Skip to main content
SpringerLink
Log in

Closed-Loop Deep Brain Stimulation for Parkinson’s Disease

  • Chapter
  • First Online:
Surgery for Parkinson's Disease

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Modolo J, et al. Using “smart stimulators” to treat Parkinson’s disease: re-engineering neurostimulation devices. Front Comput Neurosci. 2012;6:69.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Little S, Brown P. What brain signals are suitable for feedback control of deep brain stimulation in Parkinson’s disease? Ann N Y Acad Sci. 2012;1265:9–24.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Hebb AO, et al. Creating the feedback loop: closed-loop neurostimulation. Neurosurg Clin N Am. 2014;25(1):187–204.

    Article  PubMed  Google Scholar 

  4. Priori A. Technology for deep brain stimulation at a gallop. Mov Disord. 2015;30(9):1206–12.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Beudel M, Brown P. Adaptive deep brain stimulation in Parkinson’s disease. Parkinsonism Relat Disord. 2016;22(Suppl 1):S123–6.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Bergey GK, et al. Long-term treatment with responsive brain stimulation in adults with refractory partial seizures. Neurology. 2015;84(8):810–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Iskhakova L, Bergman H. Computational physiology of the basal ganglia, movement disorders and their therapy. In: Falup-Pecurariu C, et al., editors. Movement disorders curricula. Wien: Springer; 2017.

    Google Scholar 

  8. Schultz W, Dayan P, Montague PR. A neural substrate of prediction and reward. Science. 1997;275(5306):1593–9.

    Article  CAS  PubMed  Google Scholar 

  9. Meidahl AC, et al. Adaptive deep brain stimulation for movement disorders: the long road to clinical therapy. Mov Disord. 2017;32(6):810–9.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Khanna P, Carmena JM. Neural oscillations: beta band activity across motor networks. Curr Opin Neurobiol. 2015;32:60–7.

    Article  CAS  PubMed  Google Scholar 

  11. Wilson CJ. Oscillators and oscillations in the basal ganglia. Neuroscientist. 2015;21(5):530–9.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Cagnan H, Duff EP, Brown P. The relative phases of basal ganglia activities dynamically shape effective connectivity in Parkinson’s disease. Brain. 2015;138(Pt 6):1667–78.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Heinrichs-Graham E, Wilson TW. Is an absolute level of cortical beta suppression required for proper movement? Magnetoencephalographic evidence from healthy aging. NeuroImage. 2016;134:514–21.

    Article  PubMed  Google Scholar 

  14. Muthukumaraswamy SD. Functional properties of human primary motor cortex gamma oscillations. J Neurophysiol. 2010;104(5):2873–85.

    Article  PubMed  Google Scholar 

  15. Bergman H, et al. The primate subthalamic nucleus. II. Neuronal activity in the MPTP model of parkinsonism. J Neurophysiol. 1994;72(2):507–20.

    Article  CAS  PubMed  Google Scholar 

  16. Deffains M, et al. Higher neuronal discharge rate in the motor area of the subthalamic nucleus of Parkinsonian patients. J Neurophysiol. 2014;112(6):1409–20.

    Article  PubMed  Google Scholar 

  17. Moshel S, et al. Subthalamic nucleus long-range synchronization-an independent hallmark of human Parkinson’s disease. Front Syst Neurosci. 2013;7:79.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Zaidel A, et al. Subthalamic span of beta oscillations predicts deep brain stimulation efficacy for patients with Parkinson’s disease. Brain. 2010;133(Pt 7):2007–21.

    Article  PubMed  Google Scholar 

  19. Zaidel A, et al. Delimiting subterritories of the human subthalamic nucleus by means of microelectrode recordings and a hidden Markov model. Mov Disord. 2009;24(12):1785–93.

    Article  PubMed  Google Scholar 

  20. Moran A, et al. Two types of neuronal oscillations in the subthalamic nucleus of Parkinson’s disease patients. Mov Disord. 2008;23(1):S118.

    Article  Google Scholar 

  21. Moran A, et al. Real-time refinement of subthalamic nucleus targeting using Bayesian decision-making on the root mean square measure. Mov Disord. 2006;21(9):1425–31.

    Article  PubMed  Google Scholar 

  22. Eitan R, et al. Asymmetric right/left encoding of emotions in the human subthalamic nucleus. Front Syst Neurosci. 2013;7:69.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Valsky D, et al. Stop! Border ahead: automatic detection of subthalamic exit during deep brain stimulation surgery. Mov Disord. 2017;32(1):70–9.

    Article  PubMed  Google Scholar 

  24. Canessa A, et al. Striatal dopaminergic innervation regulates subthalamic Beta-oscillations and cortical-subcortical coupling during movements: preliminary evidence in subjects with Parkinson’s disease. Front Hum Neurosci. 2016;10:611.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Eusebio A, et al. Deep brain stimulation can suppress pathological synchronisation in Parkinsonian patients. J Neurol Neurosurg Psychiatry. 2011;82(5):569–73.

    Article  CAS  PubMed  Google Scholar 

  26. Kuhn AA, et al. Pathological synchronisation in the subthalamic nucleus of patients with Parkinson’s disease relates to both bradykinesia and rigidity. Exp Neurol. 2009;215(2):380–7.

    Article  PubMed  Google Scholar 

  27. Neumann WJ, et al. Subthalamic synchronized oscillatory activity correlates with motor impairment in patients with Parkinson’s disease. Mov Disord. 2016;31(11):1748–51.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Little S, et al. Beta band stability over time correlates with Parkinsonian rigidity and bradykinesia. Exp Neurol. 2012;236(2):383–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Kuhn AA, et al. Reduction in subthalamic 8-35 Hz oscillatory activity correlates with clinical improvement in Parkinson’s disease. Eur J Neurosci. 2006;23(7):1956–60.

    Article  PubMed  Google Scholar 

  30. Little S, Brown P. Closed-loop programming: human perspective. In: Vitek J, editor. Deep brain stimulation: technology and applications. London: Future Medicine; 2014. p. 79–90.

    Google Scholar 

  31. Deffains M, Iskhakova L, Katabi S, Israel Z, Bergman H. Longer β oscillatory episodes reliably identify pathological subthalamic activity in Parkinsonism. Mov Disord. 2018.

    Google Scholar 

  32. Cagnan H, et al. Stimulating at the right time: phase-specific deep brain stimulation. Brain. 2016;140(1):132–45.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Tinkhauser G, et al. The modulatory effect of adaptive deep brain stimulation on beta bursts in Parkinson’s disease. Brain. 2017;140(4):1053–67.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Little S, et al. Bilateral adaptive deep brain stimulation is effective in Parkinson’s disease. J Neurol Neurosurg Psychiatry. 2016;87(7):717–21.

    Article  PubMed  Google Scholar 

  35. Little S, et al. Adaptive deep brain stimulation in advanced Parkinson disease. Ann Neurol. 2013;74(3):449–57.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Swann NC, et al. Gamma oscillations in the hyperkinetic state detected with chronic human brain recordings in Parkinson's disease. J Neurosci. 2016;36(24):6445–58.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Lenz FA, et al. Single unit analysis of the human ventral thalamic nuclear group: correlation of thalamic “tremor cells” with the 3-6 Hz component of Parkinsonian tremor. J Neurosci. 1988;8(3):754–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Buzsaki G, Anastassiou CA, Koch C. The origin of extracellular fields and currents--EEG, ECoG, LFP and spikes. Nat Rev Neurosci. 2012;13(6):407–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Michmizos KP, Sakas D, Nikita KS. Prediction of the timing and the rhythm of the Parkinsonian subthalamic nucleus neural spikes using the local field potentials. IEEE Trans Inf Technol Biomed. 2012;16(2):190–7.

    Article  PubMed  Google Scholar 

  40. Kuhn AA, et al. The relationship between local field potential and neuronal discharge in the subthalamic nucleus of patients with Parkinson’s disease. Exp Neurol. 2005;194(1):212–20.

    Article  PubMed  Google Scholar 

  41. Weinberger M, et al. Beta oscillatory activity in the subthalamic nucleus and its relation to dopaminergic response in Parkinson’s disease. J Neurophysiol. 2006;96(6):3248–56.

    Article  PubMed  Google Scholar 

  42. Winestone JS, et al. The use of macroelectrodes in recording cellular spiking activity. J Neurosci Methods. 2012;206(1):34–9.

    Article  PubMed  Google Scholar 

  43. Marmor O, et al. Local vs. volume conductance activity of field potentials in the human subthalamic nucleus. J Neurophysiol. 2017; https://doi.org/10.1152/jn.00756.2016.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Giannicola G, et al. Subthalamic local field potentials after seven-year deep brain stimulation in Parkinson’s disease. Exp Neurol. 2012;237(2):312–7.

    Article  PubMed  Google Scholar 

  45. Priori A, et al. Adaptive deep brain stimulation (aDBS) controlled by local field potential oscillations. Exp Neurol. 2013;245:77–86.

    Article  PubMed  Google Scholar 

  46. Afshar P, et al. A translational platform for prototyping closed-loop neuromodulation systems. Front Neural Circuits. 2012;6:117.

    PubMed  Google Scholar 

  47. Rasche D, Tronnier VM. Clinical significance of invasive motor cortex stimulation for trigeminal facial neuropathic pain syndromes. Neurosurgery. 2016;79(5):655–66.

    Article  PubMed  Google Scholar 

  48. Panov F, et al. Intraoperative electrocorticography for physiological research in movement disorders: principles and experience in 200 cases. J Neurosurg. 2016;126(1):122–31.

    Google Scholar 

  49. Albin RL, Young AB, Penney JB. The functional anatomy of basal ganglia disorders. Trends Neurosci. 1989;12(10):366–75.

    Article  CAS  PubMed  Google Scholar 

  50. Cilia R, et al. Extradural motor cortex stimulation in Parkinson’s disease. Mov Disord. 2007;22(1):111–4.

    Article  PubMed  Google Scholar 

  51. De Rose M, et al. Motor cortex stimulation in Parkinson’s disease. Neurol Res Int. 2012;2012:502096.

    PubMed  PubMed Central  Google Scholar 

  52. Lefaucheur JP. Treatment of Parkinson’s disease by cortical stimulation. Expert Rev Neurother. 2009;9(12):1755–71.

    Article  PubMed  Google Scholar 

  53. Munno D, et al. Neuropsychologic assessment of patients with advanced Parkinson disease submitted to extradural motor cortex stimulation. Cogn Behav Neurol. 2007;20(1):1–6.

    Article  PubMed  Google Scholar 

  54. Zwartjes DG, et al. Motor cortex stimulation for Parkinson’s disease: a modelling study. J Neural Eng. 2012;9(5):056005.

    Article  PubMed  Google Scholar 

  55. Bentivoglio AR, et al. Unilateral extradural motor cortex stimulation is safe and improves Parkinson disease at 1 year. Neurosurgery. 2012;71(4):815–25.

    Article  PubMed  Google Scholar 

  56. Moro E, et al. Unilateral subdural motor cortex stimulation improves essential tremor but not Parkinson’s disease. Brain. 2011;134(Pt 7):2096–105.

    Article  PubMed  Google Scholar 

  57. Kern K. et al. Detecting a cortical fingerprint of Parkinson’s disease for closed-loop neuromodulation. Front Neurosci. 2016;10(110).

    Google Scholar 

  58. Boakye M. Implications of neuroplasticity for neurosurgeons. Surg Neurol. 2009;71(1):5–10.

    Article  PubMed  Google Scholar 

  59. Rosin B, et al. Closed-loop deep brain stimulation is superior in ameliorating parkinsonism. Neuron. 2011;72(2):370–84.

    Article  CAS  PubMed  Google Scholar 

  60. Boraud T. Closed-loop stimulation: the future of surgical therapy of brain disorders? Mov Disord. 2012;27(2):200.

    Article  PubMed  Google Scholar 

  61. Ryapolova-Webb E, et al. Chronic cortical and electromyographic recordings from a fully implantable device: preclinical experience in a nonhuman primate. J Neural Eng. 2014;11(1):016009.

    Article  PubMed  Google Scholar 

  62. Khanna P, et al. Neurofeedback control in Parkinsonian patients using electrocortigraphy signals accessed wirelessly with a chronic, fully implanted device. IEEE Trans Neural Syst Rehabil Eng. 2016;25(10):1715–24.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Little S, et al. Adaptive deep brain stimulation for Parkinson’s disease demonstrates reduced speech side effects compared to conventional stimulation in the acute setting. J Neurol Neurosurg Psychiatry. 2016;87(12):1388–9.

    Article  PubMed  Google Scholar 

  64. Rosa M, et al. Adaptive deep brain stimulation in a freely moving Parkinsonian patient. Mov Disord. 2015;30(7):1003–5.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Rosa M, et al. Adaptive deep brain stimulation controls levodopa-induced side effects in Parkinsonian patients. Mov Disord. 2017;32:628.

    Article  PubMed  PubMed Central  Google Scholar 

  66. Campbell GA, Crawford IL. A gated electronic switch for stimulation and recording with a single electrode. Brain Res Bull. 1980;5(4):485–6.

    Article  CAS  PubMed  Google Scholar 

  67. Ferrer AZ, Fernández-Guardiola A, Solís H. Electronic circuit breaker for recording and stimulation from same electrode. Electroencephalogr Clin Neurophysiol. 1978;45(2):299–301.

    Article  CAS  PubMed  Google Scholar 

  68. Hatzopoulos A, Theophilidis G. A simple electronic unit allowing extracellular recording and stimulation through the same wire hook or suction electrode. J Neurosci Methods. 1984;11(3):169–72.

    Article  CAS  PubMed  Google Scholar 

  69. Rossi L, et al. An electronic device for artefact suppression in human local field potential recordings during deep brain stimulation. J Neural Eng. 2007;4(2):96–106.

    Article  CAS  PubMed  Google Scholar 

  70. Stanslaski S, et al. Design and validation of a fully implantable, chronic, closed-loop neuromodulation device with concurrent sensing and stimulation. IEEE Trans Neural Syst Rehabil Eng. 2012;20(4):410–21.

    Article  PubMed  Google Scholar 

  71. Al-ani T, et al. Automatic removal of high-amplitude stimulus artefact from neuronal signal recorded in the subthalamic nucleus. J Neurosci Methods. 2011;198(1):135–46.

    Article  PubMed  Google Scholar 

  72. Harding GW. A method for eliminating the stimulus artifact from digital recordings of the direct cortical response. Comput Biomed Res. 1991;24(2):183–95.

    Article  CAS  PubMed  Google Scholar 

  73. Williams NR, Foote KD, Okun MS. STN vs. GPi deep brain stimulation: translating the rematch into clinical practice. Mov Disord Clin Pract. 2014;1(1):24–35.

    Article  PubMed  PubMed Central  Google Scholar 

  74. Odekerken VJ, et al. GPi vs STN deep brain stimulation for Parkinson disease: three-year follow-up. Neurology. 2016;86(8):755–61.

    Article  CAS  PubMed  Google Scholar 

  75. Odekerken VJ, et al. Subthalamic nucleus versus globus pallidus bilateral deep brain stimulation for advanced Parkinson's disease (NSTAPS study): a randomised controlled trial. Lancet Neurol. 2013;12(1):37–44.

    Article  PubMed  Google Scholar 

  76. Weaver FM, et al. Randomized trial of deep brain stimulation for Parkinson disease: thirty-six-month outcomes. Neurology. 2012;79(1):55–65.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Okun MS, Foote KD. Subthalamic nucleus vs globus pallidus interna deep brain stimulation, the rematch: will pallidal deep brain stimulation make a triumphant return? Arch Neurol. 2005;62(4):533–6.

    Article  PubMed  Google Scholar 

  78. Arkadir D, et al. In quest of the oscillator(s) in tremor: are we getting closer? Brain. 2014;137(Pt 12):3102–3.

    Article  PubMed  Google Scholar 

  79. Lee RG, Stein RB. Resetting of tremor by mechanical perturbations: a comparison of essential tremor and Parkinsonian tremor. Ann Neurol. 1981;10(6):523–31.

    Article  CAS  PubMed  Google Scholar 

  80. Coenen VA, et al. One-pass deep brain stimulation of dentato-rubro-thalamic tract and subthalamic nucleus for tremor-dominant or equivalent type Parkinson’s disease. Acta Neurochir. 2016;158(4):773–81.

    Article  PubMed  Google Scholar 

  81. Johnson LA, et al. Closed-loop deep brain stimulation effects on Parkinsonian motor symptoms in a non-human primate – is Beta enough? Brain Stimul. 2016;9(6):892–6.

    Article  PubMed  PubMed Central  Google Scholar 

  82. Meissner W, et al. Subthalamic high frequency stimulation resets subthalamic firing and reduces abnormal oscillations. Brain. 2005;128(Pt 10):2372–82.

    Article  PubMed  Google Scholar 

  83. Tass PA. A model of desynchronizing deep brain stimulation with a demand-controlled coordinated reset of neural subpopulations. Biol Cybern. 2003;89(2):81–8.

    Article  PubMed  Google Scholar 

  84. Tass PA. Phase resetting in medicine and biology: stochastic modelling and data analysis. Berlin: Springer; 1999.

    Book  Google Scholar 

  85. Tass PA, et al. Coordinated reset has sustained aftereffects in Parkinsonian monkeys. Ann Neurol. 2012;72(5):816–20.

    Article  PubMed  Google Scholar 

  86. Wang J, et al. Coordinated reset deep brain stimulation of subthalamic nucleus produces long-lasting, dose-dependent motor improvements in the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine non-human primate model of parkinsonism. Brain Stimul. 2016;9(4):609–17.

    Article  PubMed  Google Scholar 

  87. Adamchic I, et al. Coordinated reset neuromodulation for Parkinson’s disease: proof-of-concept study. Mov Disord. 2014;29(13):1679–84.

    Article  PubMed  PubMed Central  Google Scholar 

  88. Montaseri G, et al. Synchrony suppression in ensembles of coupled oscillators via adaptive vanishing feedback. Chaos. 2013;23(3):033122.

    Article  PubMed  Google Scholar 

  89. Popovych OV, et al. Pulsatile desynchronizing delayed feedback for closed-loop deep brain stimulation. PLoS One. 2017;12(3):e0173363.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  90. Popovych OV, Lysyansky B, Tass PA. Closed-loop deep brain stimulation by pulsatile delayed feedback with increased gap between pulse phases. Sci Rep. 2017;7(1):1033.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  91. Brocker DT, et al. Improved efficacy of temporally non-regular deep brain stimulation in Parkinson’s disease. Exp Neurol. 2013;239:60–7.

    Article  PubMed  Google Scholar 

  92. Brocker DT, et al. Optimized temporal pattern of brain stimulation designed by computational evolution. Sci Transl Med. 2017;9(371).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  93. Shimamoto SA, et al. Subthalamic nucleus neurons are synchronized to primary motor cortex local field potentials in Parkinson's disease. J Neurosci. 2013;33(17):7220–33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. de Hemptinne C, et al. Exaggerated phase-amplitude coupling in the primary motor cortex in Parkinson disease. Proc Natl Acad Sci U S A. 2013;110(12):4780–5.

    Article  PubMed  PubMed Central  Google Scholar 

  95. de Hemptinne C, et al. Therapeutic deep brain stimulation reduces cortical phase-amplitude coupling in Parkinson’s disease. Nat Neurosci. 2015;18(5):779–86.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  96. Gunduz A, et al. Proceedings of the second annual deep brain stimulation think tank: what’s in the pipeline. Int J Neurosci. 2015;125(7):475–85.

    Article  PubMed  PubMed Central  Google Scholar 

  97. Swann NC, et al. Chronic multisite brain recordings from a totally implantable bidirectional neural interface: experience in 5 patients with Parkinson’s disease. J Neurosurg. 2017; 128(2):605–16.

    Google Scholar 

  98. Lee KH, et al. Neurotransmitter release from high-frequency stimulation of the subthalamic nucleus. J Neurosurg. 2004;101(3):511–7.

    Article  CAS  PubMed  Google Scholar 

  99. Shon YM, et al. High frequency stimulation of the subthalamic nucleus evokes striatal dopamine release in a large animal model of human DBS neurosurgery. Neurosci Lett. 2010;475(3):136–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Bennet KE, et al. A diamond-based electrode for detection of neurochemicals in the human brain. Front Hum Neurosci. 2016;10:102.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  101. Jang DP, et al. Paired pulse voltammetry for differentiating complex analytes. Analyst. 2012;137(6):1428–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Koehne JE, et al. Carbon nanofiber electrode array for electrochemical detection of dopamine using fast scan cyclic voltammetry. Analyst. 2011;136(9):1802–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Chang SY, et al. Wireless fast-scan cyclic voltammetry measurement of histamine using WINCS--a proof-of-principle study. Analyst. 2012;137(9):2158–65.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Chang SY, et al. Development of the Mayo investigational neuromodulation control system: toward a closed-loop electrochemical feedback system for deep brain stimulation. J Neurosurg. 2013;119(6):1556–65.

    Article  PubMed  PubMed Central  Google Scholar 

  105. Grahn PJ, et al. A neurochemical closed-loop controller for deep brain stimulation: toward individualized smart neuromodulation therapies. Front Neurosci. 2014;8:169.

    PubMed  PubMed Central  Google Scholar 

  106. Min HK, et al. Dopamine release in the nonhuman primate caudate and putamen depends upon site of stimulation in the subthalamic nucleus. J Neurosci. 2016;36(22):6022–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Graupe D, et al. Adaptively controlling deep brain stimulation in essential tremor patient via surface electromyography. Neurol Res. 2010;32(9):899–904.

    Article  PubMed  Google Scholar 

  108. Shukla P, et al. A neural network-based design of an on-off adaptive control for deep brain stimulation in movement disorders. Conf Proc IEEE Eng Med Biol Soc. 2012;2012:4140–3.

    Google Scholar 

  109. Malekmohammadi M, et al. Kinematic adaptive deep brain stimulation for resting tremor in Parkinson’s disease. Mov Disord. 2016;31(3):426–8.

    Article  PubMed  Google Scholar 

  110. Khobragade N, Graupe D, Tuninetti D. Towards fully automated closed-loop deep brain stimulation in Parkinson’s disease patients: a LAMSTAR-based tremor predictor. Conf Proc IEEE Eng Med Biol Soc. 2015;2015:2616–9.

    Google Scholar 

  111. Contarino MF, et al. Directional steering: a novel approach to deep brain stimulation. Neurology. 2014;83(13):1163–9.

    Article  PubMed  Google Scholar 

  112. Pollo C, et al. Directional deep brain stimulation: an intraoperative double-blind pilot study. Brain. 2014;137(7):2015–26.

    Article  PubMed  Google Scholar 

  113. Bour LJ, et al. Directional recording of subthalamic spectral power densities in Parkinson’s disease and the effect of steering deep brain stimulation. Brain Stimul. 2015;8(4):730–41.

    Article  CAS  PubMed  Google Scholar 

  114. Fernández-García C, et al. Directional local field potential recordings for symptom-specific optimization of deep brain stimulation. Mov Disord. 2017;32:626.

    Article  PubMed  Google Scholar 

  115. Vansteensel MJ, et al. Fully implanted brain-computer interface in a locked-in patient with ALS. N Engl J Med. 2016;375(21):2060–6.

    Article  PubMed  PubMed Central  Google Scholar 

  116. Gates B. The road ahead. London: Penguin Books; 1995.

    Google Scholar 

  117. Palmerini L, et al. A wavelet-based approach to fall detection. Sensors (Basel). 2015;15(5):11575–86.

    Article  Google Scholar 

  118. Wu F, et al. Development of a wearable-sensor-based fall detection system. Int J Telemed Appl. 2015;2015:576364.

    PubMed  PubMed Central  Google Scholar 

  119. Schwenk M, et al. Wearable sensor-based in-home assessment of gait, balance, and physical activity for discrimination of frailty status: baseline results of the Arizona frailty cohort study. Gerontology. 2015;61(3):258–67.

    Article  PubMed  Google Scholar 

  120. Wikipedia. Smart City. Available from: https://en.wikipedia.org/wiki/Smart_city.

  121. Jalal A, Kamal S, Kim D. A depth video sensor-based life-logging human activity recognition system for elderly care in smart indoor environments. Sensors (Basel). 2014;14(7):11735–59.

    Article  Google Scholar 

  122. Siddiqi MH, et al. Video-based human activity recognition using multilevel wavelet decomposition and stepwise linear discriminant analysis. Sensors (Basel). 2014;14(4):6370–92.

    Article  Google Scholar 

  123. Kostikis N, et al. Smartphone-based evaluation of Parkinsonian hand tremor: quantitative measurements vs clinical assessment scores. Conf Proc IEEE Eng Med Biol Soc. 2014;2014:906–9.

    CAS  Google Scholar 

  124. Parviainen J, et al. Adaptive activity and environment recognition for mobile phones. Sensors (Basel). 2014;14(11):20753–78.

    Article  Google Scholar 

  125. Shoaib M, et al. Fusion of smartphone motion sensors for physical activity recognition. Sensors (Basel). 2014;14(6):10146–76.

    Article  Google Scholar 

  126. Garcia-Ceja E, et al. Long-term activity recognition from wristwatch accelerometer data. Sensors (Basel). 2014;14(12):22500–24.

    Article  Google Scholar 

  127. Wile DJ, Ranawaya R, Kiss ZH. Smart watch accelerometry for analysis and diagnosis of tremor. J Neurosci Methods. 2014;230:1–4.

    Article  PubMed  Google Scholar 

  128. Buchman AS, et al. Associations between quantitative mobility measures derived from components of conventional mobility testing and Parkinsonian gait in older adults. PLoS One. 2014;9(1):e86262.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  129. Cancela J, et al. Wearability assessment of a wearable system for Parkinson’s disease remote monitoring based on a body area network of sensors. Sensors (Basel). 2014;14(9):17235–55.

    Article  Google Scholar 

  130. Heldman DA, et al. Clinician versus machine: reliability and responsiveness of motor endpoints in Parkinson’s disease. Parkinsonism Relat Disord. 2014;20(6):590–5.

    Article  PubMed  PubMed Central  Google Scholar 

  131. Mera T, et al. Kinematic optimization of deep brain stimulation across multiple motor symptoms in Parkinson’s disease. J Neurosci Methods. 2011;198(2):280–6.

    Article  PubMed  PubMed Central  Google Scholar 

  132. Mera TO, et al. Feasibility of home-based automated Parkinson’s disease motor assessment. J Neurosci Methods. 2012;203(1):152–6.

    Article  PubMed  Google Scholar 

  133. Sanchez-Ferro A, Maetzler W. Advances in sensor and wearable technologies for Parkinson’s disease. Mov Disord. 2016;31(9):1257.

    Article  PubMed  Google Scholar 

  134. Campos-Romo A, et al. Quantitative evaluation of MPTP-treated nonhuman Parkinsonian primates in the HALLWAY task. J Neurosci Methods. 2009;177(2):361–8.

    Article  CAS  PubMed  Google Scholar 

  135. Chien SL, et al. The efficacy of quantitative gait analysis by the GAITRite system in evaluation of Parkinsonian bradykinesia. Parkinsonism Relat Disord. 2006;12(7):438–42.

    Article  PubMed  Google Scholar 

  136. Hubble RP, et al. Wearable sensor use for assessing standing balance and walking stability in people with Parkinson’s disease: a systematic review. PLoS One. 2015;10(4):e0123705.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  137. Pulliam CL, et al. Motion sensor strategies for automated optimization of deep brain stimulation in Parkinson’s disease. Parkinsonism Relat Disord. 2015;21(4):378–82.

    Article  PubMed  PubMed Central  Google Scholar 

  138. Mera TO, et al. Objective quantification of arm rigidity in MPTP-treated primates. J Neurosci Methods. 2009;177(1):20–9.

    Article  CAS  PubMed  Google Scholar 

  139. Endo T, et al. A novel method for systematic analysis of rigidity in Parkinson’s disease. Mov Disord. 2009;24(15):2218–24.

    Article  PubMed  Google Scholar 

  140. Prochazka A, et al. Measurement of rigidity in Parkinson’s disease. Mov Disord. 1997;12(1):24–32.

    Article  CAS  PubMed  Google Scholar 

  141. Baker JJ, et al. Continuous detection and decoding of dexterous finger flexions with implantable myoelectric sensors. IEEE Trans Neural Syst Rehabil Eng. 2010;18(4):424–32.

    Article  PubMed  Google Scholar 

  142. Li Y, et al. A low power, parallel wearable multi-sensor system for human activity evaluation. Proc IEEE Annu Northeast Bioeng Conf. 2015; 2015.

    Google Scholar 

  143. Tzallas AT, et al. PERFORM: a system for monitoring, assessment and management of patients with Parkinson’s disease. Sensors (Basel). 2014;14(11):21329–57.

    Article  Google Scholar 

  144. Lieber B, et al. Motion sensors to assess and monitor medical and surgical management of Parkinson disease. World Neurosurg. 2015;84(2):561–6.

    Article  PubMed  Google Scholar 

  145. Papapetropoulos S, et al. Objective monitoring of tremor and bradykinesia during DBS surgery for Parkinson disease. Neurology. 2008;70(15):1244–9.

    Article  CAS  PubMed  Google Scholar 

  146. Marceglia S, et al. Web-based telemonitoring and delivery of caregiver support for patients with Parkinson disease after deep brain stimulation: protocol. JMIR Res Protoc. 2015;4(1):e30.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Medical Neurobiology (Physiology), Institute of Medical Research Israel-Canada (IMRIC), Edmond and Lily Safra Center (ELSC) for Brain Research, The Hebrew University, Jerusalem, Israel

    R. Eitan & H. Bergman

  2. Department of Neurosurgery, Hadassah University Hospital, Jerusalem, Israel

    H. Bergman & Z. Israel

Authors
  1. R. Eitan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. H. Bergman
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Z. Israel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Z. Israel .

Editor information

Editors and Affiliations

  1. Hackensack Meridian School of Medicine at Seton Hall University, Nutley, NJ, USA

    Robert R. Goodman

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Eitan, R., Bergman, H., Israel, Z. (2019). Closed-Loop Deep Brain Stimulation for Parkinson’s Disease. In: Goodman, R. (eds) Surgery for Parkinson's Disease. Springer, Cham. https://doi.org/10.1007/978-3-319-23693-3_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-23693-3_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23692-6

  • Online ISBN: 978-3-319-23693-3

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation