Long-Lasting Desynchronization of Plastic Neural Networks by Random Reset Stimulation

摘要: Excessive neuronal synchrony is a hallmark of neurological disorders such as epilepsy and Parkinson's disease. An established treatment for medically refractory disease high-frequency (HF) deep brain stimulation (DBS). However, symptoms return shortly after cessation HF-DBS. Recently developed decoupling approaches, Random Reset (RR) stimulation, specifically target pathological connections to achieve long-lasting desynchronization. During RR temporally spatially randomized stimulus pattern administered. spatial randomization, presented so far, may be difficult realize in DBS-like setup due insufficient resolution. Motivated by recently segmented DBS electrodes with multiple sites, we present protocol that copes the limited resolution currently available depth DBS. Specifically, randomization realized delivering stimuli simultaneously L randomly selected sites out total M which will called L/M-RR stimulation. We study networks excitatory integrate-and-fire neurons spike-timing dependent plasticity means theoretical computational analysis. find yields parameter-robust Furthermore, our theory reveals strong not suitable inducing desynchronization effects. As consequence, low high frequency affect synaptic weights qualitatively different ways. Our simulations confirm these predictions show qualitative differences between are across wide range parameters, rendering intermediate frequencies most efficient. Remarkably, does rely on resolution, characterized density area, corresponding large M. In fact, performs even better at amplitudes. results provide evidence way exploit modern lead therapeutic