Here is a curious and interesting finding, best understood without mentioning either Mars or Venus. A prediction based on white matter efficiency works for the women subjects in this study, but not for the men. Do women really rely more on efficient brain operation, while men just organise their crania like a bachelor’s bedroom?
FRONTO-PARIETAL GRAY MATTER AND WHITE MATTER EFFICIENCY DIFFERENTIALLY PREDICT INTELLIGENCE IN MALES AND FEMALES* Sephira G Ryman1 , Ronald Yeo1 , Katie Witkiewitz1 , Martijn van den Heuvel2 , Marcel de Reus2 , Andrei Vakhtin1 , Ranee Flores3 , Christopher Wertz3 , Christine Meadows3 , Rex E. Jung3
1 University of New Mexico, firstname.lastname@example.org.
2 University Medical Center Utrecht.
3 University of New Mexico HSC.
Intelligence is associated with communication efficiency of a widespread neural network as well as variation in gray matter volume, particularly within the fronto-parietal regions of the human brain. Recent reports of gender differences in the relationships between brain measures and intelligence highlight the need to differentiate how the brains of males and females may rely on different neural structures when completing measures of intelligence.
The current study utilizes a network approach in conjunction with structural equation modelling to examine potential gender differences in the relationship between white matter efficiency, fronto-parietal gray matter volume, and general cognitive ability.
Two hundred and forty-four (21.77+/−3.29 years; 125 males) subjects participated in the current study. Individual brain networks were modelled on the basis of the set of reconstructed fiber tracts. Network white matter efficiency was calculated as the average inverse shortest path length across the whole network. Two confirmatory factor analyses (CFA) were conducted to identify the factor structure and measurement invariance of a general cognitive factor (GCA) and a fronto-parietal gray matter volume factor (FPG). Lastly, we proceeded to fit a structural equation model relating the GCA latent factor to both the FPG and white matter efficiency. A nested model comparison and examination of the interactions were conducted to determine if the regression coefficients differed in each gender.
Results of the GCA CFA indicate adequate model fit [χ² (5) = 6.82, p = 0.23, RMSEA = 0.04 (90% CI [0.00-0.10]), CFI = .98], with the GCA factor exhibiting metric invariance and partial scalar invariance across gender. Results of the FPG CFA indicate adequate model fit [χ²(2) = 0.36, p = 0.84, RMSEA < 0.01 (90% CI [.00-.07]), CFI = 1.00], exhibiting metric, scalar, and residual invariance across gender. A nested model comparison of the model predicting GCA before and after constraining the regression coefficients across gender resulted in a significant χ² difference between the models (χ²(2) = 8.33, p = 0.01. There was a significant relationship between FPG and CGA in males and females. In contrast, white matter efficiency significantly predicted GCA in females, but not males.
The current study aimed to identify the relationship between intelligence, fronto-parietal gray matter volume, and white matter connectivity. Results of the structural equation model highlight that the latent factor of fronto-parietal gray matter volume predicts GCA, with greater fronto-parietal gray matter corresponding to greater GCA scores. Of interest, while the relationship between fronto-parietal gray matter and GCA is consistent across males and females (with larger effect sizes in males), white matter efficiency demonstrated differential effects across gender.
The current study provides further evidence for this trade of in brain structure seen in males and females, suggesting that women likely rely more on efficient brain organization when performing measures of intelligence.