Prediction for Human Intelligence using Morphometric Characteristics of Cortical Surface: Partial Least Square Analysis.
Neuroscience. 2013 Apr 30;
Authors: Yang JJ, Yoon U, Yun H, Im K, Yong Choi Y, Ho Lee K, Park H, Hough MG, Lee JM
A number of imaging studies has reported neuroanatomical correlates of human intelligence with various morphological characteristics of cerebral cortex. However, it is not yet clear whether these morphological properties of cerebral cortex account for human intelligence. We assumed that complex structure of cerebral cortex could be explained effectively considering cortical thickness, surface area, sulcal depth and absolute mean curvature together. In 78 young healthy adults (age range: 17-27, male/female: 39/39), we used the full-scale intelligence quotient (FSIQ) and the cortical measurements calculated in native space from each subject to determine how much the combining various cortical measures explained human intelligence. Since each cortical measure is thought to be not independent but highly inter-related, we applied partial least square (PLS) regression, which is one of the most promising multivariate analysis approaches, to overcome multicollinearity among cortical measures. Our results showed that 30% of FSIQ was explained by first latent variable extracted from PLS regression analysis. Although it is difficult to relate the first derived latent variable with specific anatomy, we found that cortical thickness measures had a substantial impact on the PLS model supporting the most significant factor accounting for FSIQ. Our results presented here strongly suggest that the new predictor combining different morphometric properties of complex cortical structure is well suited for predicting human intelligence.
PMID: 23643979 [PubMed – as supplied by publisher]