Department of Psychiatry, University of Alberta, Edmonton, Alberta, Canada.
School of Psychology, University of Aberdeen, Aberdeen, Scotland, United Kingdom.
J Affect Disord. 2022 Aug 1;310:87-95. doi: 10.1016/j.jad.2022.04.122. Epub 2022 Apr 23.
Effective screening is important to combat the raising burden of depression and opens a critical time window for early intervention. Clinical use of non-verbal depression screening is nascent, yet a promising and viable candidate to supplement verbal screening. Differential self- and emotion-processing in depression patients were previously reported by non-verbal behavioural assessments, corroborated by neuroimaging findings of distinct neuroanatomical markers. Thus non-verbal validated brain-behaviour based self-emotion-related assessment data reflect physiological differences and may support individual level screening of depression.
In this pilot study (n = 84) we collected two longitudinal sessions of behavioural assessment data in a laboratory setting. Depression was assessed using Beck Depression Inventory II (BDI-II), to explore optimal screening methods with machine-learning, and to establish the validity of adapting a novel behavioural assessment focusing on self and emotions for depression screening.
The best machine-learning model achieved high performance in depression screening, 10-Fold cross-validation (CV) Area Under the receiver operating characteristic Curve (AUC) of 0.90 and balanced accuracy of 0.81, using a Gradient Boosting algorithm. Prospective prediction using a model trained with session 1 data to predict session 2 depression status achieved a 10-Fold CV AUC of 0.77 and balanced accuracy of 0.66. We also identified interpretable behavioural signatures for depression patients based on the best model.
The study supports the utility of using behavioural data as a viable and cost-effective solution for depression screening, with a potential wide range of applications in clinical settings.
有效的筛查对于应对日益增加的抑郁负担至关重要，也为早期干预开辟了关键的时间窗口。非言语性抑郁筛查在临床上还处于起步阶段，但作为一种有前途且可行的补充言语性筛查的方法，具有广阔的应用前景。先前的非言语性行为评估报告了抑郁患者在自我和情绪处理方面的差异，神经影像学研究也发现了不同的神经解剖学标志物。因此，基于非言语性的经过验证的大脑-行为自我情绪相关评估数据反映了生理差异，并可能支持个体层面的抑郁筛查。
在这项初步研究（n=84）中，我们在实验室环境中收集了两次行为评估数据的纵向数据。使用贝克抑郁量表第二版（BDI-II）评估抑郁，探索使用机器学习进行最佳筛查方法，并为适应新的关注自我和情绪的行为评估方法进行抑郁筛查建立有效性。
最佳机器学习模型在抑郁筛查中表现出了较高的性能，10 折交叉验证（CV）的接收者操作特征曲线（AUC）为 0.90，平衡准确性为 0.81，使用梯度提升算法。使用第一阶段数据训练的模型来预测第二阶段的抑郁状态，前瞻性预测的 10 折 CV AUC 为 0.77，平衡准确性为 0.66。我们还根据最佳模型确定了可解释的抑郁患者行为特征。
本研究支持将行为数据作为一种可行且具有成本效益的抑郁筛查方法的应用，具有广泛的临床应用潜力。
