Machine Learning Will Extend the Clinical Utility of Adaptive Deep Brain Stimulation

Sensing-enabled implantable pulse generators for deep brain stimulation (DBS) now make it possible to integrate invasive brain signal recordings into clinical routine. Decades of research have characterized the intricate relationship between pathological neural circuit activity and movement disorder phenomenology. Motor state-specific pathological activity has been identified in various DBS implantation targets in patients who suffer from Parkinson’s disease (PD), dystonia, Tourette syndrome, and essential tremor (ET). The fact that neural oscillations can convey information on concurrent motor signs has inspired the idea of closed-loop adaptive deep brain stimulation (aDBS). Closed-loop aDBS is designed to adapt stimulation dynamically to feedback signals and is thus contrasted with conventional open-loop DBS, where chronic stimulation parameters need be changed manually by specialized neurologists or patients and are kept constant throughout changes in medication efficacy as observed in PD patients with dopaminergic drugs. For aDBS, the presence or threshold crossing of a specific activity pattern is envisioned as the feedback signal to adapt stimulation parameters dynamically. For tremor, earlier studies have focused on kinematic measures using external accelerometers to adapt stimulation amplitude or phase. Such a single biomarker approach has the advantage that the feedback signal simultaneously represents a pathophysiologically relevant target feature. However, kinematic markers require motor signs to be present, and thus they may signal therapeutic demand later than necessary. Beyond tremor, most identified oscillatory biomarkers have been associated with specific motor signs within broader syndromes of complex neural system disorders that would benefit from complementary adaptation measures. These caveats can possibly be mitigated with machine learning toward a precision medicine approach for intelligent real-time aDBS. In this month’s issue of Movement Disorders, He et al use machine learning to decode movement and posture to inform adaptive stimulation in 8 patients with ET. Among other noteworthy accomplishments, this study is the first to demonstrate the feasibility of machine learning–based adaptive stimulation relying solely on local field potential (LFP) recordings from the DBS target in the ventral intermediate nucleus (VIM) without additional invasive recording electrodes or peripheral sensors (Fig. 1). Furthermore, it is the first study to report group results, comparing machine learning–based adaptive versus conventional continuous open-loop DBS. Controlling binary stimulation conditions (OFF vs. ON at predefined best clinical settings) with a machine learning classifier, they achieved 99% of efficacy at 39% of energy delivered when compared with conventional open-loop DBS. This corresponded to an average improvement of 52.62% tremor suppression compared to no DBS, which lies in the typical range reported in meta-analyses of the effect of DBS of VIM-DBS in ET. An important detail here is that the authors did not aim for their algorithm to react to the wellcharacterized thalamic tremor frequency activity. Instead, they followed a strategy to decode the presence © 2021 International Parkinson and Movement Disorder Society