Dimensional RDoC Modeling across the Range of Negative Mood Dysfunction

描述（由申请人提供）：整合来自大脑、表现以及自我和他人报告测量的维度参数是NIMH提出的研究领域标准（RDoC）中细化中间表型（IP）的关键。目前，IP与析取分类诊断系统不一致。事实上，重度抑郁症（MDD）和双相情感障碍（BD）NOS和阈下条件的症状在症状和影响方面有显著重叠。在认知和情感系统（IP）中可能存在共享中断，这可能会导致负面情绪中这些中断的风险。IP将基因与大脑生物学和生理学联系起来; IP还与不同情绪障碍组的子集联系起来。该提案是一个协同的，综合的和应用的一系列调查的核心领域在任何情绪障碍（AMD）的120个人，在缓解，以减少状态症状的混乱，包括所有的BD，MDD，情绪障碍，NOS，调整障碍与抑郁情绪，和阈下情绪组。这些AMD受试者将与55名个体的健康对照（HC）组组合。使用自我-他人报告、他人/临床医生报告、基于实验室的表现和脑生理学/回路（fMRI）生物标志物测量来测量RDoC矩阵的域，以解决两个目标和两个探索性目标。量表开发工具被用来证明量表的信度和结构效度。来自生物医学工程和统计机器学习的先进建模和分层技术将识别共享功能障碍核心维度的跨诊断亚组，这些亚组可以与域和子域异常以及疾病的影响联系起来。目标1研究核心（共享）功能障碍升高恐惧急性威胁（1.1）使用焦虑措施/神经质方面，情绪处理偏见，杏仁核和边缘系统的反应，情绪面孔匹配任务中的负面面孔，和功能连接的方法。在损失和损失预期（1.2）中也存在核心功能障碍，使用负面的环境损失/压力，负面的记忆偏差，以及NAcc和OFC激活来预期货币激励延迟（MID）任务中的损失，以及功能连接方法。目的2研究核心功能障碍的四个认知系统子域，注意力（2.1），工作记忆（2.2），认知控制（抑制，2.3），认知控制（干扰，2.4）测量与自我和观察者的报告，性能，VL和DLPFC和DACC激活的N-Back和参数去/不去/停止任务在功能磁共振成像。在40例按功能结局分层的AMD加20例HC中进行子域稳定性的探索性目标。探索性目标2是采集血液用于后续靶向基因实验和/或在更大规模的GWAS研究中共享。总之，本建议使用锚定在AMD谱中的功能障碍的核心特征中的维度建模，但也追求基于诊断、域和功能的分化领域。我们的策略是最佳的RDoC维度的方法进行分类AMD频谱的研究，通过整合和扩展现有的知识库，并与常用的临床工具和遗传研究的新发现的准备翻译集成。

DESCRIPTION (provided by applicant): Integration of dimensional parameters from brain, performance, and self and other-report measures is key towards refining intermediate phenotypes (IPs) within the Research Domain Criteria (RDoC) proposal by NIMH. Currently, IPs do not align well with the disjunctive categorical diagnostic systems. In reality, the symptoms of Major Depressive Disorder (MDD) and Bipolar Disorder (BD) NOS and subthreshold conditions have significant overlap in symptoms and impact. There are likely shared disruptions in cognitive and affective systems (IPs) that may confer risk for these disruptions in negative mood. IPs link genes to brain biology and physiology; IPs also link to subsets of different mood disorder groups. The proposal is a synergistic, integrated and applied series of investigations of core domains in any mood disorders (AMD) for 120 individuals, in remission to diminish state symptom confounds, including all BD, MDD, Mood Disorder, NOS, Adjustment Disorder with Depressed Mood, and subthreshold Mood groups. These AMD subjects will be combined with a healthy control (HC) group of 55 individuals. Domains of the RDoC matrix are measured using self-other- report, other/clinician report, lab-based performance, and brain physiology/circuit (fMRI) biomarker measures to address two Aims and two Exploratory Aims. Scale development tools are used to demonstrate scale reliability and construct validity. Advanced modeling and stratification techniques from biomedical engineering and statistical machine learning will identify across-diagnosis subgroups that share core dimensions of dysfunction, which can be linked to domain and subdomain abnormalities and impact of illness. Aim 1 studies core (shared) dysfunction in elevated Fear to Acute Threat (1.1) using anxiety measures/Neuroticism facets, emotion processing biases, amygdala and limbic reactivity to negative faces in the Emotion Faces Matching Task, and functional connectivity approaches. There is also a core dysfunction in Loss and Loss anticipation (1.2) using negative environmental loss/stresses, negative memory biases, and NAcc and OFC activation to anticipation of loss in the Monetary Incentive Delay (MID) task, and functional connectivity approaches. Aim 2 studies core dysfunction in four Cognitive System subdomains, Attention (2.1), Working Memory (2.2), Cognitive Control (Inhibition, 2.3), and Cognitive Control (Interference, 2.4) measured with self and observer reports, performance, and VL and DLPFC and DACC activation in the N-Back and Parametric Go/No-go/Stop tasks during fMRI. An Exploratory Aim in subdomain stability is conducted in 40 AMD stratified on functional outcome plus 20 HC. Exploratory Aim 2 is collection of blood for later targeted gene experiments and/or sharing in larger GWAS studies. In summary, the present proposal uses dimensional modeling anchored in core features of dysfunction across AMD spectrum, but also pursues areas of differentiation based upon diagnosis, domain and functioning. Our strategy is optimal for the study of RDoC dimensional approaches for classification of AMD spectrum by integrating and extending the existing knowledge base, and integrating with commonly used clinical tools and genetic studies for ready translation of novel findings.