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 tr...