Spectral and Non-Linear Analyses of Spontaneous Magnetoencephalographic Activity in Alzheimer's Disease

Poza, Jesús; Gómez, Carlos; Bachiller, Alejandro; Hornero, Roberto

doi:https://doi.org/10.1260/2040-2295.3.2.299

Journal of Healthcare Engineering

On this page

Abstract References Copyright Related Articles

Research Article | Open Access

Volume 3 | Article ID 106130 | https://doi.org/10.1260/2040-2295.3.2.299

Spectral and Non-Linear Analyses of Spontaneous Magnetoencephalographic Activity in Alzheimer's Disease

Jesús Poza,¹Carlos Gómez,¹Alejandro Bachiller,¹and Roberto Hornero¹

Received01 Apr 2011

Accepted01 Dec 2011

Abstract

Alzheimer's Disease (AD) is considered the most prevalent form of dementia. A definite AD diagnosis is established after examination of brain tissue. However, an accurate identification should be attempted to effectively apply therapeutic strategies. The aim of the present study was to perform regional analysis of spontaneous magnetoencephalographic (MEG) activity to describe brain dynamics in AD. Several spectral and non-linear parameters were calculated to obtain a comprehensive description of the spatial abnormalities in brain dynamics. Our findings showed a significant global slowing of MEG activity in AD, as well as a significant loss of irregularity and complexity in several brain regions. Spectral and non-linear parameters reached classification accuracies of around 80%. The results suggest the potential usefulness of spectral and non-linear parameters to characterize the cognitive and functional abnormalities of dementia. These parameters can yield information useful in clinical AD diagnosis and provide further insights on underlying brain dynamics.

References

J. J. Jalbert, L. A. Daiello, and K. L. Lapane, “Dementia of the Alzheimer type,” Epidemiologic Reviews, vol. 30, pp. 15–34, 2008.
View at: Google Scholar
K. Blennow, M. J. de Leon, and H. Zetterberg, “Alzheimer's disease,” Lancet, vol. 368, no. 9533, pp. 387–403, 2006.
View at: Google Scholar
R. N. Kalaria, G. E. Maestre Arizaga R, R. P. Friedland et al., “World Federation of Neurology Dementia Research Group. Alzheimer's disease and vascular dementia in developing countries: prevalence, management, and risk factors,” Lancet Neurology, vol. 7, no. 9, pp. 812–826, 2008.
View at: Google Scholar
G. McKhann, D. Drachman, M. Folstein, R. Katzman, D. Price, and E. M. Stadlan, “Clinical diagnosis of Alzheimer's disease: report of NINCDS-ADRDA work group under the auspices of department of health and human services task force on Alzheimer's disease,” Neurology, vol. 34, no. 7, pp. 939–944, 1984.
View at: Google Scholar
D. S. Knopman, S. T. DeKosky, J. L. Cummings et al., “Practice parameter: Diagnosis of dementia (an evidence-based review). Report of the Quality Standards Subcommittee of the American Academy of Neurology,” Neurology, vol. 56, no. 9, pp. 1143–1153, 2001.
View at: Google Scholar
B. Dubois, H. H. Feldman, C. Jacova et al., “Research criteria for the diagnosis of Alzheimer's disease: revising the NINCDS-ADRDA criteria,” Lancet Neurology, vol. 6, no. 8, pp. 734–746, 2007.
View at: Google Scholar
D. W. Shineman and H. M. Fillit, “Novel strategies for the prevention of dementia from Alzheimer's disease,” Dialogues in Clinical Neuroscience, vol. 11, no. 2, pp. 129–134, 2009.
View at: Google Scholar
J. L. Cummings, “Alzheimer's disease,” The New England Journal of Medicine, vol. 351, no. 1, pp. 56–67, 2004.
View at: Google Scholar
P. M. Rossini, S. Rossi, C. Babiloni, and J. Polich, “Clinical neurophysiology of aging brain: From normal aging to neurodegeneration,” Progress in Neurobiology, vol. 83, no. 6, pp. 375–400, 2007.
View at: Google Scholar
R. Hari, “Magnetoencephalography in clinical neurophysiological assessment of human cortical functions,” in Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, Ernst Niedermeyer and Fernando Lopes da Silva, Eds., pp. 1165–1197, Lippincontt Williams & Wilkins, Philadelphia, USA, 5th edition, 2005.
View at: Google Scholar
P. L. Nunez, B. M. Wingeier, and R. B. Silberstein, “Spatial-Temporal structures of human alpha rhythms: Microcurrents sources, multiscale elements, and global binding of local networks,” Human Brain Mapping, vol. 13, no. 3, pp. 125–164, 2001.
View at: Google Scholar
J. Jeong, “EEG dynamics in patients with Alzheimer's disease,” Clinical Neurophysiology, vol. 115, no. 7, pp. 1490–1505, 2004.
View at: Google Scholar
C. J. Stam, “Nonlinear dynamical analysis of EEG and MEG: Review of an emerging field,” Clinical Neurophysiology, vol. 116, no. 10, pp. 2266–2301, 2005.
View at: Google Scholar
C. J. Stam, “Use of magnetoencephalography (MEG) to study functional brain networks in neurodegenerative disorders,” Journal of the Neurological Sciences, vol. 289, no. 1-2, pp. 128–134, 2010.
View at: Google Scholar
H. W. Berendse, J. P. A. Verbunt, Scheltens Ph, B. W. van Dijk, and E. J. Jonkman, “Magnetoencephalographic analysis of cortical activity in Alzheimer's disease: a pilot study,” Clinical Neurophysiology, vol. 111, no. 4, pp. 604–612, 2000.
View at: Google Scholar
D. Osipova, J. Ahveninen, O. Jensen, A. Ylikoski, and E. Pekkonen, “Altered generation of spontaneous oscillations in Alzheimer's disease,” NeuroImage, vol. 27, no. 4, pp. 835–841, 2005.
View at: Google Scholar
A. Fernández, R. Hornero, A. Mayo, J. Poza, P. Gil-Gregorio, and T. Ortiz, “MEG spectral profile in Alzheimer's disease and mild cognitive impairment,” Clinical Neurophysiology, vol. 117, no. 2, pp. 306–314, 2006.
View at: Google Scholar
A. Fernández, R. Hornero, A. Mayo, J. Poza, F. Maestú, and T. Ortiz, “Quantitative magnetoencephalography of spontaneous brain activity in Alzheimer disease: an exhaustive frequency analysis,” Alzheimer Disease and Associated Disorders, vol. 20, no. 3, pp. 153–159, 2006.
View at: Google Scholar
W. de Haan, C. J. Stam, B. F. Jones, I. M. Zuiderwijk, B. W. van Dijk, and P. Scheltens, “Resting-state oscillatory brain dynamics in Alzheimer disease,” Journal of Clinical Neurophysiology, vol. 25, no. 4, pp. 187–193, 2008.
View at: Google Scholar
J. Poza, R. Hornero, D. Abásolo, A. Fernández, and A. Mayo, “Evaluation of spectral ratio measures from spontaneous MEG recordings in patients with Alzheimer's disease,” Computer Methods and Programs in Biomedicine, vol. 90, no. 2, pp. 137–147, 2008.
View at: Google Scholar
R. Hindriks, F. Bijma, B. W. van Dijk, Y. D. van der Werf, E. J. van Someren, and A. W. van der Vaart, “Dynamics underlying spontaneous human alpha oscillations: a data-driven approach.,” NeuroImage, vol. 57, no. 2, pp. 440–451, 2011.
View at: Google Scholar
J. Poza, R. Hornero, D. Abásolo, A. Fernández, and M. García, “Extraction of spectral based measures from MEG background oscillations in Alzheimer's disease,” Medical Engineering and Physics, vol. 29, no. 10, pp. 1073–1083, 2007.
View at: Google Scholar
D. Osipova, K. Rantanen, J. Ahveninen et al., “Source estimation of spontaneous MEG oscillations in mild cognitive impairment,” Neuroscience Letters, vol. 405, no. 1-2, pp. 57–61, 2006.
View at: Google Scholar
I. Abatzoglou, P. Anninos, A. Adamopoulos, and M. Koukourakis, “Nonlinear analysis of brain magnetoencephalographic activity in Alzheimer disease patients.,” Acta Neurologica Belgica, vol. 107, no. 2, pp. 34–39, 2007.
View at: Google Scholar
C. Gómez, R. Hornero, D. Abásolo, A. Fernández, and M. López, “Complexity analysis of the magnetoencephalogram background activity in Alzheimer's disease patients,” Medical Engineering and Physics, vol. 28, no. 9, pp. 851–859, 2006.
View at: Google Scholar
C. Gómez, R. Hornero, D. Abásolo, A. Fernández, and J. Escudero, “Analysis of MEG background activity in Alzheimer's disease using non-linear methods and ANFIS,” Annals of Biomedical Engineering, vol. 37, no. 3, pp. 586–594, 2009.
View at: Google Scholar
R. Hornero, D. Abásolo, J. Escudero, and C. Gómez, “Nonlinear analysis of electroencephalogram and magnetoencephalogram recordings in patients with Alzheimer's disease,” Philosophical Transactions of the Royal Society A, Mathematical, Physical, and Engineering Sciences, vol. 367, no. 1887, pp. 317–336, 2009.
View at: Google Scholar
A. Fernández, R. Hornero, C. Gómez et al., “Complexity analysis of spontaneous brain activity in Alzheimer's Disease and Mild Cognitive Impairment,” Alzheimer Disease and Associated Disorders, vol. 24, no. 1, pp. 1–10, 2010.
View at: Google Scholar
C. Gómez and R. Hornero, “Entropy and complexity analyses in Alzheimer's disease: an MEG study,” The Open Biomedical Engineering Journal, vol. 4, pp. 223–235, 2010.
View at: Google Scholar
C. Gómez, A. Mediavilla, R. Hornero, D. Abásolo, and A. Fernández, “Use of the Higuchi's fractal dimension for the analysis of MEG recordings from Alzheimer's disease patients,” Medical Engineering and Physics, vol. 31, no. 3, pp. 306–313, 2009.
View at: Google Scholar
A.-M. van Cappellen van Walsum, Y. A. L. Pijnenburg, H. W. Berendse et al., “A neural complexity measure applied to MEG data in Alzheimer's disease,” Clinical Neurophysiology, vol. 114, no. 6, pp. 1034–1040, 2003.
View at: Google Scholar
R. Hornero, J. Escudero, A. Fernández, J. Poza, and C. Gómez, “Spectral and Non-linear Analyses of MEG Background Activity in Patients with Alzheimer's Disease,” IEEE Transactions on Biomedical Engineering, vol. 55, no. 6, pp. 1658–1665, 2008.
View at: Google Scholar
J. Poza, J. Escudero, R. Hornero, A. Fernández, and C. I. Sanchez, “Regional analysis of spontaneous MEG rhythms in patients with Alzheimer's disease using spectral entropies,” Annals of Biomedical Engineering, vol. 36, no. 1, pp. 141–152, 2008.
View at: Google Scholar
C. Gómez, R. Hornero, D. Abásolo, A. Fernández, and J. Escudero, “Analysis of the magnetoencephalogram background activity in Alzheimer's disease patients with auto mutual information,” Computers Methods and Programs in Biomedicine, vol. 87, no. 3, pp. 239–247, 2007.
View at: Google Scholar
C. J. Stam, A. M. van Cappellen Walsum, Y. A. Pijnenburg et al., “Generalized synchronization of MEG recordings in Alzheimer's Disease: evidence for involvement of the gamma band,” Journal of Clinical Neurophysiology, vol. 19, no. 6, pp. 562–574, 2002.
View at: Google Scholar
R. Franciotti, D. Iacono, S. Della Penna et al., “Cortical rhythms reactivity in AD, LBD and normal subjects. A quantitative MEG study,” Neurobiology of Aging, vol. 27, no. 8, pp. 1100–1109, 2006.
View at: Google Scholar
T. Montez, S. S. Poil, B. F. Jones et al., “Altered temporal correlations in parietal alpha and prefrontal theta oscillations in early-stage Alzheimer disease.,” Proceedings of the National Academy of Sciences of the United States of America, vol. 106, no. 5, pp. 1614–1619, 2009.
View at: Google Scholar
C. Gómez, CJ. Stam, R. Hornero, A. Fernández, and F. Maestú, “Disturbed beta band functional connectivity in patients with mild cognitive impairment: An MEG study,” IEEE Transactions on Biomedical Engineering, vol. 56, no. 6, pp. 1683–1690, 2009.
View at: Google Scholar
C. J. Stam, W. de Haan, A. Daffertshofer et al., “Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer's disease,” Brain, vol. 132, Pt 1, pp. 213–224, 2009.
View at: Google Scholar
J. F. Alonso, J. Poza, M. A. Mañanas, S. Romero, A. Fernández, and R. Hornero, “MEG connectivity analysis in patients with Alzheimer's disease using cross mutual information and spectral coherence,” Annals of Biomedical Engineering, vol. 39, no. 1, pp. 524–536, 2010.
View at: Google Scholar
C. J. Stam, B. F. Jones, I. Manshanden et al., “Magnetoencephalographic evaluation of resting-state functional connectivity in Alzheimer's disease,” NeuroImage, vol. 32, no. 3, pp. 1335–1344, 2006.
View at: Google Scholar
B. Auer SReisberg, “The GDS/FAST staging system,” International Psychogeriatrics, vol. 9, no. 1, pp. 167–171, 1997.
View at: Google Scholar
G. Rodriguez, F. Copello, P. Vitali, G. Perego, and F. Nobili, “EEG spectral profile to stage Alzheimer's disease,” Clinical Neurophysiology, vol. 110, no. 10, pp. 1831–1837, 1999.
View at: Google Scholar
W. Klimesch, “EEG alpha and theta oscillations reflect cognitive and memory performance: a review analysis,” Brain Research Review, vol. 29, no. 2-3, pp. 169–195, 1999.
View at: Google Scholar
G. E. Powell and I. C. Percival, “A spectral entropy method for distinguishing regular and irregular motion of Hamiltonian systems,” Journal of Physics A: Mathematical and General, vol. 12, no. 11, pp. 2053–2071, 1979.
View at: Google Scholar
J. P. Eckmann and D. Ruelle, “Fundamental limitations for estimating dimensions and Lyapunov exponents in dynamical systems,” Physica D: Nonlinear Phenomena, vol. 56, no. 2-3, pp. 185–187, 1992.
View at: Google Scholar
J. S. Richman and J. R. Moorman, “Physiological time-series analysis using approximate entropy and sample entropy,” American Journal of Physiology. Heart and Circulatory Physiology, vol. 278, no. 6, pp. H2039–H2049, 2000.
View at: Google Scholar
A. Lempel and J. Ziv, “On the complexity of finite sequences,” IEEE Transactions on Information Theory, vol. 22, no. 1, pp. 75–81, 1976.
View at: Google Scholar
XS. Zhang, RJ. Roy, and EW. Jensen, “EEG complexity as a measure of depth of anesthesia for patients,” IEEE Transactions on Biomedical Engineering, vol. 48, no. 12, pp. 1424–1433, 2001.
View at: Google Scholar
R. Nagarajan, “Quantifying physiological data with Lempel-Ziv complexity - certain issues,” IEEE Transactions on Biomedical Engineering, vol. 49, no. 11, pp. 1371–1373, 2002.
View at: Google Scholar
B. Mandelbrot, Fractals: Form, Chance, and Dimension, Freeman, San Francisco, 1977.
A. Accardo, M. Affinito, M. Carrozzi, and F. Bouquet, “Use of the fractal dimension for the analysis of electroencephalographic time series,” Biological Cybernetics, vol. 77, no. 5, pp. 339–350, 1997.
View at: Google Scholar
P. Maragos and F. K. Sun, “Measuring the fractal dimension of signals: morphological covers and iterative optimization,” IEEE Transactions on Signal Processing, vol. 41, no. 1, pp. 108–121, 1983.
View at: Google Scholar
M. Katz, “Fractals and the analysis of waveforms,” Computers in Biology and Medicine, vol. 18, no. 3, pp. 145–156, 1988.
View at: Google Scholar
T. Higuchi, “Approach to an irregular time series on the basis of the fractal theory,” Physica D: Nonlinear Phenomena, vol. 31, no. 2, pp. 277–283, 1988.
View at: Google Scholar
J. W. Sleigh, D. A. Steyn-Ross, C. Grant, and G. Ludbrook, “Cortical entropy changes with general anaesthesia: theory and experiment,” Physiological Measurement, vol. 25, no. 4, pp. 921–934, 2004.
View at: Google Scholar
J. Bendat and A. Piersol, Random Data Analysis and Measurement Procedures, Wiley, New York, 2000.
A. Fernández, F. Maestú, C. Amo et al., “Focal temporoparietal slow activity in Alzheimer's disease revealed by magnetoencephalography,” Biological Psychiatry, vol. 52, no. 7, pp. 764–770, 2002.
View at: Google Scholar
A. Fernández, A. Turrero, P. Zuluaga et al., “Magnetoencephalographic parietal delta dipole density in mild cognitive impairment: preliminary results of a method to estimate the risk of developing Alzheimer disease,” Archives of Neurology, vol. 63, no. 3, pp. 427–430, 2006.
View at: Google Scholar
P. Giannakopoulos, E. Kövari, F. R. Herrmann, P. R. Hof, and C. Bouras, “Interhemispheric distribution of Alzheimer disease and vascular pathology in brain aging,” Stroke, vol. 40, no. 3, pp. 983–986, 2009.
View at: Google Scholar
C. Huang, L. O. Wahlund, T. Dierks, P. Julin, B. Winblad, and V. Jelic, “Discrimination of Alzheimer's disease and mild cognitive impairment by equivalent EEG sources: a cross-sectional and longitudinal study,” Clinical Neurophysiology, vol. 111, no. 11, pp. 1961–1967, 2000.
View at: Google Scholar
D. Osipova, J. Ahveninen, S. Kaakkola, I. P. Jääskeläinen, J. Huttunen, and E. Pekkonen, “Effects of scopolamine on MEG spectral power and coherence in elderly subjects,” Clinical Neurophysiology, vol. 114, no. 10, pp. 1902–1907, 2003.
View at: Google Scholar
N. Kannathal, M. L. Choob, U. R. Acharyab, and P. K. Sadasivana, “Entropies for detection of epilepsy in EEG,” Computer Methods and Programs in Biomedicine, vol. 80, no. 3, pp. 187–194, 2005.
View at: Google Scholar
R. G. Baraniuk, P. Flandrin, A. J. E. M. Janssen, and O. J. J. Michel, “Measuring time-frequency information content using the Rényi entropies,” IEEE Transactions on Information Theory, vol. 47, no. 4, pp. 1391–1409, 2001.
View at: Google Scholar
C. Besthorn, H. Sattel, C. Geiger-Kabisch, R. Zerfass, and H. Förstl, “Parameters of EEG dimensional complexity in Alzheimer's disease,” Electroencephalography and Clinical Neurophysiology, vol. 95, no. 2, pp. 84–89, 1995.
View at: Google Scholar
J. Escudero, D. Abásolo, R. Hornero, P. Espino, and M. López, “Analysis of electroencephalograms in Alzheimer's disease patients with multiscale entropy,” Physiological Measurement, vol. 27, no. 11, pp. 1091–1106, 2006.
View at: Google Scholar
J. Jeong, S. J. Kim, and S. H. Han, “Non-linear dynamical analysis of the EEG in Alzheimer's disease with optimal embedding dimension,” Electroencephalography and Clinical Neurophysiology, vol. 106, no. 3, pp. 220–228, 1998.
View at: Google Scholar
B. Jelles, J. H. van Birgelen, J. P. J. Slaets, R. E. M. Hekster, E. J. Jonkman, and C. J. Stam, “Decrease of non-linear structure in the EEG of Alzheimer patients compared to healthy controls,” Clinical Neurophysiology, vol. 110, no. 7, pp. 1159–1167, 1999.
View at: Google Scholar

Copyright

Copyright © 2012 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PDF Download Citation Order printed copies

Views

547

Downloads

703

Citations