Intracranial Investigation of Neural Circuity Underlying Human Mood

人类情绪背后的神经回路的颅内研究

• 批准号: 10660355
• 负责人: Kelly Rowe Bijanki
• 金额: $ 93.44万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2028-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10660355
• 关键词: Address; Affect; Affective; Area; Artificial Intelligence; Attention; Behavior; Behavioral; Belief; Black race; Brain; Brain region; Cognition; Cognitive; Complex; Data; Data Set; Deep Brain Stimulation; Diagnostic; Disease; Economic Burden; Electric Stimulation; Electrodes; Emotional; Entropy; Epilepsy; Functional disorder; Funding; Goals; Heterogeneity; Human; Individual; Inpatients; Intractable Epilepsy; Investigation; Link; Machine Learning; Measurable; Measures; Mental Depression; Mental disorders; Methods; Modeling; Monitor; Moods; Negative Valence; Neurobiology; Neurosciences; Non-linear Models; Participant; Patient Self-Report; Patients; Pattern; Performance; Population; Positive Valence; Psyche structure; Reporting; Research; Research Domain Criteria; Rewards; Sampling; Seizures; Severities; Site; Societies; Structure; Symptoms; System; Task Performances; Testing; Therapeutic; Therapeutic Intervention; Training; United States National Institutes of Health; Variant; Work; behavior influence; behavioral response; brain behavior; cognitive control; cognitive task; cohort; comorbid depression; computational neuroscience; control theory; disability; emotional stimulus; experience; human subject; improved; in silico; indexing; innovation; insight; interest; machine learning model; model building; mood regulation; multidisciplinary; nervous system disorder; neural; neural correlate; neural model; neurophysiology; neuroregulation; novel; novel strategies; recurrent neural network; response; spatiotemporal; therapy development; tool; treatment optimization; treatment strategy; treatment-resistant depression

Project Summary Depression is one of the most common disorders of mental health, affecting 7–8% of the population and causing tremendous disability to afflicted individuals and economic burden to society. In order to optimize existing treat- ments and develop improved ones, we need a deeper understanding of the mechanistic basis of this complex disorder. Previous work in this area has made important progress but has two main limitations. (1) Most studies have used non-invasive and therefore imprecise measures of brain activity. (2) Black box modeling used to link neural activity to behavior remain difficult to interpret, and although sometimes successful in describing activity within certain contexts, may not generalize to new situations, provide mechanistic insight, or efficiently guide therapeutic interventions. To overcome these challenges, we combine precise intracranial neural recordings in humans with a suite of new eXplainable Artificial Intelligence (XAI) approaches. We have assembled a team of exper- imentalists and computational experts with combined experience sufficient for this task. Our unique dataset comprises two groups of subjects: the Epilepsy Cohort consists of patients with refractory epilepsy undergoing intracranial seizure monitoring, and the Depression Cohort consists of subjects in an NIH/BRAIN-funded research trial of deep brain stimulation for treatment-resistant depression (TRD). As a whole, this dataset provides pre- cise, spatiotemporally resolved human intracranial recording and stimulation data across a wide dynamic range of depression severity. Our Aims apply a progressive approach to modeling and manipulating brain-behavior relationships. Aim 1 seeks to identify features of neural activity associated with mood states. It begins with current state-of-the-art AI models and then uses a “ladder” approach to bridge to models of increasing expressiveness while imposing mechanistically explainable structure. Whereas Aim 1 focuses on self-reported mood level as the behavioral in- dex of interest, Aim 2 uses an alternative approach of focusing on measurable neurobiological features inspired by the Research Domain Criteria (RDoC). These features, such as reward sensitivity, loss aversion, executive at- tention, etc. are extracted from behavioral task performance using a novel “inverse rational control” XAI approach. Relating these measures to neural activity patterns provides additional mechanistic and normative understanding of the neurobiology of depression. Aim 3 uses recurrent neural networks to model the consequences of richly var- ied patterns of multi-site intracranial stimulation on neural activity. It then employs an innovative “inception loop” XAI approach to derive stimulation strategies for open- and closed-loop control that can drive the neural system towards a desired, healthier state. If successful, this project would enhance our understanding of the pathophys- iology of depression and improve neuromodulatory treatment strategies. It can also be applied to a host of other neurological and psychiatric disorders, taking an important step towards XAI-guided precision neuroscience. 1

项目摘要 抑郁症是最常见的心理健康障碍之一，影响7-8%的人口， 对受害者个人造成巨大的残疾，对社会造成经济负担。为了优化现有的治疗- 我们需要更深入地了解这种复杂的机械基础， disorder.以前在这一领域的工作取得了重要进展，但有两个主要的局限性。(1)大多数研究 使用非侵入性的，因此不精确的大脑活动的措施。(2)黑盒建模用于链接 行为的神经活动仍然难以解释，尽管有时在描述活动方面是成功的， 在某些情况下，可能无法概括到新的情况，提供机械的洞察力，或有效地指导 治疗干预。 为了克服这些挑战，我们将联合收割机精确的人类颅内神经记录与 一套新的可解释的阿尔蒂官方情报（XAI）方法。我们召集了一个专家小组- imentalists和计算专家的结合经验足以完成这项任务。我们独特的数据集 包括两组受试者：癫痫组群由患有难治性癫痫的患者组成， 颅内癫痫监测，抑郁症队列由NIH/BRAIN资助研究的受试者组成 脑深部电刺激治疗难治性抑郁症（TRD）总的来说，这个数据集提供了一个预- cise，时空分辨的人类颅内记录和刺激数据，在一个广泛的动态 抑郁症的严重程度。 我们的目标是采用渐进的方法来建模和操纵大脑行为关系。要求1 旨在识别与情绪状态相关的神经活动特征。它从当前最先进的 AI建模，然后使用“阶梯”方法来桥接增加表现力的模型， 机械地解释结构。而目标1侧重于自我报告的情绪水平作为行为的影响因素， 目标2使用了另一种方法，专注于可测量的神经生物学特征， 研究领域标准（RDoC）。这些特征，如奖励敏感性、损失厌恶、管理者在- 注意力等是使用一种新的“逆理性控制”XAI方法从行为任务表现中提取的。 将这些测量与神经活动模式相关联提供了额外的机械和规范性理解 抑郁症的神经生物学。目标3使用递归神经网络来模拟丰富的变量的后果， 多部位颅内刺激对神经活动的IED模式。然后，它采用了一种创新的“初始循环”， XAI方法，用于导出可以驱动神经系统的开环和闭环控制的刺激策略 一个理想的，更健康的状态。如果成功的话，这个项目将提高我们对病理学的理解- 抑郁症和改善神经调节治疗策略。它也可以应用于其他主机 神经和精神疾病，向XAI指导的精确神经科学迈出了重要一步。 1