Data-driven models of symptom heterogeneity to empower transdiagnostic multimodal biomarker discovery in mood disorders

项目总结/摘要 我的目标是通过利用尖端的神经成像技术， 和数据驱动的分析方法，以推进精神健康领域的精准医学。在我坚强的基础上 神经成像和计算背景，该奖项的培训部分强调课程， 在精神病理学的临床和行为方面提供指导。我也将接受指导， 我对这个新兴领域的深度学习的理论和应用理解。总体研究 这项计划的目标是发展计算策略，以解释情绪障碍的异质性 以改善治疗反应生物标志物的鉴定。药物和行为反应 抗抑郁药治疗率低，可能是由于抑郁症的症状和病因异质性 由此某些治疗可以为具有特定症状群的患者提供不同的益处。 在K99阶段，我将寻求使用电休克改善个体抗抑郁反应的预测。 ECT疗法（ECT）作为治疗模型，其具有稳健和快速的抗抑郁作用。我会用核磁共振成像 为全球ECT-MRI研究协作组织收集的接受ECT的患者的临床数据 （宝石）。在目标1中，我将使用探索性因素分析来表征潜在症状维度， ECT之前、期间和之后的GEMRIC队列。预测临床结局的准确性沿着恢复 将症状维度与使用总评分评价反应的传统方法进行比较， 汉密尔顿抑郁评定量表（HDRS）。追求这一目标将扩大我对临床精神病学的理解 并为独立的目标奠定基础知识。Aim 2将扩展我的深度学习和多模式 神经成像技能，因为我开发了新的深度学习架构， GEMRIC参与者进一步改善治疗反应和认知障碍的预测， 等方面深度网络架构将发现， 潜在特征表示。R 00阶段将是我在 指导阶段，并在这些研究线上扩展。Aim 3将从一系列大型MRI 来自更广泛定义的情绪障碍患者的数据集，以识别多模态成像标记物 与跨诊断症状域相关。目标4使用目标3中的治疗组， 接受氯胺酮，睡眠剥夺，认知行为治疗和药物，以探索程度 根据这些生物标志物的治疗反应，沿着确定的转诊断症状维度定义 在目标3中，各治疗组之间共享。我预计情绪障碍的离散分类 人为地掩盖了治疗反应生物标志物的发现。实现这些目标，同时 促使我独立，并对异质性情绪障碍的治疗产生重要的见解。

PROJECT SUMMARY/ABSTRACT My goal is to pursue an independent career in computational psychiatry by leveraging cutting-edge neuroimaging and data-driven analysis approaches to advance precision medicine in mental health. To build on my strong neuroimaging and computational background, the training component of this award emphasizes coursework and mentorship in the clinical and behavioral aspects of psychopathology. I will also receive mentorship to advance my theoretical and applied understanding of deep learning in this burgeoning field. The overarching research goal in this proposal is to develop computational strategies that account for the heterogeneity of mood disorders to improve the identification of treatment-response biomarkers. Response to pharmaceutical and behavioral antidepressant treatments is low, likely due to the symptomatic and etiological heterogeneity of depression whereby certain treatments may confer differential benefits for patients having particular symptom constellations. In the K99 phase, I will seek to improve prediction of individual antidepressant response using electroconvulsive therapy (ECT), which elicits robust and rapid antidepressant effects, as the treatment model. I will use MRI and clinical data from patients undergoing ECT collected for the large the Global ECT-MRI Research Collaboration (GEMRIC). In Aim 1, I will use exploratory factor analysis to characterize latent symptom dimensions of the GEMRIC cohort before, during, and after ECT. The accuracy of predicting clinical outcomes along the recovered symptom dimensions will be compared to traditional means of evaluating response using the total score of the Hamilton Depression Rating Scale (HDRS). Pursuit of this aim will expand my understanding of clinical psychiatry and lay foundational knowledge for the independent aims. Aim 2 will expand my deep learning and multimodal neuroimaging skillsets as I develop novel deep learning architectures to fuse multimodal imaging features of GEMRIC participants to further improve predictions of treatment response and cognitive impairment following ECT. Rather than simply concatenating multimodal features together, deep network architectures will discover latent feature representations. The R00 phase will be a logical progression of the skill sets I develop in the mentored phase and expand on these lines of research. Aim 3 will draw from a collection of large-scale MRI datasets from patients with more broadly defined mood disorders to identify multimodal imaging markers associated with transdiagnostic symptom domains. Aim 4 uses treatment groups from aim 3, including patients undergoing ketamine, sleep deprivation, cognitive behavioral therapy, and pharmaceuticals, to explore the extent to which biomarkers of therapeutic response, defined along the transdiagnostic symptom dimensions identified in Aim 3, are shared across treatment groups. I anticipate that discrete categorizations of mood disorders artificially obscures discovery of treatment-response biomarkers. Fulfillment of these aims will simultaneously propel me to independence and yield important insight into the treatment of heterogeneous mood disorders.