Deep brain stimulation (DBS) has transformed the treatment of movement disorders like Parkinson's Disease (PD). Innovations in DBS technology and experimentation have fostered adaptive DBS (aDBS), which employs a closed-loop system that senses physiological biomarkers to inform precise neuromodulation and personalized therapy. This review analyzes several promising advances in aDBS, including biomarker detection, control policies, mechanisms of efficacy, and a data-driven approach using artificial intelligence to decode motor states from neural signals. Investigations into data-driven approaches have expanded biomarker detection beyond subcortical beta oscillations, leveraging other neural and kinematic signals. Future aDBS systems that accommodate multi-modal inputs have the potential to bolster therapeutic efficacy and address symptoms not addressed by beta-driven aDBS. Continuing investigation is necessary to address existing technical and computational challenges for further clinical translation.
Deep brain stimulation (DBS) requires individualized programming of stimulation parameters, a time-consuming process performed manually by clinicians with specialized training. This process limits DBS accessibility, delays treatment, and constrains the potential for next-generation technology to improve patient outcomes. This review describes technological advancements that could automate DBS programming, focusing on Parkinson's disease biomarkers that can provide objective outcome measurement and algorithms that can quickly and automatically identify optimal DBS settings. We first define key performance criteria for an automated programming system, including effectiveness, efficiency, and ease of use, and then describe and evaluate each component with respect to these criteria. We conclude that the state of current research provides a strong foundation for developing automated DBS programming. The primary remaining obstacle lies in identifying and deploying the best combination of available techniques that will overcome the high entry barrier needed for wide-scale clinical deployment and adoption.