Relationships between IQ and regional cortical gray matter thickness in healthy adults.

Laboratory of Neuro Imaging, Department of Neurology, Geffen School of Medicine at the University of California, Los Angeles (UCLA), Los Angeles, CA 90095-7334, USA.
Cerebral Cortex (Impact Factor: 8.31). 10/2007; 17(9):2163-71. DOI: 10.1093/cercor/bhl125
Source: PubMed

ABSTRACT Prior studies show positive correlations between full-scale intelligence quotient (FSIQ) and cerebral gray matter measures. Few imaging studies have addressed whether general intelligence is related to regional variations in brain tissue and the associated influences of sex. Cortical thickness may more closely reflect cytoarchitectural characteristics than gray matter density or volume estimates. To identify possible localized relationships, we examined FSIQ associations with cortical thickness at high spatial resolution across the cortex in healthy young adult (age 17-44 years) men (n = 30) and women (n = 35). Positive relationships were found between FSIQ and intracranial gray and white matter but not cerebrospinal fluid volumes. Significant associations with cortical thickness were evident bilaterally in prefrontal (Brodmann's areas [BAs] 10/11, 47) and posterior temporal cortices (BA 36/37) and proximal regions. Sex influenced regional relationships; women showed correlations in prefrontal and temporal association cortices, whereas men exhibited correlations primarily in temporal-occipital association cortices. In healthy adults, greater intelligence is associated with larger intracranial gray matter and to a lesser extent with white matter. Variations in prefrontal and posterior temporal cortical thickness are particularly linked with intellectual ability. Sex moderates regional relationships that may index dimorphisms in cognitive abilities, overall processing strategies, or differences in structural organization.

Download full-text


Available from: Robert Bilder, Jun 27, 2015
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: People with larger brains tend to score higher on tests of general intelligence (g). It is unclear, however, how much variance in intelligence other brain measurements would account for if included together with brain volume in a multivariable model. We examined a large sample of individuals in their seventies (n = 672) who were administered a comprehensive cognitive test battery. Using structural equation modelling, we related six common magnetic resonance imaging-derived brain variables that represent normal and abnormal features—brain volume, cortical thickness, white matter structure, white matter hyperintensity load, iron deposits, and microbleeds—to g and to fluid intelligence. As expected, brain volume accounted for the largest portion of variance (~ 12%, depending on modelling choices). Adding the additional variables, especially cortical thickness (+~ 5%) and white matter hyperintensity load (+~ 2%), increased the predictive value of the model. Depending on modelling choices, all neuroimaging variables together accounted for 18–21% of the variance in intelligence. These results reveal which structural brain imaging measures relate to g over and above the largest contributor, total brain volume. They raise questions regarding which other neuroimaging measures might account for even more of the variance in intelligence.
    Intelligence 08/2015; 51. DOI:10.1016/j.intell.2015.05.001 · 2.67 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Individual differences in general intelligence have been associated with differences in brain structure and function. The currently most popular theory of the neural bases of intelligence – the Parieto-Frontal Integration Theory of Intelligence (P-FIT) – describes a network of frontal and parietal brain regions as the main neural basis of intelligence. Here, we put the theory to an empirical test by conducting voxel-based quantitative meta-analyses of 12 structural and 16 functional human brain imaging studies, testing for statistically significant spatial convergence across studies. We focused our analyses on studies reporting associations between individual differences in intelligence (as assessed by established tests of psychometric intelligence) and either (a) brain activation during a cognitive task (functional meta-analysis) or (b) amount of grey matter as assessed by voxel-based morphometry (structural meta-analysis). The functional meta-analysis resulted in eight clusters distributed across both hemispheres, located in lateral frontal, medial frontal, parietal, and temporal cortices. The structural meta-analysis of VBM studies resulted in 12 clusters distributed in lateral and medial frontal, temporal, and occipital cortices, as well as in subcortical structures. Results of the functional and structural meta-analyses did not show any overlap — although both independently showed good match with the P-FIT. Based on the meta-analyses, we present an updated model for the brain bases of intelligence that extends previous models in also considering the posterior cingulate cortex and subcortical structures as relevant for intelligence, and in differentiating between positive and negative associations of intelligence and brain activation. From a critical review of original studies and methods, we derive important suggestions for future research on brain correlates of intelligence.
    Intelligence 05/2015; 51:10-27. DOI:10.1016/j.intell.2015.04.009 · 2.67 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: We report that preexisting individual differences in the cortical thickness of brain areas involved in a perceptual learning task predict the subsequent perceptual learning rate. Participants trained in a motion-discrimination task involving visual search for a "V"-shaped target motion trajectory among inverted "V"-shaped distractor trajectories. Motion-sensitive area MT+ (V5) was functionally identified as critical to the task: after 3 weeks of training, activity increased in MT+ during task performance, as measured by functional magnetic resonance imaging. We computed the cortical thickness of MT+ from anatomical magnetic resonance imaging volumes collected before training started, and found that it significantly predicted subsequent perceptual learning rates in the visual search task. Participants with thicker neocortex in MT+ before training learned faster than those with thinner neocortex in that area. A similar association between cortical thickness and training success was also found in posterior parietal cortex (PPC). © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail:
    Cerebral Cortex 01/2015; DOI:10.1093/cercor/bhu309 · 8.31 Impact Factor