Identifying Neurophysiological Markers of Emotion Regulation Relevant to Adolescent Depression Diagnosis and Prognosis: A Comparison of Classification Models

项目总结 青春期是抑郁症首次发作的高危发育期，流行病学研究 显示在这段时间内抑郁症的发病率急剧上升。考虑早些时候 抑郁症的出现与一生中更虚弱的疾病过程有关， 确定脆弱性因素对于早期和个性化干预至关重要。忧郁症是 以情绪处理的广泛变化为特征的，难以调节对 烦躁、悲伤的情绪被认为是抑郁症发生和维持的核心。vt.给出 令人信服的证据表明，情绪处理过程中的一些变化先于抑郁症的发展， 对烦躁刺激反应失调的神经生理学标记物可能会识别那些 复发和新的抑郁发作的风险最大。之前的研究证明了与事件相关的 从脑电(EEG)获得的电位(ERPs)可靠且经济地捕捉到 情绪加工的时间动力学。此外，情绪调节的研究表明，脑电标记物在 时间域和频率域都可以通过调节努力进行修改，例如认知重新评估。 尽管有这样的承诺，但复杂的情绪调节任务与延长刺激呈现导致高- 维度数据，放大了与出现相关的事件相关事件量化过程中的歧义和差异 以及抑郁症的维持。一种公认的解决方案是使用主成分分析(PCA)来 降低数据的维度并确定与任务相关的事件相关事件。然而，翻译研究正在审查 事件相关电位与情绪和抑郁之间的联系受到可复制性和临床问题的进一步限制 关联性。拟议研究的主要目标是比较分类算法，以澄清 情绪调节的神经生理学基础，优化诊断和预后预测 青少年(14-17岁)抑郁，以促进早期、个性化的干预和预防工作。 目的1：考察被试内部情绪调节对事件相关电位和时频脑电成分的影响。 目的2：反复研究哪些神经生理情绪调节特征和分类算法 最准确地对目前患有和不患有抑郁症的青少年进行基线分类。目标3：确定哪些 情绪调节特征和分类算法最准确地预测未来抑郁症的发病 青少年的发作期。探索性分析将检查潜在的性别差异并比较模型 包括情绪调节的自我报告时的表现。除了这些研究目标外，这个奖项 将支持我的博士前培训目标，以扩大我在诊断临床面试方面的发展专长， 对情绪调节的概念性理解，先进的EEG/ERP方法，以及严格的量化 技术，为我的职业生涯做好准备，成为一名成功的翻译临床研究人员。

PROJECT SUMMARY Adolescence is a high-risk developmental period for the first onset of depression, and epidemiological research reveals a sharp increase in incidence rates for depressive disorders during this time. Considering earlier onsets of depression are associated with a more debilitating course of the disorder throughout the lifespan, the identification of vulnerability factors is critical for early and personalized intervention. Depression is characterized by widespread alterations in emotion processing, and difficulties regulating responses to dysphoric, sad emotions are thought to be central to the onset and maintenance of depression. Given compelling evidence that some alterations in emotion processing precede the development of depression, neurophysiological markers of dysregulated responses to dysphoric stimuli could potentially identify those at greatest risk for recurrence and new depressive episodes. Previous research demonstrates event-related potentials (ERPs) derived from the electroencephalogram (EEG) reliably and economically capture the temporal dynamics of emotion processing. Further, studies on emotion regulation demonstrate EEG markers in both the time and frequency domains are modifiable by regulation efforts, such as cognitive reappraisal. Despite this promise, complex emotion regulation tasks with extended stimuli presentations result in high- dimensional data, amplifying ambiguities and discrepancies in quantifying ERPs relevant to the emergence and maintenance of depression. One established solution is to use principal components analysis (PCA) to reduce the dimensionality of the data and identify task-relevant ERPs. Yet, translational research examining associations between ERPs to emotion and depression is further limited by issues of replicability and clinical relevance. The primary goal of the proposed research is to compare classification algorithms to clarify the neurophysiological substrates of emotion regulation that optimize diagnostic and prognostic predictions of adolescent (age 14-17) depression in order to facilitate early, personalized intervention and prevention efforts. Aim 1: Examine within-subjects effects of emotion regulation on ERP and time-frequency EEG components. Aim 2: Iteratively investigate which neurophysiological emotion regulation features and classification algorithms most accurately classify adolescents with and without current depression at baseline. Aim 3: Identify which emotion regulation features and classification algorithms most accurately predict future onsets of depressive episodes in adolescents. Exploratory analyses will examine potential sex differences and compare model performance when including self-reports of emotion regulation. In addition to these research goals, this award will support my predoctoral training goals to expand my developing expertise in diagnostic clinical interviewing, conceptual understanding of emotion regulation, advanced EEG/ERP methods, and rigorous quantitative techniques, preparing me for a career as a successful translational clinical researcher.