Kalva Prathik, Kanja Kourtney, Metzger Brian A, Fan Xiaoxu, Cui Brian, Pascuzzi Bailey, Magnotti John, Mocchi Madaline, Mathura Raissa, Bijanki Kelly R
Department of Neurosurgery, Baylor College of Medicine, Houston, Texas.
Department of Psychology, Swarthmore College, Swarthmore, Pennsylvania.
Biol Psychiatry Cogn Neurosci Neuroimaging. 2024 Jul 18. doi: 10.1016/j.bpsc.2024.07.004.
To mitigate limitations of self-reported mood assessments, we introduce a novel affective bias task. The task quantifies instantaneous emotional state by leveraging the phenomenon of affective bias, in which people interpret external emotional stimuli in a manner consistent with their current emotional state. This study establishes task stability in measuring and tracking depressive symptoms in clinical and nonclinical populations. Initial assessment in a large nonclinical sample established normative ratings. Depressive symptoms were measured and compared with task performance in a nonclinical sample, as well as in a clinical cohort of individuals who were undergoing surgical evaluation for severe epilepsy. In both cohorts, a stronger negative affective bias was associated with a higher Beck Depression Inventory-II score. The affective bias task exhibited high stability and interrater reliability as well as construct validity in predicting depression levels in both cohorts, suggesting that the task is a reliable proxy for mood and a diagnostic tool for detecting depressive symptoms.
为了减轻自我报告情绪评估的局限性，我们引入了一种新型的情感偏差任务。该任务通过利用情感偏差现象来量化瞬时情绪状态，即人们以与其当前情绪状态一致的方式解释外部情绪刺激。本研究确立了该任务在测量和跟踪临床及非临床人群抑郁症状方面的稳定性。在一个大型非临床样本中的初步评估确定了标准评分。在一个非临床样本以及一组正在接受严重癫痫手术评估的临床个体中，测量了抑郁症状并将其与任务表现进行比较。在这两个队列中，更强的负面情感偏差与更高的贝克抑郁量表第二版得分相关。情感偏差任务在预测两个队列的抑郁水平方面表现出高稳定性、评分者间信度以及结构效度，这表明该任务是情绪的可靠替代指标，也是检测抑郁症状的诊断工具。
