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Published on: August 12, 2018
Optimal deep brain stimulation of the subthalamic nucleus--a computational study.
Xiao-Jiang Feng1, Eric Shea-Brown, Brian Greenwald
1Department of Chemistry, Princeton University, Princeton, NJ 08544, USA.
Journal of Computational Neuroscience
|May 8, 2007
Summary
Researchers found that alternative deep brain stimulation (DBS) patterns, not just high-frequency pulses, can effectively treat Parkinson's disease (PD) motor symptoms with lower energy. This discovery offers new possibilities for optimizing DBS therapy.
Area of Science:
- Computational Neuroscience
- Neuroscience
- Biophysics
Background:
- Deep brain stimulation (DBS) using high-frequency pulse trains is a standard treatment for Parkinson's disease (PD) motor symptoms.
- The subthalamic nucleus is a common target for DBS in PD treatment.
- Pathological network dynamics in PD involve burstlike and synchronized GPi synaptic outputs.
Purpose of the Study:
- To computationally investigate alternative temporal patterns of DBS inputs for treating Parkinson's disease.
- To determine if lower-amplitude, non-conventional DBS waveforms can be as effective as standard high-frequency stimulation.
- To identify optimized DBS inputs using a closed-loop learning algorithm.
Main Methods:
- Utilized a biophysically-based model of spiking cells in the basal ganglia.
- Assessed DBS performance by analyzing its effect on GPi synaptic outputs modulating thalamocortical cells.
- Evaluated DBS impact on GPi cell auto- and cross-correlograms.
- Employed a nonlinear closed-loop learning algorithm to identify minimal-strength DBS inputs.
Main Results:
- Computational evidence suggests alternative DBS temporal patterns can be effective with lower amplitudes.
- Optimized DBS inputs successfully ceased pathological modulation of thalamocortical cells by GPi outputs.
- Identified effective DBS inputs that differ from the regular, entrained firing associated with conventional high-frequency DBS.
- Heterogeneity in network dynamics and DBS input strength did not preclude optimized solutions.
Conclusions:
- Alternative temporal patterns of DBS may offer an effective, lower-amplitude treatment for Parkinson's disease motor symptoms.
- A model-free learning algorithm can identify optimized DBS inputs, potentially applicable in experimental and clinical settings.
- This research opens avenues for developing more energy-efficient and personalized DBS therapies.
