Computational analysis of two novel deep brain stimulation pulsing patterns on a thalamocortical network model of Parkinson's disease.

Deep brain stimulation (DBS) at high frequencies has revolutionized efforts to alleviate Parkinson's disease symptoms for approximately 30 years. Since then, there has been vast investigation into the mechanisms of action of DBS. Recently, synaptic suppression was found to play a pivotal role in the fundamental mechanisms underlying DBS. Based on this understanding, researchers introduced two novel DBS pulsing strategies that use a minimal number of stimuli. In contrast to conventional DBS (cDBS) pulsing, which employs continuous high-frequency pulses (>100 Hz), the two novel methods incorporate changes in pulsing frequency and on/off pulsing periods. In this computational study, we investigated the network effects of these two suggested patterns using an updated version of a biophysically realistic thalamocortical network model of DBS. Both suggested pulsing patterns significantly reduced the exaggerated beta power (∼13 Hz-30 Hz oscillations) in the motor cortex, with careful consideration of the intensity of the stimulating pulses. In addition, they significantly reduced the level of network synchronization. We compared these findings with the effects of 20 and 130 Hz cDBS on our network model and did not observe effects contrary to those of 130 Hz cDBS. The two suggested patterns, which were computationally successful in reproducing known DBS network effects, could potentially increase the battery life of DBS device and reduce the microlesion effect associated with long-term cDBS pulsing. These outcomes, however, require confirmation in further studies.