TY - JOUR
T1 - Leveraging Normative Personality Data and Machine Learning to Examine the Brain Structure Correlates of Obsessive-Compulsive Personality Disorder Traits
AU - Moreau, Allison L.
AU - Gorelik, Aaron J.
AU - Knodt, Annchen
AU - Barch, Deanna M.
AU - Hariri, Ahmad R.
AU - Samuel, Douglas B.
AU - Oltmanns, Thomas F.
AU - Hatoum, Alexander S.
AU - Bogdan, Ryan
N1 - Publisher Copyright:
© 2024 American Psychological Association
PY - 2024
Y1 - 2024
N2 - 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 whetherML-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-FFOCISF 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.
AB - 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 whetherML-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-FFOCISF 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.
KW - brain structure
KW - machine learning
KW - magnetic resonance imaging
KW - neuroimaging
KW - obsessive-compulsive personality disorder
UR - http://www.scopus.com/inward/record.url?scp=85208246917&partnerID=8YFLogxK
U2 - 10.1037/abn0000919
DO - 10.1037/abn0000919
M3 - Article
C2 - 39480334
AN - SCOPUS:85208246917
SN - 2769-7541
VL - 133
SP - 656
EP - 666
JO - Journal of Psychopathology and Clinical Science
JF - Journal of Psychopathology and Clinical Science
IS - 8
ER -