Zhang Zhanqi, Chou Chi K, Rosberg Holden, Perry William, Young Jared W, Minassian Arpi, Mishne Gal, Aoi Mikio
Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA.
Department of Mathematics, La Jolla, CA.
medRxiv. 2025 Apr 14:2024.11.14.24317348. doi: 10.1101/2024.11.14.24317348.
Recent technologies for quantifying behavior have revolutionized animal studies in social, cognitive, and pharmacological neurosciences. However, comparable studies in understanding human behavior, especially in psychiatry, are lacking. In this study, we utilized data-driven machine learning to analyze natural, spontaneous open-field human behaviors in people with euthymic bipolar disorder (BD) and non-BD participants. Our computational paradigm identified representations of distinct sets of actions (motifs) that capture the physical activities of both groups of participants. We propose novel measures for quantifying dynamics, variability, and stereotypy in BD behaviors. These fine-grained behavioral features reflect patterns of cognitive functions of BD and better predict BD compared with traditional ethological and psychiatric measures and action recognition approaches. This research represents a significant computational advancement in human ethology, enabling the quantification of complex behaviors in real-world conditions and opening new avenues for characterizing neuropsychiatric conditions from behavior.
近期用于量化行为的技术彻底改变了社会、认知和药理神经科学领域的动物研究。然而，在理解人类行为方面，尤其是在精神病学领域，缺乏类似的研究。在本研究中，我们利用数据驱动的机器学习来分析处于心境正常的双相情感障碍（BD）患者和非BD参与者的自然、自发的旷场行为。我们的计算范式识别出了不同动作集（基序）的表征，这些表征捕捉了两组参与者的身体活动。我们提出了用于量化BD行为中的动态性、变异性和刻板性的新方法。与传统的行为学和精神病学测量方法以及动作识别方法相比，这些细粒度的行为特征反映了BD的认知功能模式，并且能更好地预测BD。这项研究代表了人类行为学在计算方面的重大进展，能够在现实世界条件下对复杂行为进行量化，并为从行为特征描述神经精神疾病开辟了新途径。
