Intracranial Investigation of Neural Circuity Underlying Human Mood

Study 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 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 A

Primary Outcome

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

Conditions Studied

Interventions

• DEVICE Brain Stimulation
• DEVICE sEEG Stimulation

Study Locations (1)