Leveraging Normative Personality Data and Machine Learning to Examine the Brain Structure Correlates of Obsessive-Compulsive Personality Disorder Traits

• Published in: Journal of psychopathology and clinical science 2024-11, Vol.133 (8), p.656-666
• Main Authors: Moreau, Allison L., Gorelik, Aaron J., Knodt, Annchen, Barch, Deanna M., Hariri, Ahmad R., Samuel, Douglas B., Oltmanns, Thomas F., Hatoum, Alexander S., Bogdan, Ryan
• Format: Article
• Language: English
• Subjects: Adult; Aged; Brain - diagnostic imaging; Brain - pathology; Brain Size; Brain structure; College students; Compulsive Personality Disorder - pathology; Compulsive Personality Disorder - psychology; Conscientiousness; Female; Five factor model; Human; Humans; Machine Learning; Magnetic Resonance Imaging; Male; Medical imaging; Middle Aged; Neo Personality Inventory; Neuroanatomy; Neuroimaging; Neuroticism; Obsessive compulsive disorder; Obsessive Compulsive Personality Disorder; Older people; Openness; Personality - physiology; Personality Correlates; Personality disorders; Personality Inventory; Personality traits; Phenotypes; Psychopathology; Young Adult
• ISSN: 2769-7541; 2769-755X; 2769-755X
• DOI: 10.1037/abn0000919

Brain structure correlates of obsessive-compulsive personality disorder (OCPD) remain poorly understood as limited OCPD assessment has precluded well-powered studies. Here, we tested whether machine learning (ML; elastic net regression, gradient boosting machines, support vector regression with linear and radial kernels) could estimate OCPD scores from personality data and whether ML-predicted scores are associated with indices of brain structure (cortical thickness and surface area and subcortical volumes). Among older adults (ns = 898-1,606) who completed multiple OCPD assessments, ML elastic net regression with Revised NEO Personality Inventory personality items as features best predicted Five-Factor Obsessive-Compulsive Inventory-Short Form (FFOCI-SF) scores, root-mean-squared error (RMSE)/SD = 0.66; performance generalized to a sample of college students (n = 175; RMSE/SD = 0.51). Items from all five-factor model personality traits contributed to predicted FFOCI-SF (p-FFOCI-SF) scores; conscientiousness and openness items were the most influential. In college students (n = 1,253), univariate analyses of cortical thickness, surface area, and subcortical volumes revealed only a positive association between p-FFOCI-SF and right superior frontal gyrus cortical thickness after adjusting for multiple testing (b = 2.21, p = .0014; all other |b|s < 1.04; all other ps > .009). Multivariate ML models of brain features predicting FFOCI, conscientiousness, and neuroticism performed poorly (RMSE/SDs > 1.00). These data reveal that all five-factor model traits contribute to maladaptive OCPD traits and identify greater right superior frontal gyrus cortical thickness as a promising correlate of OCPD for future study. Broadly, this study highlights the utility of ML to estimate unmeasured psychopathology phenotypes in neuroimaging data sets but that our application of ML to neuroimaging may not resolve unreliable associations and small effects characteristic of univariate psychiatric neuroimaging research. General Scientific SummaryIndices of obsessive-compulsive personality disorder (OCPD) and its underlying dimensions can be predicted by applying machine learning to personality data. OCPD traits have small correlations with brain structure; only a positive association with superior frontal gyrus cortical thickness was significant. Multivariate machine learning models using brain features to predict OCPD traits were not robust.