Leveraging normative personality data and machine learning to examine the brain structure correlates of obsessive-compulsive personality disorder traits.

ABSTRACT: 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 (R

SUBMITTER: Moreau AL 

PROVIDER: S-EPMC11964300 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature