Deep Brain Stimulation (DBS) has been a longstanding adjunctive therapy for movement disorders like Parkinson’s disease, yet its precise mechanisms of action have remained elusive. In a recent study published in the journal Neuromodulation, Dr. Milad Lankarany and his team have introduced a computational model that accurately predicts the dynamics of neuronal activity during DBS across various frequencies.
This research not only provides insights into DBS parameters’ effects on neuronal activity but also offers a framework for optimizing stimulation parameters to enhance clinical outcomes.
DBS entails the implantation of electrodes in specific brain regions, delivering electrical impulses to regulate abnormal brain activity and alleviate movement disorder symptoms by adjusting neuron firing.
Despite DBS’s established benefits, understanding its therapeutic mechanisms has posed a challenge. Existing models have offered qualitative interpretations of experimental data but have lacked quantitative precision in capturing neuronal activity dynamics across different DBS frequencies.
Dr. Milad Lankarany, affiliated professor at the University of Toronto, a Scientist at the Krembil Brain Institute, and an Affiliate Scientist at the KITE Research Institute, spearheaded the development of a novel firing rate model capable of accurately tracking neurons’ instantaneous firing rates during DBS.
“Our motivation is to predict how neurons, at the cellular level, respond to different frequencies of DBS. If this can be achieved, we can scale up the model and predict how neurons respond to DBS at the network level,” explained Dr. Milad Lankarany.
The study leveraged data from key nuclei implicated in movement disorders—subthalamic nucleus (STN), substantia nigra pars reticulata (SNr), and ventral intermediate nucleus (Vim)—across a spectrum of DBS frequencies (5 to 200Hz). By incorporating short-term synaptic plasticity (STP) into the model, the researchers achieved unprecedented accuracy in simulating the immediate impact of DBS pulses.
“Our work is significant because an accurate physiological model of DBS effects is critical in understanding the underlying biophysical mechanisms and further translational studies. DBS models have been prevalent since about 20 years ago, but there have been very few models that can accurately and fully track clinical data. In the future, our model can be further developed to characterize more detailed physiological mechanisms, and to be implemented in closed-loop control systems that optimize DBS settings.” noted Yupeng Tian, Ph.D. candidate at the Institute of Biomedical Engineering and lead author of the study.
These findings represent a notable advancement in understanding the intricate dynamics of DBS-induced neuronal activity, with broad implications for improving the treatment of movement disorders and beyond.