Lozano-Goupil Juliette, Shankman Stewart A, Walther Sebastian, Wuethrich Florian, Maher Riley E, Grzelak Lauren N, Mittal Vijay A
Department of Psychology, Northwestern University, Evanston, IL, USA.
Department of Psychology, Northwestern University, Evanston, IL, USA; Stephen M. Stahl Center for Psychiatric Neuroscience, Department of Psychiatry and Behavioral Sciences, Northwestern University, Chicago, IL, USA.
J Affect Disord. 2025 Jun 11;389:119684. doi: 10.1016/j.jad.2025.119684.
Nonverbal behavior plays a crucial role in social interactions and may provide insights into Major Depressive Disorder (MDD). While previous research suggests that hand gesture deficits are linked to depression, it remains unclear whether these deficits are state-dependent or persist beyond active illness. This study utilized an automated, video-based tool to quantify spontaneous hand gestures in individuals with current (cMDD) and remitted (rMDD) MDD during oral expression. A total of 145 participants (97 rMDD and 49 cMDD) completed a recorded gesture-elicitation task, and hand movement trajectories were extracted using video-based body tracking. Results revealed that individuals with current MDD exhibited significantly fewer gestures per minute compared to remitted individuals (p = .016, d = 0.38). Furthermore, gesture frequency negatively correlated with depressive symptom severity (r = -0.17, p = .046) and observational measures of psychomotor retardation (r = -0.23, p = .012). These findings suggest that gesture deficits are more strongly tied to the active state of depression rather than serving as a marker of vulnerability or a scar from previous depressive episodes. Automated gesture analysis provides an objective and scalable method for assessing nonverbal behavior in MDD. Future research should explore its clinical utility as a biomarker for symptom severity and treatment response.
非言语行为在社会互动中起着至关重要的作用，并且可能为重度抑郁症（MDD）提供相关见解。虽然先前的研究表明手势缺陷与抑郁症有关，但这些缺陷是与当前状态相关还是在疾病缓解后仍然存在尚不清楚。本研究使用了一种基于视频的自动化工具，对患有当前重度抑郁症（cMDD）和缓解期重度抑郁症（rMDD）的个体在口头表达过程中的自发手势进行量化。共有145名参与者（97名rMDD和49名cMDD）完成了一项有记录的手势诱发任务，并使用基于视频的身体跟踪技术提取了手部运动轨迹。结果显示，与缓解期个体相比，当前患有MDD的个体每分钟的手势明显更少（p = 0.016，d = 0.38）。此外，手势频率与抑郁症状严重程度呈负相关（r = -0.17，p = 0.046），与精神运动迟缓的观察指标呈负相关（r = -0.23，p = 0.012）。这些发现表明，手势缺陷与抑郁症的活跃状态联系更为紧密，而不是作为易感性的标志或先前抑郁发作留下的痕迹。自动化手势分析为评估MDD中的非言语行为提供了一种客观且可扩展的方法。未来的研究应探索其作为症状严重程度和治疗反应生物标志物的临床效用。
