Mapping connectomes for disordered emotional states

绘制紊乱情绪状态的连接体图

• 批准号: 9314855
• 负责人: Leanne Williams
• 金额: $ 78.47万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-19 至 2021-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9314855
• 关键词: Acute; Address; Adherence; Affect; Age; Age-Years; Algorithms; Amygdaloid structure; Anatomy; Anhedonia; Anterior; Anxiety; Behavior; Behavioral; Behavioral Symptoms; Brain; Categories; Classification; Cognitive; Corpus striatum structure; Data; Data Set; Diagnostic; Diffusion; Dimensions; Disease; Dorsal; Down-Regulation; Emotional; Emotional disorder; Emotions; Evaluation; Foundations; Fright; Functional Imaging; Human; Image; Insula of Reil; Machine Learning; Magnetic Resonance Imaging; Maps; Medial; Mental Depression; Methods; Modality; Modeling; National Institute of Mental Health; Nature; Negative Valence; Neurobiology; Neuronal Dysfunction; Neurosciences; Parietal Lobe; Participant; Patient Self-Report; Patients; Performance; Positive Valence; Precentral gyrus; Prefrontal Cortex; Principal Component Analysis; Protocols documentation; Psychopathology; Reaction; Recruitment Activity; Reproducibility; Research Domain Criteria; Resources; Rewards; Sampling; Seeds; Short-Term Memory; Structure; Subgroup; Symptoms; System; Taxonomy; Technology; United States National Institutes of Health; aged; anxiety symptoms; base; burden of illness; cognitive function; cognitive reappraisal; cognitive system; cohesion; connectome; data management; data sharing; disability; disability burden; emotional experience; executive function; experience; follow-up; hedonic; high dimensionality; human imaging; incentive salience; individual patient; interest; learning strategy; network dysfunction; network models; outcome prediction; physical symptom; predict clinical outcome; response; social; treatment choice; white matter

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.

项目总结/摘要 我们的目标是使用HCP协议来获取并公开一个大型的成像、行为和 情绪紊乱患者的症状数据。我们还将开发和公开新的 研究连接体解体如何在神经元水平上引起这些无序状态的方法 个别病人。由增强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。这种混乱的情绪状态 多个诊断类别，并伴随认知功能的中断。不 因此，令人惊讶的是，这些形式的精神病理学是残疾的主要原因。解决这些 我们的调查策略是由研究领域标准（RDoC）倡议带头 的NIMH。我们专注于三个RDoC域和结构：1）负价中的急性威胁 系统（NVS）域，与对恐惧和焦虑的身体症状的自动反应相关的结构; 2） 正价系统（PVS）域中的奖励评估和响应，这是一个涉及 激励显著性，享乐反应和快感缺失症状;和3）工作记忆内的 认知系统（CS）域，一个涉及认知反刍自上而下调节的结构， 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 共享我们结合互补的专业知识，包括（1）最先进的MRI技术和数据 管理系统;（2）领域领先的可再生神经科学中心;（3）领先的跟踪记录 大规模神经放射学联盟;（4）RDoC知情的大规模成像方法的领导者 抑郁和焦虑;（5）开创高维数据的统计方法。我们的目标是 (1)使用HCP协议获取300名年龄在22-25岁之间的人的多模态数据， 经历不同程度的严重威胁，失去奖励价值/反应能力，以及在 工作记忆，（2）阐明连接体，症状和行为之间的关系的性质， 与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体如何联合收割机结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神经生物学模型，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提供基础， 根据神经功能障碍的类型选择治疗方法，而不是依赖于 神经生物学的不可知论者。