Motor Control in Parkinson’s Disease

Characterization of motor performance in Parkinson’s disease can can help elucidate faulty feedback mechanisms in the brain. Linear dynamical systems have been shown to be effective models of manual pursuit tracking, and system parameters such as damping ratio and natural frequency potential biomarkers. High-fidelity characterization of motor processes, and correlation of these models with observable brain processes (e.g., through fMRI or EEG data) can provide insight into compensatory mechanisms in the brain in Parkinson’s disease. This is joint work with Dr. Martin J. McKeown and Dr. Z. Jane Wang at UBC.

Related lab publications

S. Lee, D. Kim, D. Svenkeson, G. Parras, M. Oishi, M.J. McKeown, “Multifaceted Effects of Noisy Galvanic Vestibular Stimulation on Manual Tracking Behavior in Parkinson’s Disease,” Frontiers in Systems Neuroscience, vol. 9, p. 1-5, February 2015.

D. Svenkeson, B. Sena, M. Oishi, S. Pappu, H. Yonas, “A Novel Use of Transfer Function Estimation for Early Assessment of Brain Injury Outcome,” IEEE Transactions on Biomedical Engieering, vol. 61, no. 9, p. 2413-2421, April 2014.

J. Stevenson, C. Lee, B.S. Lee, P. Talebifard, E. Ty, K. Aseeva, M. Oishi, and M.J. McKeown, “Excessive sensitivity to uncertain visual input in L-dopa induced dyskinesias in Parkinson’s disease: further implications for cerebellar involvement,” Frontiers in Movement Disorders, vol. 5, no. 8, p. 1-13, February 2014.

N. Baradaran, S. N. Tan, A. Liu, A. Ashoori, S. Palmer, Z. J. Wang, M. Oishi, M. J. McKeown, “Parkinson’s disease rigidity: relation to brain connectivity and motor performance,” Frontiers in Movement Disorders, vol. 4, no. 67, p. 1-9, June 2013.

A. Ashoori, M. J. McKeown, and M. Oishi, “Switched manual pursuit tracking tasks to measure motor performance in Parkinson’s disease,” IET Control Theory and Applications, vol. 5, no. 17, p. 1970-1977, November 2011.

J. K. R. Stevenson, P. TalebiFard, E. Ty, M. Oishi, and M. J. McKeown, “Dyskinetic Parkinson’s disease patients demonstrate motor abnormalities off medication,” Experimental Brain Research, vol. 214, no. 3, p. 471-479, October 2011.

M. Oishi, P. TalebiFard, and M. J. McKeown, “Assessing manual pursuit tracking in Parkinson’s disease via linear dynamical systems,” Annals of Biomedical Engineering, vol. 39, no. 8, p. 2263-2273, August 2011.

M. Oishi, N. Matni, A. Ashoori, and M. J. McKeown, “Switching restrictions for stability despite switching delay: Application to switched tracking tasks in Parkinson’s disease,” Journal of Nonlinear Systems and Applications, Special issue on hybrid systems, vol. 2, no. 1-2, p. 16-25, 2011.

J. K. R. Stevenson, M. Oishi, S. Farajian, E. Cretu, E. Ty, and M. J. McKeown, “Response to sensory uncertainty in Parkinson’s disease: A marker of cerebellar dysfunction?European Journal for Neuroscience, vol. 83, no. 2, p. 298-305, February 2011.

W. Au, N. Li, M. Oishi, and M. J. McKeown, “L-dopa induces underdamped motor responses in Parkinson’s disease,” Experimental Brain Research, vol. 202, no.3, p.553-559, May 2010.

Related lab conference proceedings

J. D. Gleason, M. Oishi, M. Simkulet, T. Arunas, L. Brown, S. Brueck, and R. F. Karlicek, “A novel smart lighting clinical testbed,” in IEEE Int’l Conference of the Engineering in Medicine and Biology Society, Jeju, Korea, July 2017, pp. 4317–4320.

M. Oishi, C. Gonzalez, D. Svenkeson, D. Kim, and M.J. McKeown, “Detection of manual tracking submovements in Parkinson’s disease through hybrid optimization,” In the Proceedings of the IFAC Conference on Analysis and Design of Hybrid Systems, Atlanta, Georgia, October 2015, vol. 48, no. 27, p. 291-297.

M. Oishi, A. Ashoori, and M.J. McKeown, “Mode detection in switched pursuit tracking tasks: Hybrid estimation to measure performance in Parkinson’s disease,” In the Proceedings of the IEEE Conference on Decision and Control, Atlanta, GA, December 2010, p. 2124-2130.