Structural Connectivity of Positive and Negative Emotions: Secondary Analysis of the Human Connectome Project through the RDoC Lens
积极和消极情绪的结构连通性:通过 RDoC 镜头对人类连接组项目进行二次分析
基本信息
- 批准号:9979106
- 负责人:
- 金额:$ 15.8万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2020
- 资助国家:美国
- 起止时间:2020-04-01 至 2022-03-31
- 项目状态:已结题
- 来源:
- 关键词:AddressAffectAffectiveAmygdaloid structureAnxietyAreaBrainCategoriesCell NucleusDataData AnalysesData ReportingData SetDepressed moodDevelopmentDiagnosisDiffusion Magnetic Resonance ImagingDimensionsDiseaseEconomic BurdenEmotionsFoundationsFrightFunctional disorderGoalsHealthHippocampus (Brain)HumanImageImpaired healthImpairmentInterventionInvestigationKnowledgeLeadMagnetic Resonance ImagingMeasuresMedialMental DepressionMental HealthMental disordersMethodsMood DisordersNational Institute of Mental HealthNegative ValenceNeurologicNeurosciencesNeurosciences ResearchParticipantPatient Self-ReportPatternPersonal SatisfactionPhysical shapePhysiologicalPositive ValencePrediction of Response to TherapyPrefrontal CortexProcessPsychopathologyPsychophysiologyPublic HealthReportingResearchResearch Domain CriteriaResearch PersonnelResolutionRewardsRiskSamplingScienceStructureSurfaceSymptomsTechniquesTranslational ResearchUnited States National Institutes of HealthWorkbasebiobehaviorbrain morphologyclinical translationcomorbidityconnectomedata de-identificationdesigneffective therapyemotion regulationexperiencefunctional disabilityhuman dataimprovedinnovationlensmortalitynegative affectneuroimagingnovelphysical conditioningpositive emotional stateprotective effectpsychologicpublic repositoryresponsesecondary analysistreatment response
项目摘要
Project Summary / Abstract
Depression and anxiety are highly prevalent and frequently co-occur. Moreover, they are associated with
functional impairment and physical health risks, including early mortality. Despite their significance to public
health, treatment for depression and anxiety remains insufficient. Understanding of these illnesses, their
comorbidity, and how to most effectively treat them has been limited by categorical approaches to diagnosis
and research and by reliance on subjective reports. In addition, the field has historically focused on negative
factors, failing to account for the unique effects of positive psychological processes. Positive affect,
independent of negative affect, benefits both psychological and physical health. However, the biobehavioral
processes by which positive affect confers these protective effects have not been fully elucidated. The
Research Domain Criteria (RDoC) include both negative and positive valence domains and require use of
multiple units of analyses (e.g., both self-report and neurological measures). RDoC thus offers a framework for
advancing the science of affective processes that are relevant to both wellbeing and mental illness.
Examination of relationships between imaging and self-report data is a critical next step. Our team has
developed a novel method of data-driven techniques that can be applied to MRI data to allow quantitative
investigation of affective processes. High-resolution structural connectome (HRSC) mapping using diffusion
weighted imaging enables the analysis of single-subject and group-level structural connectivity at more than
50,000 points along the cortex and surfaces of the deep nuclei. HSRC mapping provides a unique opportunity
to examine patterns of structural connectivity, as a means of advancing our understanding of the circuitry
underlying self-reported affect. In response to PAR-17-158, “Secondary Data Analyses to Explore NIMH
Research Domain Criteria (R03),” we propose to analyze neuroimaging and self-report data from the Human
Connectome Project (HCP), a publicly available dataset collected as part of the NIH Blueprint for Neuroscience
Research. We will apply our innovative HRSC technique to examine constructs within the RDoC negative and
positive valence domains. Using data from HCP participants (N=1206) who completed neuroimaging and NIH
Toolbox measures of both negative and positive affect, we will examine the unique associations between
negative and positive affect with brain structural connectivity, and evaluate whether positive affect moderates
the associations between brain structural connectivity and negative affect. The results will provide a novel and
quantitative understanding of psychophysiological processes underlying both negative and positive affect,
across a broad range of emotion profiles. These methods and findings have long-term potential for contributing
to the identification of neuroaffective predictors of psychiatric illness and response to treatment.
项目摘要/摘要
项目成果
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