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）矩阵描绘了反映人类基本维度的一般结构 从正常到异常的行为功能。RDoC矩阵按域组织这些结构 (e.g.,正效价和社会处理系统）和分析单元（即，从基因，到分子，细胞，电路， 生理学、行为、自我报告、范例），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特定疾病的 并且不直接与RDoC构建体连接。在精神分裂症（SZ）中，阴性症状领域，包括无意识， 快感缺乏、不合群、失语症和情感迟钝（5因素模型），已经进行了一些详细的研究。最近一个理论 阴性症状域和RDoC结构之间的映射将无意志、快感缺乏和无意志与阳性症状域联系起来， 配价系统以及对社会过程系统的影响。然而，所提出的行为之间的映射 （阴性症状领域）和大脑结构/电路尚未进行测试或验证;无论是在SZ中，还是在其他 神经精神疾病，如双相情感障碍（BD）或重度抑郁症（MDD）。早期的研究表明， 更简约的阴性症状2因素模型，其中无意志，快感缺乏和社交障碍与一个 动机和快乐（MAP）因素，以及与表达（EXP）因素相关的情感迟钝和失语症。值得注意的是， 除了不合群之外，这些因素似乎映射到RDoC中的积极效价和社会过程系统上 矩阵;为与阴性症状相关的拟议RDoC矩阵结构提供额外支持。之间的映射 阴性症状域的不同解释（例如，5-因素和2因素模型）和大脑结构/电路 也没有进行。利用全球合作的ENIGMA（增强神经成像遗传学 通过荟萃分析）联盟和CONOMAC（协作信息学和神经成像套件工具包， 匿名计算）计算平台，该建议将联合收割机结合神经成像和临床措施， 精神分裂症（SZ）、双相情感障碍（BD）和重度抑郁症（MDD）的阴性症状，以验证 扩展了重性精神疾病阴性症状域的RDoC矩阵表示。我们提取关节 每个可分离（子）结构的多模态特征，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，我们将映射结构，扩散张量成像，和静息态泛函 磁共振成像测量大脑结构/电路到阴性症状行为测量。成功 完成这项提案的目标将确定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的失调 突破了传统诊断的界限