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 healthy youth from the multi-site Pediatric Imaging, Neurocognition and Genetics (PING) cohort study. The trained model …

Date

2018

Authors

Farshid Sepehrband, Kirsten M Lynch, Ryan P Cabeen, Clio Gonzalez-Zacarias, Lu Zhao, Mike D'Arcy, Carl Kesselman, Megan M Herting, Ivo D Dinov, Arthur W Toga, Kristi A Clark

Journal

Neuroimage

Volume

172

Pages

217-227

Publisher

Academic Press