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 treatments and develop improved ones, the investigators 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, the investigators combine precise intracranial neural recordings in humans with a suite of new eXplainable Artificial Intelligence (XAI) approaches. The investigators have assembled a team of experimentalists 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 precise, 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. Beginning 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 index 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 attention, 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 varied patterns of multi-site intracranial stimulation on neural activity. Then employing 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 pathophysiology of depression and improve neuromodulatory treatment strategies. This can also be applied to a host of other neurological and psychiatric disorders, taking an important step towards XAI-guided precision neuroscience.

We apply novel modeling approaches to unique human intracranial data with the goal of understanding the neural basis of depression. Aims 1 and 2 apply complementary approaches to achieve the goal of relating mood states to neural dynamics. Aim 3 then models the effect of stimulation to derive causal understanding of the systems response to modulation. In Aim 1, we decode mood state obtained from subject self report to produce reliable neural correlates of mood. To do so in an informative way, we use a ladder of models to build from conventional AI models, with their known limitations, to novel mechanistically explainable dynamic models. In Aim 2, we use an alternative transdiagnostic approach inspired by the RDoC. Rather than measuring depression as variations in self reported mood and symptoms, we study how depression manifests in behavior. In particular, we examine performance on a suite of tasks targeted to reveal patients characteristics along neurobiologically relevant axes of Positive Valence, Negative Valence, and Cognitive Systems. We apply our novel inverse rational control methodology to infer the subjects internal models of tasks from observed behavior. This process then allows us to estimate neural correlates of relevant (RDoC-based) latent parameters such as reward sensitivity, loss aversiveness, cognitive flexibility, etc. Side by side comparison of results from Aims 1 and 2 will thus allow synergistic understanding of the brain behavior relationships related to mood and depression. To improve our therapeutic interventions, in Aim 3 we will quantify brain responses to electrical stimulation.

To model and explain these measurements, we will apply recurrent neural networks to network responses measured from high entropy stimulation patterns to build predictive models of neural responses to stimulation. We then use our novel inception loop strategy to generate optimized open and closed loop stimulation paradigms to coax the network from unhealthy (depressed) to healthier (euthymic) state.

Inclusion Criteria:

• Epilepsy cohort: adult patients scheduled to undergo intracranial seizure monitoring who provide informed consent
• Depression cohort: patients enrolled in our DBS for depression trial