Using Phase Response Curves to Optimize Deep Brain Stimulation
Undergraduate Institution and Major/Degree:
B.A., Biology & Chemistry, 2010, St. Olaf College, Northfield, MN
Senior Scientist, Medtronic
Postdoctoral Fellow, University of Oxford, Oxford, England
Theoden Netoff, Ph.D.
I am interested in understanding the mechanisms of action of deep brain stimulation (DBS) for Parkinson's Disease (PD). Synchrony in certain nuclei of the basal ganglia is characteristic in PD. Our lab believes that DBS works to chaotically desynchronize these populations by stimulating at specific frequencies to induce chaotic responses. To measure the effect of the stimulus pulse, we measure phase response curves, which shows how the stimulus advances the phase of the neuron depending on the phase at which the stimulus was applied. To collect this data we perform whole-cell patch clamp experiments.
DBS is used to treat symptoms of medication-refractory PD. Currently, it takes many post-operative visits to tune the DBS parameters, and there is a need for a closed-loop approach to tuning based on physiological data. The PRC could be a good way to help clinicians determine the optimal stimulus waveforms and patters based on patient-specific physiological responses. This would cut down the amount of time spent in the clinic and may improve efficacy of the treatment.
- Martha Flanders
- David Redish
- Jim Ashe
- Teresa Nick
- Tay Netoff
Courses Taken Beyond the Core Courses:
- Society for Neuroscience - Fall 2010
Selected Publications and Presentations:
- Holt AB, Netoff TI. Computational models of deep brain stimulation for Parkinson’s disease. Drug Discovery Today: Disease Models. 2017;19:31-36.
- Holt AB, Wilson D, Shinn M, Moehlis J, Netoff TI. Phasic burst stimulation: A closed-loop approach to tuning deep brain stimulation parameters for Parkinson’s disease. PLOS Comput Biol. 2016;12(7):e1005011.
- Holt AB, Wilson D, Shinn M, Moehlis J, Netoff TI. A closed loop approach to tuning deep brain stimulation parameters for Parkinson's disease. PLoS Comput Biol. 2016;12(7):e1005011.
- Wilson D, Holt AB, Netoff TI, Moehlis J. Optimal entrainment of heterogeneous noisy neurons. Front Neurosci. 2015;9:192.
- Holt AB, Netoff TI. Origins and suppression of oscillations in a computational model of Parkinson's disease. J. Comput Neurosci. 2014;37:505-521.
- Holt AB, Netoff TI. Computational modeling of epilepsy for an experimental neurologist. Exp Neurol. 2013;244:75-86.
- Johnson MD, Lim HH, Netoff TI, Connolly AT, Johnson N, Roy A, Holt AB, Lim KO, Carey JR, Vitek JL, He B. Neuromodulation for brain disorders: challenges and opportunities. IEEE Trans Biomed Eng. 2013;60(3):610-624.
Awards and Honors:
- IGERT Neuroengineering Associate 2013-Present
- Organization for Computational Neuroscience Travel Award 2015
- Poster Award, Neuromodulation Symposium, 2015
- 1st place (September 2014) - Medtronic Interdisciplinary Healthcare Case Competition
- 2014 MNDrive Neuromodulation Predoctoral Fellowship
- Shoreview, MN