ArticleNeuroImage2018
Neuroanatomical morphometric characterization of sex differences in youth using statistical learning.
Article in NeuroImage, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Sex differences in global metrics of brain size across the lifespan.Frontiers in neuroscience · 2026Article
- Critical role of EEG signals in assessment of sex-specific insights in neurological diagnostics via machine learning approach.Scientific reports · 2025Article
- "Precision Medicine" and the Failed Search for Binary Brain Sex Differences to Address Gender Behavioral Health Disparities.American journal of human biology : the official journal of the Human Biology Council · 2025Review
- Sex, gender diversity, and brain structure in early adolescence.Human brain mapping · 2024Article
- Resolving Heterogeneity in Posttraumatic Stress Disorder Using Individualized Structural Covariance Network Analysis.Depression and anxiety · 2024Article
- Classifying sex with volume-matched brain MRI.Neuroimage. Reports · 2023Article
- The sleep EEG envelope is a novel, neuronal firing-based human biomarker.Scientific reports · 2022Article
- The sexual brain, genes, and cognition: A machine-predicted brain sex score explains individual differences in cognitive intelligence and genetic influence in young children.Human brain mapping · 2022Article
- Three-dimensional self-attention conditional GAN with spectral normalization for multimodal neuroimaging synthesis.Magnetic resonance in medicine · 2021Article
- Global and Regional Changes in Perivascular Space in Idiopathic and Familial Parkinson's Disease.Movement disorders : official journal of the Movement Disorder Society · 2021Article
- Volumetric distribution of perivascular space in relation to mild cognitive impairment.Neurobiology of aging · 2021Article
- Fast Polynomial Approximation of Heat Kernel Convolution on Manifolds and Its Application to Brain Sulcal and Gyral Graph Pattern Analysis.IEEE transactions on medical imaging · 2020Article
- Article
- Sex Matters: A Multivariate Pattern Analysis of Sex- and Gender-Related Neuroanatomical Differences in Cis- and Transgender Individuals Using Structural Magnetic Resonance Imaging.Cerebral cortex (New York, N.Y. : 1991) · 2020Article
- Imputation Strategy for Reliable Regional MRI Morphological Measurements.Neuroinformatics · 2020Article
- Nonparenchymal fluid is the source of increased mean diffusivity in preclinical Alzheimer's disease.Alzheimer's & dementia (Amsterdam, Netherlands) · 2019Article
- Blood-brain barrier breakdown is an early biomarker of human cognitive dysfunction.Nature medicine · 2019Article
- Gender Identification of Human Cortical 3-D Morphology Using Hierarchical Sparsity.Frontiers in human neuroscience · 2019Article
- Intracranial volume: To adjust or not to adjust? It is not a matter of if, but how.Imaging neuroscience (Cambridge, Mass.)Article
- Because the machine can discriminate: How machine learning serves and transforms biological explanations of human difference.Big data & societyArticle
Corrections and comments
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Authors and funding
11 authors.
Funding
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).
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