]  Paul J. Laurienti,et al.  The Brain as a Complex System: Using Network Science as a Tool for Understanding the Brain , 2011, Brain Connect..

[11]  R. Kahn,et al.  Efficiency of Functional Brain Networks and Intellectual Performance , 2009, The Journal of Neuroscience.

[12]  David J. Broadhurst,et al.  Exploiting the 1, 440-fold symmetry of the master two-loop diagram , 1986 .

[13]  O. Sporns.  Networks of the Brain , 2010 .

[14]  Fenna M. Krienen,et al.  Opportunities and limitations of intrinsic functional connectivity MRI , 2013, Nature Neuroscience.

[15]  Dustin Scheinost,et al.  Sex differences in normal age trajectories of functional brain networks , 2015, Human brain mapping.

[16]  Justin L. Vincent,et al.  Distinct brain networks for adaptive and stable task control in humans , 2007, Proceedings of the National Academy of Sciences.

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

[18]  J. Rapoport,et al.  The anatomical distance of functional connections predicts brain network topology in health and schizophrenia. , 2013, Cerebral cortex.

[19]  R. Kahn,et al.  Functionally linked resting‐state networks reflect the underlying structural connectivity architecture of the human brain , 2009, Human brain mapping.

[20]  N. Volkow,et al.  Functional connectivity density mapping , 2010, Proceedings of the National Academy of Sciences.

[21]  Alan C. Evans,et al.  Age- and Gender-Related Differences in the Cortical Anatomical Network , 2009, The Journal of Neuroscience.

[22]  Paul J Laurienti,et al.  Analyzing complex functional brain networks: Fusing statistics and network science to understand the brain*† , 2013, Statistics surveys.

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

[24]  Kevin Murphy,et al.  The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced? , 2009, NeuroImage.

[25]  R. Cameron Craddock,et al.  A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics , 2013, NeuroImage.

[26]  Andreas Daffertshofer,et al.  Comparing Brain Networks of Different Size and Connectivity Density Using Graph Theory , 2010, PloS one.

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

[28]  M. Fox,et al.  The global signal and observed anticorrelated resting state brain networks. , 2009, Journal of neurophysiology.

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

[30]  Thomas E. Nichols,et al.  Brain Network Analysis: Separating Cost from Topology Using Cost-Integration , 2011, PloS one.

[31]  Keith A. Johnson,et al.  Cortical Hubs Revealed by Intrinsic Functional Connectivity: Mapping, Assessment of Stability, and Relation to Alzheimer's Disease , 2009, The Journal of Neuroscience.

[32]  Edward T. Bullmore,et al.  Age-related changes in modular organization of human brain functional networks , 2009, NeuroImage.

[33]  Edward T. Bullmore,et al.  Efficiency and Cost of Economical Brain Functional Networks , 2007, PLoS Comput. Biol..

[34]  Dustin Scheinost,et al.  BOLD signal and functional connectivity associated with loving kindness meditation , 2014, Brain and behavior.

[35]  Xenophon Papademetris,et al.  Graph-theory based parcellation of functional subunits in the brain from resting-state fMRI data , 2010, NeuroImage.

[36]  Jonathan D. Power,et al.  Multi-task connectivity reveals flexible hubs for adaptive task control , 2013, Nature Neuroscience.

[37]  O. Sporns,et al.  Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.

[38]  Edward T. Bullmore,et al.  Frontiers in Systems Neuroscience Systems Neuroscience , 2022 .

[39]  Dustin Scheinost,et al.  The intrinsic connectivity distribution: A novel contrast measure reflecting voxel level functional connectivity , 2012, NeuroImage.

[40]  Massimo Marchiori,et al.  Economic small-world behavior in weighted networks , 2003 .

[41]  E. Bullmore,et al.  Hierarchical Organization of Human Cortical Networks in Health and Schizophrenia , 2008, The Journal of Neuroscience.

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

[43]  Thomas T. Liu,et al.  A component based noise correction method (CompCor) for BOLD and perfusion based fMRI , 2007, NeuroImage.

[44]  Steen Moeller,et al.  ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging , 2014, NeuroImage.

[45]  Danielle Smith Bassett,et al.  Small-World Brain Networks , 2006, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.