Machine learning of brain gray matter differentiates sex in a large forensic sample

Nathaniel E. Anderson, Keith A. Harenski, Carla L. Harenski, Michael R. Koenigs, Jean Decety, Vince D. Calhoun, Kent A. Kiehl

Research output: Contribution to journalArticle

Abstract

Differences between males and females have been extensively documented in biological, psychological, and behavioral domains. Among these, sex differences in the rate and typology of antisocial behavior remains one of the most conspicuous and enduring patterns among humans. However, the nature and extent of sexual dimorphism in the brain among antisocial populations remains mostly unexplored. Here, we seek to understand sex differences in brain structure between incarcerated males and females in a large sample (n = 1,300) using machine learning. We apply source-based morphometry, a contemporary multivariate approach for quantifying gray matter measured with magnetic resonance imaging, and carry these parcellations forward using machine learning to classify sex. Models using components of brain gray matter volume and concentration were able to differentiate between males and females with greater than 93% generalizable accuracy. Highly differentiated components include orbitofrontal and frontopolar regions, proportionally larger in females, and anterior medial temporal regions proportionally larger in males. We also provide a complimentary analysis of a nonforensic healthy control sample and replicate our 93% sex discrimination. These findings demonstrate that the brains of males and females are highly distinguishable. Understanding sex differences in the brain has implications for elucidating variability in the incidence and progression of disease, psychopathology, and differences in psychological traits and behavior. The reliability of these differences confirms the importance of sex as a moderator of individual differences in brain structure and suggests future research should consider sex specific models.

Original languageEnglish (US)
Pages (from-to)1496-1506
Number of pages11
JournalHuman Brain Mapping
Volume40
Issue number5
DOIs
StatePublished - Apr 1 2019
Externally publishedYes

Keywords

  • MRI
  • antisocial behavior
  • gender
  • machine learning
  • sex
  • source-based morphometry

ASJC Scopus subject areas

  • Anatomy
  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Neurology
  • Clinical Neurology

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  • Cite this

    Anderson, N. E., Harenski, K. A., Harenski, C. L., Koenigs, M. R., Decety, J., Calhoun, V. D., & Kiehl, K. A. (2019). Machine learning of brain gray matter differentiates sex in a large forensic sample. Human Brain Mapping, 40(5), 1496-1506. https://doi.org/10.1002/hbm.24462