Prediction of Human Intelligence using Morphometric Characteristics of Cerebral Cortex

A number of studies from imaging data have reported neuroanatomical correlates of intelligence. However, studies related to intelligence assessing all possible aspects of cortical structure have been little addressed. We examined the relationship between human intelligence and morphometric characteristics of cortical surface such as gray matter volume, surface area, thickness, convolution, and sulcal depth with 79 young people (Age range: 17-27, Male/Female: 39/39). All cortical measures were calculated each lobe region and hemisphere in native space. Our study also addressed to predict the human intelligence using the morphometric characteristics correlated to intelligence quotient (IQ). We applied partial least squares to analyze complex information obtained from different structural properties of the cerebral cortex. We found that there were no significant relationships between each cortical measurement and IQ while considering age, gender and intracranial volume (ICV). When we examined the same regression model excluding ICV, some cortical measurements were associated with IQ frequently in frontal and temporal lobe. It might conclude that human intelligence is significantly correlated with the cortical complexity coming from brain size, rather than cortical shape itself. The combination of various cortical measurements explained 52.57% of IQ. The results showed that they were more useful for prediction of human intelligence than alone. Our findings may suggest that makes it Manuscript received July 15, 2011; revised August 17, 2011. This work was supported by the Korea Science and Engineering Foundation (KOSEF) NRL program grant funded by the Korean Government (MEST) (R0A-2007-000-20068-0) and a grant from the Korea Health 21 R and D Project, Ministry of Health and Welfare, Republic of Korea (A050079). Jin-ju Yang is with the Department of Biomedical Engineering, Hanyang University, Seoul, South Korea (e-mail: [email protected]). Uicheul Yoon was with Department of Biomedical Engineering, Hanyang University, Seoul, South Korea. He is now with the Department of Biomedical Engineering, College of Health and Medical Science, Catholic University, Daegu, South Korea (e-mail: [email protected]). HyukJin Yun is with the Department of Biomedical Engineering, Hanyang University, Seoul, South Korea (e-mail: [email protected]). Kiho Im was with Department of Biomedical Engineering, Hanyang University, Seoul, South Korea. He is now with the Division of Newborn Medicine, Children’s Hospital Boston, Harvard Medical School, Boston, MA, 02115, USA, Center for Fetal Neonatal Neuroimaging and the Developmental Science, Children’s Hospital Boston, Harvard Medical School, Boston, MA, USA (e-mail: [email protected]). Yu Yong Choi is with the Bioimaging Research Center, Gwangju Institute of Science and Technology, Gwangju, South Korea (e-mail: [email protected]). Sun I. Kim is with the Department of Biomedical Engineering, Hanyang University, Seoul, South Korea (e-mail: [email protected]). Kun Ho Lee is the Department of Marine Life Science, Chosun University, Gwangju , South Korea (e-mail: [email protected]). Jong-Min Lee (corresponding author to provide phone: 82-2-2220-0697; fax: 82-2-2296-5943; e-mail: [email protected]) is with the Department of Biomedical Engineering, Hanyang University, Seoul, South Korea. possible to predict human intelligence using Magnetic resonance imaging (MRI).