Global features of functional brain networks change with contextual disorder

It is known that features of stimuli in the environment affect the strength of functional connectivity in the human brain. However, investigations to date have not converged in determining whether these also impact functional networks' global features, such as modularity strength, number of modules, partition structure, or degree distributions. We hypothesized that one environmental attribute that may strongly impact global features is the temporal regularity of the environment, as prior work indicates that differences in regularity impact regions involved in sensory, attentional and memory processes. We examined this with an fMRI study, in which participants passively listened to tonal series that had identical physical features and differed only in their regularity, as defined by the strength of transition structure between tones. We found that series-regularity induced systematic changes to global features of functional networks, including modularity strength, number of modules, partition structure, and degree distributions. In tandem, we used a novel node-level analysis to determine the extent to which brain regions maintained their within-module connectivity across experimental conditions. This analysis showed that primary sensory regions and those associated with default-mode processes are most likely to maintain their within-module connectivity across conditions, whereas prefrontal regions are least likely to do so. Our work documents a significant capacity for global-level brain network reorganization as a function of context. These findings suggest that modularity and other core, global features, while likely constrained by white-matter structural brain connections, are not completely determined by them.

[1]  Jennifer A. Mangels,et al.  Predictive Codes for Forthcoming Perception in the Frontal Cortex , 2006, Science.

[2]  P. Vitz,et al.  PREFERENCES FOR RATES OF INFORMATION PRESENTED BY SEQUENCES OF TONES. , 1964, Journal of experimental psychology.

[3]  S. Fortunato,et al.  Resolution limit in community detection , 2006, Proceedings of the National Academy of Sciences.

[4]  Rafael Malach,et al.  Stimulus-free thoughts induce differential activation in the human default network , 2011, NeuroImage.

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

[6]  Jonathan D. Cohen,et al.  Improved Assessment of Significant Activation in Functional Magnetic Resonance Imaging (fMRI): Use of a Cluster‐Size Threshold , 1995, Magnetic resonance in medicine.

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

[8]  Peter A. Bandettini,et al.  Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI , 2006, NeuroImage.

[9]  P. Vitz Affect as a function of stimulus variation. , 1966, Journal of experimental psychology.

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

[11]  O. Sporns,et al.  Key role of coupling, delay, and noise in resting brain fluctuations , 2009, Proceedings of the National Academy of Sciences.

[12]  C. Kelly,et al.  The extrinsic and intrinsic functional architectures of the human brain are not equivalent. , 2013, Cerebral cortex.

[13]  Olaf Sporns,et al.  Network attributes for segregation and integration in the human brain , 2013, Current Opinion in Neurobiology.

[14]  Jordan B. Peterson,et al.  Psychological entropy: a framework for understanding uncertainty-related anxiety. , 2012, Psychological review.

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

[16]  C D Frith,et al.  On the benefits of not trying: brain activity and connectivity reflecting the interactions of explicit and implicit sequence learning. , 2005, Cerebral cortex.

[17]  Paul J. Laurienti,et al.  Changes in global and regional modularity associated with increasing working memory load , 2014, Front. Hum. Neurosci..

[18]  L. Hubert,et al.  Comparing partitions , 1985 .

[19]  Benjamin H. Good,et al.  Performance of modularity maximization in practical contexts. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[20]  Martin Suter,et al.  Small World , 2002 .

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

[22]  Bernard Mazoyer,et al.  Patterns of hemodynamic low-frequency oscillations in the brain are modulated by the nature of free thought during rest , 2012, NeuroImage.

[23]  M. Masson,et al.  Using confidence intervals in within-subject designs , 1994, Psychonomic bulletin & review.

[24]  Scott T. Grafton,et al.  Wandering Minds: The Default Network and Stimulus-Independent Thought , 2007, Science.

[25]  Scott T. Grafton,et al.  Swinging in the brain: shared neural substrates for behaviors related to sequencing and music , 2003, Nature Neuroscience.

[26]  Stephen M. Smith,et al.  A Bayesian model of shape and appearance for subcortical brain segmentation , 2011, NeuroImage.

[27]  Danielle S Bassett,et al.  Brain graphs: graphical models of the human brain connectome. , 2011, Annual review of clinical psychology.

[28]  Paul J. Laurienti,et al.  Changes in Cognitive State Alter Human Functional Brain Networks , 2011, Front. Hum. Neurosci..

[29]  R W Cox,et al.  AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. , 1996, Computers and biomedical research, an international journal.

[30]  Karl J. Friston,et al.  A Dual Role for Prediction Error in Associative Learning , 2008, Cerebral cortex.

[31]  H. Nusbaum,et al.  Task-dependent organization of brain regions active during rest , 2009, Proceedings of the National Academy of Sciences.

[32]  Aiden E. G. F. Arnold,et al.  Spatial and temporal functional connectivity changes between resting and attentive states , 2015, Human brain mapping.

[33]  A. Zalesky,et al.  Competitive and cooperative dynamics of large-scale brain functional networks supporting recollection , 2012, Proceedings of the National Academy of Sciences.

[34]  Jessica A. Grahn,et al.  Finding and Feeling the Musical Beat: Striatal Dissociations between Detection and Prediction of Regularity , 2012, Cerebral cortex.

[35]  Uri Hasson,et al.  Connectivity in the human brain dissociates entropy and complexity of auditory inputs , 2015, NeuroImage.

[36]  C. J. Honeya,et al.  Predicting human resting-state functional connectivity from structural connectivity , 2009 .

[37]  Manfred G Kitzbichler,et al.  Cognitive Effort Drives Workspace Configuration of Human Brain Functional Networks , 2011, The Journal of Neuroscience.

[38]  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.

[39]  Viviana Betti,et al.  Natural Scenes Viewing Alters the Dynamics of Functional Connectivity in the Human Brain , 2013, Neuron.

[40]  Jennifer A. Mangels,et al.  Neocortical Connectivity during Episodic Memory Formation , 2006, PLoS biology.

[41]  Edward T. Bullmore,et al.  Neuroinformatics Original Research Article , 2022 .

[42]  Martin Meyer,et al.  Language in the brain at rest: new insights from resting state data and graph theoretical analysis , 2014, Front. Hum. Neurosci..

[43]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[44]  Ludmila I. Kuncheva,et al.  Using diversity in cluster ensembles , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[45]  Siyuan Liu,et al.  Embodied Comprehension of Stories: Interactions between Language Regions and Modality-specific Neural Systems , 2014, Journal of Cognitive Neuroscience.

[46]  Habib Benali,et al.  Relating Structure and Function in the Human Brain: Relative Contributions of Anatomy, Stationary Dynamics, and Non-stationarities , 2014, PLoS Comput. Biol..

[47]  J. Antrobus INFORMATION THEORY AND STIMULUS‐INDEPENDENT THOUGHT , 1968 .

[48]  Karl J. Friston,et al.  Behavioral / Systems / Cognitive Striatal Prediction Error Modulates Cortical Coupling , 2010 .

[49]  S. Tobimatsu,et al.  Efficiency of a "small-world" brain network depends on consciousness level: a resting-state FMRI study. , 2014, Cerebral cortex.

[50]  Uri Hasson,et al.  Neural systems mediating recognition of changes in statistical regularities , 2012, NeuroImage.

[51]  Alan C. Evans,et al.  Revealing modular architecture of human brain structural networks by using cortical thickness from MRI. , 2008, Cerebral cortex.

[52]  Santo Fortunato,et al.  Consensus clustering in complex networks , 2012, Scientific Reports.

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

[54]  Yury Shtyrov,et al.  Fast reconfiguration of high-frequency brain networks in response to surprising changes in auditory input. , 2012, Journal of neurophysiology.

[55]  Walter Schneider,et al.  Identifying the brain's most globally connected regions , 2010, NeuroImage.

[56]  William M. Rand,et al.  Objective Criteria for the Evaluation of Clustering Methods , 1971 .

[57]  C. Fiebach,et al.  Predicting errors from reconfiguration patterns in human brain networks , 2012, Proceedings of the National Academy of Sciences.

[58]  Paul J. Laurienti,et al.  Comparison of characteristics between region-and voxel-based network analyses in resting-state fMRI data , 2010, NeuroImage.

[59]  John D E Gabrieli,et al.  A Corticostriatal Neural System Enhances Auditory Perception through Temporal Context Processing , 2012, The Journal of Neuroscience.

[60]  E. Bullmore,et al.  Adaptive reconfiguration of fractal small-world human brain functional networks , 2006, Proceedings of the National Academy of Sciences.

[61]  G H Glover,et al.  Image‐based method for retrospective correction of physiological motion effects in fMRI: RETROICOR , 2000, Magnetic resonance in medicine.

[62]  Rafael Malach,et al.  Extrinsic and intrinsic systems in the posterior cortex of the human brain revealed during natural sensory stimulation. , 2007, Cerebral cortex.

[63]  Ning Zhong,et al.  Changes in the brain intrinsic organization in both on-task state and post-task resting state , 2012, NeuroImage.

[64]  Viktor K. Jirsa,et al.  Noise during Rest Enables the Exploration of the Brain's Dynamic Repertoire , 2008, PLoS Comput. Biol..

[65]  Scott T. Grafton,et al.  Dynamic reconfiguration of human brain networks during learning , 2010, Proceedings of the National Academy of Sciences.

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