Control Algorithms for Adaptive Deep Brain Stimulation in Parkinson's Disease

Thirty years on from the introduction of deep brain stimulation as a therapy for Parkinson's disease, adaptive deep brain stimulation (aDBS) is poised to transform neural stimulation for the treatment of motor disorders. Implementation of aDBS has been facilitated by the identification of biomarkers that reflect patients' clinical states and the introduction of hardware allowing simultaneous sensing and stimulation. However, the most appropriate algorithms for aDBS remain unclear. This review provides an overview of control algorithms used in computational, animal and human studies of aDBS in Parkinson's disease to date. Although shown to be effective and well-tolerated, only relatively simple aDBS algorithms have been used clinically. Computational studies have explored a wider range of more advanced algorithms which promise superior performance but need to be rigorously tested and compared against clinically-tested techniques before translating to patients. The need for comparative studies is further driven by advances in machine learning, which promise patient-specific programming solutions but require large datasets and have high computational requirements. Understanding the advantages and limitations of these different algorithms is critical for aDBS to reach its potential and enable personalized treatment to provide maximum patient benefit.