Computational Models of Closed–Loop Deep Brain Stimulation

Abstract

Deep Brain Stimulation (DBS) is a neurosurgical intervention that sends electrical signals to the brain to effectively alleviate the symptoms of neurological disorders such as Parkinson’s disease. Although the conventional high frequency DBS shows remarkable therapeutic success, it is desirable to overcome the downsides of such a form of open–loop DBS. Using computational models, we explore a closed–loop DBS paradigm, multi–site delayed feedback stimulation (MDFS), that may potentially overcome the drawbacks of constant high frequency DBS. We first develop a biological-faithful computational network model of basal ganglia and thalamus in parkinsonian conditions. The model mimics the pathological neuronal activity observed in the basal ganglia in parkinsonian conditions, such as increased firing rate, bursting patterns, and synchronization. We then evaluate the outcome of closed–loop MDFS being applied to the parkinsonian network by examining both quantitative measures of neurons in the basal ganglia and the relay error of thalamocortical (TC) neurons. Our computational results show that closed–loop MDFS significantly diminish TC relay error by breaking the bursting pattern and desynchronizing the synchronized clusters in the basal ganglia. The design of MDFS suggests that it is superior to open–loop stimulation in that not only the stimulation signal is guided by changes in neuronal activities specific to disorders being treated, but also MDFS shows much lower energy consumption compared with the conventional high frequency DBS. To support the computational results and feasibility of closed–loop DBS, we further review some previous work that validates the evaluation measure of TC relay error and some recent experimental studies that validate the on–demand type of DBS.