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、自我和其他报告测量的维度参数是在NIMH的研究领域标准(RDoC)提案中提炼中间表型(IP)的关键。目前，IP与析取式分类诊断系统不能很好地匹配。在现实中，严重抑郁障碍(MDD)和双相情感障碍(BD)的症状和阈值下状况在症状和影响上有显著重叠。在认知和情感系统(IP)中可能存在共同的干扰，这可能会增加负面情绪中这些干扰的风险。IP将基因与大脑生物学和生理学联系起来；IP也与不同情绪障碍群体的子集联系在一起。该提案是对120名个体的任何情绪障碍(AMD)核心领域的协同、集成和应用系列研究，以缓解状态症状混杂，包括所有BD、MDD、情绪障碍、NOS、情绪调节障碍伴抑郁情绪和阈值下情绪组。这些AMD受试者将与55人组成的健康对照组(HC)相结合。RDoC矩阵的领域使用自我其他报告、其他/临床医生报告、基于实验室的表现和脑生理/电路(FMRI)生物标记物措施来测量，以解决两个目标和两个探索性目标。量表开发工具用于验证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，这可能与领域和亚域异常以及疾病的影响有关。目的1使用焦虑测量/神经质方面、情绪加工偏差、杏仁核和边缘对情绪面孔匹配任务中负面面孔的反应，以及功能连接方法，研究从恐惧到急性威胁(1.1)的核心(共同)功能障碍。在损失和损失预期方面也存在核心功能障碍(1.2)，使用负环境损失/压力、负记忆偏向、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。目的研究认知系统四个亚域的核心功能障碍，即注意(2.1)、工作记忆(2.2)、认知控制(2.3)和认知控制(干扰，2.4)，测量自我和观察者报告、操作，以及N-back和参数Go/No-Go/Stop任务中VL、DLPFC和DACC的激活。亚域稳定性的探索性目标是在40例AMD患者中进行功能结果分层加20例HC。探索性目标2是为以后的靶向基因实验和/或在更大的GWAS研究中共享收集血液。综上所述，本提案使用维度建模，立足于AMD谱系中的功能障碍的核心特征，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的知识库，并与常用的临床工具和基因研究相结合，以准备好新发现的翻译，从而为AMD谱分类的RDoC维度方法的研究提供最佳选择。

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.