Abstract
Deep Brain Stimulation (DBS) is an important tool in the treatment of pharmacologically resistant neurological movement disorders such as essential tremor (ET) and Parkinson's disease (PD). However, the open-loop design of current systems may be holding back the true potential of invasive neuromodulation. In the last decade we have seen an explosion of activity in the use of feedback to “close the loop” on neuromodulation in the form of adaptive DBS (aDBS) systems that can respond to the patient's therapeutic needs. In this paper we summarize the accomplishments of a 5-year study at the University of Washington in the use of neural feedback from an electrocorticography strip placed over the sensorimotor cortex. We document our progress from an initial proof of hardware all the way to a fully implanted adaptive stimulation system that leverages machine-learning approaches to simplify the programming process. In certain cases, our systems out-performed current open-loop approaches in both power consumption and symptom suppression. Throughout this effort, we collaborated with neuroethicists to capture patient experiences and take them into account whilst developing ethical aDBS approaches. Based on our results we identify several key areas for future work. “Graded” aDBS will allow the system to smoothly tune the stimulation level to symptom severity, and frequent automatic calibration of the algorithm will allow aDBS to adapt to the time-varying dynamics of the disease without additional input from a clinician. Additionally, robust computational models of the pathophysiology of ET will allow stimulation to be optimized to the nuances of an individual patient's symptoms. We also outline the unique advantages of using cortical electrodes for control and the remaining hardware limitations that need to be overcome to facilitate further development in this field. Over the course of this study we have verified the potential of fully-implanted, cortically driven aDBS as a feasibly translatable treatment for pharmacologically resistant ET.
Highlights
Essential Tremor (ET) is one of the most common neurological movement disorders
We found that the benefit to adaptive DBS (aDBS) control of the addition of ventral intermediate nucleus of the thalamus (VIM) data was not worth the loss in sampling rate, since local field potentials (LFPs) data from the VIM was heavily contaminated by stimulation artifacts
We provide our outlook on what the key hardware and ethical challenges that need to be solved in aDBS for essential tremor (ET)
Summary
Essential Tremor (ET) is one of the most common neurological movement disorders. By some estimates, it affects as much as 1% of the world’s adult population and up to 4.5% of the senior population to some extent (Louis and Ferreira, 2010). Adaptive DBS (aDBS) offers to solve many of the limitations of cDBS systems (Arlotti et al, 2016; Meidahl et al, 2017) In this approach, stimulation is delivered in a closed-loop format that allows the system to adapt to the patient’s state. ET is a attractive application for this approach since the primary symptom, tremor, manifests itself almost exclusively during movement This clearly defines the periods when stimulation would be the most beneficial, greatly reducing the complexity of the control problem to be solved. This review paper is intended to provide a an overview of the development process and preliminary clinical results from start to finish
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