Skip to main content
SpringerLink
Log in

Complexity Analysis of Resting-State MEG Activity in Early-Stage Parkinson’s Disease Patients

  • Published:
Annals of Biomedical Engineering Aims and scope Submit manuscript

Abstract

The aim of the present study was to analyze resting-state brain activity in patients with Parkinson’s disease (PD), a degenerative disorder of the nervous system. Magnetoencephalography (MEG) signals were recorded with a 151-channel whole-head radial gradiometer MEG system in 18 early-stage untreated PD patients and 20 age-matched control subjects. Artifact-free epochs of 4 s (1250 samples) were analyzed with Lempel–Ziv complexity (LZC), applying two- and three-symbol sequence conversion methods. The results showed that MEG signals from PD patients are less complex than control subjects’ recordings. We found significant group differences (p-values <0.01) for the 10 major cortical areas analyzed (e.g., bilateral frontal, central, temporal, parietal, and occipital regions). In addition, using receiver-operating characteristic curves with a leave-one-out cross-validation procedure, a classification accuracy of 81.58% was obtained. In order to investigate the best combination of LZC results for classification purposes, a forward stepwise linear discriminant analysis with leave-one out cross-validation was employed. LZC results (three-symbol sequence conversion) from right parietal and temporal brain regions were automatically selected by the model. With this procedure, an accuracy of 84.21% (77.78% sensitivity, 90.0% specificity) was achieved. Our findings demonstrate the usefulness of LZC to detect an abnormal type of dynamics associated with PD.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5

Similar content being viewed by others

References

  1. Aboy, M., R. Hornero, D. Abásolo, and D. Álvarez. Interpretation of the Lempel–Ziv complexity measure in the context of biomedical signal analysis. IEEE Trans. Biomed. Eng. 53:2282–2288, 2006.

    Article  PubMed  Google Scholar 

  2. Anninos, P. A., A. V. Adamopoulos, A. Kotini, and N. Tsagas. Nonlinear analysis of brain activity in magnetic influenced Parkinson patients. Brain Topogr. 13:135–144, 2000.

    Article  PubMed  CAS  Google Scholar 

  3. Bosboom, J. L., D. Stoffers, C. J. Stam, B. W. van Dijk, J. Verbunt, H. W. Berendse, and E. Ch. Wolters. Resting state oscillatory brain dynamics in Parkinson’s disease: an MEG study. Clin. Neurophysiol. 117:2521–2531, 2006.

    Article  PubMed  CAS  Google Scholar 

  4. Bosboom, J. L., D. Stoffers, E. Ch. Wolters, C. J. Stam, and H. W. Berendse. MEG resting state functional connectivity in Parkinson’s disease related dementia. J. Neural Transm. 116:193–202, 2009.

    Article  PubMed  CAS  Google Scholar 

  5. Cassidy, M., and P. Brown. Task-related EEG–EEG coherence depends on dopaminergic activity in Parkinson’s disease. Neuroreport 26:703–707, 2001.

    Article  Google Scholar 

  6. Eckmann, J. P., and D. Ruelle. Fundamental limitations for estimating dimensions and Lyapunov exponents in dynamical systems. Physica D 56:185–187, 1992.

    Article  Google Scholar 

  7. Ferenets, R., T. Lipping, A. Anier, V. Jäntti, S. Melto, and S. Hovilehto. Comparison of entropy and complexity measures for the assessment of depth of sedation. IEEE Trans. Biomed. Eng. 53:1067–1077, 2006.

    Article  PubMed  Google Scholar 

  8. Fernández, A., R. Hornero, C. Gómez, A. Turrero, P. Gil-Gregorio, J. Matías-Santos, and T. Ortiz. Complexity analysis of spontaneous brain activity in Alzheimer disease and mild cognitive impairment: an MEG study. Alzheimer Dis. Assoc. Disord. 24:182–189, 2010.

    Article  PubMed  Google Scholar 

  9. Fernández, A., J. Quintero, R. Hornero, P. Zuluaga, M. Navas, C. Gómez, J. Escudero, N. García-Campos, J. Biederman, and T. Ortiz. Complexity analysis of spontaneous brain activity in attention-deficit/hyperactivity disorder: diagnostic implications. Biol. Psychiatry 65:571–577, 2009.

    Article  PubMed  Google Scholar 

  10. Gómez, C., R. Hornero, D. Abásolo, A. Fernández, and J. Escudero. Analysis of MEG background activity in Alzheimer’s disease using nonlinear methods and ANFIS. Ann. Biomed. Eng. 37:586–594, 2009.

    Article  PubMed  Google Scholar 

  11. Gómez, C., R. Hornero, D. Abásolo, A. Fernández, and M. López. Complexity analysis of the magnetoencephalogram background activity in Alzheimer’s disease patients. Med. Eng. Phys. 28:851–859, 2006.

    Article  PubMed  Google Scholar 

  12. Gusev, V. D., L. A. Nemytikova, and N. A. Chuzhanova. On the complexity measures of genetic sequences. Bioinformatics 15:994–999, 1999.

    Article  PubMed  CAS  Google Scholar 

  13. Hämäläinen, M., R. Hari, R. J. Ilmoniemi, J. Knuutila, and O. V. Lounasmaa. Magnetoencephalograpy—theory, instrumentation, and applications to noninvasive studies of the working human brain. Rev. Mod. Phys. 65:413–497, 1993.

    Article  Google Scholar 

  14. Hornero, R., D. Abásolo, J. Escudero, and C. Gómez. Nonlinear analysis of electroencephalogram and magnetoencephalogram recordings in patients with Alzheimer’s disease. Philos. Trans. R. Soc. A 367:317–336, 2009.

    Article  Google Scholar 

  15. Hughes, A. J., Y. Ben-Shlomo, S. E. Daniel, and A. J. Lees. What features improve the accuracy of clinical diagnosis in Parkinson’s disease: a clinicopathologic study. Neurology 42:1142–1146, 1992.

    PubMed  CAS  Google Scholar 

  16. Hughes, A. J., Y. Ben-Shlomo, S. E. Daniel, and A. J. Lees. Improved accuracy of clinical diagnosis of Lewy body Parkinson’s disease. Neurology 57:1497–1499, 2001.

    PubMed  CAS  Google Scholar 

  17. Jankovic, J. Parkinson’s disease: clinical features and diagnosis. J. Neurol. Neurosurg. Psychiatry 79:368–376, 2008.

    Article  PubMed  CAS  Google Scholar 

  18. Jeong, J., J. H. Chae, S. Y. Kim, and S. H. Han. Nonlinear dynamic analysis of the EEG in patients with Alzheimer’s disease and vascular dementia. J. Clin. Neurophysiol. 18:58–67, 2001.

    Article  PubMed  CAS  Google Scholar 

  19. Kaspar, F., and H. G. Schuster. Easily calculable measure for the complexity of spatiotemporal patterns. Phys. Rev. A 36:842–848, 1987.

    Article  PubMed  Google Scholar 

  20. Kotini, A., P. Anninos, A. Adamopoulos, and P. Prassopoulos. Low-frequency MEG activity and MRI evaluation in Parkinson’s disease. Brain Topogr. 18:59–63, 2005.

    Article  PubMed  CAS  Google Scholar 

  21. Kyriazis, M. Practical applications of chaos theory to the modulation of human ageing: nature prefers chaos to regularity. Biogerontology 4:75–90, 2003.

    Article  PubMed  Google Scholar 

  22. Lempel, A., and J. Ziv. On the complexity of finite sequences. IEEE Trans. Inf. Theory 22:75–81, 1976.

    Article  Google Scholar 

  23. Méndez, M. A., P. Zuluaga, R. Hornero, C. Gómez, J. Escudero, A. Rodríguez-Palancas, T. Ortiz, and A. Fernández. Complexity analysis of spontaneous brain activity: effects of depression and antidepressant treatment. J. Psychopharm. 2011. doi:10.1177/0269881111408966.

  24. Moore, D. J., A. B. West, V. L. Dawson, and T. M. Dawson. Molecular pathophysiology of Parkinson’s disease. Annu. Rev. Neurosci. 28:57–87, 2005.

    Article  PubMed  CAS  Google Scholar 

  25. Müller, V., W. Lutzenberger, F. Pulvermüller, B. Mohr, and N. Birbaumer. Investigation of brain dynamics in Parkinson’s disease by methods derived from nonlinear dynamics. Exp. Brain Res. 137:103–110, 2001.

    Article  PubMed  Google Scholar 

  26. Nutt, J. G., and G. F. Wooten. Diagnosis and initial management of Parkinson’s disease. N. Engl. J. Med. 353:1021–1027, 2005.

    Article  PubMed  CAS  Google Scholar 

  27. Pezard, L., R. Jech, and E. Růzicka. Investigation of non-linear properties of multichannel EEG in the early stages of Parkinson’s disease. Clin. Neurophysiol. 112:38–45, 2001.

    Article  PubMed  CAS  Google Scholar 

  28. Rapp, P. E., A. M. Albano, I. D. Zimmerman, and M. A. Jiminez-Montano. Phase-randomized surrogates can produce spurious identifications of nonrandom structure. Phys. Lett. A 192:27–33, 1994.

    Article  Google Scholar 

  29. Serizawa, K., S. Kamei, A. Morita, M. Hara, T. Mizutani, H. Yoshihashi, M. Yamaguchi, J. Takeshita, and K. Hirayanagi. Comparison of quantitative EEGs between Parkinson disease and age-adjusted normal controls. J. Clin. Neurophysiol. 25:361–366, 2008.

    Article  PubMed  Google Scholar 

  30. Silberstein, P., A. Pogosyan, A. A. Kühn, G. Hotton, S. Tisch, A. Kupsch, P. Dowsey-Limousin, M. I. Hariz, and P. Brown. Cortico-cortical coupling in Parkinson’s disease and its modulation by therapy. Brain 128:1277–1291, 2005.

    Article  PubMed  Google Scholar 

  31. Simon, R., M. D. Radmacher, K. Dobbin, and L. M. McShane. Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. J. Natl Cancer Inst. 95:14–18, 2003.

    Article  PubMed  CAS  Google Scholar 

  32. Stam, C. J. Use of magnetoencephalography (MEG) to study functional brain networks in neurodegenerative disorders. J. Neurol. Sci. 289:128–134, 2010.

    Article  PubMed  CAS  Google Scholar 

  33. Stam, C. J., B. Jelles, H. A. Achtereekte, S. A. Rombouts, J. P. Slaets, and R. W. Keunen. Investigation of EEG non-linearity in dementia and Parkinson’s disease. Electroencephalogr. Clin. Neurophysiol. 95:309–317, 1995.

    Article  PubMed  CAS  Google Scholar 

  34. Stam, K. J., D. L. Tavy, B. Jelles, H. A. Achtereekte, J. P. Slaets, and R. W. Keunen. Non-linear dynamical analysis of multichannel EEG: clinical applications in dementia and Parkinson’s disease. Brain Topogr. 7:141–150, 1994.

    Article  PubMed  CAS  Google Scholar 

  35. Stoffers, D., J. L. Bosboom, J. B. Deijen, E. C. Wolters, H. W. Berendse, and C. J. Stam. Slowing of oscillatory brain activity is a stable characteristic of Parkinson’s disease without dementia. Brain 130:1847–1860, 2007.

    Article  PubMed  CAS  Google Scholar 

  36. Stoffers, D., J. L. Bosboom, J. B. Deijen, E. C. Wolters, C. J. Stam, and H. W. Berendse. Increased cortico-cortical functional connectivity in early-stage Parkinson’s disease: an MEG study. Neuroimage 41:212–222, 2008.

    Article  PubMed  CAS  Google Scholar 

  37. Yan, R., and R. X. Gao. Complexity as a measure for machine health evaluation. IEEE Trans. Instrum. Meas. 53:1327–1334, 2004.

    Article  Google Scholar 

  38. Zhang, X. S., R, J. Roy, and E. W. Jensen. EEG complexity as a measure of depth of anesthesia for patients. IEEE Trans. Biomed. Eng. 48:1424–1433, 2001.

    Article  PubMed  CAS  Google Scholar 

  39. Zozor, S., P. Ravier, and O. Buttelli. On Lempel–Ziv complexity for multidimensional data analysis. Physica A 345:285–302, 2005.

    Google Scholar 

Download references

Acknowledgments

This research was supported in part by the Ministerio de Ciencia e Innovación (Spain) under projects TEC2008-02241 and TEC2011-22987, the Dutch Parkinson Foundation (Parkinson Patiënten Vereniging), and the Dutch Foundation for Brain Research (Nederlandse Hersenstichting, grant no. 11F03(2)05). The authors wish to thank Diederick Stoffers for collecting the dataset used in this study.

Author information

Authors and Affiliations

  1. Biomedical Engineering Group, E. T. S. Ingenieros de Telecomunicación, University of Valladolid, Paseo Belén 15, 47011, Valladolid, Spain

    Carlos Gómez & Roberto Hornero

  2. Department of Neurology, VU University Medical Centre, Amsterdam, The Netherlands

    Kim T. E. Olde Dubbelink & Henk W. Berendse

  3. Department of Clinical Neurophysiology, VU University Medical Centre, Amsterdam, The Netherlands

    Cornelis J. Stam

  4. Centre for Biomedical Engineering, Mechanical, Medical & Aerospace Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, UK

    Daniel Abásolo

Authors
  1. Carlos Gómez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kim T. E. Olde Dubbelink
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cornelis J. Stam
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Daniel Abásolo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Henk W. Berendse
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Roberto Hornero
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Carlos Gómez.

Additional information

Associate Editor Leonidas D. Iasemidis oversaw the review of this article.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gómez, C., Olde Dubbelink, K.T.E., Stam, C.J. et al. Complexity Analysis of Resting-State MEG Activity in Early-Stage Parkinson’s Disease Patients. Ann Biomed Eng 39, 2935–2944 (2011). https://doi.org/10.1007/s10439-011-0416-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10439-011-0416-0

Keywords

Search

Navigation