Closed-loop Optimized rTMS for Depression

Overview

Targeted and individualized treatments for mental health disorders are critically needed. Repetitive transcranial magnetic stimulation (rTMS) represents the front-line of new and innovative approaches to normalizing dysfunctional brain networks in those with mental illness. rTMS is FDA-approved for depression and obsessive-compulsive disorder with clinical trials underway for PTSD and addiction, among others. However, remission rates are suboptimal and ideal stimulation parameters are unknown. We recently completed a randomized, double blind clinical trial and a depression severity biomarker that predicts clinical outcome. The overarching goal of this study is to develop the first broadly generalizable platform for real-time biomarker monitoring and personalized rTMS treatment. We plan to recruit patients with medication-resistant depression and in perform a four-phase, cross-over, double-blind, placebo-controlled trial to 1) identify how standard and optimized rTMS patterns engage the depression severity biomarker, and 2) determine the dose-response of these rTMS patterns. Findings from this study will provide the basis for a double-blind, randomized clinical trial comparing rTMS optimized to the individual against standard rTMS.

Nearly 50% of all Americans will suffer from a mental health disorder during their lifetimes. Brain stimulation treatments, including repetitive transcranial magnetic stimulation (rTMS), are increasingly used to normalize dysfunctional brain circuits in these disorders. Mechanistically, rTMS is thought to work by changing the synaptic strength of neurons, referred to as brain plasticity. Despite the variety of disorders targeted and significant between-patient heterogeneity, rTMS is currently applied in a manner that is one-size-fits-all (without any individual optimization of the stimulation pattern) and open-loop (fixed schedule of stimulation pattern, with no measurement or adjustment during rTMS). We believe that the response rate of rTMS for depression, which is at present \<50%, can be improved through personalized brain stimulation that enhances target engagement and maximizes plasticity. To personalize brain stimulation, one must (1) measure and monitor brain changes in real-time; (2) determine the optimal stimulation patterns for inducing brain changes; (3) develop adaptive treatments to drive desired changes on an individual patient-specific level over time. Such personalization of brain stimulation will increase our mechanistic understanding of brain plasticity to improve efficacy in non-responders to standard treatments. The primary goals of my research program are to (1) discover brain biomarkers that predict progression to clinical remission, and (2) develop closed-loop treatment algorithms that optimize these biomarkers and improve clinical outcomes. We will focus on depression as it is the leading cause of disability worldwide and and medications are ineffective or not tolerated for close to half of these patients. Leveraging my previous work, we propose three stages of development of personalized brain stimulation using single pulses of TMS combined with electroencephalography (TMS-EEG) to generate a causal measurement of brain state that can easily be translated to the clinic. The TMS-EEG depression severity biomarker that I recently discovered occurs 30 milliseconds after administering a TMS test pulse (p30) in the fronto-parietal network (FPN), a region implicated in depression. The degree of suppression of this p30 signal predicted clinical outcome in depressed patients following rTMS treatment. Additionally, a single stimulation session was sufficient to suppress the p30 during and for 30 minutes after stimulation. This work indicates that p30 suppression can be monitored in real-time and has the potential to support empiric treatment optimization. Findings from this study will provide the basis for a double-blind, randomized clinical trial of closed-loop rTMS against standard rTMS.