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: 10661086
• 负责人: ALEXANDER JOSEPH SHACKMAN
• 金额: $ 77.06万
• 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/10661086
• 关键词: Acute; Address; Amygdaloid structure; Anxiety; Anxiety Disorders; Architecture; Arousal; 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; Regional Anatomy; 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; adjudication; anxiety symptoms; biobehavior; brain based; clinically relevant; computerized tools; data streams; digital; innovation; interest; multidisciplinary; neural; neural circuit; neuroeconomics; neuroimaging; neuroregulation; novel; predictive modeling; racial diversity; recruit; research and development; response; segregation; 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.

尽管对有效性的担忧日益加剧，但NIMH研究领域标准（RDOC）框架却起着关键 在组织基础，翻译和临床研究中的作用。 RDOC恐惧和焦虑的方法是绝对的： 威胁是急性或潜力；参与杏仁核或茎端的床核（BST）； 并引起恐惧或焦虑。最近的工作对这一二进制观点产生了怀疑，刺激了发展 替代方法。维度模型指出，威胁响应的平滑连续体有所不同 感知到的危险 - 从绝对安全到正在进行的攻击。人们认为危险的看法是从中出现的 威胁接近性，概率和确定性的参数估计，这些估计是在弱分离中计算的 皮质层状电路。迄今为止，还没有系统地进行计算的努力 实施这些竞争模型并比较其有效性。此外，两种模型都突出了 威胁不确定性的重要性，他们没有指定哪种。计算精神病学认识2 数学上不同的不确定性：风险和模棱两可。哪个与威胁更相关 反应性及其如何映射到潜在的神经生物学上是未知的。解决这些基本 问题，我们将招募一个大致多样化的社区样本，这些样本富含恐惧/焦虑症状。二 参数威胁 - 观察范式将使我们能够简单地探测对分类敏感的电路 （RDOC）和威胁的维度变化。智能手机表型将评估现实世界 威胁暴露，不确定性和困扰。 A1。我们将测试一系列关于 威胁敏感的大脑电路的结构。我们将使用理论驱动的计算建模超越 二进制威胁类别；确定对风险，歧义性和其他威胁方面敏感的区域；和 用恐惧和动画的迹象和符号探索逐审的关系。 A2。 RDOC意味着急性和 潜在威胁以不同的大脑活动模式表示；确实，这是 创建单独的RDOC构造。维度模型预测了实质性的相似性。多毒素机器 - 学习方法提供了裁决这些主张的严格含义，并阐明了 Amygdala，BST和其他地区。 A3。融合fMRI和智能手机数据流将使我们能够建立 威胁和特定大脑区域的特定方面与现实世界困扰的相关性。我们还将探索 神经影像学指标与恐惧和动画相关的诊断，符号和特征之间的关系。 意义。极端的恐惧和动画是人类痛苦和发病率的主要原因。这个项目将 提供了一个潜在的变革机会，以开发第一个计算基础的恐惧模型和 焦虑。它将有助于调整持续的理论辩论，验证一种新的概念方法 其他读出和物种为新型翻译模型和临床研究奠定了基础，优先考虑 神经调节和其他治疗剂开发的新目标，并指导RDOC 2.0的开发。