Cortical thickness correlates of specific cognitive performance accounted for by the general factor of intelligence in healthy children aged 6 to 18

Abstract

Prevailing psychometric theories of intelligence posit that individual differences in cognitive performance are attributable to three main sources of variance: the general factor of intelligence (g), cognitive ability domains, and specific test requirements and idiosyncrasies. Cortical thickness has been previously associated with g. In the present study, we systematically analyzed associations between cortical thickness and cognitive performance with and without adjusting for the effects of g in a representative sample of children and adolescents (N=207, Mean age=11.8; SD=3.5; Range=6 to 18.3 years). Seven cognitive tests were included in a measurement model that identified three first-order factors (representing cognitive ability domains) and one second-order factor representing g. Residuals of the cognitive ability domain scores were computed to represent g-independent variance for the three domains and seven tests. Cognitive domain and individual test scores as well as residualized scores were regressed against cortical thickness, adjusting for age, gender and a proxy measure of brain volume. g and cognitive domain scores were positively correlated with cortical thickness in very similar areas across the brain. Adjusting for the effects of g eliminated associations of domain and test scores with cortical thickness. Within a psychometric framework, cortical thickness correlates of cognitive performance on complex tasks are well captured by g in this demographically representative sample.

Figure 1

Measurement model for the cognitive ability measures.

Figure 2

Distribution of g-independent cognitive ability domain scores. Note the approximately normal distribution.

Figure 3

Results of cortical thickness regressed against g. An FDR threshold of 0.05 is used to control for multiple comparisons across the brain. Colors, representing q values (which are metrics used for FDR that can be viewed as analogues to p values), are superimposed on left and right lateral average surface templates generated from the ICBM152 dataset. Results are corrected for gender, age, total brain volume and scanner.

Figure 4

Results of cortical thickness regressed against each of the three domains before (left) and after (right) controlling for g. Here also, an FDR threshold of 0.05 is used to control for multiple comparisons across the brain and colors, representing q values, are superimposed on left and right lateral average surface templates generated from the ICBM152 dataset. Results are corrected for gender, age, total brain volume and scanner.

Figure 5

Results of cortical thickness regressed against the Passage Comprehension, Matrix Reasoning, Block Design, and Vocabulary tests before (periphery) and after (center) controlling for g. Here also, an FDR threshold of 0.05 is used to control for multiple comparisons and colors, representing q values, are superimposed on left and right lateral average surface templates generated from the ICBM152 dataset. Results are corrected for gender, age, scanner, and a proxy measure of brain volume.

Figure A1 (Appendix)

Plot of the association between Mean Cortical Thickness and Cortical Gray Matter Volume

Figure A2 (Appendix)

Figures showing the association between g and cortical thickness after controlling for pBV (White Matter + Intracerebral CSF + Subcortical Gray Matter) or TBV (White Matter + Intracerebral CSF + Cortical and Subcortical Gray Matter). The top right g map is identical to Figure 3 and is reproduced here for convenience to facilitate comparisons between figures.