Deep brain stimulation: a review of the open neural engineering challenges
Vissani M, Isaias IU, Mazzoni A
Journal of Neural Engineering · 2020 · doi:10.1088/1741-2552/abb581
Deep brain stimulation (DBS) is an established and valid therapy for a variety of pathological conditions ranging from motor to cognitive disorders. Still, much of the DBS-related mechanism of action is far from being understood, and there are several side effects of DBS whose origin is unclear. In the last years DBS limitations have been tackled by a variety of approaches, including adaptive deep brain stimulation (aDBS), a technique that relies on using chronically implanted electrodes on 'sensing mode' to detect the neural markers of specific motor symptoms and to deliver on-demand or modulate the stimulation parameters accordingly. Here we review the state of the art of the several approaches to improve DBS and summarize the main challenges toward the development of an effective aDBS therapy. We discuss models of basal ganglia disorders pathogenesis, hardware and software improvements for conventional DBS, and candidate neural and non-neural features and related control strategies for aDBS. We identify the main operative challenges toward optimal DBS, such as accurate target localization, increased spatial resolution of stimulation, development of in silico tests for DBS, identification of specific motor-symptom biomarkers, in particular assessing how LFP oscillations relate to behavioral dysfunctions, and clarifying how stimulation affects the cortico-basal-ganglia-thalamic network in order to design optimal stimulation patterns. This roadmap leads neural engineers new to the field toward the most relevant open issues of DBS, while in-depth readers may find a careful comparison of the advantages and drawbacks of the most recent attempts to improve DBS-related neuromodulatory strategies.