Precision Functional Mapping in Treatment-Resistant Depression

难治性抑郁症(TRD)，定义为反复对治疗无效，是最致命的 严重抑郁障碍(MDD)的形式，并声称发病率和死亡率不成比例。这个 抑郁症的巨大代价促使人们长达数十年的时间寻找有意义的生物标记物。 然而，尽管付出了巨大的努力，而且功能性脑成像作为一种非侵入性工具的前景 全脑测量，抑郁症的生物标记物的鉴定已经准备好临床使用。 难以捉摸。进展的主要障碍包括典型MDD的大量表型异质性。 队列以及不可靠和不精确的成像措施。在他毕业和实习的几年里， 这项建议的主要研究人员开发了精确功能神经成像的新方法，称为 ‘精密功能图’(PFM)，此前已被应用于健康人的脑组织研究 个人。目前的K99/R00独立之路奖励提案旨在应用这些新的 方法解决这些先前的局限性，确定TRD的病理特征和状态生物标记物。在……里面 为了实现这些目标，制定了一项全面的职业发展议程，以培训专业人员 在评估和管理这一重病人口方面。PI还将学习如何实施 生态上有效的工具，用于精确的行为和精神评估。这些培训目标将会得到实现 在查尔斯·康威博士、迪安娜·巴克博士、埃里克·伦泽博士、尼科·多森巴赫博士和 亚伯拉罕·斯奈德，精神病学评估、精神病理学维度、人类受试者方面的世界专家 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：研究方法。在训练K阶段期间，PI将获取扩展和重复的原则证明数据集 特征明确的TRD患者、治疗有效的MDD患者和健康人的静息fMRI数据 对照，以优化单一受试者功能网络图的定量再现性 来自静息状态的功能连接(RSFC；目标1)。在颁奖的R00阶段，一大群人(n=90) 将对TRD、治疗敏感型MDD和健康对照组进行评估，以确定脑成像特征 难治性抑郁症的生物标志物(目标2)。将状态与特征生物标记物分离，密集时间 在TRD患者接受治疗的同时，将获得TRD患者的成像数据和EMA。 电休克疗法(ECT)治疗(目标3)。总而言之，这些研究很可能揭示潜在的 疾病的网络特征，并将决定RSFC是否可以可靠地用作TRD的生物标志物。 这项提案中列出的工具和方法自然将扩展到对其他治疗的评估 行为模式和神经精神障碍。通过这次培训和培训获得的技能和专业知识 随着研究计划的实施，PI将处于有利地位，可以作为内科科学家追求独立的职业生涯。

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.