Neuroanatomical morphometric characterization of sex differences in youth using statistical learning

• Published in: NeuroImage (Orlando, Fla.) Vol. 172; pp. 217 - 227
• Main Authors: Sepehrband, Farshid, Lynch, Kirsten M., Cabeen, Ryan P., Gonzalez-Zacarias, Clio, Zhao, Lu, D'Arcy, Mike, Kesselman, Carl, Herting, Megan M., Dinov, Ivo D., Toga, Arthur W., Clark, Kristi A.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.05.2018; Elsevier Limited
• Subjects: Adolescent; Age; Anatomy; Big Data; Brain - anatomy & histology; Brain architecture; Brain research; Child; Childrens health; Classification; Cortex; Cortical morphology; Datasets; Female; Females; FreeSurfer; Gender differences; GLM; Humans; Image Interpretation, Computer-Assisted - methods; Learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Males; Mathematical models; Medical imaging; Mental health; Multivariate modeling; Neuroimaging; Neurological disorders; NMR; Nuclear magnetic resonance; Pediatrics; Sex Characteristics; Sex difference; Sex differences; Sexes; Support Vector Machine; SVM; Young Adult; GLM; SVM; Sex difference; FreeSurfer; Cortical morphology; Multivariate modeling
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2018.01.065

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). Visualization of neuroanatomical differences of sex by combining the following three statistical values: correlation of the neuroanatomical features with brain size as assessed by estimating Spearman's correlation with estimated total intracranial volume (x-axis), sex-related discriminatory indices derived from the SVM model (y-axis), and the univariate sex-related differences obtained from the GLM analysis (radius of spheres = negative log of the p-value). PLS: Paracentral lobule and sulcus, aMCC: middle-anterior part of the cingulate cortex, mOG: medial occipital gyrus, AG: angular gyrus, PP: Planum polare of the superior temporal gyrus, sPL: superior parietal lobe, WM: white matter hemisphere. Superscripts refers to left (L), right (R) hemispheres. Interactive version of the plot is presented online on the Plotly website (https://plot.ly/∼sepehrband/50/neuroanatomy-of-sex-difference/). [Display omitted] •Neuroanatomical sex differences in youth is modeled using a statistical learning approach.•Results indicate the advantageous of multivariate analysis over univariate analysis.•Cortical thickness and mean curvature measures revealed sex differences that were unrelated to brain size.•Most discriminative brain areas were angular and occipital gyri and paracentral lobule.•The source code for the analysis performed in this study has been made available.