access icon free Controllability and observability analysis of basal ganglia model and feedback linearisation control

Deep brain stimulation (DBS) is a clinical remedy to control tremor in Parkinson's disease. In DBS, one of the two main areas of basal ganglia (BG) is stimulated. This stimulation is produced with no feedback of the tremor and often causes a wide range of unpleasant side effects. Using a feedback signal from tremor, the stimulatory signal can be reduced or terminated to avoid extra stimulation and as a result decrease the side effects. To design a closed-loop controller for the non-linear BG model, a complete study of controllability and observability of this system is presented in this study. This study shows that the BG model is controllable and observable. The authors also propose the idea of stimulating the two BG areas simultaneously. A two-part controller is then designed: a feedback linearisation controller for subthalamic nucleus stimulation and a partial state feedback controller for globus pallidus internal stimulation. The controllers are designed to decrease three indicators: the hand tremor, the level of delivered stimulation signal in disease condition, and the ratio of the level of delivered stimulation signal in health condition to disease condition. Considering these three indicators, the simulation results show satisfactory performance.

Inspec keywords: bioelectric phenomena; diseases; linearisation techniques; neurophysiology; feedback; brain; closed loop systems; medical control systems; controllers

Other keywords: Parkinson's disease; controllability analysis; globus pallidus internal stimulation; feedback signal; observability analysis; disease condition; basal ganglia model; partial state feedback controller; nonlinear BG model; closed-loop controller; two-part controller; subthalamic nucleus stimulation; feedback linearisation controller; feedback linearisation control; clinical remedy; deep brain stimulation; tremor control; delivered stimulation signal

Subjects: Biological and medical control systems; Bioelectric signals; Electrical activity in neurophysiological processes; Electrodiagnostics and other electrical measurement techniques; Controllers

References

    1. 1)
      • 20. Kent, A.R., Grill, W.M.: ‘Instrumentation to record evoked potentials for closed-loop control of deep brain stimulation’, Conf. Proc. IEEE Eng. Med. Biol. Soc., 2011, 2011, pp. 67776780.
    2. 2)
      • 25. Edwards, R., Beuter, A., Glass, L.: ‘Parkinsonian tremor and simplification in network dynamics’, Bull. Math. Biol., 1999, 61, (1), pp. 157177.
    3. 3)
      • 28. Beuter, A., Vasilakos, K.: ‘Tremor: is Parkinson's disease a dynamical disease?’, Chaos, 1995, 5, (1), pp. 3542.
    4. 4)
      • 30. Titcombe, M.S., Edwards, R., Beuter, A.: ‘Mathematical modelling of parkinsonian tremor’, Nonlinear Stud., 2004, 11, (3), pp. 363384.
    5. 5)
      • 27. Fukumoto, I.: ‘Computer simulation of Parkinsonian tremor’, J. Biomed. Eng., 1986, 8, (1), pp. 4955.
    6. 6)
      • 33. Isidori, A.: ‘Nonlinear control systems’ (Springer Science & Business Media, 2013).
    7. 7)
      • 21. Carron, R., Chaillet, A., Filipchuk, A., et al: ‘Closing the loop of deep brain stimulation’, Front. Syst. Neurosci., 2013, 7, p. 112.
    8. 8)
      • 15. Mehanna, R., Lai, E.C.: ‘Deep brain stimulation in Parkinson's disease’, Transl. Neurodegener., 2013, 2, (1), p. 22.
    9. 9)
      • 31. Rouhollahi, K., Emadi Andani, M., Karbassi, S.M., et al: ‘Designing a robust backstepping controller for rehabilitation in Parkinson's disease: a simulation study’, IET Syst. Biol., 2016, 10, (4), pp. 136146.
    10. 10)
      • 3. Titcombe, M.S., Glass, L., Guehl, D., et al: ‘Dynamics of Parkinsonian tremor during deep brain stimulation’, Chaos, 2001, 11, (4), pp. 766773.
    11. 11)
      • 26. Gurfinkel, V.S., Osovets, S.M.: ‘Mechanisms of generation of oscillations in the tremor form of Parkinsonism’, Biofizika, 1973, 18, (4), p. 731.
    12. 12)
      • 7. Lee, J.: ‘A closed-loop deep brain stimulation device with a logarithmic pipeline ADC’ (ProQuest, 2008).
    13. 13)
      • 24. Asai, Y., Nomura, T., Abe, K., et al: ‘Classification of dynamics of a model of motor coordination and comparison with Parkinson's disease data’, Biosystems, 2003, 71, (1), pp. 1121.
    14. 14)
      • 10. Kang, G., Lowery, M.M.: ‘Conductance-based model of the basal ganglia in Parkinson's disease’, 2009, pp. 1515.
    15. 15)
      • 19. Terman, D., Rubin, J.E., Yew, A.C., et al: ‘Activity patterns in a model for the subthalamopallidal network of the basal ganglia’, J. Neurosci., 2002, 22, (7), pp. 29632976.
    16. 16)
      • 4. Davidson, C.M., De Paor, A.M., Lowery, M.M.: ‘Insights from control theory into deep brain insights from control theory into deep brain’. ELEKTRO, 2012, pp. 27.
    17. 17)
      • 1. Berns, G.S., Sejnowski, T.J.: ‘How the basal ganglia make decisions’, in Damasio, A.R., Damasio, H., Christen, Y. (EDs): ‘Neurobiology of decision-making’ (Springer Berlin Heidelberg, 1996), pp. 101113.
    18. 18)
      • 13. Krouchev, N.I., Danner, S.M., Vinet, A., et al: ‘Energy-optimal electrical-stimulation pulses shaped by the least-action principle’, PLoS ONE, 2014, 9, (3), pp. e90480.
    19. 19)
      • 5. Hall, J.E.: ‘Guyton and Hall textbook of medical physiology’ (Elsevier Health Sciences, 2010).
    20. 20)
      • 6. Bolam, P., Ingham, C., Magill, P.: ‘Dynamic model of basal ganglia functions and Parkinson's disease’, in Bolam, J.P., Ingham, C.A., Magill, P.J. (Eds.): ‘The basal ganglia VIII’, vol. 56, Advances in behavioral biology (Springer, 2005).
    21. 21)
      • 32. Rouhollahi, K., Emadi Andani, M., Karbassi, S.M., et al: ‘Design of robust adaptive controller and feedback error learning for rehabilitation in Parkinson's disease: a simulation study’, IET Syst. Biol., 2017, 11, (1), pp. 1929.
    22. 22)
      • 14. Muniz, A.M.S., Liu, W., Liu, H., et al: ‘Assessment of the effects of subthalamic stimulation in Parkinson disease patients by artificial neural network’, Conf. Proc. IEEE Eng. Med. Biol. Soc., 2009, 2009, pp. 56735676.
    23. 23)
      • 11. Haeri, M., Sarbaz, Y., Gharibzadeh, S.: ‘Modeling the Parkinson's tremor and it treatments’, J. Theor. Biol., 2005, 236, (3), pp. 311322.
    24. 24)
      • 18. Rehan, M., Hong, K.S.: ‘Modeling and automatic feedback control of tremor: adaptive estimation of deep brain stimulation’, PLoS ONE, 2013, 8, (4), pp. e62888.
    25. 25)
      • 16. Zhang, D., Poignet, P., Widjaja, F., et al: ‘Neural oscillator based control for pathological tremor suppression via functional electrical stimulation’, Control Eng. Pract., 2011, 19, (1), pp. 7488.
    26. 26)
      • 8. Suri, R.E., Albani, C., Glattfelder, A.H.: ‘A dynamic model of motor basal ganglia functions’, Biol. Cybern., 1997, 76, (6), pp. 451458.
    27. 27)
      • 2. De Paor, A.M., Lowery, M.M.: ‘Analysis of the mechanism of action of deep brain stimulation using the concepts of dither injection and the equivalent nonlinearity’, IEEE Trans. Biomed. Eng., 2009, 56, (11), pp. 27172720.
    28. 28)
      • 29. Chagdes, J.R., Rietdyk, S., Jeffrey, M.H., et al: ‘Dynamic stability of a human standing on a balance board’, J. Biomech., 2013, 46, (15), pp. 25932602.
    29. 29)
      • 23. Karabacak, O., Sengor, N.S.: ‘A dynamical model of a cognitive function: action selection’. 16th IFAC Congress, 2005.
    30. 30)
      • 12. Santaniello, S., Fiengo, G., Glielmo, L., et al: ‘Closed-loop control of deep brain stimulation: a simulation study’, IEEE Trans. Neural Syst. Rehabil. Eng., 2011, 19, (1), pp. 1524.
    31. 31)
      • 17. Vrutangkumar, V.S., Sachin, G., Harish, J.P.M.: ‘Linking increased response time to rest tremors in Parkinson's disease: a feedback control perspective’, 2015, eprint arXiv:1403.0296.
    32. 32)
      • 22. Gillies, A.J.: ‘The role of the subthalamic nucleus in the basal ganglia’, 1995.
    33. 33)
      • 9. Lourens, M.A.J.: ‘Neural network dynamics in Parkinson's disease’ (University of Twente, 2013).
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