title: Toward closed-loop optimization of deep brain stimulation for Parkinson's disease: concepts and lessons from a computational model.
abstract: 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.
authors.affiliation: Department of Chemistry, Princeton University, Princeton, NJ 08544, USA.
authors.forename: Xiao-Jiang
authors.initials: XJ
authors.lastname: Feng
authors.forename: Brian
authors.initials: B
authors.lastname: Greenwald
authors.forename: Herschel
authors.initials: H
authors.lastname: Rabitz
authors.forename: Eric
authors.initials: E
authors.lastname: Shea-Brown
authors.forename: Robert
authors.initials: R
authors.lastname: Kosut
mesh_terms: 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