Linking brain network reconfiguration and intelligence: Are we there yet?

Recent applications of dynamic network analyses to functional neuroimaging data have revealed relationships between a number of cognition conditions and the dynamic reconfiguration of brain networks. Here we critically review such applications of network neuroscience to intelligence. After providing an overview of network neuroscience, we center our discussion around the recently proposed Network Neuroscience Theory of Intelligence (Barbey, 2017). We evaluate and review existing empirical support for the theses made by this theory and argue that while studies strongly suggest their plausibility, evidence to date has largely been indirect. We propose avenues for future research to directly evaluate these theses by overcoming the methodological and analytical shortcomings of previous studies. In doing so, our goal is to stimulate future empirical investigations and present valuable ways forward in the network neuroscience of intelligence.

[1]  Rex E. Jung,et al.  Distributed brain sites for the g-factor of intelligence , 2006, NeuroImage.

[2]  Keith A. Johnson,et al.  Cortical Hubs Revealed by Intrinsic Functional Connectivity: Mapping, Assessment of Stability, and Relation to Alzheimer's Disease , 2009, The Journal of Neuroscience.

[3]  Mark D'Esposito,et al.  Reconfiguration of brain network architecture to support executive control in aging , 2016, Neurobiology of Aging.

[4]  Stephen M. Smith,et al.  Advances and Pitfalls in the Analysis and Interpretation of Resting-State FMRI Data , 2010, Front. Syst. Neurosci..

[5]  Xiao Liu,et al.  Co-activation patterns in resting-state fMRI signals , 2018, NeuroImage.

[6]  Jessica R. Cohen The behavioral and cognitive relevance of time-varying, dynamic changes in functional connectivity , 2017, NeuroImage.

[7]  A. Jensen,et al.  The nature of psychometric g: Unitary process or a number of independent processes? , 1991 .

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

[9]  Kristof Kovacs,et al.  Process Overlap Theory: A Unified Account of the General Factor of Intelligence , 2016 .

[10]  Gustavo Deco,et al.  Functional connectivity dynamics: Modeling the switching behavior of the resting state , 2015, NeuroImage.

[11]  Sharon L. Thompson-Schill,et al.  A Functional Cartography of Cognitive Systems , 2015, PLoS Comput. Biol..

[12]  Jessica R. Cohen,et al.  The Segregation and Integration of Distinct Brain Networks and Their Relationship to Cognition , 2016, The Journal of Neuroscience.

[13]  T. Salthouse Relations between cognitive abilities and measures of executive functioning. , 2005, Neuropsychology.

[14]  D. Bassett,et al.  Dynamic reconfiguration of frontal brain networks during executive cognition in humans , 2015, Proceedings of the National Academy of Sciences.

[15]  Ian J. Deary,et al.  The Stability of Intelligence From Childhood to Old Age , 2014 .

[16]  B. Sahakian,et al.  Angular default mode network connectivity across working memory load , 2017, Human brain mapping.

[17]  R. Haier,et al.  The Parieto-Frontal Integration Theory (P-FIT) of intelligence: Converging neuroimaging evidence , 2007, Behavioral and Brain Sciences.

[18]  Fabio Pasqualetti,et al.  Optimal trajectories of brain state transitions , 2016, NeuroImage.

[19]  D. Bassett,et al.  A Network Neuroscience of Human Learning: Potential to Inform Quantitative Theories of Brain and Behavior , 2017, Trends in Cognitive Sciences.

[20]  Danielle S. Bassett,et al.  Brain Network Adaptability across Task States , 2014, PLoS Comput. Biol..

[21]  Danielle S. Bassett,et al.  Brain state expression and transitions are related to complex executive cognition in normative neurodevelopment , 2018, NeuroImage.

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

[23]  M. Greicius,et al.  Decoding subject-driven cognitive states with whole-brain connectivity patterns. , 2012, Cerebral cortex.

[24]  Johann Daniel Kruschwitz,et al.  General, crystallized and fluid intelligence are not associated with functional global network efficiency: A replication study with the human connectome project 1200 data set , 2018, NeuroImage.

[25]  S. Rossi,et al.  Efficiency of weak brain connections support general cognitive functioning , 2014, Human brain mapping.

[26]  P. Thompson,et al.  Understanding human intelligence by imaging the brain. , 2013 .

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

[28]  Danielle S. Bassett,et al.  Functional Network Dynamics of the Language System , 2016, Cerebral cortex.

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

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

[31]  Yong He,et al.  Individual differences and time-varying features of modular brain architecture , 2017, NeuroImage.

[32]  Danielle S Bassett,et al.  Detection of functional brain network reconfiguration during task-driven cognitive states , 2016, NeuroImage.

[33]  Dimitri Van De Ville,et al.  The dynamic functional connectome: State-of-the-art and perspectives , 2017, NeuroImage.

[34]  Maxwell A. Bertolero,et al.  The diverse club , 2017, Nature Communications.

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

[36]  Kaustubh Supekar,et al.  Dynamic Reconfiguration of Structural and Functional Connectivity Across Core Neurocognitive Brain Networks with Development , 2011, The Journal of Neuroscience.

[37]  Viviana Betti,et al.  Dynamic reorganization of human resting-state networks during visuospatial attention , 2015, Proceedings of the National Academy of Sciences.

[38]  Vince D. Calhoun,et al.  Chronnectomic patterns and neural flexibility underlie executive function , 2017, NeuroImage.

[39]  Jun Li,et al.  Brain Anatomical Network and Intelligence , 2009, NeuroImage.

[40]  Xiaoping Hu,et al.  Behavioral Relevance of the Dynamics of the Functional Brain Connectome , 2014, Brain Connect..

[41]  Danielle S Bassett,et al.  Learning-induced autonomy of sensorimotor systems , 2014, Nature Neuroscience.

[42]  O. Sporns,et al.  Network hubs in the human brain , 2013, Trends in Cognitive Sciences.

[43]  Jean M. Vettel,et al.  Network Approaches to Understand Individual Differences in Brain Connectivity: Opportunities for Personality Neuroscience , 2018, Personality Neuroscience.

[44]  Leonardo L. Gollo,et al.  Time-resolved resting-state brain networks , 2014, Proceedings of the National Academy of Sciences.

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

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

[47]  C. Spearman General intelligence Objectively Determined and Measured , 1904 .

[48]  Aaron Kucyi,et al.  Just a thought: How mind-wandering is represented in dynamic brain connectivity , 2017, NeuroImage.

[49]  Andrew Zalesky,et al.  Reconfiguration of Brain Network Architectures between Resting-State and Complexity-Dependent Cognitive Reasoning , 2017, The Journal of Neuroscience.

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

[51]  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.

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

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

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

[55]  Olaf Sporns,et al.  The small world of the cerebral cortex , 2007, Neuroinformatics.

[56]  I. Deary,et al.  The neuroscience of human intelligence differences , 2010, Nature Reviews Neuroscience.

[57]  Peter A. Bandettini,et al.  Task-based dynamic functional connectivity: Recent findings and open questions , 2017, NeuroImage.

[58]  Jun Li,et al.  Brain spontaneous functional connectivity and intelligence , 2008, NeuroImage.

[59]  Danna Zhou,et al.  d. , 1934, Microbial pathogenesis.

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

[61]  Danielle S. Bassett,et al.  A mechanistic model of connector hubs, modularity and cognition , 2018, Nature Human Behaviour.

[62]  R. Cattell,et al.  Refinement and test of the theory of fluid and crystallized general intelligences. , 1966, Journal of educational psychology.

[63]  General , 1970 .

[64]  R. Kahn,et al.  Efficiency of Functional Brain Networks and Intellectual Performance , 2009, The Journal of Neuroscience.

[65]  Mark Newman,et al.  Networks: An Introduction , 2010 .

[66]  Olaf Sporns,et al.  Dynamic fluctuations coincide with periods of high and low modularity in resting-state functional brain networks , 2015, NeuroImage.

[67]  Hesheng Liu,et al.  State-dependent variability of dynamic functional connectivity between frontoparietal and default networks relates to cognitive flexibility , 2016, Neuroscience.

[68]  Baxter P. Rogers,et al.  Analyzing the association between functional connectivity of the brain and intellectual performance , 2015, Front. Hum. Neurosci..

[69]  Russell A. Poldrack,et al.  The low dimensional dynamic and integrative core of cognition in the human brain , 2018 .

[70]  Abraham Z. Snyder,et al.  A default mode of brain function: A brief history of an evolving idea , 2007, NeuroImage.

[71]  A. Miyake,et al.  Not All Executive Functions Are Related to Intelligence , 2006, Psychological science.

[72]  R. Nathan Spreng,et al.  The Shifting Architecture of Cognition and Brain Function in Older Adulthood , 2019, Perspectives on psychological science : a journal of the Association for Psychological Science.

[73]  S. Rombouts,et al.  Consistent resting-state networks across healthy subjects , 2006, Proceedings of the National Academy of Sciences.

[74]  Xi-Nian Zuo,et al.  Reliable intrinsic connectivity networks: Test–retest evaluation using ICA and dual regression approach , 2010, NeuroImage.

[75]  Edward T. Bullmore,et al.  Small-World Brain Networks Revisited , 2016, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

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

[77]  A. Stevens,et al.  Functional Brain Network Modularity Captures Inter- and Intra-Individual Variation in Working Memory Capacity , 2012, PloS one.

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

[79]  Ulrike Basten,et al.  Efficient hubs in the intelligent brain: Nodal efficiency of hub regions in the salience network is associated with general intelligence , 2017 .

[80]  A. Miyake,et al.  The Nature and Organization of Individual Differences in Executive Functions , 2012, Current directions in psychological science.

[81]  Lorena R. R. Gianotti,et al.  Functional brain network efficiency predicts intelligence , 2012, Human brain mapping.

[82]  Aaron Kucyi,et al.  Dynamic functional connectivity of the default mode network tracks daydreaming , 2014, NeuroImage.

[83]  J. Carroll No demonstration that g is not unitary, but there's more to the story :comment on Kranzler and Jensen , 1991 .

[84]  Danielle S. Bassett,et al.  Dynamic graph metrics: Tutorial, toolbox, and tale , 2017, NeuroImage.

[85]  E. Thompson,et al.  Neurophenomenology Integrating Subjective Experience and Brain Dynamics in the Neuroscience of Consciousness , 2003 .

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

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

[88]  Kaustubh Supekar,et al.  Distinct Global Brain Dynamics and Spatiotemporal Organization of the Salience Network , 2016, PLoS biology.

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

[90]  Michael W. Cole,et al.  Lateral Prefrontal Cortex Contributes to Fluid Intelligence Through Multinetwork Connectivity , 2015, Brain Connect..

[91]  Michael W. Cole,et al.  Global Connectivity of Prefrontal Cortex Predicts Cognitive Control and Intelligence , 2012, The Journal of Neuroscience.

[92]  A. Barbey Network Neuroscience Theory of Human Intelligence , 2018, Trends in Cognitive Sciences.

[93]  R. Cotterill CyberChild - A simulation test-bed for consciousness studies , 2003 .

[94]  Lucina Q. Uddin,et al.  Demystifying cognitive flexibility: Implications for clinical and developmental neuroscience , 2015, Trends in Neurosciences.

[95]  E. Santarnecchi,et al.  Bridge Over Troubled Water: Commenting on Kovacs and Conway's Process Overlap Theory , 2016 .

[96]  O. Sporns,et al.  Network neuroscience , 2017, Nature Neuroscience.

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

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

[99]  Fabio Pasqualetti,et al.  Control of brain network dynamics across diverse scales of space and time. , 2019, Physical review. E.

[100]  P. Ackerman,et al.  Working Memory and Intelligence : The Same or Different Constructs ? , 2005 .

[101]  Laura C. Buchanan,et al.  Tracking ongoing cognition in individuals using brief, whole-brain functional connectivity patterns , 2015, Proceedings of the National Academy of Sciences.