AN EFFICIENT STOCHASTIC BASED MODEL FOR SIMULATING MICROELECTRODE RECORDINGS OF THE DEEP BRAIN - Modelling and Analysis

K. J. Weegink, J. J. Varghese, P. A. Bellette, T. Coyne, P. A. Silburn, P. A. Meehan

2012

Abstract

We have developed a computationally efficient stochastic model for simulating microelectrode recordings, including electronic noise and neuronal noise from the local field of 3000 neurons. From this we have shown that for a neuron network model spiking with a stationary Weibull distribution the power spectrum can change from exhibiting periodic behaviour to non-stationary behaviour as the distribution shape is changed. It is shown that the windowed power spectrum of the model follows an analytical result prediction in the range of 100-5000 Hz. The analysis of the simulation is compared to the analysis of real patient interoperative sub-thalamic nucleus microelectrode recordings. The model runs approximately 200 times faster compared to existing models that can reproduce power spectral behaviour. The results indicate that a spectrogram of the real patient recordings can exhibit non-stationary behaviour that can be re-created using this efficient model in real time.

References

  1. Akingba, A. G., Wang, D., Chen, P.-s., Neves, H., & Montemago, C. (2003). Application of nanoelectrodes in recording biopotentials. Paper presented at the IEEE-NANO 2003.
  2. Banta, E. (1964). A note on the correlation function of nonindependent, overlapping pulse trains. Information Theory, IEEE Transactions on, 10(2), 160-161.
  3. Bedard, C., & Destexhe, A. (2009). Macrascopic Models of Local Field Potentials and the Apparent 1/f Noise in Brain Activity. Biophysical Journal, 96, 2589-2603.
  4. Bedard, C., Kroger, H., & Destexhe, A. (2004). Modeling Extracellular Field Potentials and the FrequencyFiltering Properties of Extracellular Space. Biophysical Journal, 86, 1829-1842.
  5. Câteau, H., & Reyes, A. D. (2006). Relation between Single Neuron and Population Spiking Statistics and Effects on Network Activity. Physical Review Letters, 96(5), 058101.
  6. Coyne, T., Silburn, P. A., Cook, R., Silberstein, P., Mellick, G., Sinclair, F.,& Stowell, P. (2006). Rapid subthalamic nucleus deep brain stimulation lead placement utilising CT/MRI fusion, microelectrode recording and test stimulation. Acta Neurochirurgica Suppl(99), 49-50.
  7. Eusebio, A., & Brown, P. (2009). Synchronisation in the beta frequency-band--the bad boy of parkinsonism or an innocent bystander? Experimental Neurology, 217(1), 1-3.
  8. Feng, X.-J., Shea-Brown, E., Greenwald, B., Kosut, R., & Rabitz, H. (2007). Optimal deep brain stimulation of the subthalamic nucleus-a computational study. J. Comput. Neurosci.(23), 265-282.
  9. Garonzik, I. M., Ohara, S., Hua, S. E., & Lenz, F. A. (2004). Microelectrode Techniques: Single-Cell and Field Potential Recordings. In Z. Israel & K. J. Burchiel (Eds.), Microelectrode recordings in movement disorder surgery (Vol. 1). New York: Thieme Medical Publishers, Inc.
  10. Hines, M. L., & Carnevale, N. T. (1997). The NEURON Simulation Environment. Neural Computation, 9(6), 1179-1209. doi: 10.1162/neco.1997.9.6.1179
  11. Izhikevich, E. M. (2007a). Dynamical Systems in Neuroscience. Cambridge: MIT Press.
  12. Izhikevich, E. M. (2007b). Solving the distal reward problem through linkage of STDP and dopamine signaling. Cerebral Cortex, October(17), 2443-2452.
  13. Li, C. (2011). A Model of Neuronal Intrinsic Plasticity. Autonomous Mental Development, IEEE Transactions on, PP(99), 1-1.
  14. Long, L. N., & Fang, G. (2010). A Review of Biologically Plausible Neuron Models for Spiking Neural Networks. Paper presented at the AIAA InfoTech@Aerospace Conference, Atlanta, GA.
  15. McKeegan, D. E. F. (2002). Spontaneous and odour evoked activity in single avian olfactory bulb neurones. Brain Research, 929(1), 48-58. doi: 10.1016/s0006-8993(01)03376-5
  16. McNames, J. (2004). Microelectrode Signal Analysis Techniques for Improved Localization. In Z. Israel & K. J. Burchiel (Eds.), Microelectrode recordings in movement disorder surgery (Vol. 1). New York: Thieme Medical Publishers, Inc.
  17. Meehan, P. A., & Bellette, P. A. (2009). Chaotic Signal Analysis of Parkinson's Disease STN Brain Signals. Paper presented at the Topics in Chaotic Systems.
  18. Meehan, P. A., Bellette, P. A., Bradley, A. P., Castner, J. E., Chenery, H. J., Copland, D. A.,& Silburn, P. A. (2011). Investigation of the Non-Markovity Spectrum as a Cognitive Processing Measure of Deep Brain Microelectrode Recordings. Paper presented at the BIOSIGNALS 2011- International Conference on BioInspired Systems and Signal Processing, Rome, Italy.
  19. Milstein, J., Mormann, F., Fried, I., & Koch, C. (2009). Neuronal Shot Noise and Brownian 1/f2 Behavior in the Local Field Potential. PLoS One, 4(2), e4338 4331-4335.
  20. Perkel, D. H., Gerstein, G. L., & Moore, G. P. (1967a). Neuronal Spike Trains and Stochastic Point Processes I. Biophys J., 7(4), 391-418.
  21. Perkel, D. H., Gerstein, G. L., & Moore, G. P. (1967b). Neuronal Spike Trains and Stochastic Point Processes II. Biophys J., 7(4), 419-440.
  22. Rouse, A. G., Stanslaski, S. R., Cong, P., Jensen, R. M., Afshar, P., Ullestad, D., & Denison, T. J. (2011). A Chronic Generalizaed Bi-directional Brain-Machine Interface. J. Neural Eng., 8(036018).
  23. Rubin, J. E., & Terman, D. (2004). High Frequency Stimulation of the Subthalamic Nucleus Eliminates Pathological Thalamic Rhythmicity in a Computational Model. Journal of Computational Neuroscience(16), 211-235.
  24. Santaniello, S., Fiengo, G., Glielmo, L., & Catapano, G. (2008). A biophysically inspired microelectrode recording-based model for the subthalamic nucleus activity in Parkinson's disease. Biomedical Signal Processing and Control(3), 203-211.
  25. Stevens, C. F., & Zador, A. M. (1998). Input synchrony and the irregular firing of cortical neurons. Nature Neuroscience 1, 210 - 217. doi: 10.1038/659
  26. Terman, D., Rubin, J. E., Yew, A. C., & Wilson, C. J. (2002). Activity Patterns in a Model for the Subthalamopallidal Network of the Basal Ganglia. The Journal of Neuroscience, 7(22), 2963-2976.
  27. Varghese, J. J., Weegink, K. J., Bellette, P. A., Meehan, P. A., Coyne, T., & Silburn, P. A. (2011). Theoretical & Experimental Analysis of the Non Markov Parameter to Detect Low Frequency Synchronisation in Time Series Analysis. Paper presented at the Engineering in Medicine and Biology Society, Annual International Conference of the IEEE, Boston Massachusetts.
Download


Paper Citation


in Harvard Style

J. Weegink K., Varghese J., Bellette P., Coyne T., Silburn P. and Meehan P. (2012). AN EFFICIENT STOCHASTIC BASED MODEL FOR SIMULATING MICROELECTRODE RECORDINGS OF THE DEEP BRAIN - Modelling and Analysis . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2012) ISBN 978-989-8425-89-8, pages 76-84. DOI: 10.5220/0003782400760084


in Bibtex Style

@conference{biosignals12,
author={K. J. Weegink and J. J. Varghese and P. A. Bellette and T. Coyne and P. A. Silburn and P. A. Meehan},
title={AN EFFICIENT STOCHASTIC BASED MODEL FOR SIMULATING MICROELECTRODE RECORDINGS OF THE DEEP BRAIN - Modelling and Analysis},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2012)},
year={2012},
pages={76-84},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003782400760084},
isbn={978-989-8425-89-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2012)
TI - AN EFFICIENT STOCHASTIC BASED MODEL FOR SIMULATING MICROELECTRODE RECORDINGS OF THE DEEP BRAIN - Modelling and Analysis
SN - 978-989-8425-89-8
AU - J. Weegink K.
AU - Varghese J.
AU - Bellette P.
AU - Coyne T.
AU - Silburn P.
AU - Meehan P.
PY - 2012
SP - 76
EP - 84
DO - 10.5220/0003782400760084