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. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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利用规范性人格数据和机器学习来检查强迫性人格障碍特征的大脑结构相关性。


强迫型人格障碍 （OCPD） 的大脑结构相关性仍然知之甚少，因为有限的 OCPD 评估排除了有把握度的研究。在这里，我们测试了机器学习（ML;弹性网络回归、梯度提升机、线性和径向核的支持向量回归）是否可以从人格数据中估计 OCPD 分数，以及 ML 预测分数是否与大脑结构指数（皮质厚度和表面积以及皮质下体积）相关。在完成多次 OCPD 评估的老年人 （ns = 898-1,606） 中，以修订后的 NEO 人格量表人格项目为特征的 ML 弹性网回归最能预测五因素强迫症量表简表 （FFOCI-SF） 分数，均方根误差 （RMSE）/SD = 0.66;表现推广到大学生样本 （n = 175;RMSE/SD = 0.51）。来自所有五因素模型人格特质的项目都有助于预测的 FFOCI-SF （p-FFOCI-SF） 分数;责任心和开放性项目最具影响力。在大学生 （n = 1,253） 中，皮质厚度、表面积和皮质下体积的单变量分析显示，在调整多次测试后，p-FFOCI-SF 与右额上回皮质厚度之间仅呈正相关 （b = 2.21，p = .0014;所有其他 |b|s < 1.04;所有其他 ps > .009）。预测 FFOCI、责任心和神经质的大脑特征的多变量 ML 模型表现不佳 （RMSE/SDs > 1.00）。这些数据表明，所有五因素模型特征都会导致适应不良的 OCPD 特征，并确定更大的右额上回皮质厚度是 OCPD 的一个有前途的相关性，用于未来研究。 从广义上讲，这项研究强调了 ML 在神经影像学数据集中估计未测量的精神病理学表型的效用，但我们将 ML 应用于神经影像学可能无法解决单变量精神病学神经影像学研究的不可靠关联和小效应特征。（PsycInfo 数据库记录 （c） 2024 APA，保留所有权利）。