Conserved Ising Model on the Human Connectome

Dynamical models implemented on the large scale architecture of the human brain may shed light on how function arises from the underlying structure. This is the case notably for simple abstract models, such as the Ising model. We compare the spin correlations of the Ising model and the empirical functional brain correlations, both at the single link level and at the modular level, and show that their match increases at the modular level in anesthesia, in line with recent results and theories. Moreover, we show that at the peak of the specific heat (the \it{critical state}) the spin correlations are minimally shaped by the underlying structural network, explaining how the best match between structure and function is obtained at the onset of criticality, as previously observed. These findings confirm that brain dynamics under anesthesia shows a departure from criticality and could open the way to novel perspectives when the conserved magnetization is interpreted in terms of an homeostatic principle imposed to neural activity.

[1]  Maurizio Corbetta,et al.  Resting-State Functional Connectivity Emerges from Structurally and Dynamically Shaped Slow Linear Fluctuations , 2013, The Journal of Neuroscience.

[2]  Viktor K. Jirsa,et al.  The Virtual Brain: a simulator of primate brain network dynamics , 2013, Front. Neuroinform..

[3]  Gustavo Deco,et al.  Role of local network oscillations in resting-state functional connectivity , 2011, NeuroImage.

[4]  M. Boly,et al.  Breakdown of within- and between-network Resting State Functional Magnetic Resonance Imaging Connectivity during Propofol-induced Loss of Consciousness , 2010, Anesthesiology.

[5]  D. Chialvo Emergent complex neural dynamics , 2010, 1010.2530.

[6]  Steven C.R. Williams,et al.  Proc. Intl. Soc. Mag. Reson. Med. , 2012 .

[7]  Steven Laureys,et al.  A role for the default mode network in the bases of disorders of consciousness , 2012, Annals of neurology.

[8]  Helen H. Shen,et al.  Core Concept: Resting-state connectivity , 2015, Proceedings of the National Academy of Sciences.

[9]  D. Long Networks of the Brain , 2011 .

[10]  Gustavo Deco,et al.  How anatomy shapes dynamics: a semi-analytical study of the brain at rest by a simple spin model , 2012, Front. Comput. Neurosci..

[11]  S Laureys,et al.  A default mode of brain function in altered states of consciousness. , 2012, Archives italiennes de biologie.

[12]  D. Chialvo,et al.  Ising-like dynamics in large-scale functional brain networks. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[13]  Thomas Benner,et al.  Diffusion imaging with prospective motion correction and reacquisition , 2011, Magnetic resonance in medicine.

[14]  Andrea Taroni,et al.  Statistical physics: 90 years of the Ising model , 2015 .

[15]  K. Kawasaki Diffusion Constants near the Critical Point for Time-Dependent Ising Models. I , 1966 .

[16]  R. Glauber Time‐Dependent Statistics of the Ising Model , 1963 .

[17]  M. Meilă Comparing clusterings---an information based distance , 2007 .

[18]  G. Deco,et al.  Ongoing Cortical Activity at Rest: Criticality, Multistability, and Ghost Attractors , 2012, The Journal of Neuroscience.

[19]  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.

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

[21]  M. A. Muñoz,et al.  A novel brain partition highlights the modular skeleton shared by structure and function , 2014, Scientific Reports.

[22]  Jari Saramäki,et al.  Graph coarse‐graining reveals differences in the module‐level structure of functional brain networks , 2016, The European journal of neuroscience.

[23]  Edward T. Bullmore,et al.  Deep sleep divides the cortex into opposite modes of anatomical–functional coupling , 2016, Brain Structure and Function.

[24]  Luc Berthouze,et al.  Markers of criticality in phase synchronization , 2014, Front. Syst. Neurosci..

[25]  O. Sporns,et al.  Mapping the Structural Core of Human Cerebral Cortex , 2008, PLoS biology.

[26]  Karl J. Friston,et al.  Structural and Functional Brain Networks: From Connections to Cognition , 2013, Science.

[27]  Jorge Hidalgo,et al.  Information-based fitness and the emergence of criticality in living systems , 2013, Proceedings of the National Academy of Sciences.

[28]  M. Sigman,et al.  Signature of consciousness in the dynamics of resting-state brain activity , 2015, Proceedings of the National Academy of Sciences.

[29]  M. A. Muñoz,et al.  Griffiths phases and the stretching of criticality in brain networks , 2013, Nature Communications.

[30]  Justin L. Vincent,et al.  Intrinsic functional architecture in the anaesthetized monkey brain , 2007, Nature.

[31]  S. Redner,et al.  A Kinetic View of Statistical Physics , 2010 .

[32]  T. Bossomaier,et al.  Information flow in a kinetic Ising model peaks in the disordered phase. , 2013, Physical review letters.

[33]  Richard F. Betzel,et al.  Modular Brain Networks. , 2016, Annual review of psychology.

[34]  G. Deco,et al.  Emerging concepts for the dynamical organization of resting-state activity in the brain , 2010, Nature Reviews Neuroscience.

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

[36]  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.

[37]  Michael Breakspear,et al.  Critical role for resource constraints in neural models , 2014, Front. Syst. Neurosci..

[38]  Dante R Chialvo,et al.  Brain organization into resting state networks emerges at criticality on a model of the human connectome. , 2012, Physical review letters.

[39]  O Sporns,et al.  Predicting human resting-state functional connectivity from structural connectivity , 2009, Proceedings of the National Academy of Sciences.

[40]  Anthony R. McIntosh,et al.  The Virtual Brain: Modeling Biological Correlates of Recovery after Chronic Stroke , 2015, Front. Neurol..

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

[42]  Steven Laureys,et al.  Large-scale signatures of unconsciousness are consistent with a departure from critical dynamics , 2015, Journal of The Royal Society Interface.

[43]  Olaf Sporns,et al.  Weight-conserving characterization of complex functional brain networks , 2011, NeuroImage.

[44]  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.

[45]  Daniele Marinazzo,et al.  Information Transfer and Criticality in the Ising Model on the Human Connectome , 2014, PloS one.