Intelligence is associated with the modular structure of intrinsic brain networks

General intelligence is a psychological construct that captures in a single metric the overall level of behavioural and cognitive performance in an individual. While previous research has attempted to localise intelligence in circumscribed brain regions, more recent work focuses on functional interactions between regions. However, even though brain networks are characterised by substantial modularity, it is unclear whether and how the brain’s modular organisation is associated with general intelligence. Modelling subject-specific brain network graphs from functional MRI resting-state data (N = 309), we found that intelligence was not associated with global modularity features (e.g., number or size of modules) or the whole-brain proportions of different node types (e.g., connector hubs or provincial hubs). In contrast, we observed characteristic associations between intelligence and node-specific measures of within- and between-module connectivity, particularly in frontal and parietal brain regions that have previously been linked to intelligence. We propose that the connectivity profile of these regions may shape intelligence-relevant aspects of information processing. Our data demonstrate that not only region-specific differences in brain structure and function, but also the network-topological embedding of fronto-parietal as well as other cortical and subcortical brain regions is related to individual differences in higher cognitive abilities, i.e., intelligence.

[1]  L. Gottfredson Mainstream science on intelligence: An editorial with 52 signatories, history, and bibliography , 1997 .

[2]  V. Menon,et al.  Saliency, switching, attention and control: a network model of insula function , 2010, Brain Structure and Function.

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

[4]  Michael W. Cole,et al.  Activity flow over resting-state networks shapes cognitive task activations , 2016, Nature Neuroscience.

[5]  Jesse A. Brown,et al.  Altered functional and structural brain network organization in autism☆ , 2012, NeuroImage: Clinical.

[6]  V. Menon Large-scale brain networks and psychopathology: a unifying triple network model , 2011, Trends in Cognitive Sciences.

[7]  Andreas Wagner,et al.  Specialization Can Drive the Evolution of Modularity , 2010, PLoS Comput. Biol..

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

[9]  Andreas Daffertshofer,et al.  Comparing Brain Networks of Different Size and Connectivity Density Using Graph Theory , 2010, PloS one.

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

[11]  O. Sporns Contributions and challenges for network models in cognitive neuroscience , 2014, Nature Neuroscience.

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

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

[14]  Mariano Sigman,et al.  A small world of weak ties provides optimal global integration of self-similar modules in functional brain networks , 2011, Proceedings of the National Academy of Sciences.

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

[16]  U. Ziemann,et al.  Working memory performance of early MS patients correlates inversely with modularity increases in resting state functional connectivity networks , 2014, NeuroImage.

[17]  E. Wagenmakers,et al.  A default Bayesian hypothesis test for correlations and partial correlations , 2012, Psychonomic bulletin & review.

[18]  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.

[19]  Jonathan D. Power,et al.  Network measures predict neuropsychological outcome after brain injury , 2014, Proceedings of the National Academy of Sciences.

[20]  Leslie G. Valiant,et al.  Evolvability , 2009, JACM.

[21]  F Sprenger,et al.  Screened radiative corrections from hyperfine-split dielectronic resonances in lithiumlike scandium. , 2008, Physical review letters.

[22]  Andrew R. A. Conway,et al.  Working memory capacity and fluid intelligence are strongly related constructs: comment on Ackerman, Beier, and Boyle (2005). , 2005, Psychological bulletin.

[23]  Ulrike Basten,et al.  Where smart brains are different: A quantitative meta-analysis of functional and structural brain imaging studies on intelligence , 2015 .

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

[25]  M. Greicius,et al.  Resting-state functional connectivity reflects structural connectivity in the default mode network. , 2009, Cerebral cortex.

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

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

[28]  Paul M. Thompson,et al.  Development of brain structural connectivity between ages 12 and 30: A 4-Tesla diffusion imaging study in 439 adolescents and adults , 2013, NeuroImage.

[29]  Winfried Schlee,et al.  Top-Down Modulation of the Auditory Steady-State Response in a Task-Switch Paradigm , 2008, Front. Hum. Neurosci..

[30]  R. Turner,et al.  Deficient approaches to human neuroimaging , 2014, Front. Hum. Neurosci..

[31]  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.

[32]  B. Biswal,et al.  Functional connectivity in the motor cortex of resting human brain using echo‐planar mri , 1995, Magnetic resonance in medicine.

[33]  G. Glover,et al.  Dissociable Intrinsic Connectivity Networks for Salience Processing and Executive Control , 2007, The Journal of Neuroscience.

[34]  Andrea Lancichinetti,et al.  Community detection algorithms: a comparative analysis: invited presentation, extended abstract , 2009, VALUETOOLS.

[35]  Ulrike Basten,et al.  Intelligence is differentially related to neural effort in the task-positive and the task-negative brain network , 2013 .

[36]  L. M. M.-T. Theory of Probability , 1929, Nature.

[37]  B T Thomas Yeo,et al.  The modular and integrative functional architecture of the human brain , 2015, Proceedings of the National Academy of Sciences.

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

[39]  Cornelis J. Stam,et al.  Disrupted modular brain dynamics reflect cognitive dysfunction in Alzheimer's disease , 2012, NeuroImage.

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

[41]  Mark D'Esposito,et al.  Focal Brain Lesions to Critical Locations Cause Widespread Disruption of the Modular Organization of the Brain , 2012, Journal of Cognitive Neuroscience.

[42]  J. Rouder,et al.  Default Bayes Factors for Model Selection in Regression , 2012, Multivariate behavioral research.

[43]  O. Sporns,et al.  Identification and Classification of Hubs in Brain Networks , 2007, PloS one.

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

[45]  Yong He,et al.  Topologically Reorganized Connectivity Architecture of Default-Mode, Executive-Control, and Salience Networks across Working Memory Task Loads. , 2016, Cerebral cortex.

[46]  Margaret D. King,et al.  The NKI-Rockland Sample: A Model for Accelerating the Pace of Discovery Science in Psychiatry , 2012, Front. Neurosci..

[47]  Rex E. Jung,et al.  Graph Metrics of Structural Brain Networks in Individuals with Schizophrenia and Healthy Controls: Group Differences, Relationships with Intelligence, and Genetics , 2016, Journal of the International Neuropsychological Society.

[48]  Jonathan S. Adelstein,et al.  Personality Is Reflected in the Brain's Intrinsic Functional Architecture , 2011, PloS one.

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

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

[51]  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 .

[52]  E A Leicht,et al.  Community structure in directed networks. , 2007, Physical review letters.

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

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

[55]  Cedric E. Ginestet,et al.  Statistical network analysis for functional MRI: summary networks and group comparisons , 2013, Front. Comput. Neurosci..

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

[57]  Hod Lipson,et al.  The evolutionary origins of modularity , 2012, Proceedings of the Royal Society B: Biological Sciences.

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

[59]  Ian J. Deary,et al.  Intelligence Predicts Health and Longevity, but Why? , 2004 .

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

[61]  Charl Linssen,et al.  Network-tools: Large-scale Brain Network Analysis in Python , 2015 .