General intelligence is associated with working memory-related brain activity: new evidence from a large sample study

Psychometric intelligence is closely related to working memory capacity. Here we aim to determine the associations of neural activation patterns during the N-back working memory paradigm with psychometric intelligence and working memory performance. We solved the statistical problems of previous studies using (1) a large cohort of 1235 young adults and (2) robust voxel-by-voxel permutation-based statistics at the whole-brain level. Many of the significant correlations were weak, and our findings were not consistent with those of previous studies. We observed that many of the significant correlations involved brain areas in the periphery or boundaries between the task-positive network (TPN) and task-negative network (TNN), suggesting that the expansion of the TPN or TNN is associated with greater cognitive ability. Lower activity in TPN and less task-induced deactivation (TID) in TNN were associated with greater cognitive ability. These findings indicate that subjects with greater cognitive ability have a lower brain response to task demand, consistent with the notion that TID in TNN reflects cognitive demand but partly inconsistent with the prevailing neural efficiency theory. One exception was the pre-supplementary motor area, which plays a key role in cognitive control and sequential processing. In this area, intelligent subjects demonstrated greater activity related to working memory, suggesting that the pre-supplementary motor area plays a unique role in the execution of working memory tasks in intelligent subjects.

[1]  Kathryn M. McMillan,et al.  N‐back working memory paradigm: A meta‐analysis of normative functional neuroimaging studies , 2005, Human brain mapping.

[2]  R. Coppola,et al.  Physiological characteristics of capacity constraints in working memory as revealed by functional MRI. , 1999, Cerebral cortex.

[3]  G. Pazour,et al.  Ror2 signaling regulates Golgi structure and transport through IFT20 for tumor invasiveness , 2017, Scientific Reports.

[4]  Bharat B. Biswal,et al.  Inter-individual differences in resting-state functional connectivity predict task-induced BOLD activity , 2010, NeuroImage.

[5]  R. Engle,et al.  Individual differences in working memory capacity and what they tell us about controlled attention, general fluid intelligence, and functions of the prefrontal cortex. , 1999 .

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

[7]  Yasuyuki Taki,et al.  Verbal working memory performance correlates with regional white matter structures in the frontoparietal regions , 2011, Neuropsychologia.

[8]  M. Rietschel,et al.  Cortical thickness of superior frontal cortex predicts impulsiveness and perceptual reasoning in adolescence , 2013, Molecular Psychiatry.

[9]  Richard J. Haier,et al.  Brain networks for working memory and factors of intelligence assessed in males and females with fMRI and DTI , 2010 .

[10]  R. Cattell,et al.  Abilities : Their Structure , Growth , and Action , 2015 .

[11]  A. Baddeley Working memory: looking back and looking forward , 2003, Nature Reviews Neuroscience.

[12]  Brian A. Nosek,et al.  Power failure: why small sample size undermines the reliability of neuroscience , 2013, Nature Reviews Neuroscience.

[13]  Yasuyuki Taki,et al.  Degree centrality and fractional amplitude of low-frequency oscillations associated with Stroop interference , 2015, NeuroImage.

[14]  Yasuyuki Taki,et al.  Failing to deactivate: The association between brain activity during a working memory task and creativity , 2011, NeuroImage.

[15]  A. Schleicher,et al.  Mapping of human and macaque sensorimotor areas by integrating architectonic, transmitter receptor, MRI and PET data. , 1995, Journal of anatomy.

[16]  P. Cowen,et al.  The effect of the serotonin transporter polymorphism (5-HTTLPR) on amygdala function: a meta-analysis , 2013, Molecular Psychiatry.

[17]  Edward Vul,et al.  Reply to Comments on “Puzzlingly High Correlations in fMRI Studies of Emotion, Personality, and Social Cognition” , 2009, Perspectives on psychological science : a journal of the Association for Psychological Science.

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

[19]  A. Neubauer,et al.  Intelligence and neural efficiency , 2009, Neuroscience & Biobehavioral Reviews.

[20]  Brian A. Nosek,et al.  Power failure: why small sample size undermines the reliability of neuroscience , 2013, Nature Reviews Neuroscience.

[21]  Abraham Z. Snyder,et al.  Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion , 2012, NeuroImage.

[22]  Yasuyuki Taki,et al.  Association between resting-state functional connectivity and empathizing/systemizing , 2014, NeuroImage.

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

[24]  R. Kawashima,et al.  Amygdala and cingulate structure is associated with stereotype on sex-role , 2015, Scientific Reports.

[25]  Yasuyuki Taki,et al.  Global associations between regional gray matter volume and diverse complex cognitive functions: evidence from a large sample study , 2017, Scientific Reports.

[26]  Hans Knutsson,et al.  Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates , 2016, Proceedings of the National Academy of Sciences.

[27]  Yasuyuki Taki,et al.  The Relationship between Processing Speed and Regional White Matter Volume in Healthy Young People , 2015, PloS one.

[28]  Benjamin Thyreau,et al.  White matter structures associated with empathizing and systemizing in young adults , 2013, NeuroImage.

[29]  A. Miyake,et al.  Models of Working Memory: Mechanisms of Active Maintenance and Executive Control , 1999 .

[30]  Yasuyuki Taki,et al.  Resting state functional connectivity associated with trait emotional intelligence , 2013, NeuroImage.

[31]  C. Semenza,et al.  Supplementary motor area as key structure for domain-general sequence processing: A unified account , 2017, Neuroscience & Biobehavioral Reviews.

[32]  Yasuyuki Taki,et al.  Cognitive and neural correlates of the 5-repeat allele of the dopamine D4 receptor gene in a population lacking the 7-repeat allele , 2015, NeuroImage.

[33]  Yasuyuki Taki,et al.  Neural Correlates of the Difference between Working Memory Speed and Simple Sensorimotor Speed: An fMRI Study , 2012, PloS one.

[34]  M. Ichikawa,et al.  Corrigendum: Dynamics of microdroplets over the surface of hot water , 2015, Scientific Reports.

[35]  Ian J. Deary,et al.  Exploring Possible Neural Mechanisms of Intelligence Differences Using Processing Speed and Working Memory Tasks: An fMRI Study. , 2009 .

[36]  S. Martens,et al.  Rethinking neural efficiency: effects of controlling for strategy use. , 2007, Behavioral neuroscience.

[37]  Peter Kirsch,et al.  Test–retest reliability of evoked BOLD signals from a cognitive–emotive fMRI test battery , 2012, NeuroImage.

[38]  Yasuyuki Taki,et al.  Associations among imaging measures (2): The association between gray matter concentration and task‐induced activation changes , 2014, Human brain mapping.

[39]  Rüdiger J Seitz,et al.  Functional modularity of the medial prefrontal cortex: involvement in human empathy. , 2006, Neuropsychology.

[40]  J. Callicott,et al.  Age-related alterations in default mode network: Impact on working memory performance , 2010, Neurobiology of Aging.

[41]  J. Gabrieli,et al.  Hyperactivity and hyperconnectivity of the default network in schizophrenia and in first-degree relatives of persons with schizophrenia , 2009, Proceedings of the National Academy of Sciences.

[42]  Jörn Diedrichsen,et al.  Detecting and adjusting for artifacts in fMRI time series data , 2005, NeuroImage.

[43]  Andrew R. A. Conway,et al.  Journal of Experimental Psychology : General Neural Mechanisms of Interference Control Underlie the Relationship Between Fluid Intelligence and Working Memory Span , 2011 .

[44]  Randall W Engle,et al.  Working memory, short-term memory, and general fluid intelligence: a latent-variable approach. , 1999, Journal of experimental psychology. General.

[45]  J. Binder,et al.  A Parametric Manipulation of Factors Affecting Task-induced Deactivation in Functional Neuroimaging , 2003, Journal of Cognitive Neuroscience.

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

[47]  N. Ramsey,et al.  Working memory capacity in schizophrenia: a parametric fMRI study , 2004, Schizophrenia Research.

[48]  Paul J. Laurienti,et al.  An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets , 2003, NeuroImage.

[49]  C. Kennard,et al.  Functional role of the supplementary and pre-supplementary motor areas , 2008, Nature Reviews Neuroscience.

[50]  Pamela K. Smith,et al.  Models of visuospatial and verbal memory across the adult life span. , 2002, Psychology and aging.

[51]  Joseph A Maldjian,et al.  Precentral gyrus discrepancy in electronic versions of the Talairach atlas , 2004, NeuroImage.

[52]  J. Raven,et al.  Manual for Raven's progressive matrices and vocabulary scales , 1962 .

[53]  N. Tzourio-Mazoyer,et al.  Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.

[54]  P. Strick,et al.  Motor areas of the medial wall: a review of their location and functional activation. , 1996, Cerebral cortex.

[55]  Yasuyuki Taki,et al.  Effects of Training of Processing Speed on Neural Systems , 2011, The Journal of Neuroscience.

[56]  S. Tajima,et al.  Characterization of large and small-plaque variants in the Zika virus clinical isolate ZIKV/Hu/S36/Chiba/2016 , 2017, Scientific Reports.