Neuroanatomical morphometric characterization of sex differences in youth using statistical learning.

ABSTRACT: Exploring neuroanatomical sex differences using a multivariate statistical learning approach can yield insights that cannot be derived with univariate analysis. While gross differences in total brain volume are well-established, uncovering the more subtle, regional sex-related differences in neuroanatomy requires a multivariate approach that can accurately model spatial complexity as well as the interactions between neuroanatomical features. Here, we developed a multivariate statistical learning model using a support vector machine (SVM) classifier to predict sex from MRI-derived regional neuroanatomical features from a single-site study of 967 healthy youth from the Philadelphia Neurodevelopmental Cohort (PNC). Then, we validated the multivariate model on an independent dataset of 682 hea

SUBMITTER: Sepehrband F 

PROVIDER: S-EPMC5967879 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature