Neuroanatomical morphometric characterization of sex differences in youth using statistical learning

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

doi:10.1016/j.neuroimage.2018.01.065

Neuroanatomical morphometric characterization of sex differences in youth using statistical learning

Neuroimage. 2018 May 15:172:217-227. doi: 10.1016/j.neuroimage.2018.01.065. Epub 2018 Feb 3.

Authors

Affiliations

¹ Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA. Electronic address: farshid.sepehrband@loni.usc.edu.
² Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA; Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA.
³ Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA.
⁴ USC Information Sciences Institute, University of Southern California, Los Angeles, CA, USA.
⁵ Department of Preventive Medicine, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA; Department of Pediatrics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA.
⁶ Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA; Statistics Online Computational Resource, Department of Health Behavior and Biological, University of Michigan, Ann Arbor, MI, USA.

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 exhibited an 83% cross-validated prediction accuracy, and correctly predicted the sex of 77% of the subjects from the independent multi-site dataset. Results showed that cortical thickness of the middle occipital lobes and the angular gyri are major predictors of sex. Results also demonstrated the inferential benefits of going beyond classical regression approaches to capture the interactions among brain features in order to better characterize sex differences in male and female youths. We also identified specific cortical morphological measures and parcellation techniques, such as cortical thickness as derived from the Destrieux atlas, that are better able to discriminate between males and females in comparison to other brain atlases (Desikan-Killiany, Brodmann and subcortical atlases).

Keywords: Cortical morphology; FreeSurfer; GLM; Multivariate modeling; SVM; Sex difference.

Publication types

Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Adolescent
Brain / anatomy & histology*
Child
Female
Humans
Image Interpretation, Computer-Assisted / methods*
Magnetic Resonance Imaging / methods
Male
Sex Characteristics*
Support Vector Machine*
Young Adult

Abstract

Publication types

MeSH terms

Grants and funding