4.7 Article

Characteristic cortical thickness patterns in adolescents with autism spectrum disorders: Interactions with age and intellectual ability revealed by canonical correlation analysis

期刊

NEUROIMAGE
卷 60, 期 3, 页码 1890-1901

出版社

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2012.01.120

关键词

Autism spectrum disorders; Kernel canonical correlation analysis; Varimax rotation; Cortical thickness; Developmental change

资金

  1. National Institutes of Health, National Institute of Mental Health (NIMH)

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To investigate patterns and correlates of cortical thickness in adolescent males with autism spectrum disorders (ASD) versus matched typically developing controls, we applied kernel canonical correlation analysis to whole brain cortical thickness with the explaining variables of diagnosis, age, full-scale IQ and their interactions. The analysis found that canonical variates (patterns of cortical thickness) correlated with each of these variables. The diagnosis- and age-by-diagnosis-related canonical variates showed thinner cortex for participants with ASD, which is consistent with previous studies using a univariate analysis. In addition, the multivariate statistics found larger affected regions with higher sensitivity than those found using univariate analysis. An IQ-related effect was also found with the multivariate analysis. The effects of IQ and age-by-IQ interaction on cortical thickness differed between the diagnostic groups. For typically developing adolescents, IQ was positively correlated with cortical thickness in orbitofrontal, postcentral and superior temporal regions, and greater thinning with age was seen in dorsal frontal areas in the superior IQ (> 120) group. These associations between IQ and cortical thickness were not seen in the ASD group. Differing relationships between IQ and cortical thickness implies independent associations between measures of intelligence and brain structure in ASD versus typically developing controls. We discuss these findings vis-a-vis prior results obtained utilizing univariate methods. Published by Elsevier Inc.

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