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

识别与青少年抑郁症诊断和预后相关的情绪调节的神经生理标志物：分类模型的比较

• 批准号: 10617686
• 负责人: Lindsay Dickey
• 金额: $ 3.46万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-16 至 2025-05-15
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10617686
• 关键词: Adolescence; Adolescent; Age; Age of Onset; Award; Classification; Clinical; Clinical Research; Complex; Data; Depressed mood; Depressive disorder; Development; Diagnosis; Diagnostic; Dimensions; Disease; Early Intervention; Electrodes; Electroencephalography; Emotions; Event-Related Potentials; Frequencies; Future; Goals; Image; Impairment; Incidence; Individual; Individual Differences; Intervention; Interview; Literature; Longevity; Maintenance; Measures; Mental Depression; Methodology; Methods; Modeling; Motivation; Neurosciences; Neurosciences Research; Parents; Participant; Patient Self-Report; Performance; Prevention; Principal Component Analysis; Process; Prognosis; Psychopathology; Recurrence; Refractory; Regulation; Research; Research Personnel; Risk; Role; Sampling; Scientist; Sex Differences; Stimulus; Structure; Techniques; Time; Training; Translational Research; aged; career; child depression; classification algorithm; clinical diagnostics; clinical translation; clinically relevant; cognitive reappraisal; depressive symptoms; early onset; emotion regulation; epidemiology study; experience; high risk; indexing; individualized prevention; innovation; insight; multidimensional data; neurophysiology; novel; personalized intervention; pre-doctoral; prognostic; prospective; recruit; response; statistical learning

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.

项目摘要 青春期是抑郁症首次发病的高危发展期，流行病学研究表明， 揭示了在此期间抑郁症发病率的急剧上升。考虑到早些时候 抑郁症的发作与整个生命周期中更衰弱的疾病过程有关， 查明脆弱性因素对于早期和个性化干预至关重要。抑郁症是 其特征是情绪处理的广泛改变，以及难以调节对 烦躁、悲伤的情绪被认为是抑郁症发作和持续的中心因素。给定 令人信服的证据表明，情绪处理的一些变化先于抑郁症的发展， 对烦躁刺激的失调反应的神经生理学标志物可能会识别出那些在 复发和新的抑郁发作的风险最大。先前的研究表明， 从脑电图（EEG）导出的ERP可靠且经济地捕获了 情绪处理的时间动态此外，对情绪调节的研究表明， 时域和频域都可以通过调节努力（例如认知重新评估）来修改。 尽管如此，复杂的情绪调节任务与扩展的刺激呈现导致高- 三维数据，放大模糊性和差异，量化ERP相关的出现 和抑郁症的维持。一个既定的解决方案是使用主成分分析（PCA）， 减少数据的维度，识别与任务相关的ERP。然而，翻译研究 ERP与情绪和抑郁之间的关联进一步受到可复制性和临床问题的限制。 本案无关所提出的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底物，优化了对 青少年（14-17岁）抑郁症，以促进早期，个性化的干预和预防工作。 目的1：考察情绪调节对ERP和时频EEG成分的被试内效应。 目的2：迭代研究神经生理学情绪调节特征和分类算法 最准确地将基线时有和没有抑郁的青少年分类。目标3：确定 情绪调节特征和分类算法最准确地预测未来抑郁发作 在青少年中发作。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，该奖项 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专业知识， 情绪调节的概念性理解，先进的EEG/ERP方法，以及严格的定量 技术，准备我的职业生涯作为一个成功的转化临床研究人员。