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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

基本信息

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
项目类别：
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

项目摘要

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.

项目总结