Computational Investigation of Adaptive Deep Brain Stimulation

Christopher Y. Thang, Paul A. Meehan

2015

Abstract

Deep Brain Stimulation of the sub-thalamic nucleus (STN) has been proven to be effective at reducing symptoms of patients with Parkinson’s disease (PD). Currently an implanted pulse generator provides chronic electrical stimulation to the STN via an electrode and the stimulation parameters are chosen heuristically. Closed-loop Deep Brain Stimulation (DBS) has been proposed as an improvement to this, utilising neural signal feedback to select stimulation parameters, adjust the duration of stimulation and achieve better patient outcomes more efficiently. In this research, potential neural feedback signals were investigated using a computational simulation of the basal ganglia. It was found that the interspike-interval in the globus pallidus externus provided a possible metric for ‘on’ and ‘off’ states in Parkinson’s disease. This parameter was subsequently implemented as neural feedback in an adaptive closed-loop DBS simulation and was shown to be effective. In particular, the thalamic relaying capability was evaluated using an Error Index (EI) and the adaptive DBS was found to reduce the EI to 2%, which compared with 20% for the PD case without DBS. This was achieved using 58% of the stimulation time used during continuous DBS, indicating a large reduction in DBS energy requirements. This selection and implementation of a potential neural feedback parameter will assist in developing improved implanted DBS pulse generators.

References

  1. Calabresi, P., B. Picconi, A. Tozzi, V. Ghiglieri and M. Di Filippo (2014). "Direct and indirect pathways of basal ganglia: a critical reappraisal." Nat Neurosci 17(8): 1022-1030.
  2. Carron, R., A. Chaillet, A. Filipchuk, W. Pasillas-Lépine and C. Hammond (2013). "Closing the loop of deep brain stimulation." Frontiers in Systems Neuroscience 7: 112.
  3. Coyne, T., P. Silburn, R. Cook, P. Silberstein, G. Mellick, F. Sinclair, G. Fracchia, D. Wasson and P. Stanwell (2006). "Rapid subthalamic nucleus deep brain stimulation lead placement utilising CT/MRI fusion, microelectrode recording and test stimulation." Acta Neurochirurgica Supplement 99: 49-50.
  4. Golomb, D. and J. Rinzel (1993). "Dynamics of globally coupled inhibitory neurons with heterogeneity." Physical Review E 48(6): 4810-4814.
  5. Huntington's Outreach Project for Education, S. U. (2010). "HOPES Brain Tutorial - Basal Ganglia." Retrieved 14 November, 2014, from http://hopes .stanford.edu/sites/hopes/files/f_ab18bslgang.gif.
  6. Kühn, A. A., A. Kupsch, G. H. Schneider and P. Brown (2006). "Reduction in subthalamic 8-35 Hz oscillatory activity correlates with clinical improvement in Parkinson's disease." European Journal of Neuroscience 23(7): 1956-1960.
  7. Little, S., J. FitzGerald, A. L. Green, T. Z. Aziz, P. Brown, A. Pogosyan, S. Neal, B. Zavala, L. Zrinzo, M. Hariz, T. Foltynie, P. Limousin and K. Ashkan (2013). "Adaptive deep brain stimulation in advanced Parkinson disease." Annals of Neurology 74(3): 449- 457.
  8. Marjama-Lyons, J. and M. Okun (2014). Parkinson's Disease: Guide to Deep Brain Stimulation Therapy, National Parkinson Foundation.
  9. McConnell, G. C., R. Q. So, J. D. Hilliard, P. Lopomo and W. M. Grill (2012). "Effective deep brain stimulation suppresses low-frequency network oscillations in the basal ganglia by regularizing neural firing patterns." The Journal of neuroscience : the official journal of the Society for Neuroscience 32(45): 15657-15668.
  10. Meehan, P. A., P. A. Bellette, A. P. Bradley, J. E. Castner, H. J. Chenery, D. A. Copland, J. D. Varghese, T. Coyne and P. A. Silburn (2011). Investigation of the non-markovity spectrum as a cognitive processing measure of deep brain microelectrode recordings. Biosignals 2011. Rome, Italy, SciTePress: 144-150.
  11. Rubin, J. E. and D. Terman (2004). "High frequency stimulation of the subthalamic nucleus eliminates pathological thalamic rhythmicity in a computational model." Journal of Computational Neuroscience 16(3): 211-235.
  12. So, R. Q., A. R. Kent and W. M. Grill (2012). "Relative contributions of local cell and passing fiber activation and silencing to changes in thalamic fidelity during deep brain stimulation and lesioning: a computational modeling study." Journal of Computational Neuroscience 32(3): 499-519.
  13. Terman, D., J. E. Rubin, A. C. Yew and C. J. Wilson (2002). "Activity patterns in a model for the subthalamopallidal network of the basal ganglia." The Journal of Neuroscience 22(7): 2963-2976.
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Paper Citation


in Harvard Style

Y. Thang C. and A. Meehan P. (2015). Computational Investigation of Adaptive Deep Brain Stimulation . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2015) ISBN 978-989-758-069-7, pages 66-75. DOI: 10.5220/0005212400660075


in Bibtex Style

@conference{biosignals15,
author={Christopher Y. Thang and Paul A. Meehan},
title={Computational Investigation of Adaptive Deep Brain Stimulation},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2015)},
year={2015},
pages={66-75},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005212400660075},
isbn={978-989-758-069-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2015)
TI - Computational Investigation of Adaptive Deep Brain Stimulation
SN - 978-989-758-069-7
AU - Y. Thang C.
AU - A. Meehan P.
PY - 2015
SP - 66
EP - 75
DO - 10.5220/0005212400660075