Mapping connectomes for disordered emotional states

项目摘要/摘要 我们的目标是使用HCP协议获取并公开成像、行为和 情绪紊乱患者的症状数据。我们还将开发和公布新的 检查连接体的失序如何在 个别病人。由强化的消极情绪或失去积极情绪引起的精神病理学 情感体验影响着全球超过4亿人。这种无序的情绪状态跨越了 多种诊断类别，并伴有认知功能障碍。不 因此，令人惊讶的是，这些形式的精神病理学是导致残疾的主要原因。要解决这些问题 问题我们的调查战略是由研究领域标准(RDoC)倡议带头的 被NIMH。我们专注于三个RDoC领域和构造：1)负价中的严重威胁 系统(NVS)领域，与对恐惧和焦虑的身体症状的自动反应有关的结构；2) 正价系统(PVS)领域内的奖励估值和反应性，这是一个涉及 激励性突显、享乐反应和快感缺乏症状；以及3)工作记忆中的 认知系统(CS)域，这一概念意味着自上而下地调节认知沉思和 担心。我们的方法基于严格遵守HPC协议和对数据的坚定承诺 分享。我们联合互补的专业知识，包括(1)最先进的核磁共振技术和数据 管理系统；(2)领域领先的可复制神经科学中心；(3)领先的 大型神经放射学联盟；(4)RDoC知情的大规模成像方法的领导者 抑郁症和焦虑症；以及(5)开创性的高维数据统计方法。我们的目标是 (1)使用HCP协议获取300名22-25岁以下人群的多模式数据 经历不同程度的严重威胁、失去奖励价值/响应性，以及 工作记忆，(2)阐明连接、症状和基于行为的关系的本质 与感兴趣的RDoC结构相关的网络，以及(3)开发数据驱动的机器学习 发现这些结构的连接如何组合在一起形成自然组织的方法 成群的人。我们的数据将推进一个神经生物学模型，该模型将网络功能障碍映射到特定的 行为和症状。这一模式将为最终指导更多的分类和 根据神经功能障碍的类型进行治疗选择，而不是依赖于 神经生物学的不可知论者。

PROJECT SUMMARY/ABSTRACT Our objective is to use HCP protocols to acquire and make public a large dataset of imaging, behavioral, and symptom data from patients with disordered emotional states. We will also develop and make public new methods for examining how connectome disorganization gives rise to these disordered states at the level of the individual patient. Psychopathology arising from enhanced negative emotion or from the loss of positive emotional experience affects over 400 million people globally. Such states of disordered emotion cut across multiple diagnostic categories and are compounded by accompanying disruptions in cognitive function. Not surprisingly, therefore, these forms of psychopathology are a leading cause of disability. To address these issues our investigative strategy is informed by the Research Domain Criteria (RDoC) initiative spearheaded by NIMH. We focus on three RDoC domains and constructs: 1) acute threat within the Negative Valence System (NVS) domain, a construct relevant to automatic reactions to fear and physical symptoms of anxiety; 2) reward valuation and responsiveness within the Positive Valence System (PVS) domain, a construct involving incentive salience, hedonic responses and symptoms of anhedonia; and 3) working memory within the Cognitive System (CS) domain, a construct that implicates top-down regulation of cognitive rumination and worry. Our approach is grounded in strict adherence to HPC protocols and a strong commitment to data sharing. We unite complementary expertise, including (1) state-of-the-art MRI technology and data management systems; (2) a field-leading Center for Reproducible Neuroscience; (3) a track record in leading large-scale neuroradiology consortia; (4) leaders in RDoC-informed approaches to large-scale imaging in depression and anxiety; and (5) pioneering statistical approaches for high-dimensional data. Our aims are to (1) use the HCP protocols to acquire multi-modal data for 300 people aged 22-25 years of age who are experiencing varying degrees of acute threat, loss of reward valuation/responsiveness, and difficulties in working memory, (2) elucidate the nature of the relations among connectomes, symptoms, and behavior based on networks related to the RDoC constructs of interest, and (3) to develop data-driven, machine-learning methods to discover how connectomes for these constructs combine together to form naturally organized clusters of people. Our data will advance a neurobiological model that maps network dysfunctions to specific behaviors and symptoms. This model will provide a foundation for ultimately guiding more classifications and treatment choices according to types of neural dysfunction rather than relying on diagnostic categories that are agnostic to neurobiology.