Major depressive disorder is a common psychiatric illness. At present, there are no objective, non-verbal, automated markers that can reliably track treatment response. Here, we explore the use of video analysis of facial expressivity in a cohort of severely depressed patients before and after deep brain stimulation (DBS), an experimental treatment for depression. We introduced a set of variability measurements to obtain unsupervised features from muted video recordings, which were then leveraged to build predictive models to classify three levels of severity in the patients’ recovery from depression. Multiscale entropy was utilized to estimate the variability in pixel intensity level at various time scales. A dynamic latent variable model was utilized to learn a low-dimensional representation of factors that describe the dynamic relationship between high-dimensional pixels in each video frame and over time. Finally, a novel elastic net ordinal regression model was trained to predict the severity of depression, as independently rated by standard rating scales. Our results suggest that unsupervised features extracted from these video recordings, when incorporated in an ordinal regression predictor, can discriminate different levels of depression severity during ongoing DBS treatment. Objective markers of patient response to treatment have the potential to standardize treatment protocols and enhance the design of future clinical trials.

中文翻译：

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通过动态面部特征对重度抑郁症恢复过程中的抑郁严重程度进行分类

重度抑郁症是一种常见的精神疾病。目前，没有客观的、非语言的、自动化的标记可以可靠地跟踪治疗反应。在这里，我们探讨了在深度脑刺激 (DBS)（一种抑郁症的实验性治疗）前后对严重抑郁症患者进行面部表情视频分析的应用。我们引入了一组可变性测量，以从静音视频记录中获取无监督特征，然后利用这些特征来构建预测模型，以将患者从抑郁症中恢复的严重程度分为三个级别。多尺度熵被用来估计不同时间尺度的像素强度水平的变化。利用动态潜在变量模型来学习描述每个视频帧中高维像素之间随时间变化的动态关系的因素的低维表示。最后，训练了一个新的弹性净序数回归模型来预测抑郁症的严重程度，由标准评定量表独立评定。我们的结果表明，从这些视频记录中提取的无监督特征，当纳入有序回归预测因子时，可以在正在进行的 DBS 治疗期间区分不同程度的抑郁症严重程度。患者对治疗反应的客观标记有可能使治疗方案标准化并加强未来临床试验的设计。训练了一个新的弹性净序数回归模型来预测抑郁症的严重程度，由标准评定量表独立评定。我们的结果表明，从这些视频记录中提取的无监督特征，当纳入有序回归预测因子时，可以在正在进行的 DBS 治疗期间区分不同程度的抑郁症严重程度。患者对治疗反应的客观标记有可能使治疗方案标准化并加强未来临床试验的设计。训练了一种新的弹性净序数回归模型来预测抑郁症的严重程度，由标准评定量表独立评定。我们的结果表明，从这些视频记录中提取的无监督特征，当纳入有序回归预测因子时，可以在正在进行的 DBS 治疗期间区分不同程度的抑郁症严重程度。患者对治疗反应的客观标记有可能使治疗方案标准化并加强未来临床试验的设计。