Topology of genetic associations between regional gray matter volume and intellectual ability: Evidence for a high capacity network

Intelligence is associated with a network of distributed gray matter areas including the frontal and parietal higher association cortices and primary processing areas of the temporal and occipital lobes. Efficient information transfer between gray matter regions implicated in intelligence is thought to be critical for this trait to emerge. Genetic factors implicated in intelligence and gray matter may promote a high capacity for information transfer. Whether these genetic factors act globally or on local gray matter areas separately is not known. Brain maps of phenotypic and genetic associations between gray matter volume and intelligence were made using structural equation modeling of 3T MRI T1-weighted scans acquired in 167 adult twins of the newly acquired U-TWIN cohort. Subsequently, structural connectivity analyses (DTI) were performed to test the hypothesis that gray matter regions associated with intellectual ability form a densely connected core. Gray matter regions associated with intellectual ability were situated in the right prefrontal, bilateral temporal, bilateral parietal, right occipital and subcortical regions. Regions implicated in intelligence had high structural connectivity density compared to 10,000 reference networks (p=0.031). The genetic association with intelligence was for 39% explained by a genetic source unique to these regions (independent of total brain volume), this source specifically implicated the right supramarginal gyrus. Using a twin design, we show that intelligence is genetically represented in a spatially distributed and densely connected network of gray matter regions providing a high capacity infrastructure. Although genes for intelligence have overlap with those for total brain volume, we present evidence that there are genes for intelligence that act specifically on the subset of brain areas that form an efficient brain network.

[1]  Anders Skrondal,et al.  Likelihood Ratio Tests in Behavioral Genetics: Problems and Solutions , 2006, Behavior genetics.

[2]  L Penke,et al.  Brain white matter tract integrity as a neural foundation for general intelligence , 2012, Molecular Psychiatry.

[3]  Aaron Carass,et al.  Erratum to: The Java Image Science Toolkit (JIST) for Rapid Prototyping and Publishing of Neuroimaging Software , 2010, Neuroinformatics.

[4]  Anders M. Dale,et al.  Cortical Thickness Is Influenced by Regionally Specific Genetic Factors , 2010, Biological Psychiatry.

[5]  Dorret I. Boomsma,et al.  Heritability of DTI and MTR in nine-year-old children , 2010, NeuroImage.

[6]  Steven C. R. Williams,et al.  Mapping IQ and gray matter density in healthy young people , 2004, NeuroImage.

[7]  Dorret I Boomsma,et al.  Testing causality in the association between regular exercise and symptoms of anxiety and depression. , 2008, Archives of general psychiatry.

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

[9]  K. Taylor,et al.  Genome-Wide Association , 2007, Diabetes.

[10]  R. Kahn,et al.  The association between brain volume and intelligence is of genetic origin , 2002, Nature Neuroscience.

[11]  Chris Rorden,et al.  Lesion Mapping of Cognitive Abilities Linked to Intelligence , 2009, Neuron.

[12]  D. Nyholt A simple correction for multiple testing for single-nucleotide polymorphisms in linkage disequilibrium with each other. , 2004, American journal of human genetics.

[13]  M. Goodale,et al.  Separate visual pathways for perception and action , 1992, Trends in Neurosciences.

[14]  Aldo Rustichini,et al.  Subcortical intelligence: Caudate volume predicts IQ in healthy adults , 2015, Human brain mapping.

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

[16]  L. Peltonen,et al.  Classical twin studies and beyond , 2002, Nature Reviews Genetics.

[17]  John Fox,et al.  OpenMx: An Open Source Extended Structural Equation Modeling Framework , 2011, Psychometrika.

[18]  Nikos Makris,et al.  Automatically parcellating the human cerebral cortex. , 2004, Cerebral cortex.

[19]  Stefan Skare,et al.  How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging , 2003, NeuroImage.

[20]  Karen J. Ferguson,et al.  Intracranial capacity and brain volumes are associated with cognition in healthy elderly men , 2002, Neurology.

[21]  Rex E. Jung,et al.  Gray matter correlates of fluid, crystallized, and spatial intelligence: Testing the P-FIT model , 2009 .

[22]  R. Haier,et al.  Human intelligence and brain networks , 2010, Dialogues in clinical neuroscience.

[23]  Kun Ho Lee,et al.  Neural correlates of superior intelligence: Stronger recruitment of posterior parietal cortex , 2006, NeuroImage.

[24]  Lars T Westlye,et al.  Intellectual abilities and white matter microstructure in development: A diffusion tensor imaging study , 2010, Human brain mapping.

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

[26]  Jerry L. Prince,et al.  Resolution of crossing fibers with constrained compressed sensing using diffusion tensor MRI , 2012, NeuroImage.

[27]  Michael C Neale,et al.  Structural brain magnetic resonance imaging of pediatric twins , 2007, Human brain mapping.

[28]  Chad E. Forbes,et al.  An integrative architecture for general intelligence and executive function revealed by lesion mapping. , 2012, Brain : a journal of neurology.

[29]  S. F. Witelson,et al.  The exceptional brain of Albert Einstein , 1999, The Lancet.

[30]  R. Kahn,et al.  Genes contributing to subcortical volumes and intellectual ability implicate the thalamus , 2014, Human brain mapping.

[31]  R. Kahn,et al.  Tract-based analysis of magnetization transfer ratio and diffusion tensor imaging of the frontal and frontotemporal connections in schizophrenia. , 2010, Schizophrenia bulletin.

[32]  Newell,et al.  A neural basis for general intelligence , 2000, American journal of ophthalmology.

[33]  A. Toga,et al.  Genetics of brain structure and intelligence. , 2005, Annual review of neuroscience.

[34]  Anderson M. Winkler,et al.  Cortical thickness or grey matter volume? The importance of selecting the phenotype for imaging genetics studies , 2010, NeuroImage.

[35]  Rainer Goebel,et al.  Genetic Contribution to Variation in Cognitive Function: An fMRI Study in Twins , 2009, Science.

[36]  R. Haier,et al.  Finding the g-factor in brain structure using the method of correlated vectors , 2006 .

[37]  Glenn Geher,et al.  Emotional intelligence and the identification of emotion , 1996 .

[38]  O. Sporns,et al.  The economy of brain network organization , 2012, Nature Reviews Neuroscience.

[39]  E. T. Bullmore,et al.  Genetic Contributions to Regional Variability in Human Brain Structure: Methods and Preliminary Results , 2002, NeuroImage.

[40]  Rex E. Jung,et al.  Structural brain variation and general intelligence , 2004, NeuroImage.

[41]  Alan C. Evans,et al.  Genetic Contributions to Human Brain Morphology and Intelligence , 2006, The Journal of Neuroscience.

[42]  Dieter Vaitl,et al.  Relationship between regional hemodynamic activity and simultaneously recorded EEG‐theta associated with mental arithmetic‐induced workload , 2007, Human brain mapping.

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

[44]  Tyrone D. Cannon,et al.  Genetic influences on brain structure , 2001, Nature Neuroscience.

[45]  P. Sham,et al.  The future of association studies: gene-based analysis and replication. , 2004, American journal of human genetics.

[46]  Arthur W Toga,et al.  Relationships between IQ and regional cortical gray matter thickness in healthy adults. , 2007, Cerebral cortex.

[47]  C. Lebel,et al.  Lateralization of the arcuate fasciculus from childhood to adulthood and its relation to cognitive abilities in children , 2009, Human brain mapping.

[48]  Lorna M. Lopez,et al.  Genome-wide association studies establish that human intelligence is highly heritable and polygenic , 2011, Molecular Psychiatry.

[49]  Cheuk Y. Tang,et al.  Gray Matter and Intelligence Factors: Is There a Neuro-g?. , 2009 .

[50]  Martijn P. van den Heuvel,et al.  Estimating false positives and negatives in brain networks , 2013, NeuroImage.

[51]  R. Kahn,et al.  Quantitative genetic modeling of variation in human brain morphology. , 2001, Cerebral cortex.

[52]  Guillén Fernández,et al.  Genes Encoding Heterotrimeric G-proteins Are Associated with Gray Matter Volume Variations in the Medial Frontal Cortex , 2012, Cerebral cortex.

[53]  A. Dale,et al.  Distinct genetic influences on cortical surface area and cortical thickness. , 2009, Cerebral cortex.

[54]  Michael C. Neale,et al.  Methodology for Genetic Studies of Twins and Families , 1992 .

[55]  R. Sternberg Beyond IQ: A Triarchic Theory of Human Intelligence , 1984 .

[56]  R. Kahn,et al.  Heritability of structural brain network topology: A DTI study of 156 twins , 2014, Human brain mapping.

[57]  Stefan Skare,et al.  A Model-Based Method for Retrospective Correction of Geometric Distortions in Diffusion-Weighted EPI , 2002, NeuroImage.

[58]  D. Glahn,et al.  Fractional anisotropy of water diffusion in cerebral white matter across the lifespan , 2012, Neurobiology of Aging.

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

[60]  Yong He,et al.  BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics , 2013, PloS one.

[61]  Pak Chung Sham,et al.  Analytic approaches to twin data using structural equation models , 2002, Briefings Bioinform..

[62]  Ian J. Deary,et al.  The Stability of Intelligence From Age 11 to Age 90 Years , 2013, Psychological science.

[63]  J. Li,et al.  Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix , 2005, Heredity.

[64]  Anders M. Dale,et al.  Genetic and environmental influences on the size of specific brain regions in midlife: The VETSA MRI study , 2010, NeuroImage.

[65]  Alan C. Evans,et al.  Brain Plasticity and Intellectual Ability Are Influenced by Shared Genes , 2010, The Journal of Neuroscience.

[66]  Hidenao Fukuyama,et al.  Functional roles of the cingulo-frontal network in performance on working memory , 2004, NeuroImage.

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

[68]  A. Dale,et al.  Hierarchical Genetic Organization of Human Cortical Surface Area , 2012, Science.

[69]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[70]  Alan C. Evans,et al.  Changes in thickness and surface area of the human cortex and their relationship with intelligence. , 2015, Cerebral cortex.

[71]  Agatha D. Lee,et al.  Genetics of Brain Fiber Architecture and Intellectual Performance , 2009, The Journal of Neuroscience.

[72]  R. Kahn,et al.  Genetic influences on human brain structure: A review of brain imaging studies in twins , 2007, Human brain mapping.

[73]  P. Thompson,et al.  Neurobiology of intelligence: science and ethics , 2004, Nature Reviews Neuroscience.

[74]  A. Dale,et al.  Effects of age on volumes of cortex, white matter and subcortical structures , 2005, Neurobiology of Aging.

[75]  R. Murray,et al.  Substantial Genetic Overlap Between Neurocognition and Schizophrenia , 2007 .

[76]  O. Sporns,et al.  Mapping the Structural Core of Human Cerebral Cortex , 2008, PLoS biology.

[77]  D Rudrauf,et al.  Distributed neural system for general intelligence revealed by lesion mapping , 2010, Proceedings of the National Academy of Sciences.

[78]  Alan C. Evans,et al.  Overlapping and segregating structural brain abnormalities in twins with schizophrenia or bipolar disorder. , 2012, Archives of general psychiatry.

[79]  Katie L McMahon,et al.  Genetic and Environmental Influences on Neuroimaging Phenotypes: A Meta-Analytical Perspective on Twin Imaging Studies , 2012, Twin Research and Human Genetics.

[80]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[81]  Armin Scheurich,et al.  Association of Structural Global Brain Network Properties with Intelligence in Normal Aging , 2014, PloS one.

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

[83]  M. Bar,et al.  The role of the parahippocampal cortex in cognition , 2013, Trends in Cognitive Sciences.