Patient Specific Parameter Optimization of Thalamic Stimulation for Treatment of Epilepsy
Abstract number :
3.186
Submission category :
3. Neurophysiology / 3E. Brain Stimulation
Year :
2022
Submission ID :
2205101
Source :
www.aesnet.org
Presentation date :
12/5/2022 12:00:00 PM
Published date :
Nov 22, 2022, 05:28 AM
Authors :
Tay Netoff, PhD – University of Minnesota; Alec Jonason, BS – Research Coordinator, Neurosurgery, University of Minnesota; Jacob Hanson, BA – Rocky Vista University School of Osteopathic Medicine; James Jean, BS – Neurosurgery – University of Minnesota; Luke Sabal, BS – University of Minnesota; Reid Johnson, BS – University of Minnesota; Robert McGovern, MD – Neurosurgery – University of Minnesota
Rationale: Deep brain stimulation (DBS) of the anterior nucleus of the thalamus (ANT) is clinically approved for treatment of epilepsy resulting in an average decrease in seizure frequency of 40% in the first year, but few patients achieve seizure freedom. Implantable neural stimulators have many parameters, such as stimulation amplitude, frequency and pulse width, which could potentially be tuned to improve efficacy. However, there is no systematic process to guide epileptologists through optimization. Stimulation of ANT in animal models has shown almost immediate changes in excitability in the circuit of Papez, which we hypothesize is a biomarker that could be used to optimize stimulation parameters. Medtronic’s DBS Percept system allows for recording during stimulation and streaming the data to a computer for further analysis, which can be used in an optimization loop. Bayesian optimization (BayesOpt) is a machine learning algorithm that is widely used for efficient optimization over a bounded parameter range when acquiring data is expensive and computational time is relatively cheap. We have used BayesOpt for optimizing stimulation settings in animal models and clinical trials.
Methods: Patients with medically refractory epilepsy who have received a Medtronic Percept DBS device have their devices programed in the clinic to stimulate at different pulse widths, frequencies, and amplitudes. Stimulation amplitude is adjusted to keep energy the same across all stimulation settings. Local field potentials of the ANT are recorded and run through BayesOpt, which will suggest optimized stimulation parameters.
Results: Our first experimental evidence shows that adjusting stimulation settings significantly modulates brain activity and may be used to select improved settings. Power measured in the 𝛼-band (D), 𝛽/𝛾-band (E), and broadband (F), show strong differences in measured power at different stimulation frequencies, suggesting that these power spectral density analyses may be used to guide programming by using stimulation settings that maximally suppress activity.
Conclusions: The continuation of this study will further optimize stimulation settings and test if settings that suppress LFP activity of the ANT results in suppression of epileptiform activity, and ultimately, seizures, in order to lead to improved surgical outcomes in medically refractory epilepsy patients with neuromodulation devices.
Funding: This study was funded by MnDRIVE, a collaboration between the University of Minnesota and the state of Minnesota.
Neurophysiology