Intracranial Investigation of Neural Circuity Underlying Human Mood

项目摘要 抑郁症是最常见的精神健康障碍之一，影响到7%-8%的人口，并导致 fl的巨大残疾给个人和社会带来了经济负担。为了优化现有的治疗- 我们需要更深入地了解这种复合体的机制基础 无序。以前在这一领域的工作取得了重要进展，但有两个主要局限。(1)大多数研究 使用了非侵入性的、因此不精确的大脑活动测量方法。(2)用于链接的黑盒建模 神经活动对行为仍然很难解释，尽管有时在描述活动方面是成功的 在某些情况下，不能对新情况一概而论，不能提供机械性的见解，也不能fi科学地指导 治疗性干预。 为了克服这些挑战，我们将人类精确的颅内神经记录与 一套新的可解释的艺术智能(ArtifiSocial Intelligence，XAI)方法。我们组织了一支经验丰富的团队- 有经验的评论家和计算专家对这项任务很有经验。我们独一无二的数据集 包括两组受试者：癫痫队列由接受治疗的难治性癫痫患者组成 颅内癫痫监测，抑郁症队列由NIH/脑资助研究的受试者组成 脑深部刺激治疗难治性抑郁症的试验。作为一个整体，此数据集提供了预 跨广泛动态的时空分辨率人体颅内记录和刺激数据 抑郁症严重程度的范围。 我们的目标是采用一种渐进的方法来建模和操纵大脑与行为的关系。目标1 寻求确定与情绪状态相关的神经活动的特征。它从目前最先进的技术开始 AI模型，然后使用“阶梯”方法连接到增加表现力的模型，同时施加 力学上可解释的结构。而目标1专注于自我报告的情绪水平，作为行为方面的- 有趣的是，Aim 2使用了另一种方法，专注于可测量的神经生物学特征 根据研究领域标准(RDoC)。这些特征，如奖励敏感度、损失厌恶、高管- 使用一种新的“逆向理性控制”XAI方法从行为任务绩效中提取紧张等信息。 将这些措施与神经活动模式联系起来提供了额外的机械性和规范性理解 抑郁症的神经生物学。目标3使用递归神经网络对富变化的后果进行建模- 多部位颅内电刺激对神经活动的影响。然后，它采用了创新的“初始循环” XAI方法推导出可驱动神经系统的开环和闭环控制的刺激策略 向着一个理想的、更健康的状态前进。如果成功，这个项目将加强我们对病理物理的了解- 改善抑郁症的心理状态和改善神经调节治疗策略。它还可以应用于许多其他 神经和精神障碍，向XAI引导的精确神经科学迈出了重要的一步。 1

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