Human cognition involves the dynamic integration of neural activity and neuromodulatory systems

The human brain integrates diverse cognitive processes into a coherent whole, shifting fluidly as a function of changing environmental demands. Despite recent progress, the neurobiological mechanisms responsible for this dynamic system-level integration remain poorly understood. Here we investigated the spatial, dynamic, and molecular signatures of system-wide neural activity across a range of cognitive tasks. We found that neuronal activity converged onto a low-dimensional manifold that facilitates the execution of diverse task states. Flow within this attractor space was associated with dissociable cognitive functions, unique patterns of network-level topology, and individual differences in fluid intelligence. The axes of the low-dimensional neurocognitive architecture aligned with regional differences in the density of neuromodulatory receptors, which in turn relate to distinct signatures of network controllability estimated from the structural connectome. These results advance our understanding of functional brain organization by emphasizing the interface between neural activity, neuromodulatory systems, and cognitive function.Neuronal activity across task states converges onto a low-dimensional manifold. Flow within this attractor space covaries with network-level topology, fluid intelligence, and regional differences in the density of neuromodulatory receptors.

[1]  G. P. King,et al.  Extracting qualitative dynamics from experimental data , 1986 .

[2]  Karl J. Friston,et al.  Functional Connectivity: The Principal-Component Analysis of Large (PET) Data Sets , 1993, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[3]  G. Edelman,et al.  Consciousness and Complexity , 1998 .

[4]  G Tononi,et al.  Measures of degeneracy and redundancy in biological networks. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[5]  Jonathan D. Cohen,et al.  Computational perspectives on dopamine function in prefrontal cortex , 2002, Current Opinion in Neurobiology.

[6]  Henry D I Abarbanel,et al.  False neighbors and false strands: a reliable minimum embedding dimension algorithm. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[7]  Michel Le Van Quyen,et al.  Disentangling the dynamic core: a research program for a neurodynamics at the large-scale , 2003 .

[8]  C. Chabris,et al.  Neural mechanisms of general fluid intelligence , 2003, Nature Neuroscience.

[9]  Anthony Randal McIntosh,et al.  Partial least squares analysis of neuroimaging data: applications and advances , 2004, NeuroImage.

[10]  R. Malach,et al.  Intersubject Synchronization of Cortical Activity During Natural Vision , 2004, Science.

[11]  Jonathan D. Cohen,et al.  An integrative theory of locus coeruleus-norepinephrine function: adaptive gain and optimal performance. , 2005, Annual review of neuroscience.

[12]  R. Guimerà,et al.  Functional cartography of complex metabolic networks , 2005, Nature.

[13]  Kristina M. Visscher,et al.  A Core System for the Implementation of Task Sets , 2006, Neuron.

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

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

[16]  E. Ott,et al.  Low dimensional behavior of large systems of globally coupled oscillators. , 2008, Chaos.

[17]  Jörn Diedrichsen,et al.  A probabilistic MR atlas of the human cerebellum , 2009, NeuroImage.

[18]  T. Robbins,et al.  The neuropsychopharmacology of fronto-executive function: monoaminergic modulation. , 2009, Annual review of neuroscience.

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

[20]  V. Brezina Beyond the wiring diagram: signalling through complex neuromodulator networks , 2010, Philosophical Transactions of the Royal Society B: Biological Sciences.

[21]  D. Heeger,et al.  Slow Cortical Dynamics and the Accumulation of Information over Long Timescales , 2012, Neuron.

[22]  Christopher P. Said,et al.  Top-down attention switches coupling between low-level and high-level areas of human visual cortex , 2012, Proceedings of the National Academy of Sciences.

[23]  Russell A. Poldrack,et al.  Discovering Relations Between Mind, Brain, and Mental Disorders Using Topic Mapping , 2012, PLoS Comput. Biol..

[24]  Karl J. Friston,et al.  Free Energy, Precision and Learning: The Role of Cholinergic Neuromodulation , 2013, The Journal of Neuroscience.

[25]  Jonathan D. Power,et al.  Evidence for Hubs in Human Functional Brain Networks , 2013, Neuron.

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

[27]  O. Sporns,et al.  An Anatomical Substrate for Integration among Functional Networks in Human Cortex , 2013, The Journal of Neuroscience.

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

[29]  Viktor K. Jirsa,et al.  Emergent Dynamics from Spiking Neuron Networks through Symmetry Breaking of Connectivity , 2013, PloS one.

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

[31]  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.

[32]  Linda Douw,et al.  Task-dependent reorganization of functional connectivity networks during visual semantic decision making , 2014, Brain and behavior.

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

[34]  Byron M. Yu,et al.  Dimensionality reduction for large-scale neural recordings , 2014, Nature Neuroscience.

[35]  Viktor Jirsa,et al.  Functional architectures and structured flows on manifolds: a dynamical framework for motor behavior. , 2014, Psychological review.

[36]  Leonardo L. Gollo,et al.  Dwelling quietly in the rich club: brain network determinants of slow cortical fluctuations , 2015, Philosophical Transactions of the Royal Society B: Biological Sciences.

[37]  Russell A. Poldrack,et al.  Estimation of dynamic functional connectivity using Multiplication of Temporal Derivatives , 2015, NeuroImage.

[38]  Theodore H. Lindsay,et al.  Global Brain Dynamics Embed the Motor Command Sequence of Caenorhabditis elegans , 2015, Cell.

[39]  Jean M. Vettel,et al.  Controllability of structural brain networks , 2014, Nature Communications.

[40]  Kristin Branson,et al.  Whole-central nervous system functional imaging in larval Drosophila , 2015, Nature Communications.

[41]  Russell A. Poldrack,et al.  Dynamic fluctuations in global brain network topology characterize functional states during rest and behavior , 2015, 1511.02976.

[42]  M. Malmierca,et al.  Editorial: Neuromodulation of executive circuits , 2015, Front. Neural Circuits.

[43]  G. Tononi,et al.  Stimulus Set Meaningfulness and Neurophysiological Differentiation: A Functional Magnetic Resonance Imaging Study , 2014, bioRxiv.

[44]  R. Poldrack,et al.  From Brain Maps to Cognitive Ontologies: Informatics and the Search for Mental Structure. , 2016, Annual review of psychology.

[45]  Ben D. Fulcher,et al.  A transcriptional signature of hub connectivity in the mouse connectome , 2016, Proceedings of the National Academy of Sciences.

[46]  Olaf Sporns,et al.  Comparative Connectomics , 2016, Trends in Cognitive Sciences.

[47]  Timothy O. Laumann,et al.  Generation and Evaluation of a Cortical Area Parcellation from Resting-State Correlations. , 2016, Cerebral cortex.

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

[49]  James M. Shine,et al.  Subcortical contributions to large-scale network communication , 2016, Neuroscience & Biobehavioral Reviews.

[50]  Thomas Stieglitz,et al.  Dynamic reconfiguration of cortical functional connectivity across brain states , 2017, Scientific Reports.

[51]  Annika L A Nichols,et al.  A global brain state underlies C. elegans sleep behavior , 2017, Science.

[52]  Viktor K. Jirsa,et al.  Symmetry Breaking in Space-Time Hierarchies Shapes Brain Dynamics and Behavior , 2017, Neuron.

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

[54]  Michael C. Avery,et al.  Neuromodulatory Systems and Their Interactions: A Review of Models, Theories, and Experiments , 2017, Front. Neural Circuits.

[55]  M. Breakspear Dynamic models of large-scale brain activity , 2017, Nature Neuroscience.

[56]  Leonardo L. Gollo,et al.  Criticality in the brain: A synthesis of neurobiology, models and cognition , 2017, Progress in Neurobiology.

[57]  William H. Thompson,et al.  Simulations to benchmark time-varying connectivity methods for fMRI , 2018, PLoS Comput. Biol..

[58]  Maurizio Corbetta,et al.  Warnings and caveats in brain controllability , 2017, NeuroImage.

[59]  Michael Breakspear,et al.  The modulation of neural gain facilitates a transition between functional segregation and integration in the brain , 2017, bioRxiv.

[60]  Russell A. Poldrack,et al.  Principles of dynamic network reconfiguration across diverse brain states , 2017, NeuroImage.

[61]  Timothy D. Verstynen,et al.  Population-averaged atlas of the macroscale human structural connectome and its network topology , 2018, NeuroImage.