Deep brain stimulation is a clinically effective treatment, but the selection of the parameters of stimulation remains a significant clinical challenge and many recipients are deprived of benefit due to non-optimal parameter selection. This project will advance an innovative approach to automatic selection of stimulation parameters based on monitoring of neural signals in the brain during stimulation. We propose to conduct first in human recordings of brain signals and evaluate them as a possible biomarker for the effectiveness of deep brain stimulation (DBS). DBS - an implanted brain pacemaker - is an effective therapy for essential tremor and Parkinson's disease. Present DBS systems operate in an open-loop fashion;a clinician sets the stimulation parameters, and patients receive invariant stimulation 24 h/day indefinitely. Selection of stimulation parameters is a non- systematic process that requires substantial time and clinical expertise and often results in sub-optimal outcomes. Further, in applications like treatment of epilepsy or depression, there may be no overt or immediate changes to guide selection of stimulation parameters. Closed-loop DBS, where the system adjusts parameters automatically and in a manner responsive to the needs of the patient, has the potential to improve outcomes by maintaining treatment during fluctuations in medication status, as the disease progresses, or as the response to DBS changes over time. The objective of the proposed project is to determine the feasibility of using recordings of neural activity, obtained using the same electrodes implanted to deliver stimulation, as a feedback signal for closed-loop control of DBS. We will conduct recordings of EEG-like brain activity during DBS and correlate changes in the amplitude and character of these signals with changes in symptoms. These experiments use innovative hardware that we developed and validated in animal studies and a novel intraoperative setting that allows direct connection to the DBS brain lead. The outcome will provide clinical validation of the suitability of brain electrical activity s a biomarker for the effectiveness of DBS. We will also conduct complementary animal studies and computational modeling to determine the source(s) of the neural signals constituting the biomarker. The outcome will provide insight into the mechanisms of action of DBS relevant to the design of future DBS systems.
Deep brain stimulation is a clinically effective treatment, but the selection of the parameters of stimulation remains a significant clinical challenge and many recipients are deprived of benefit due to non-optimal parameter selection. This project will advance an innovative approach to automatic selection of stimulation parameters based on monitoring of neural signals in the brain during stimulation.
|Hoang, Kimberly B; Cassar, Isaac R; Grill, Warren M et al. (2017) Biomarkers and Stimulation Algorithms for Adaptive Brain Stimulation. Front Neurosci 11:564|
|Brocker, David T; Swan, Brandon D; So, Rosa Q et al. (2017) Optimized temporal pattern of brain stimulation designed by computational evolution. Sci Transl Med 9:|
|Kumaravelu, Karthik; Brocker, David T; Grill, Warren M (2016) A biophysical model of the cortex-basal ganglia-thalamus network in the 6-OHDA lesioned rat model of Parkinson's disease. J Comput Neurosci 40:207-29|
|Deeb, Wissam; Giordano, James J; Rossi, Peter J et al. (2016) Proceedings of the Fourth Annual Deep Brain Stimulation Think Tank: A Review of Emerging Issues and Technologies. Front Integr Neurosci 10:38|
|Grill, Warren M (2015) Model-based analysis and design of waveforms for efficient neural stimulation. Prog Brain Res 222:147-62|
|Kent, Alexander R; Swan, Brandon D; Brocker, David T et al. (2015) Measurement of evoked potentials during thalamic deep brain stimulation. Brain Stimul 8:42-56|
|Kent, Alexander R; Grill, Warren M (2014) Analysis of deep brain stimulation electrode characteristics for neural recording. J Neural Eng 11:046010|
|Swan, Brandon D; Grill, Warren M; Turner, Dennis A (2014) Investigation of deep brain stimulation mechanisms during implantable pulse generator replacement surgery. Neuromodulation 17:419-24; discussion 424|
|Gross, Robert E; McDougal, Margaret E (2013) Technological advances in the surgical treatment of movement disorders. Curr Neurol Neurosci Rep 13:371|
|Kent, Alexander R; Grill, Warren M (2013) Neural origin of evoked potentials during thalamic deep brain stimulation. J Neurophysiol 110:826-43|
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