Using Theory- and Data-Driven Neurocomputational Approaches and Digital Phenotyping to Understand RDoC Acute and Potential Threat

使用理论和数据驱动的神经计算方法和数字表型来了解 RDoC 急性和潜在威胁

• 批准号: 10537200
• 负责人: ALEXANDER JOSEPH SHACKMAN
• 金额: $ 78.36万
• 依托单位: UNIV OF MARYLAND, COLLEGE PARK
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-06 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10537200
• 关键词: Acute; Address; Amygdaloid structure; Anatomy; Anxiety; Anxiety Disorders; Architecture; Arousal; Base of the Brain; Basic Science; Black, Indigenous, People of Color; Brain; Brain imaging; Brain region; Categories; Cell Nucleus; Cellular Phone; Classification; Clinical Research; Communities; Computational Technique; Computer Models; Consensus; Data; Development; Diagnosis; Dimensions; Dissociation; Distal; Distress; Ecological momentary assessment; Fright; Functional Magnetic Resonance Imaging; Galvanic Skin Response; Human; Individual; Intervention; Lesion; Life; Machine Learning; Maps; Mathematics; Modeling; Moods; Morbidity - disease rate; National Institute of Mental Health; Nature; Neuroanatomy; Neurobiology; Outcome; Pathology; Patients; Pattern; Perception; Performance; Phenotype; Play; Probability; Psychiatry; Psychopathology; Public Health; Research; Research Domain Criteria; Resources; Risk; Role; Safety; Sampling; Series; Signs and Symptoms; Structure of terminal stria nuclei of preoptic region; Symptoms; System; Testing; Time; Training; Translational Research; Uncertainty; Variant; Work; adjudicate; anxiety symptoms; biobehavior; clinically relevant; computerized tools; data streams; digital; innovation; interest; multidisciplinary; neural circuit; neuroeconomics; neuroimaging; neuroregulation; novel; predictive modeling; racial diversity; recruit; relating to nervous system; research and development; response; theories; therapeutic development; therapy development; tool; trait; translational model; virtual

Despite growing concerns about validity, the NIMH Research Domain Criteria (RDoC) framework plays a key role in organizing basic, translational, and clinical research. RDoC’s approach to fear and anxiety is categorical: threat is either acute or potential; engages either the Amygdala or the bed nucleus of the stria terminalis (BST); and elicits either fear or anxiety. Recent work casts doubt on this binary perspective, spurring the development of alternative approaches. Dimensional models posit that threat responses vary along a smooth continuum of perceived danger—from absolutely safety to on-going attack. Danger perceptions are thought to emerge from parametric estimates of threat proximity, probability, and certainty, which are computed in weakly segregated cortico-subcortical circuits. To date, there have been no systematic, well-powered efforts to computationally implement these competing models and compare their validity. Furthermore, while both models highlight the importance of threat uncertainty, they do not specify which kind. Computational psychiatry recognizes 2 mathematically distinct kinds of uncertainty: Risk and Ambiguity. Which of these is more relevant to threat reactivity and how they map onto the underlying neurobiology is unknown. To address these fundamental questions, we will recruit a racially diverse community sample enriched for elevated fear/anxiety symptoms. Two parametric threat-anticipation paradigms will allow us to simultaneously probe circuits sensitive to categorical (RDoC) and dimensional variation in threat for the first time. Smartphone phenotyping will assess real-world threat exposure, uncertainty, and distress. A1. We will test a series of competing predictions about the architecture of threat-sensitive brain circuits. We will use theory-driven computational modeling to go beyond binary threat categories; identify regions sensitive to risk, ambiguity, and other dimensional facets of threat; and explore trial-by-trial relations with signs and symptoms of fear and anxiety. A2. RDoC implies that Acute and Potential Threat are represented in different patterns of brain activity; indeed, this was the major rationale for creating separate RDoC constructs. Dimensional models predict substantial similarities. Multivoxel machine- learning approaches provide a rigorous means of adjudicating these claims and clarifying the importance of the Amygdala, BST, and other regions. A3. Fusing the fMRI and smartphone data-streams will enable us to establish the relevance of specific facets of threat and specific brain regions to real-world distress. We will also explore relations between neuroimaging metrics and fear- and anxiety-related diagnoses, symptoms, and traits. Significance. Extreme fear and anxiety are leading causes of human misery and morbidity. This project will provide a potentially transformative opportunity to develop the first computationally grounded model of fear and anxiety. It will help adjudicate on-going theoretical debates, validate a new conceptual approach for use with other read-outs and species, set the stage for new kinds of translational models and clinical studies, prioritize new targets for neuromodulation and other therapeutics development, and guide the development of RDoC 2.0.

尽管对有效性的关注越来越多，但NIMH研究领域标准（RDoC）框架在其中起着关键作用。 在组织基础、转化和临床研究中发挥作用。RDoC对恐惧和焦虑的态度是明确的： 威胁是急性的或潜在的;涉及杏仁核或终纹床核（BST）; 消除恐惧或焦虑。最近的研究对这种二元观点提出了质疑， 的替代方法。三维模型表明，威胁响应沿着一个平滑的连续体变化， 从绝对安全到持续攻击。人们认为，对危险的感知来自于 威胁接近度、概率和确定性的参数估计，这些参数是在弱隔离环境中计算的 皮质-皮质下回路到目前为止，还没有系统的，强大的努力来计算 实现这些竞争模型并比较它们的有效性。此外，虽然这两种模式都突出了 威胁不确定性的重要性，他们没有具体说明是哪种。计算精神病学承认2 数学上不同的不确定性：风险和模糊性。哪一个与威胁更相关 反应性以及它们如何映射到潜在的神经生物学是未知的。为了解决这些基本问题 问题，我们将招募一个种族多样化的社区样本，丰富的恐惧/焦虑症状。两 参数威胁预期范式将使我们能够同时探测对分类敏感的电路。 （RDoC）和威胁的维度变化。智能手机表型分析将评估现实世界 威胁暴露、不确定性和痛苦。A1.我们将测试一系列关于 对威胁敏感的大脑回路的结构。我们将使用理论驱动的计算建模来超越 二进制威胁类别;识别对风险、模糊性和威胁的其他维度方面敏感的区域;以及 探索逐个试验与恐惧和焦虑症状的关系。A2. RDoC意味着急性和 潜在的威胁表现在大脑活动的不同模式中;事实上，这是 创建单独的RDoC结构。维度模型预测了大量的相似性。多像素机器- 学习方法提供了一种严格的手段来评判这些主张，并澄清 杏仁核、BST和其他区域。A3.融合功能磁共振成像和智能手机数据流将使我们能够建立 威胁的特定方面和特定大脑区域与现实世界痛苦的相关性。我们还将探索 神经影像学指标与恐惧和焦虑相关的诊断，症状和特征之间的关系。 意义极度恐惧和焦虑是人类痛苦和发病的主要原因。该项目将 提供了一个潜在的变革机会，以开发第一个基于计算的恐惧模型， 焦虑它将有助于评判正在进行的理论辩论，验证一种新的概念方法， 其他读出和物种，为新型转化模型和临床研究奠定基础， 为神经调节和其他治疗药物的开发提供新的靶点，并指导RDoC 2.0的开发。