Received: 11 April 2018 Revised: 5 September 2018 Accepted: 27 October 2018 DOI: 10.1002/hbm.24462 RESEARCH ARTICLE Machine learning of brain gray matter differentiates sex in a large forensic sample Nathaniel E. Anderson1 | Keith A. Harenski1 |CarlaL.Harenski1 | Michael R. Koenigs2 | Jean Decety3 | Vince D. Calhoun1,4 | Kent A. Kiehl1,4 1The Mind Research Network & Lovelace Biomedical and Environmental Research Abstract Institute, Albuquerque, New Mexico Differences between males and females have been extensively documented in biological, psy- 2University of Wisconsin-Madison, Madison, chological, and behavioral domains. Among these, sex differences in the rate and typology of Wisconsin antisocial behavior remains one of the most conspicuous and enduring patterns among humans. 3 University of Chicago, Chicago, Illinois However, the nature and extent of sexual dimorphism in the brain among antisocial populations 4 University of New Mexico, Albuquerque, remains mostly unexplored. Here, we seek to understand sex differences in brain structure New Mexico between incarcerated males and females in a large sample (n = 1,300) using machine learning. Correspondence Nathaniel Anderson, The Mind Research We apply source-based morphometry, a contemporary multivariate approach for quantifying Network & Lovelace Biomedical and gray matter measured with magnetic resonance imaging, and carry these parcellations forward Environmental Research Institute, using machine learning to classify sex. Models using components of brain gray matter volume Albuquerque, NM. and concentration were able to differentiate between males and females with greater than 93% Email:
[email protected] generalizable accuracy. Highly differentiated components include orbitofrontal and frontopolar Funding information National Institute of Biomedical Imaging and regions, proportionally larger in females, and anterior medial temporal regions proportionally Bioengineering, Grant/Award Number: larger in males.