TY - JOUR
T1 - Optimal deep brain stimulation of the subthalamic nucleus - A computational study
AU - Feng, Xiao Jiang
AU - Shea-Brown, Eric
AU - Greenwald, Brian
AU - Kosut, Robert
AU - Rabitz, Herschel
N1 - Funding Information:
Acknowledgements We thank Dr. Jonathan Rubin for helpful discussions during the project and insightful comments on an earlier version of this manuscript. We also thank Dr. David Terman for his insights, as well as for providing the simulation code from the paper (Terman et al. 2002). We are indebted to Drs. Karen Sigvardt and Vikki Wheelock for their important comments, advice, and references throughout this project. E.B. was supported by a NSF Mathematical Sciences Postdoctoral Research Fellowship and holds a Career Award at the Scientific Interface from the Burroughs Wellcome Fund. H.R. and X.F. acknowledge support from the National Science Foundation.
PY - 2007/12
Y1 - 2007/12
N2 - Deep brain stimulation (DBS) of the subthalamic nucleus, typically with periodic, high frequency pulse trains, has proven to be an effective treatment for the motor symptoms of Parkinson's disease (PD). Here, we use a biophysically-based model of spiking cells in the basal ganglia (Terman et al., Journal of Neuroscience, 22, 2963-2976, 2002; Rubin and Terman, Journal of Computational Neuroscience, 16, 211-235, 2004 to provide computational evidence that alternative temporal patterns of DBS inputs might be equally effective as the standard high-frequency waveforms, but require lower amplitudes. Within this model, DBS performance is assessed in two ways. First, we determine the extent to which DBS causes Gpi (globus pallidus pars interna) synaptic outputs, which are burstlike and synchronized in the unstimulated Parkinsonian state, to cease their pathological modulation of simulated thalamocortical cells. Second, we evaluate how DBS affects the GPi cells' auto- and cross-correlograms. In both cases, a nonlinear closed-loop learning algorithm identifies effective DBS inputs that are optimized to have minimal strength. The network dynamics that result differ from the regular, entrained firing which some previous studies have associated with conventional high-frequency DBS. This type of optimized solution is also found with heterogeneity in both the intrinsic network dynamics and the strength of DBS inputs received at various cells. Such alternative DBS inputs could potentially be identified, guided by the model-free learning algorithm, in experimental or eventual clinical settings.
AB - Deep brain stimulation (DBS) of the subthalamic nucleus, typically with periodic, high frequency pulse trains, has proven to be an effective treatment for the motor symptoms of Parkinson's disease (PD). Here, we use a biophysically-based model of spiking cells in the basal ganglia (Terman et al., Journal of Neuroscience, 22, 2963-2976, 2002; Rubin and Terman, Journal of Computational Neuroscience, 16, 211-235, 2004 to provide computational evidence that alternative temporal patterns of DBS inputs might be equally effective as the standard high-frequency waveforms, but require lower amplitudes. Within this model, DBS performance is assessed in two ways. First, we determine the extent to which DBS causes Gpi (globus pallidus pars interna) synaptic outputs, which are burstlike and synchronized in the unstimulated Parkinsonian state, to cease their pathological modulation of simulated thalamocortical cells. Second, we evaluate how DBS affects the GPi cells' auto- and cross-correlograms. In both cases, a nonlinear closed-loop learning algorithm identifies effective DBS inputs that are optimized to have minimal strength. The network dynamics that result differ from the regular, entrained firing which some previous studies have associated with conventional high-frequency DBS. This type of optimized solution is also found with heterogeneity in both the intrinsic network dynamics and the strength of DBS inputs received at various cells. Such alternative DBS inputs could potentially be identified, guided by the model-free learning algorithm, in experimental or eventual clinical settings.
KW - Basal ganglia
KW - Deep brain stimulation
KW - Neural network model
KW - Numerical optimization
KW - Parkinson's disease
KW - Subthalamic nucleus
UR - http://www.scopus.com/inward/record.url?scp=35248895229&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=35248895229&partnerID=8YFLogxK
U2 - 10.1007/s10827-007-0031-0
DO - 10.1007/s10827-007-0031-0
M3 - Article
C2 - 17484043
AN - SCOPUS:35248895229
SN - 0929-5313
VL - 23
SP - 265
EP - 282
JO - Journal of Computational Neuroscience
JF - Journal of Computational Neuroscience
IS - 3
ER -