ENIGMA-COINSTAC: Advanced Worldwide Transdiagnostic Analysis of Valence System Brain CircuitsPD

ENIGMA-COINSTAC：价系统脑回路的先进全球跨诊断分析PD

• 批准号: 10252236
• 负责人: VINCE D CALHOUN
• 金额: $ 2.61万
• 依托单位: GEORGIA STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-02 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10252236
• 关键词: Activities of Daily Living; Affect; Algorithms; Amygdaloid structure; Anhedonia; Anterior; Aphasia; Behavior; Behavioral; Behavioral Research; Behavioral Symptoms; Bipolar Disorder; Brain; Cells; Classification; Clinical; Clinical Research; Clinical Trials; Computer Analysis; Computing Methodologies; Consensus; Corpus striatum structure; Data; Data Analyses; Decentralization; Development; Diagnostic; Diffusion Magnetic Resonance Imaging; Dimensions; Disease; Dorsal; Face; Functional Magnetic Resonance Imaging; Functional disorder; Future; Genes; Human; Image; Image Analysis; Individual; Inferior frontal gyrus; Informatics; Insula of Reil; Intervention; Joints; Link; Machine Learning; Major Depressive Disorder; Major Mental Illness; Maps; Measures; Medial; Mental Depression; Mental disorders; Meta-Analysis; Modeling; Motivation; National Institute of Mental Health; Neurobiology; Normal Range; Outcome; Participant; Patient Self-Report; Patients; Physiology; Play; Positive Valence; Psychopathology; Research Domain Criteria; Research Project Summaries; Resources; Rest; Role; Schizophrenia; Severities; Social Processes; Specificity; Standardization; Structure; Superior temporal gyrus; Symptoms; System; Testing; Therapeutic; United States Food and Drug Administration; Validation; Variant; Work; analysis pipeline; base; behavior measurement; brain circuitry; brain dysfunction; computational platform; dimensional analysis; disability; druggable target; frontal lobe; imaging genetics; improved; mood symptom; multimodality; neural circuit; neuroimaging; neuropsychiatric disorder; neuropsychiatry; novel; open source; patient stratification; pleasure; quality assurance; relating to nervous system; research study; social; tool; working group

Project Summary The Research Domain Criteria (RDoC) matrix delineates general constructs, that reflect basic dimensions of human behavioral functioning that can range from normal to abnormal. The RDoC matrix organizes these constructs by domains (e.g., positive valence and social processing systems) and units of analysis (i.e., from genes, to molecules, cells, circuits, physiology, behavior, self-report, paradigms) such that they can be systematically studied at multiple levels of analysis. Most clinical research studies, to date, have employed standardized symptom assessments, which are often disorder specific and not directly linked to RDoC constructs. In schizophrenia (SZ), negative symptom domains, including avolition, anhedonia, asociality, alogia, and blunted affect (5 factor model), have been studied in some detail. Recently a theoretical mapping between negative symptom domains and RDoC constructs linked avolition, anhedonia, and avolition to positive valence system, and alogia and flat affect to the social processes system. However, the proposed mappings between behavior (negative symptom domains) and brain structures/circuitry have not been tested or validated; either in SZ, or in other neuropsychiatric illnesses such as bipolar disorder (BD) or major depressive disorder (MDD). Earlier work suggested a more parsimonious 2-factor model of negative symptoms, in which avolition, anhedonia, and asociality were linked to a motivation and pleasure (MAP) factor, and and blunted affect andalogia linked to an expressive (EXP) factor. Of note, with the exception of asociality, these factors appear to map onto positive valence and social processes systems in the RDoC matrix; lending additional support to the proposed RDoC matrix structure related to negative symptoms. Mappings between different interpretations of negative symptom domains (e.g., 5-factor and 2-factor models) and brain structures/circuitry have also not been conducted. Leveraging the worldwide collaborative ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) consortium and the COINSTAC (Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation) computational platform, this proposal will combine neuroimaging and clinical measures of negative symptoms across schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD), to validate and extend the RDoC matrix representation of negative symptom domains in major mental illness. We extract joint multimodal features for each separable (sub)construct, evaluate them for their relationship with the behavior, and then use them in a subsequent cross-validation analysis. Subsequently, we evaluate their single subject prediction power. Through these powerful computational methods, we will map structural, diffusion tensor imaging, and resting state functional magnetic resonance imaging measures of brain structures/circuitry to negative symptom behavioral measures. Successful completion of this proposal’s aims will identify distinct and overlapping neural circuits associated with negative symptom domains, will test integrative models of functioning, and identify dysregulation in psychopathology-related mechanisms that cut across traditional diagnostic boundaries.

项目摘要 研究领域标准(RDoC)矩阵描述了反映人的基本维度的一般结构 行为功能的范围从正常到异常。RDoC矩阵按域组织这些结构 (例如，正价和社会处理系统)和分析单元(即，从基因，到分子，细胞，电路， 生理学、行为学、自我报告、范式)，以便可以在多个分析水平上对它们进行系统研究。 到目前为止，大多数临床研究都采用了标准化的症状评估，这些评估通常是针对疾病的。 并且不直接链接到RDoC构造。在精神分裂症(SZ)中，阴性症状领域，包括撕裂， 快感缺失、无社交、冷漠和钝化情感(5因素模型)已经被详细研究过了。最近有一种理论认为 阴性症状域与RDoC结构之间的映射将性欲减退、快感缺乏和性欲缺失与阳性联系起来 对社会过程系统的冷漠和平淡的影响。然而，建议的行为之间的映射 (阴性症状领域)和大脑结构/电路尚未经过测试或验证；无论是在深圳，还是在其他 神经精神疾病，如双相情感障碍(BD)或严重抑郁障碍(MDD)。早期的研究表明 更节俭的阴性症状的两因素模型，在该模型中，贪婪、快感缺失和无社交与 动机和愉悦(MAP)因素，以及与表达(EXP)因素相关的钝性情感痛感。值得注意的是， 除了非社会性，这些因素似乎映射到RDoC中的正价和社会过程系统 矩阵；为拟议的与阴性症状有关的RDoC矩阵结构提供额外支持。之间的映射 对阴性症状域(例如，5因素和2因素模型)和大脑结构/回路的不同解释 也没有进行过。利用全球合作之谜(增强神经成像遗传学 通过Meta分析)联盟和COINSTAC(协作信息学和神经成像套件工具包，用于 匿名计算)计算平台，这一提议将结合神经成像和临床测量 精神分裂症(SZ)、双相情感障碍(BD)和严重抑郁障碍(MDD)的阴性症状，以验证 并扩展了重大精神疾病阴性症状域的RDoC矩阵表示。我们抽出关节 每个可分离(子)构造的多模式要素，评估它们与行为的关系，然后使用 在随后的交叉验证分析中。随后，我们评估了它们的单主题预测能力。穿过 这些强大的计算方法，我们将映射结构、扩散张量成像和静止态泛函 脑部结构/回路的磁共振成像测量到阴性症状的行为测量。成功 完成这项提案的目标将识别与阴性症状相关的不同和重叠的神经回路 领域，将测试功能的综合模型，并确定精神病理学相关机制中的失调 这突破了传统诊断的界限。