Precision Functional Mapping in Treatment-Resistant Depression

难治性抑郁症（TRD）定义为对治疗反复无效， 严重抑郁症（MDD）的形式，并声称发病率和死亡率的比例不成比例。的 抑郁症的巨大代价促使人们花了几十年时间寻找有意义的生物标志物。 然而，尽管做出了大量的努力，而且功能性脑成像作为一种非侵入性的工具， 全脑测量，识别抑郁症的生物标志物，准备临床使用仍然存在 难以捉摸。进展的主要障碍包括典型MDD中大量的表型异质性 队列和不可靠和不精确的成像测量。在他的研究生和实习期间， 这项提案的主要研究者开发了精确的功能性神经成像的新方法，称为 “精确功能映射”（PFM），以前曾被应用于研究健康人的大脑组织。 个体目前的K99/R 00独立之路奖提案旨在应用这些新的 方法来解决这些以前的局限性，并确定TRD病理学的特征和状态生物标志物。在 为了实现这些目标，已经制定了一个全面的职业发展议程来培训PI 在评估和管理这一严重疾病的人口。PI还将学习如何实施 生态有效的工具，用于精确的行为和精神评估。这些培训目标将得到实现。 在Charles Conway博士、Deanna Barch博士、Eric Lenze博士、Nico Dosenbach博士和 Abraham Snyder，世界精神病评估专家，精神病理学的维度，人类受试者 研究设计、生态瞬时评估（EMA）、认知神经科学和功能神经成像 方法.在训练K阶段，PI将获得扩展和重复的原理验证数据集。 对特征明确的TRD患者、治疗反应性MDD患者和健康人的静息fMRI数据 对照，以优化单受试者功能网络图的定量再现性， 静息状态功能连接（RSFC; Aim 1）。在该奖项的R 00阶段，一个大型队列（n=90） 将对TRD、治疗反应性MDD和健康对照进行评价，以确定脑成像特征 难治性抑郁症的生物标志物（目标2）。为了将状态与特征生物标志物分开，密集时间 在TRD患者接受治疗时，将采集TRD患者的成像数据以及EMA 电休克治疗（ECT）治疗（目的3）。总之，这些研究很可能揭示了潜在的 它将确定RSFC是否可以可靠地用作TRD的生物标志物。 本提案中提出的工具和方法自然将扩展到对其他治疗方法的评价。 方式和神经精神疾病。凭借通过培训获得的技能和专业知识， 研究计划，PI将很好地定位，追求作为一个独立的职业医生科学家。

Treatment-resistant depression (TRD), defined as repeated failure to respond to treatment, is the most virulent form of major depressive disorder (MDD), and claims a disproportionate fraction of morbidity and mortality. The overwhelming costs of depression have motivated a decades-long search for meaningful biomarkers. However, despite substantial effort, and the promise of functional brain imaging as a non-invasive tool for whole-brain measurements, the identification of biomarkers of depression for ready clinical use has remained elusive. The major impediments to progress include substantial phenotypic heterogeneity in typical MDD cohorts and unreliable and imprecise imaging measures. During his graduate and residency years, the principal investigator of this proposal developed novel methods for precise functional neuroimaging, known as `precision functional mapping' (PFM), which have been previously applied to study brain organization in healthy individuals. The present K99/R00 Pathway to Independence award proposal is designed to apply these new methods to address these previous limitations and identify trait and state biomarkers of pathology in TRD. In order to achieve these goals, a comprehensive career development agenda has been structured to train the PI in the evaluation and management of this severely ill population. The PI will also learn how to implement ecologically valid tools for precise behavioral and psychiatric assessments. These training aims will be pursued with the guidance and mentorship of Drs. Charles Conway, Deanna Barch, Eric Lenze, Nico Dosenbach, and Abraham Snyder, world experts in psychiatric assessment, dimensions of psychopathology, human subjects study design, ecological momentary assessment (EMA), cognitive neuroscience, and functional neuroimaging methods. During the training K phase, the PI will acquire a proof-of-principle dataset of extended and repeated resting fMRI data on well-characterized TRD patients, treatment-responsive MDD patients, and healthy controls, in order to optimize the quantitative reproducibility of single-subject functional network maps derived from resting state functional connectivity (RSFC; Aim 1). In the R00 phase of the award, a large cohort (n=90) of TRD, treatment-responsive MDD and healthy controls will be evaluated to identify brain imaging trait biomarkers of treatment-resistant depression (Aim 2). To separate state from trait biomarkers, dense time courses of imaging data, as well as EMA, will be acquired on TRD patients while they undergo electroconvulsive therapy (ECT) treatment (Aim 3). Together, these studies are likely to reveal underlying network features of disease and will determine whether RSFC can be reliably used as a biomarker for TRD. The tools and approach laid out in this proposal will naturally extend to evaluations of other treatment modalities and neuropsychiatric disorders. With the skills and expertise gained through this training and research program, the PI will be well-positioned to pursue an independent career as a physician scientist.