Intracranial Investigation of Neural Circuity Underlying Human Mood

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.

Both patients in the depression and epilepsy cohort will have implanted intracranial stereo-EEG (sEEG) electrodes as part of their clinical trial and regular clinical care, respectively, The depression cohort will also have deep brain stimulation (DBS) leads implanted as part of their trial. We will deliver stimulation via the DBS and sEEG electrodes. We will adhere to well known safety parameters.

Both patients in the depression and epilepsy cohort will have implanted intracranial stereo-EEG (sEEG) electrodes as part of their clinical trial and regular clinical care. We will deliver stimulation via the sEEG electrodes. We will adhere to well known safety parameters.

Across both Depression and Eplipsy Cohorts, we will measure naturally occurring mood variations by periodically administering the Computerized Adaptive Test Depression Inventory (CAT-DI) mood assessment tool. Its adaptive nature makes CAT-DI fast to administer (1 minute) while maintaining precision and correlation with conventional depression scales such as the Hamilton Depression Inventory. We will administer CAT-DI 7-10 times per day to capture natural variations in mood over hours to days. We will also induce mood variation by allowing subjects to watch a series of short (45-60 sec) videos with emotionally valenced content spanning negative to positive (72 total videos).

Intracranial recordings will be performed to assess LFPs from the sEEG electrodes in Depression and Epilepsy Cohorts for the following behavioral tasks that will help investigating the underlying mechanisms of mood decoding: Affective Bias Task: Subjects rate intensity and valence of faces' expressions along a visual analog scale. Stimuli are presented in 2 blocks. This task serves as a standard emotion recognition task that may engage the Positive and Negative Valence network & a behavioral read-out of affective modulation. Probabilistic Cognitive Control Task: Subjects see moving colored dots and in alternate blocks indicate the majority color or majority motion direction. They enter their response using left or right arrows corresponding with the colors of the dots. Foraging task: Subjects travel between a number of sites to forage for rewards. At each "site", reward is only available at certain unknown times following a telegraph process.

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