Toward closed-loop optimization of deep brain stimulation for Parkinson's disease: concepts and lessons from a computational model.
Deep brain stimulation (DBS) of the subthalamic nucleus with periodic, high-frequency pulse trains is an increasingly standard therapy for advanced Parkinson's disease. Here, we propose that a closed-loop global optimization algorithm may identify novel DBS waveforms that could be more effective than their high-frequency counterparts. We use results from a computational model of the Parkinsonian basal ganglia to illustrate general issues relevant to eventual clinical or experimental tests of such an algorithm. Specifically, while the relationship between DBS characteristics and performance is highly complex, global search methods appear able to identify novel and effective waveforms with convergence rates that are acceptably fast to merit further investigation in laboratory or clinical settings.
Feng
Xiao-Jiang
XJ
Department of Chemistry, Princeton University, Princeton, NJ 08544, USA.
Greenwald
Brian
B
Rabitz
Herschel
H
Shea-Brown
Eric
E
Kosut
Robert
R
D000465:Algorithms; D001479:Basal Ganglia / Q000503:physiopathology*; D003198:Computer Simulation; D020000:Decision Support Systems, Clinical; D046690:Deep Brain Stimulation / Q000379:methods*; D004569:Electroencephalography / Q000379:methods; D005246:Feedback; D006801:Humans; D008959:Models, Neurological; D010300:Parkinson Disease / Q000503:physiopathology* / Q000628:therapy*; D013813:Therapy, Computer-Assisted / Q000379:methods*; D016896:Treatment Outcome