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

ENIGMA-COINSTAC：价系统脑回路的先进全球跨诊断分析

• 批准号: 10410073
• 负责人: VINCE D CALHOUN
• 金额: $ 5.41万
• 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 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10410073
• 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; Factor Analysis; Fibrinogen; 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; Pathway Analysis; Patient Self-Report; Patients; Physiology; Play; Positive Valence; Psychopathology; Research Domain Criteria; Research Project Summaries; Resources; Rest; Role; Schizophrenia; Severities; Social Processes; Specificity; Standardization; Statistical Methods; Structure; Superior temporal gyrus; Symptoms; System; Testing; 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; therapeutic target; 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）矩阵描述了一般的结构，这些结构反映了人类的基本维度 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）和分析单位（即，从基因到分子，细胞，电路， 生理学，行为，自我报告，范例），使它们可以在多个分析层面上系统地研究。 迄今 而不是直接与RDOC构建体相连。在精神分裂症（SZ）中，包括抽应在内的负面症状领域， Anhedonia，联想，Alogia和Bluet的情感（5因子模型）已在一些详细的研究中进行了研究。最近一个理论 负面症状域和RDOC构造之间的映射与Anhedonia相关联，并向积极的绘制 价系统，Alogia和对社会过程系统的影响。但是，行为之间的拟议映射 （负症状域）和大脑结构/电路尚未测试或验证；在SZ或其他 神经精神疾病，例如躁郁症（BD）或主要抑郁症（MDD）。较早的工作建议 更简约的2因子模型的负面症状模型，其中的气流，抗抗肿瘤和联想与A相关 动机和愉悦（地图）因素，并且蓝色影响Andalogia与表达（EXP）因素有关。值得注意的是 除关联外，这些因素似乎映射到RDOC的正价和社会过程系统 矩阵;向拟议的与负症状有关的拟议的RDOC矩阵结构提供额外支持。之间的映射 负面症状域（例如5因子和2因子模型）和大脑结构/电路的不同解释 也没有进行。利用全球合作谜（增强神经成像遗传学） 通过荟萃分析）财团和CoInstac（合作信息学和神经成像套件工具包 匿名计算）计算平台，该建议将结合神经影像学和临床测量 精神分裂症（SZ），躁郁症（BD）和主要抑郁症（MDD）的负面症状，以验证 并扩展主要精神疾病中负症状域的RDOC矩阵表示。我们提取关节 每个单独（子）结构的多模式特征，对它们与行为的关系进行评估，然后使用 它们在随后的交叉验证分析中。随后，我们评估了他们的单一主题预测能力。通过 这些强大的计算方法，我们将绘制结构，扩散张量成像和静止状态功能 磁共振成像测量大脑结构/电路为负面症状行为测量。成功的 该提案目标的完成将确定与负面症状相关的明显和重叠的神经回路 域，将测试功能的集成模型，并确定与精神病理学相关机制中的失调 跨越了传统的诊断边界。