The Human Brain Traverses a Common Activation-Pattern State Space Across Task and Rest

Much of our lives are spent in unconstrained rest states, yet cognitive brain processes are primarily investigated using task-constrained states. It may be possible to utilize the insights gained from experimental control of task processes as reference points for investigating unconstrained rest. To facilitate comparison of rest and task functional magnetic resonance imaging data, we focused on activation amplitude patterns, commonly used for task but not rest analyses. During rest, we identified spontaneous changes in temporally extended whole-brain activation-pattern states. This revealed a hierarchical organization of rest states. The top consisted of two competing states consistent with previously identified "task-positive" and "task-negative" activation patterns. These states were composed of more specific states that repeated over time and across individuals. Contrasting with the view that rest consists of only task-negative states, task-positive states occurred 40% of the time while individuals "rested," suggesting task-focused activity may occur during rest. Together our results suggest that brain activation dynamics form a general hierarchy across task and rest, with a small number of dominant general states reflecting basic functional modes and a variety of specific states potentially reflecting a wide variety of cognitive processes.

[1]  Thomas E. Nichols,et al.  Functional connectomics from resting-state fMRI , 2013, Trends in Cognitive Sciences.

[2]  Sung-Hyuk Cha Comprehensive Survey on Distance/Similarity Measures between Probability Density Functions , 2007 .

[3]  Evan M. Gordon,et al.  On the Stability of BOLD fMRI Correlations , 2016, Cerebral cortex.

[4]  Mark W. Woolrich,et al.  Network modelling methods for FMRI , 2011, NeuroImage.

[5]  J. Smallwood,et al.  The restless mind. , 2006, Psychological bulletin.

[6]  Carl T. Bergstrom,et al.  The map equation , 2009, 0906.1405.

[7]  B. Biswal,et al.  Functional connectivity of default mode network components: Correlation, anticorrelation, and causality , 2009, Human brain mapping.

[8]  J. Duyn,et al.  Time-varying functional network information extracted from brief instances of spontaneous brain activity , 2013, Proceedings of the National Academy of Sciences.

[9]  David T. Jones,et al.  Non-Stationarity in the “Resting Brain’s” Modular Architecture , 2012, PloS one.

[10]  Timothy O. Laumann,et al.  Functional Network Organization of the Human Brain , 2011, Neuron.

[11]  Catie Chang,et al.  Decomposition of spontaneous brain activity into distinct fMRI co-activation patterns , 2013, Front. Syst. Neurosci..

[12]  W. K. Simmons,et al.  Circular analysis in systems neuroscience: the dangers of double dipping , 2009, Nature Neuroscience.

[13]  Xiao-Jing Wang Neurophysiological and computational principles of cortical rhythms in cognition. , 2010, Physiological reviews.

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

[15]  Han Yuan,et al.  Spatiotemporal dynamics of the brain at rest — Exploring EEG microstates as electrophysiological signatures of BOLD resting state networks , 2012, NeuroImage.

[16]  Birte U. Forstmann,et al.  A Neural Model of Mind Wandering , 2016, Trends in Cognitive Sciences.

[17]  R. Poldrack Can cognitive processes be inferred from neuroimaging data? , 2006, Trends in Cognitive Sciences.

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

[19]  J. S. Guntupalli,et al.  Decoding neural representational spaces using multivariate pattern analysis. , 2014, Annual review of neuroscience.

[20]  Craig E. L. Stark,et al.  When zero is not zero: The problem of ambiguous baseline conditions in fMRI , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[21]  Bradley Voytek,et al.  Cycle-by-cycle analysis of neural oscillations. , 2019, Journal of neurophysiology.

[22]  J. Haynes A Primer on Pattern-Based Approaches to fMRI: Principles, Pitfalls, and Perspectives , 2015, Neuron.

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

[24]  Jessica A. Turner,et al.  Neuroinformatics Original Research Article , 2022 .

[25]  Stephen José Hanson,et al.  Decoding the Large-Scale Structure of Brain Function by Classifying Mental States Across Individuals , 2009, Psychological science.

[26]  Stephen M. Smith,et al.  Temporally-independent functional modes of spontaneous brain activity , 2012, Proceedings of the National Academy of Sciences.

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

[28]  J. Smallwood,et al.  Back to the future: Autobiographical planning and the functionality of mind-wandering , 2011, Consciousness and Cognition.

[29]  Martin Rosvall,et al.  Multilevel Compression of Random Walks on Networks Reveals Hierarchical Organization in Large Integrated Systems , 2010, PloS one.

[30]  Olaf Sporns,et al.  Synchronization dynamics and evidence for a repertoire of network states in resting EEG , 2012, Front. Comput. Neurosci..

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

[32]  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.

[33]  Imran N. Junejo,et al.  Learning Self-Similarities for Action Recognition Using Conditional Random Fields , 2010 .

[34]  A. Dale,et al.  Whole Brain Segmentation Automated Labeling of Neuroanatomical Structures in the Human Brain , 2002, Neuron.

[35]  C. Furusawa,et al.  A Dynamical-Systems View of Stem Cell Biology , 2012, Science.

[36]  M. Raichle Two views of brain function , 2010, Trends in Cognitive Sciences.

[37]  Anders M. Dale,et al.  Sequence-independent segmentation of magnetic resonance images , 2004, NeuroImage.

[38]  Vince D. Calhoun,et al.  Dynamic modeling of neuronal responses in fMRI using cubature Kalman filtering , 2011, NeuroImage.

[39]  M. Corbetta,et al.  Control of goal-directed and stimulus-driven attention in the brain , 2002, Nature Reviews Neuroscience.

[40]  Bharat B. Biswal,et al.  The oscillating brain: Complex and reliable , 2010, NeuroImage.

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

[42]  Á. Pascual-Leone,et al.  Microstates in resting-state EEG: Current status and future directions , 2015, Neuroscience & Biobehavioral Reviews.

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

[44]  D. Schacter,et al.  The Brain's Default Network , 2008, Annals of the New York Academy of Sciences.

[45]  Ludvig Bohlin,et al.  Community detection and visualization of networks with the map equation framework , 2014 .

[46]  Eswar Damaraju,et al.  Tracking whole-brain connectivity dynamics in the resting state. , 2014, Cerebral cortex.

[47]  Stephen M Smith,et al.  Correspondence of the brain's functional architecture during activation and rest , 2009, Proceedings of the National Academy of Sciences.

[48]  Biyu J. He Spontaneous and Task-Evoked Brain Activity Negatively Interact , 2013, The Journal of Neuroscience.

[49]  Xiaohong Shen,et al.  Instantaneous Brain Dynamics Mapped to a Continuous State Space , 2017 .

[50]  Stephen M. Smith,et al.  Brain network dynamics are hierarchically organized in time , 2017, Proceedings of the National Academy of Sciences.

[51]  S. Dehaene,et al.  Characterizing the dynamics of mental representations: the temporal generalization method , 2014, Trends in Cognitive Sciences.

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

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

[54]  Jian Yang,et al.  Using brain imaging to track problem solving in a complex state space , 2012, NeuroImage.

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

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

[57]  Justin L. Vincent,et al.  Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

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

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

[60]  Kevin S. Brown,et al.  Cooperation between the default mode network and the frontal–parietal network in the production of an internal train of thought , 2012, Brain Research.

[61]  K. Christoff,et al.  Experience sampling during fMRI reveals default network and executive system contributions to mind wandering , 2009, Proceedings of the National Academy of Sciences.

[62]  Maurizio Corbetta,et al.  The human brain is intrinsically organized into dynamic, anticorrelated functional networks. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[63]  Catie Chang,et al.  Introducing co-activation pattern metrics to quantify spontaneous brain network dynamics , 2015, NeuroImage.

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

[65]  David A. Leopold,et al.  Dynamic functional connectivity: Promise, issues, and interpretations , 2013, NeuroImage.

[66]  Russell A. Poldrack,et al.  Large-scale automated synthesis of human functional neuroimaging data , 2011, Nature Methods.