SPARSE BUMP MODELING OF MILDAD PATIENTS - Modeling Transient Oscillations in the EEG of Patients with Mild Alzheimer’s Disease

François-Benoit Vialatte, Charles François Vincent Latchoumane, Nigel Hudson, Sunil Wimalaratna, Jordi Solé-Casals, Jaeseung Jeong, Andrzej Cichocki

Abstract

We explore the potential of bump modeling to extract transient local synchrony in EEG, as a marker for mildAD (mild Alzheimer’s disease). EEG signals of patients with mildAD are transformed to a wavelet timefrequency representation, and afterwards a sparsification process (bump modeling) extracts time-frequency oscillatory bursts. We observed that organized oscillatory events contain stronger discriminative signatures than averaged spectral EEG statistics for patients in a probable early stage of Alzheimer’s disease. Specifically, bump modeling enhanced the difference between mildAD patients and age-matched control subjects in the θ and β frequency ranges.This effect is consistent with previous results obtained on other databases.

References

  1. Bas¸ar, E. (1980). EEG-brain dynamics: Relation between EEG and brain evoked potentials. Elsevier, Amsterdam.
  2. Barlow, J. (1993). The Electroencephalogram: Its Patterns and Origins. MIT Press, Cambridge MA, USA.
  3. Caplan, J., Madsen, J., Raghavachari, S., and Kahana, M. (2001). Distinct patterns of brain oscillations underlie two basic parameters of human maze learning. J Neurophysiol, 86(1):368-380.
  4. Düzel, E., Habib, R., Schott, B., Schoenfeld, A., Lobaugh, N., McIntosh, A. R., Scholz, M., and Heinze, H. J. (2003). A multivariate, spatiotemporal analysis of electromagnetic time-frequency data of recognition memory. Neuroimage, 18:185-197.
  5. Fernández, A., Arrazola, J., Maestú, F., Amo, C., GilGregorio, P., Wienbruch, C., and Ortiz, T. (2003). Correlations of hippocampal atrophy and focal lowfrequency magnetic activity in alzheimer disease: volumetric mr imaging-magnetoencephalographic study. AJNR Am J Neuroradiol, 24(3):481-487.
  6. Helkala, E., Hänninen, T., Hallikainen, M., Könönen, M., Laakso, M., Hartikainen, P., Soininen, H., Partanen, J., Partanen, K., Vainio, P., and Riekkinen, P. S. (1996). Slow-wave activity in the spectral analysis of the electroencephalogram and volumes of hippocampus in subgroups of alzheimer's disease patients. Behav Neurosci, 110(6):1235-1243.
  7. Henderson, G., Ifeachor, E., Hudson, N., Goh, C., Outram, N., Wimalaratna, S., Del Percio, C., and Vecchio, F. (2006). Development and assessment of methods for detecting dementia using the human electroencephalogram. IEEE Transaction on Biomedical Engineering, 53:1557-1568.
  8. Jeong, J. (2004). Eeg dynamics in patients with alzheimers disease. Clinical Neurophysiology, 115:1490-1505.
  9. Kronland-Martinet, R., Morlet, J., and Grossmann, A. (1988). Analysis of sound patterns through wavelet transforms. International Journal on Pattern Recognition and Artificial Intelligence, 1(2):273-301.
  10. Li, X., Yao, X., Fox, J., and Jefferys, J. (2007). Interaction dynamics of neuronal oscillations analysed using wavelet transforms. Journal of Neuroscience Methods, 160(1):178-185.
  11. Mallat, S. (1999). A wavelet tour of signal processing, 2nd edition. Academic Press, New York.
  12. Moratti, S., Clementz, B., Gao, Y., Ortiz, T., and Keil, A. (2007). Neural mechanisms of evoked oscillations: Stability and interaction with transient events. Human Brain Mapping, 28(12):1318-1333.
  13. Nikulin, V., Linkenkaer-Hansen, K., Nolte, G., Lemm, S., Müller, K., Ilmoniemi, R., and Curio, G. (2007). A novel mechanism for evoked responses in the human brain. European Journal of Neuroscience, 25(10):3146-3154.
  14. Percival, D. and Walden, A. (2000). Wavelet Methods for Time Series Analysis. Cambridge University Press, New York.
  15. Quiroga, R., Sakowitz, O., Bas¸ar, E., and Schürmann, M. (2001). Wavelet transform in the analysis of the frequency composition of evoked potentials. Brain Research Protocols, 8:16-24.
  16. Slobounov, S., Hallett, M., Cao, C., and Newell, K. (2008). Modulation of cortical activity as a result of voluntary postural sway direction: An eeg study. Neuroscience Letters, 442(3):309-313.
  17. Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions (with discussion). Journal of the Royal Statistical Society B, 36:111-147.
  18. Tallon-Baudry, C., Bertrand, O., Delpuech, C., and Pernier, J. (1996). Stimulus specificity of phase-locked and non-phase-locked 40 hz visual responses in human. Journal of Neuroscience, 16:4240-4249.
  19. Uhlhaas, P. and Singer, W. (2006). Neural synchrony in brain disorders: relevance for cognitive dysfunctions and pathophysiology. Neuron, 52:155-168.
  20. Vialatte, F., Bakardjian, H., Prasad, R., and Cichocki, A. (in press 2009a). Eeg paroxysmal gamma waves during bhramari pranayama: A yoga breathing technique. Consciousness and Cognition.
  21. Vialatte, F., Dauwels, J., Maurice, M., Yamaguchi, Y., and Cichocki, A. (2009b). On the synchrony of steady state visual evoked potentials and oscillatory burst events. Cognitive Neurodynamics, 3(3):251-261.
  22. Vialatte, F., Martin, C., Dubois, R., Haddad, J., Quenet, B., Gervais, R., and G, D. (2007). A machine learning approach to the analysis of time-frequency maps, and its application to neural dynamics. Neural Networks, 20:194-209.
  23. Vialatte, F., Maurice, M., and Cichocki, A. (2008a). Why sparse bump models? In Proceedings of OHBM meeting: June 15-19 2008, Melbourne, Australia - Neuroimage, 41(S1):S159.
  24. Vialatte, F., Solé-Casals, J., and Cichocki, A. (2008b). Eeg windowed statistical wavelet scoring for evaluation and discrimination of muscular artifacts. Physiological Measurements, 29(12):1435-1452.
  25. Vialatte, F., Solé-Casals, J., Dauwels, J., Maurice, M., and Cichocki, A. (2009c). Bump time-frequency toolbox: a toolbox for time-frequency oscillatory bursts extraction in electrophysiological signals. BMC Neuroscience, 10(46).
  26. Vialatte, F. B., Solé-Casals, J., Maurice, M., Latchoumane, C., Hudson, N., Wimalaratna, S., Jeong, J., and Andrzej, C. (2009d). Improving the quality of eeg data in patients with alzheimers disease using ica. In Proceedings of t15th International Conference on Neural Information Processing, ICONIP, Auckland, New Zealand, November 25-28 2008 - LNCS, Part II, 5507:979-986.
Download


Paper Citation


in Harvard Style

Vialatte F., François Vincent Latchoumane C., Hudson N., Wimalaratna S., Solé-Casals J., Jeong J. and Cichocki A. (2010). SPARSE BUMP MODELING OF MILDAD PATIENTS - Modeling Transient Oscillations in the EEG of Patients with Mild Alzheimer’s Disease . In Proceedings of the Third International Conference on Bio-inspired Systems and Signal Processing - Volume 1: Special Session on Neural Signals of Brain Disorde, (BIOSTEC 2010) ISBN 978-989-674-018-4, pages 479-484. DOI: 10.5220/0002755104790484


in Bibtex Style

@conference{special session on neural signals of brain disorde10,
author={François-Benoit Vialatte and Charles François Vincent Latchoumane and Nigel Hudson and Sunil Wimalaratna and Jordi Solé-Casals and Jaeseung Jeong and Andrzej Cichocki},
title={SPARSE BUMP MODELING OF MILDAD PATIENTS - Modeling Transient Oscillations in the EEG of Patients with Mild Alzheimer’s Disease},
booktitle={Proceedings of the Third International Conference on Bio-inspired Systems and Signal Processing - Volume 1: Special Session on Neural Signals of Brain Disorde, (BIOSTEC 2010)},
year={2010},
pages={479-484},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002755104790484},
isbn={978-989-674-018-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Third International Conference on Bio-inspired Systems and Signal Processing - Volume 1: Special Session on Neural Signals of Brain Disorde, (BIOSTEC 2010)
TI - SPARSE BUMP MODELING OF MILDAD PATIENTS - Modeling Transient Oscillations in the EEG of Patients with Mild Alzheimer’s Disease
SN - 978-989-674-018-4
AU - Vialatte F.
AU - François Vincent Latchoumane C.
AU - Hudson N.
AU - Wimalaratna S.
AU - Solé-Casals J.
AU - Jeong J.
AU - Cichocki A.
PY - 2010
SP - 479
EP - 484
DO - 10.5220/0002755104790484