Centralized and distributed cognitive task processing in the human connectome

A key question in modern neuroscience is how cognitive changes in a human brain can be quantified and captured by functional connectivity (FC). A systematic approach to measure pairwise functional distance at different brain states is lacking. This would provide a straightforward way to quantify differences in cognitive processing across tasks; also, it would help in relating these differences in task-based FCs to the underlying structural network. Here we propose a framework, based on the concept of Jensen-Shannon divergence, to map the task-rest connectivity distance between tasks and resting-state FC. We show how this information theoretical measure allows for quantifying connectivity changes in distributed and centralized processing in functional networks. We study resting state and seven tasks from the Human Connectome Project dataset to obtain the most distant links across tasks. We investigate how these changes are associated with different functional brain networks, and use the proposed measure to infer changes in the information-processing regimes. Furthermore, we show how the FC distance from resting state is shaped by structural connectivity, and to what extent this relationship depends on the task. This framework provides a well-grounded mathematical quantification of connectivity changes associated with cognitive processing in large-scale brain networks. Author Summary A key question in modern neuroscience is how cognitive changes in a human brain can be quantified and captured by functional connectivity (FC). Here we propose a framework, based on Jensen-Shannon divergence, to define “connectivity distance” and to infer about brain network reconfiguration across different tasks with respect to resting state, and to explore changes in centralized and distributed processing in FCs. Three functional networks (dorsal attention, frontoparietal and DMN) showed major changes in distributed processing and minor changes in centralized processing. Changes in centralized processing depend on the underlying structural connectivity weights and structural path “hiddenness.” These findings suggest that the cognitive “switch” between resting state and task states is a complex interplay between maximally and minimally distant functional connections, and the underlying structure.

[1]  Jacques-Donald Tournier,et al.  Diffusion tensor imaging and beyond , 2011, Magnetic resonance in medicine.

[2]  Marjorie Jacobs,et al.  Default mode , 2016, Neurology.

[3]  Mark Jenkinson,et al.  The minimal preprocessing pipelines for the Human Connectome Project , 2013, NeuroImage.

[4]  Timothy O. Laumann,et al.  Methods to detect, characterize, and remove motion artifact in resting state fMRI , 2014, NeuroImage.

[5]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[6]  T. Schreiber,et al.  Surrogate time series , 1999, chao-dyn/9909037.

[7]  Edward T. Bullmore,et al.  Fundamentals of Brain Network Analysis , 2016 .

[8]  Marisa O. Hollinshead,et al.  The organization of the human cerebral cortex estimated by intrinsic functional connectivity. , 2011, Journal of neurophysiology.

[9]  Steen Moeller,et al.  Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project , 2013, NeuroImage.

[10]  Danielle S. Bassett,et al.  Structurally-Constrained Relationships between Cognitive States in the Human Brain , 2014, PLoS Comput. Biol..

[11]  Danielle S. Bassett,et al.  Subgraphs of functional brain networks identify dynamical constraints of cognitive control , 2017 .

[12]  Ludovica Griffanti,et al.  Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers , 2014, NeuroImage.

[13]  Jan Sijbers,et al.  Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data , 2014, NeuroImage.

[14]  Stefan Skare,et al.  How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging , 2003, NeuroImage.

[15]  Peter Harremoës,et al.  Properties of Classical and Quantum Jensen-Shannon Divergence , 2009 .

[16]  Michael Cole,et al.  Cognitive task information is transferred between brain regions via resting-state network topology , 2017 .

[17]  Denis Le Bihan,et al.  Looking into the functional architecture of the brain with diffusion MRI , 2003, Nature Reviews Neuroscience.

[18]  Steen Moeller,et al.  The Human Connectome Project: A data acquisition perspective , 2012, NeuroImage.

[19]  Michael Breakspear,et al.  Whole-brain analytic measures of network communication reveal increased structure-function correlation in right temporal lobe epilepsy , 2016, NeuroImage: Clinical.

[20]  Timothy Edward John Behrens,et al.  Task-free MRI predicts individual differences in brain activity during task performance , 2016, Science.

[21]  K Sneppen,et al.  Communication boundaries in networks. , 2005, Physical review letters.

[22]  Noah D. Brenowitz,et al.  Whole-brain, time-locked activation with simple tasks revealed using massive averaging and model-free analysis , 2012, Proceedings of the National Academy of Sciences.

[23]  Mark W. Woolrich,et al.  Resting-state fMRI in the Human Connectome Project , 2013, NeuroImage.

[24]  Nancy Kanwisher,et al.  Structural Connectivity Fingerprints Predict Cortical Selectivity for Multiple Visual Categories across Cortex. , 2016, Cerebral cortex.

[25]  Steen Moeller,et al.  Advances in diffusion MRI acquisition and processing in the Human Connectome Project , 2013, NeuroImage.

[26]  M. Breakspear,et al.  The connectomics of brain disorders , 2015, Nature Reviews Neuroscience.

[27]  Krzysztof J. Gorgolewski,et al.  The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance , 2015, Neuron.

[28]  Joaquín Goñi,et al.  Mapping the functional connectome traits of levels of consciousness , 2016, NeuroImage.

[29]  K. Sneppen,et al.  Searchability of networks. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

[31]  J. E. Tanner,et al.  Spin diffusion measurements : spin echoes in the presence of a time-dependent field gradient , 1965 .

[32]  Mark W. Woolrich,et al.  FSL , 2012, NeuroImage.

[33]  Ninon Burgos,et al.  New advances in the Clinica software platform for clinical neuroimaging studies , 2019 .

[34]  Evan M. Gordon,et al.  Evidence for Two Independent Factors that Modify Brain Networks to Meet Task Goals. , 2016, Cell reports.

[35]  Alan Connelly,et al.  SIFT: Spherical-deconvolution informed filtering of tractograms , 2013, NeuroImage.

[36]  Timothy O. Laumann,et al.  Informatics and Data Mining Tools and Strategies for the Human Connectome Project , 2011, Front. Neuroinform..

[37]  Alan Connelly,et al.  SIFT2: Enabling dense quantitative assessment of brain white matter connectivity using streamlines tractography , 2015, NeuroImage.

[38]  Enrico Amico,et al.  Mapping hybrid functional-structural connectivity traits in the human connectome , 2017, Network Neuroscience.

[39]  Dustin Scheinost,et al.  Can brain state be manipulated to emphasize individual differences in functional connectivity? , 2017, NeuroImage.

[40]  Abraham Z. Snyder,et al.  Function in the human connectome: Task-fMRI and individual differences in behavior , 2013, NeuroImage.

[41]  Dominik Endres,et al.  A new metric for probability distributions , 2003, IEEE Transactions on Information Theory.

[42]  Mason A. Porter,et al.  Task-Based Core-Periphery Organization of Human Brain Dynamics , 2012, PLoS Comput. Biol..

[43]  Olaf Sporns,et al.  THE HUMAN CONNECTOME: A COMPLEX NETWORK , 2011, Schizophrenia Research.

[44]  B T Thomas Yeo,et al.  Reconfigurable task-dependent functional coupling modes cluster around a core functional architecture , 2014, Philosophical Transactions of the Royal Society B: Biological Sciences.

[45]  Rachid Deriche,et al.  Optimal Design of Multiple Q-shells experiments for Diffusion MRI , 2011 .

[46]  P. Harremoes,et al.  Properties of Classical and Quantum Jensen-Shannon Divergence , 2008, 0806.4472.

[47]  Michael W. Cole,et al.  Higher Intelligence Is Associated with Less Task-Related Brain Network Reconfiguration , 2016, The Journal of Neuroscience.

[48]  M. V. D. Heuvel,et al.  Exploring the brain network: A review on resting-state fMRI functional connectivity , 2010, European Neuropsychopharmacology.

[49]  Michael W. Cole,et al.  Activity flow over resting-state networks shapes cognitive task activations , 2016, Nature Neuroscience.

[50]  Alan Connelly,et al.  Anatomically-constrained tractography: Improved diffusion MRI streamlines tractography through effective use of anatomical information , 2012, NeuroImage.

[51]  Richard F. Betzel,et al.  Structure–function relationships during segregated and integrated network states of human brain functional connectivity , 2018, Brain Structure and Function.

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

[53]  Essa Yacoub,et al.  The WU-Minn Human Connectome Project: An overview , 2013, NeuroImage.

[54]  G. Edelman,et al.  A measure for brain complexity: relating functional segregation and integration in the nervous system. , 1994, Proceedings of the National Academy of Sciences of the United States of America.

[55]  Richard F. Betzel,et al.  Resting-brain functional connectivity predicted by analytic measures of network communication , 2013, Proceedings of the National Academy of Sciences.

[56]  M. Fox,et al.  Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging , 2007, Nature Reviews Neuroscience.

[57]  Paul Suetens,et al.  Global tractography of multi-shell diffusion-weighted imaging data using a multi-tissue model , 2015, NeuroImage.

[58]  Jin Yu,et al.  Functional connectivity of resting brain , 2000 .

[59]  G L Shulman,et al.  INAUGURAL ARTICLE by a Recently Elected Academy Member:A default mode of brain function , 2001 .

[60]  I. Vajda,et al.  A new class of metric divergences on probability spaces and its applicability in statistics , 2003 .

[61]  Olaf Sporns,et al.  Network-Level Structure-Function Relationships in Human Neocortex , 2016, Cerebral cortex.

[62]  Walter Schneider,et al.  The cognitive control network: Integrated cortical regions with dissociable functions , 2007, NeuroImage.

[63]  Jonathan D. Power,et al.  Intrinsic and Task-Evoked Network Architectures of the Human Brain , 2014, Neuron.

[64]  Scott T. Grafton,et al.  Structural foundations of resting-state and task-based functional connectivity in the human brain , 2013, Proceedings of the National Academy of Sciences.

[65]  Vito Latora,et al.  Structural reducibility of multilayer networks , 2015, Nature Communications.

[66]  Alejandro Ribeiro,et al.  Functional Alignment with Anatomical Networks is Associated with Cognitive Flexibility , 2016, Nature Human Behaviour.

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

[68]  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.

[69]  Jesper Andersson,et al.  A multi-modal parcellation of human cerebral cortex , 2016, Nature.

[70]  Zeynep M. Saygin,et al.  Anatomical connectivity patterns predict face-selectivity in the fusiform gyrus , 2011, Nature Neuroscience.

[71]  Alan Connelly,et al.  MRtrix: Diffusion tractography in crossing fiber regions , 2012, Int. J. Imaging Syst. Technol..

[72]  M. Raichle The brain's default mode network. , 2015, Annual review of neuroscience.