Identification and analysis of local field potentials in Parkinson's disease under nonlinear delayed feedback stimulation

Deep brain stimulation (DBS) is a clinically recognized electrical therapy for the treatment of several neural disorders, e.g., Parkinson's disease (PD), essential tremor, and compulsive disorders. High frequency (HF) STN DBS (Deep Brain Stimulation in SubThalamic Nucleus) is currently used to reduce the motor symptoms of PD, but the actual procedures for the selection of the stimulation parameters do not allow to cope with the disorder fluctuations while a poor knowledge of the cellular mechanisms of DBS has partially limited the development of new stimulation profiles. Popovych et al. [1] recently hypothesized that DBS works by reducing the level of synchronization among the neuronal firing patterns within the target site and proposed a desynchronization-based closed loop strategy for the generation of the DBS input (Nonlinear Delay Feedback Stimulation, NDFS). We review their work by introducing a Systems approach to the analysis of the stimulation performances, quantify the desynchronizing effects of the NDFS through the autoregressive (AR) model identification, and compare the results with those achieved by the actual HF DBS. Simulation results confirm the effectiveness of the approach by Popovych and coworkers.