Format

Send to

Choose Destination

See 1 citation found by title matching your search:

Hum Brain Mapp. 2019 Apr 1;40(5):1496-1506. doi: 10.1002/hbm.24462. Epub 2018 Nov 15.

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

Author information

1
The Mind Research Network & Lovelace Biomedical and Environmental Research Institute, Albuquerque, New Mexico.
2
University of Wisconsin-Madison, Madison, Wisconsin.
3
University of Chicago, Chicago, Illinois.
4
University of New Mexico, Albuquerque, New Mexico.

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.

KEYWORDS:

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

PMID:
30430711
PMCID:
PMC6397072
[Available on 2020-04-01]
DOI:
10.1002/hbm.24462

Supplemental Content

Full text links

Icon for Wiley
Loading ...
Support Center