Mehta Marishka M, Hakimi Navid, Pena Orestes, Torres Taylor, Goldman Carter M, Lavalley Claire A, Stewart Jennifer L, Berg Hannah, Ironside Maria, Paulus Martin P, Aupperle Robin, Smith Ryan
Laureate Institute for Brain Research, Tulsa, OK, US.
University of Tulsa, Tulsa, OK, US.
Comput Psychiatr. 2025 Sep 5;9(1):159-186. doi: 10.5334/cpsy.131. eCollection 2025.
Psychiatric disorders are highly heterogeneous and often co-morbid, posing specific challenges for effective treatment. Recently, computational modeling has emerged as a promising approach for characterizing sources of this heterogeneity, which could potentially aid in clinical differentiation. In this study, we tested whether computational mechanisms of decision-making under approach-avoidance conflict (AAC) - where behavior is expected to have both positive and negative outcomes - may have utility in this regard. We first carried out a set of pre-registered modeling analyses in a sample of 480 individuals who completed an established AAC task. These analyses aimed to replicate cross-sectional and longitudinal results from a prior dataset (N = 478) - suggesting that mechanisms of decision uncertainty () and emotion conflict () differentiate individuals with depression, anxiety, substance use disorders, and healthy comparisons. We then combined the prior and current datasets and employed a stacked machine learning approach to assess whether these computational measures could successfully perform out-of-sample classification between diagnostic groups. This revealed above-chance differentiation between affective and substance use disorders (balanced accuracy > 0.688), both in the presence and absence of co-morbidities. These results demonstrate the predictive utility of computational measures in characterizing distinct mechanisms of psychopathology and may point to novel treatment targets.
精神疾病具有高度异质性且常常共病，这给有效治疗带来了特殊挑战。最近，计算建模已成为一种很有前景的方法，用于刻画这种异质性的来源，这可能有助于临床鉴别。在本研究中，我们测试了在趋避冲突（AAC）下的决策计算机制——即行为预期既有积极结果又有消极结果——在这方面是否有用。我们首先在480名完成既定AAC任务的个体样本中进行了一组预先注册的建模分析。这些分析旨在复制先前数据集（N = 478）的横断面和纵向结果，表明决策不确定性机制（）和情绪冲突机制（）可区分患有抑郁症、焦虑症、物质使用障碍的个体以及健康对照者。然后，我们将先前和当前的数据集合并，并采用堆叠机器学习方法来评估这些计算指标是否能够成功地在诊断组之间进行样本外分类。这揭示了在存在和不存在共病的情况下，情感障碍和物质使用障碍之间的区分高于随机水平（平衡准确率> 0.688）。这些结果证明了计算指标在刻画精神病理学不同机制方面的预测效用，并可能指向新的治疗靶点。
