A Novel Framework for Network-Targeted Neuropsychiatric Deep Brain Stimulation

Abstract

Deep brain stimulation (DBS) has emerged as a promising therapy for neuropsychiatric illnesses, including depression and obsessive-compulsive disorder, but has shown inconsistent results in prior clinical trials. We propose a shift away from the empirical paradigm for developing new DBS applications, traditionally based on testing brain targets with conventional stimulation paradigms. Instead, we propose a multimodal approach centered on an individualized intracranial investigation adapted from the epilepsy monitoring experience, which integrates comprehensive behavioral assessment, such as the Research Domain Criteria proposed by the National Institutes of Mental Health. In this paradigm-shifting approach, we combine readouts obtained from neurophysiology, behavioral assessments, and self-report during broad exploration of stimulation parameters and behavioral tasks to inform the selection of ideal DBS parameters. Such an approach not only provides a foundational understanding of dysfunctional circuits underlying symptom domains in neuropsychiatric conditions but also aims to identify generalizable principles that can ultimately enable individualization and optimization of therapy without intracranial monitoring.

Keywords: deep brain stimulation; depression; neuromodulation; neuropsychiatry; stereoelectroencephalography.

FIGURE.

Intracranial recordings from sEEG electrodes in brain regions implicated in TRD, such as the dorsomedial, temporal and ventromedial cortex, as well as the orbitofrontal cortex, to name a few. Ideally, recordings from the aforementioned brain regions would be obtained in gray matter structures at the termini of white matter tracts, as we conceive of the DBS targets as white matter targets. The goal of these electrophysiological recordings is to help understand the pathophysiological network dynamics at the individual patient level. This NMU-based platform also allows appreciation of the network response to stimulation across a broad range of parameters. These 2 pieces of information can be combined to optimize DBS parameter selection.