Department of Psychology, Louisiana State University, Baton Rouge, LA.
Louisiana State University, Center for Computation and Technology, Baton Rouge, LA.
Schizophr Bull. 2021 Jan 23;47(1):44-53. doi: 10.1093/schbul/sbaa065.
Negative symptoms are a critical, but poorly understood, aspect of schizophrenia. Measurement of negative symptoms primarily relies on clinician ratings, an endeavor with established reliability and validity. There have been increasing attempts to digitally phenotype negative symptoms using objective biobehavioral technologies, eg, using computerized analysis of vocal, speech, facial, hand and other behaviors. Surprisingly, biobehavioral technologies and clinician ratings are only modestly inter-related, and findings from individual studies often do not replicate or are counterintuitive. In this article, we document and evaluate this lack of convergence in 4 case studies, in an archival dataset of 877 audio/video samples, and in the extant literature. We then explain this divergence in terms of "resolution"-a critical psychometric property in biomedical, engineering, and computational sciences defined as precision in distinguishing various aspects of a signal. We demonstrate how convergence between clinical ratings and biobehavioral data can be achieved by scaling data across various resolutions. Clinical ratings reflect an indispensable tool that integrates considerable information into actionable, yet "low resolution" ordinal ratings. This allows viewing of the "forest" of negative symptoms. Unfortunately, their resolution cannot be scaled or decomposed with sufficient precision to isolate the time, setting, and nature of negative symptoms for many purposes (ie, to see the "trees"). Biobehavioral measures afford precision for understanding when, where, and why negative symptoms emerge, though much work is needed to validate them. Digital phenotyping of negative symptoms can provide unprecedented opportunities for tracking, understanding, and treating them, but requires consideration of resolution.
阴性症状是精神分裂症的一个关键但理解不足的方面。阴性症状的测量主要依赖于临床医生的评分，这是一项具有既定可靠性和有效性的工作。越来越多的人试图使用客观的生物行为技术对阴性症状进行数字表型分析，例如，使用计算机对声音、言语、面部、手部和其他行为进行分析。令人惊讶的是，生物行为技术和临床医生的评分只是适度相关，个别研究的结果往往无法复制或与直觉相悖。在本文中，我们在 4 个案例研究中记录和评估了这种缺乏一致性的情况，在一个包含 877 个音频/视频样本的档案数据集中，并在现有文献中进行了评估。然后，我们根据“分辨率”来解释这种分歧，这是生物医学、工程和计算科学中的一个关键心理计量学特性，定义为区分信号各个方面的精度。我们展示了如何通过跨各种分辨率对数据进行缩放来实现临床评分和生物行为数据之间的收敛。临床评分反映了一种不可或缺的工具，它将大量信息整合到可操作的、但“低分辨率”的有序评分中。这使得可以查看阴性症状的“全貌”。不幸的是，它们的分辨率无法以足够的精度进行缩放或分解，以隔离阴性症状的时间、环境和性质，以达到许多目的（即，看到“树木”）。生物行为测量对于理解阴性症状何时、何地以及为何出现提供了前所未有的机会，尽管还需要进行大量工作来验证它们。阴性症状的数字表型分析可以为跟踪、理解和治疗它们提供前所未有的机会，但需要考虑分辨率。
