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 抑郁症是心理健康最常见的疾病之一，影响了7-8％的人口，并导致 巨大的残疾，对社会和社会的经济焚烧。为了优化现有的治疗 - 我们需要更深入地了解这一复合物的机械基础 紊乱。该领域的先前工作取得了重要进展，但有两个主要局限性。 （1）大多数研究 已经使用了非侵入性，因此暗示了大脑活动的度量。 （2）用于链接的黑匣子建模 行为的神经活动仍然很难解释，尽管有时成功地描述了活动 在某些情况下，可能不会推广到新情况，提供机械洞察力或有效指南 治疗干预措施。 为了克服这些挑战，我们将人类的精确颅内神经记录结合在一起 一套新的可解释的人工智能（XAI）方法。我们已经组建了一个专家团队 具有足够经验的iMentalists和计算专家为此任务提供了足够的能力。我们独特的数据集 由两组受试者组成：癫痫队组由患有难治性癫痫的患者组成 颅内癫痫发作监测，抑郁群由NIH/脑资助研究中的受试者组成 对抗治疗抑郁症（TRD）的深脑刺激试验。总体而言，此数据集提供 CISE，在广泛的动态上，在空间上解决的人类内记录和刺激数据 抑郁严重程度的范围。 我们的目标采用渐进式方法来建模和操纵脑行为关系。目标1 试图确定与情绪状态相关的神经活动的特征。它始于当前的最新 AI模型，然后使用“梯子”方法来桥接提高表现力的模型，同时强加 机械上的解释结构。而AIM 1则专注于自我报告的情绪水平，作为行为的影响 感兴趣的DEX，AIM 2使用另一种方法来专注于受启发的可测量神经生物学特征 根据研究领域标准（RDOC）。这些功能，例如奖励灵敏度，损失厌恶，执行官 使用新颖的“反理性控制” XAI方法从行为任务绩效中提取陈述等。 将这些措施与神经活动模式联系起来提供了更多的机械和正常理解 抑郁症神经生物学。 AIM 3使用复发性神经元网络来模拟丰富的var-的后果 多部位颅内刺激对神经元活性的IED模式。然后，它采用了创新的“ Inception Loop” XAI方法来得出可以驱动神经系统的开环控制和闭环控制的刺激策略 走向理想，更健康的状态。如果成功，该项目将增强我们对病理局的理解 - 抑郁症的生物学并改善神经调节治疗策略。它也可以应用于其他许多 神经和精神疾病，朝着XAI指导的精度神经科学迈出了重要一步。 1