Prediction for human intelligence using morphometric characteristics of cortical surface: Partial least square analysis

• Published in: Neuroscience Vol. 246; pp. 351 - 361
• Main Authors: Yang, J.-J., Yoon, U., Yun, H.J., Im, K., Choi, Y.Y., Lee, K.H., Park, H., Hough, M.G., Lee, J.-M.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Ltd 29.08.2013; Elsevier
• Subjects: Biological and medical sciences; Cerebral Cortex - anatomy & histology; Cerebral Cortex - physiology; cortical thickness; curvature; Echo-Planar Imaging - methods; Female; Forecasting; Fundamental and applied biological sciences. Psychology; human intelligence; Humans; Intelligence - physiology; Least-Squares Analysis; Male; Neurology; partial least square regression; sulcal depth; surface area; Vertebrates: nervous system and sense organs; Young Adult; sulcal depth; RMSEp; ICA; cortical thickness; IQ; P-FIT; RMSE; AAL; partial least square regression; PRESS; PCA; curvature; surface area; human intelligence; FSIQ; BAs; RESS; PLS; WAIS; Predicted Residual Estimated Sum of Squares; root mean squared error of the predicted residual sum; Parieto-Frontal Integration Theory; independent component analysis; Brodmann areas; Residual Estimated Sum of Squares; root mean squared error of the residual sum; partial least square; Wechsler Adult Intelligence Scale Revised; full-scale intelligence quotient; automated anatomical labeling; intelligence quotient; principal component analysis; Human; Intelligence
• ISSN: 0306-4522; 1873-7544; 1873-7544
• DOI: 10.1016/j.neuroscience.2013.04.051

•Structural properties of cerebral cortex could account for human intelligence.•Considering several structural indices together was helpful in providing complementary information from multiple perspectives.•Partial least square (PLS) regression was used to overcome multicollinearity among cortical measures. A number of imaging studies have reported neuroanatomical correlates of human intelligence with various morphological characteristics of the cerebral cortex. However, it is not yet clear whether these morphological properties of the cerebral cortex account for human intelligence. We assumed that the complex structure of the 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 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 the 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.