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、自我和他人报告测量的维度参数是在NIMH研究领域标准（RDoC）提案中精炼中间表型（IPs）的关键。目前，ip不能很好地与分离分类诊断系统保持一致。在现实中，重度抑郁症（MDD）和双相情感障碍（BD） NOS和阈下症状在症状和影响上有显著的重叠。在认知和情感系统（IPs）中可能存在共同的干扰，这可能会给这些干扰带来负面情绪的风险。IPs将基因与大脑生物学和生理学联系起来；IPs还与不同情绪障碍群体的子集有关。该提案是对120名处于缓解期的任何情绪障碍（AMD）的核心领域进行协同、整合和应用的一系列研究，以减少状态症状的混淆，包括所有双相障碍、重度抑郁症、情绪障碍、NOS、抑郁情绪调节障碍和阈下情绪组。这些AMD受试者将与55人的健康对照组（HC）组合并。RDoC矩阵的域使用自我-他人报告、他人/临床医生报告、基于实验室的表现和脑生理/回路（fMRI）生物标志物测量来测量，以解决两个目标和两个探索性目标。使用量表开发工具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。目的1使用焦虑测量/神经质方面、情绪加工偏差、情绪面孔匹配任务中杏仁核和边缘对负面面孔的反应以及功能连接方法研究了从恐惧到急性威胁（1.1）的核心（共享）功能障碍。在损失和损失预期中也存在核心功能障碍（1.2），使用负环境损失/压力、负记忆偏差、NAcc和OFC激活来预测货币激励延迟（MID）任务中的损失，以及功能连接方法。目的2研究四个认知系统子域的核心功能障碍，即注意力（2.1）、工作记忆（2.2）、认知控制（抑制，2.3）和认知控制（干扰，2.4），通过自我和观察者报告、表现、VL、DLPFC和DACC在fMRI中N-Back和参数Go/No-go/Stop任务中的激活来测量。对40例AMD的子域稳定性进行了探索性研究，这些AMD按功能结果和20例HC分层。探索性目标2是收集血液用于以后的靶向基因实验和/或在更大的GWAS研究中共享。综上所述，本建议使用基于AMD频谱功能障碍核心特征的维度建模，但也追求基于诊断、领域和功能的区分领域。我们的策略是通过整合和扩展现有知识库，并与常用的临床工具和遗传学研究相结合，以随时翻译新发现，来研究RDoC维度方法用于AMD谱分类的最佳策略。

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.