Practical design of closed-loop DBS algorithms using systematic in silico verification

Closed-loop deep brain stimulation is a promising new treatment for several neurological disorders, including Parkinson's disease.

Closed-loop approaches offer the potential to not only match or even surpass the effectiveness of currently available treatments, but also promise longer battery life with fewer treatment-induced side effects.

Despite preliminary experimental studies supported by computational analyses demonstrating its efficacy, there is no consensus on the best algorithm for controlling deep brain stimulation. This lack of a clear direction delays the implementation and clinical adoption of the technique.

Moreover, without an understanding of the effectiveness of the various control approaches available, it is difficult to identify the most appropriate control scheme.

To address this issue, this project aims to design a novel algorithm for closed-loop DBS and demonstrate that it outperforms the currently proposed approaches in terms of total energy use and symptom reduction.

The demonstration will be conducted using a state-of-the art verification environment, where the proposed stimulation algorithms and their hardware implementations will be tested against a range of computational models of parkinsonian brain, providing objective measures of efficacy and efficiency of the proposed stimulation approaches.