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

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.

Figure 1

Series represents a generic human brain, sliced top to bottom in 4 mm slices. Colored regions represent the gray matter volume (modulated) associated with a specific component derived from source‐based morphometry. Blue represents component areas with higher loading for males (t = 14.02, p < .001). Orange represents component areas with higher loading for females (t = 10.77, p < .001). Higher loadings are dimensionally proportional to higher volume in these regions, but do not represent direct differences between males and females (see Figure 3). Colored bars are z‐values for the spatial extent of the individual components across subjects [Color figure can be viewed at http://wileyonlinelibrary.com]

Figure 2

Series represents a generic human brain, sliced top to bottom in 4 mm slices. Colored regions represent the gray matter density (unmodulated) associated with a specific component derived from source‐based morphometry. Blue represents component areas with higher loading for males (t = 6.02, p < .001). Orange represents component areas with higher loading for females (t = 4.53, p < .001). Higher loadings are dimensionally proportional to higher gray matter concentration in these regions, but do not represent direct differences between males and females (see Figure 3). Colored bars are z‐values for the spatial extent of the individual components across subjects [Color figure can be viewed at http://wileyonlinelibrary.com]

Figure 3

t‐Tests demonstrating volumetric comparisons between males and females in the incarcerated (a) and nonincarcerated (b: Genomics Superstruct) samples. Orange maps show gray matter relatively larger in females; blue maps show gray matter relatively larger in males. These figures are intended as a succinct summary of regional gray matter differences for ease of viewing; however, classification models were based on 57 components of gray matter derived from source‐based morphometry (e.g., Figures 1 and 2) [Color figure can be viewed at http://wileyonlinelibrary.com]