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Journal of Computational Neuroscience

, Volume 29, Issue 1–2, pp 63–72 | Cite as

Application of matched filtering to identify behavioral modulation of brain oscillations

  • Catherine Stamoulis
  • Andrew G. Richardson
Article

Abstract

Brain oscillations modulated by motor behaviors are coupled to steady-state and other potentially unrelated to movement oscillations, with energy in the same frequency bands as the signals of interest. We applied matched filtering, a quasi-optimum signal detection technique, to decouple and extract movement-related signals from local field potentials (LFPs) recorded in monkey motor cortical areas during the execution of a visually instructed reach-out task. Using a matched-filterbank, we examined coupling and interference of pre-movement and initial steady-state oscillations with movement-induced signals. Once these signal contributions were eliminated, we were able to identify significant correlations of the residual signals with behavioral parameters, which appeared attenuated by pre-movement signal interference in the raw LFPs. Specifically, the maximum and minimum amplitudes of filtered LFPs were directly modulated by peak movement velocity and micro-movements, respectively, identified in recorded hand velocity profiles. In addition, we identified phase correlations between signals during the delay (when the instructional cue was presented) and movement intervals, as well as modulation of LFP phase by movement direction. For pairs of orthogonal movement directions, corresponding LFP signals were consistently out of phase. Finally, β-band energy which is typically reduced during movement execution, possibly partly due to destructive interference between the modulated by behavior signal and unrelated oscillations, appeared to be recovered in the filtered signals.

Keywords

Motor system Local field potentials Brain oscillations Matched filtering 

References

  1. Allen, R. L., & Mills, D. W. (2004). Signal analysis: Time, frequency, scale and structure. New York: Wiley.Google Scholar
  2. Babiloni, C., Carducci, F., Cincotti, F., Rossini, P., Neuper, C., Pfurtscheller, G., et al. (1999). Human movement related potentials vs desynchronization of EEG alpha rhythm: A high resolution EEG study. Neuroimage, 10, 658–665.CrossRefPubMedGoogle Scholar
  3. Baker, S. N., Kilner, J. M., Pinches, E. M., & Lemon, R. N. (1999). The role of synchrony and oscillations in the motor output. Experimental Brain Research, 128, 109–117.CrossRefGoogle Scholar
  4. Baker, S. N., Pinches, E. M., & Lemon, N. (2003). Synchronization in monkey motor cortex during a precision task. II. Effect of oscillatory activity on corticospinal output. Journal of Neurophysiology, 89, 1941–1953.CrossRefPubMedGoogle Scholar
  5. Funk, C. C., Theiler, J, Roberts, D. A., & Borel, C. C. (2001). Clustering to improve matched filter detection of weak gas plumes in hyperspectral thermal imagery. IEEE Transactions on Geoscience and Remote Sensing, 39(7), 1410–1420.CrossRefGoogle Scholar
  6. Hatsopoulos, N. G., et al. (2006). Decoding continuous and discrete motor behaviors using motor and premotor cortical ensembles. Journal of Neurophysiology, 96, 1658–1663.CrossRefGoogle Scholar
  7. Juday, R. D. (2001). Generality of matched-filtering and minimum Euclidean distance projection for optical pattern recognition. Journal of the Optical Society of America, 18(8), 1882–1896.CrossRefPubMedGoogle Scholar
  8. Lopes da Silva, F. H., Ten Brock, W., & Van Hulten, K. (1976). EEG non-stationarities detected by inverse filtering in scalp and cortical recordings of epileptics. In P. Kellayway, & I. Petersen (Eds.), Quantitative analytic studies in epilepsy (pp. 375–387). New York: Raven.Google Scholar
  9. Mitzdorf, U. (1985) Current source-density method and application in cat cerebral cortex: Investigation of evoked potentials and EEG phenomena. Physiological Reviews, 65, 37–100.PubMedGoogle Scholar
  10. Niedermeyr, E., & Lopes da Silva, F. H. (2004). Electroencephalography: Basic principles, clinical applications and related fields (5th ed.). Philadelphia: Lippincott, Williams & Wilkins.Google Scholar
  11. O’Leary, J., & Hatsopoulos, N. (2006). Early visuomotor representations revealed from evoked local field potentials in motor and premotor cortical areas. Journal of Neurophysiology, 96, 1492–1506.CrossRefPubMedGoogle Scholar
  12. Richardson, A. G. (2007). Role of the precentral cortex in adapting behavior to different mechanical environments. Ph.D. Thesis, Massachusetts Institute of Technology.Google Scholar
  13. Rickert, J., et al. (2005). Encoding of movement direction in different frequency ranges of motor cortical local field potentials. Journal of Neuroscience, 25(3), 8815–8824.CrossRefPubMedGoogle Scholar
  14. Roux, S., et al. (2006). The pre-movement component of motor cortical local field potentials reflects the level of expectancy. Behavioural Brain Research, 169, 335–351.CrossRefPubMedGoogle Scholar
  15. Rubino, D., Robins, K. A., & Hatsopoulos, N. G. (2006). Propagating waves mediate information transfer in the motor cortex. Nature Neuroscience, 9(12), 1549–1557.CrossRefPubMedGoogle Scholar
  16. Sanes, J. N., & Donoghue, J. P. (1993). Oscillations in local field potentials of the primate motor cortex during voluntary movement. Proceedings of the National Academy of Sciences of the United States of America, 90, 4470–4474.CrossRefPubMedGoogle Scholar
  17. Sirota, A., Montgomery, S., Isomura, Y., Zugaro, M., & Buzsaki, G. (2008). Entrainment of neocortical neurons and gamma oscillations by the hippocampal theta rhythm. Neuron, 60(4), 683–697.CrossRefPubMedGoogle Scholar
  18. Spiesberger, J. L. (2001). The matched-lag filter: Detecting broadband signals with auto- and cross-correlation functions. Journal of the Acoustic Society of America, 109(5), Pt. 1, 1997–2007.CrossRefGoogle Scholar
  19. Stamoulis, C., & Chang, B. S. (2009). Application of matched-filtering to extract EEG features and decouple signal contributions from multiple seizures in brain malformations. In Proceedings of the 4th international conference on neural engineering. IEEE.Google Scholar
  20. Van Trees, H. L. (2003). Detection, estimation and modulation theory. New York: Wiley.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Department of BiostatisticsHarvard School of Public HealthBostonUSA
  2. 2.McGovern Institute for Brain ResearchMITCambridgeUSA

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