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

数据驱动的症状异质性模型可促进情绪障碍的跨诊断多模式生物标志物发现

• 批准号: 10011838
• 负责人: Benjamin Seavey Cutler Wade
• 金额: $ 10.29万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-06 至 2022-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10011838
• 关键词: Anhedonia; Antidepressive Agents; Anxiety Disorders; Architecture; Award; Behavioral; Biological Markers; Bipolar Disorder; Brain; Categories; Clinical; Clinical Data; Cognitive; Cognitive Therapy; Collaborations; Collection; Data; Data Set; Databases; Development; Diagnostic; Dimensions; Disease; Disease remission; Electroconvulsive Therapy; Etiology; Factor Analysis; Foundations; Functional Imaging; Goals; Hamilton Rating Scale for Depression; Heterogeneity; Hippocampus (Brain); Image; Impaired cognition; Individual; Infusion procedures; Intervention; Ketamine; Knowledge; Left; Magnetic Resonance Imaging; Maps; Measures; Mental Depression; Mental Health; Mentors; Mentorship; Methods; Modality; Modeling; Mood Disorders; Multimodal Imaging; National Institute of Mental Health; Negative Valence; Neurobiology; Participant; Patients; Pattern; Pharmacologic Substance; Pharmacotherapy; Phase; Post-Traumatic Stress Disorders; Prediction of Response to Therapy; Psychiatry; Psychopathology; Research; Research Domain Criteria; Rest; Severity of illness; Sleep Deprivation; Sleeplessness; Structure; Symptoms; System; Therapeutic Intervention; Training; Transcend; Treatment Side Effects; antidepressant effect; anxious; biological systems; biomarker discovery; biomarker identification; career; classification algorithm; cohort; convolutional neural network; data archive; deep learning; disability; effective therapy; electric field; hippocampal morphometry; imaging biomarker; improved; insight; learning strategy; machine learning method; multimodality; network architecture; neurobiological mechanism; neuroimaging; novel; personalized intervention; personalized medicine; precision medicine; predict clinical outcome; predicting response; random forest; relating to nervous system; response; response biomarker; side effect; skills; statistical and machine learning; support vector machine; therapy outcome; treatment group; treatment response; treatment strategy

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.

项目摘要/摘要 我的目标是通过利用尖端神经影像来从事计算精神病学独立职业 以及数据驱动的分析方法，以提高心理健康方面的精确医学。以我的坚强为基础 神经影像学和计算背景，该奖项的培训部分强调课程和 心理病理学的临床和行为方面的指导。我还将获得指导以进步 在这个新兴领域，我对深度学习的理论和应用理解。总体研究 该提案中的目标是制定计算策略来解释情绪障碍的异质性 改善治疗响应生物标志物的识别。对药物和行为的反应 抗抑郁药的治疗较低，可能是由于抑郁症的症状和病因异质性 因此，某些治疗可能会给患有特定症状星座的患者带来差异益处。 在K99阶段，我将寻求使用电击弹射的单个抗抑郁反应的预测 治疗（ECT），它引起了鲁棒和快速的抗抑郁作用，作为治疗模型。我将使用MRI和 全球ECT-MRI研究合作的临床数据来自接受ECT的患者 （宝石）。在AIM 1中，我将使用探索性因素分析来表征 在ECT之前，期间和之后的宝石队列。预测沿恢复的临床结果的准确性 症状维度将与传统的评估反应方法的方法进行比较 汉密尔顿抑郁量表量表（HDRS）。追求这一目标将扩大我对临床精神病学的理解 和独立目标的基础知识。 AIM 2将扩大我的深度学习和多模式 当我开发新颖的深度学习体系结构以融合多模式成像特征时，神经影像学技能 宝石参与者进一步改善了治疗反应和认知障碍的预测 ect。深层网络体系结构不会简单地将多模式特征串在一起，而是会发现 潜在特征表示。 R00阶段将是我在 指导阶段并扩展这些研究路线。 AIM 3将从大规模MRI的集合中获取 来自具有更广泛定义的情绪障碍患者的数据集，以识别多模式成像标记 与转诊症状域有关。 AIM 4使用AIM 3的治疗组，包括患者 接受氯胺酮，睡眠剥夺，认知行为疗法和药物，以探索程度 沿着经诊断症状尺寸定义的治疗反应的生物标志物已确定 在AIM 3中，在治疗组之间共享。我预计情绪障碍的离散分类 人为地掩盖了治疗响应生物标志物的发现。这些目标的实现将同时 促使我独立，并对治疗异质情绪障碍的治疗产生重要的见解。

项目成果

Anterior default mode network and posterior insular connectivity is predictive of depressive symptom reduction following serial ketamine infusion.

• DOI: 10.1017/s0033291722001313
• 发表时间: 2022-09
• 期刊: PSYCHOLOGICAL MEDICINE
• 影响因子: 6.9
• 作者: Wade, Benjamin S. C.;Loureiro, Joana;Sahib, Ashish;Kubicki, Antoni;Joshi, Shantanu H.;Hellemann, Gerhard;Espinoza, Randall T.;Woods, Roger P.;Congdon, Eliza;Narr, Katherine L.
• 通讯作者: Narr, Katherine L.

Individual Prediction of Optimal Treatment Allocation Between Electroconvulsive Therapy or Ketamine using the Personalized Advantage Index.

使用个性化优势指数对电惊厥疗法或氯胺酮之间的最佳治疗分配进行个体预测。

• DOI: 10.21203/rs.3.rs-3682009/v1
• 发表时间: 2023
• 期刊: Research square
• 作者: Wade,Benjamin;Pindale,Ryan;Camprodon,Joan;Luccarelli,James;Li,Shuang;Meisner,Robert;Seiner,Stephen;Henry,Michael
• 通讯作者: Henry,Michael