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Resting EEG Discrimination of Early Stage Alzheimer’s Disease from Normal Aging Using Inter-Channel Coherence Network Graphs

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Abstract

Amnestic mild cognitive impairment (MCI) is a degenerative neurological disorder at the early stage of Alzheimer’s disease (AD). This work is a pilot study aimed at developing a simple scalp-EEG-based method for screening and monitoring MCI and AD. Specifically, the use of graphical analysis of inter-channel coherence of resting EEG for the detection of MCI and AD at early stages is explored. Resting EEG records from 48 age-matched subjects (mean age 75.7 years)—15 normal controls (NC), 16 with early-stage MCI, and 17 with early-stage AD—are examined. Network graphs are constructed using pairwise inter-channel coherence measures for delta–theta, alpha, beta, and gamma band frequencies. Network features are computed and used in a support vector machine model to discriminate among the three groups. Leave-one-out cross-validation discrimination accuracies of 93.6% for MCI vs. NC (p < 0.0003), 93.8% for AD vs. NC (p < 0.0003), and 97.0% for MCI vs. AD (p < 0.0003) are achieved. These results suggest the potential for graphical analysis of resting EEG inter-channel coherence as an efficacious method for noninvasive screening for MCI and early AD.

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References

  1. Anoop, A., P. K. Singh, R. S. Jacob, and S. K. Maji. CSF biomarkers for Alzheimer’s disease diagnosis. Int. J. Alzheimer Dis. 1–12:2010, 2010.

    Google Scholar 

  2. Averbeck, B. B., and M. Seo. The statistical neuroanatomy of frontal networks in the macaque. PLoS Comput. Biol. 4:e1000050, 2008.

    Article  PubMed  Google Scholar 

  3. Bassett, D. S., and E. T. Bullmore. Small-world brain networks. Neuroscientist 12:512–523, 2006.

    Article  PubMed  Google Scholar 

  4. Bishop, C. M. Pre-processing and feature extraction. In: Neural Networks for Pattern Recognition. New York, NY: Oxford University Press, Inc., 2008, pp. 295–329.

  5. Braitenberg, V., and A. Schüz. 1998. Cortex: Statistics and Geometry of Neuronal Connectivity, 2nd ed., reviewed by L. Gary. Berlin: Springer, 1998, 249 pp.

  6. Brenner, R. P., R. F. Ulrich, D. G. Spiker, R. J. Sclabassi, C. F. Reynolds, III, R. S. Marin, and F. Boller. Computerized EEG spectral analysis in elderly normal, demented and depressed subjects. Electroencephalogr. Clin. Neurophysiol. 64:483–492, 1986.

    Article  PubMed  CAS  Google Scholar 

  7. Bullmore, E., and O. Sporns. Complex brain networks: theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10:186–198, 2009.

    Article  PubMed  CAS  Google Scholar 

  8. Farrarini, L., I. M. Veer, E. Baerends, M. J. van Tol, R. J. Renken, N. J. van der Wee, D. J. Veltman, A. Aleman, F. G. Zitman, B. W. Penninx, M. A. van Buchem, J. H. Reiber, S. A. Rombouts, and J. Miles. Hierarchical functional modularity in the resting-state human brain. Hum. Brain Mapp. 30:2220–2231, 2009.

    Article  Google Scholar 

  9. Girvan, M., and M. E. J. Newman. Community structure in social and biological networks. Proc. Natl. Acad. Sci. U.S.A. 99:7821–7826, 2002.

    Article  PubMed  CAS  Google Scholar 

  10. He, B. Neural signal processing. In: Neural Engineering. New York, NY: Kluwer Academic/Plenum Publishers, 2005, pp. 193–221.

  11. Hellwig, B. A. A quantitative analysis of the local connectivity between pyramidal neurons in layers 2/3 of the rat visual cortex. Biol. Cybern. 82:111–121, 2000.

    Article  PubMed  CAS  Google Scholar 

  12. Jelic, V., S. E. Johansson, O. Almkvist, M. Shigeta, P. Julin, A. Nordberg, B. Winblad, and L. O. Wahlund. Quantitative electroencephalography in mild cognitive impairment: longitudinal changes and possible prediction of Alzheimer’s disease. Neurobiol. Aging 21:533–540, 2000.

    Article  PubMed  CAS  Google Scholar 

  13. Jeong, J. EEG dynamics in patients with Alzheimer’s disease. Clin. Neurophysiol. 115:1490–1505, 2004.

    Article  PubMed  Google Scholar 

  14. Meunier, D., S. Archard, A. Morcom, and E. Bullmore. Age-related changes in modular organization of human brain functional networks. Neuroimage 44:715–723, 2009.

    Article  PubMed  Google Scholar 

  15. Petersen, R. Mild Cognitive Impairment. New York, NY: Oxford Press, 288 pp., 2003.

  16. Petersen, R. C., J. E. Parisi, D. W. Dickson, K. A. Johnson, D. S. Knopman, B. F. Boeve, G. A. Jicha, R. J. Ivnik, G. E. Smith, E. G. Tangalos, H. Braak, and E. Kokmen. Neuropathologic features of amnestic mild cognitive impairment. Arch. Neurol. 63:665–672, 2006.

    Article  PubMed  Google Scholar 

  17. Reijneveld, J. C., S. C. Ponten, H. W. Berendse, and C. J. Stam. The application of graph theoretical analysis to complex networks in the brain. Clin. Neurophysiol. 118:2317–2331, 2007.

    Article  PubMed  Google Scholar 

  18. Salvador, R., J. Suckling, M. R. Coleman, J. D. Pickard, D. Menon, and E. Bullmore. Neurophysiological architecture of functional magnetic resonance images of human brain. Cereb. Cortex 15:1332–1342, 2005.

    Article  PubMed  Google Scholar 

  19. Signorino, M., E. Pucci, N. Belardinelli, G. Nolfe, and F. Angeleri. EEG spectral analysis in vascular and Alzheimer’s dementia. Electroencephalogr. Clin. Neurophysiol. 94:313–325, 1995.

    Article  PubMed  CAS  Google Scholar 

  20. Snaedal, J., G. H. Johannesson, T. E. Gudmundsson, S. Gudmundsson, T. H. Pajdak, and K. Johnsen. The use of EEG in Alzheimer’s disease, with and without scopolamine—a pilot study. Clin. Neurophysiol. 121:836–841, 2010.

    Article  PubMed  CAS  Google Scholar 

  21. Soininen, H., J. Partanen, V. Laulumaa, E. L. Helkala, M. Laakso, and P. J. Riekkinen. Longitudinal EEG spectral analysis in early stage of Alzheimer’s disease. Electroencephalogr. Clin. Neurophysiol. 72:290–297, 1989.

    Article  PubMed  CAS  Google Scholar 

  22. Sporns, O., D. R. Chialvo, M. Kaiser, and C. C. Hilgetag. Organization, development and function of complex brain networks. Trends Cogn. Sci. 8:418–425, 2004.

    Article  PubMed  Google Scholar 

  23. Stam, C. J., and J. C. Reijneveld. Graph theoretical analysis of complex of complex networks in the brain. Nonlinear Biomed. Phys. 1:3, 2007.

    Article  PubMed  Google Scholar 

  24. Tononi, G., O. Sporns, and G. M. Edelman. A measure for brain complexity: relating functional segregation and integration in the nervous system. Proc. Natl. Acad. Sci. U.S.A. 91:5033–5037, 1994.

    Article  PubMed  CAS  Google Scholar 

  25. Waldemar, G., B. Dubois, M. Emre, J. Georges, I. G. McKeith, M. Rossor, P. Scheltens, P. Tariska, and B. Winbald. Recommendations for the diagnosis and management of Alzheimer’s disease and other disorders associated with dementia: EFNS guideline. Eur. J. Neurol. 14:e1–e26, 2007.

    Article  PubMed  CAS  Google Scholar 

  26. Zetterberg, H., N. Mattson, and K. Blennow. Cerebrospinal fluid analysis should be considered in patients with cognitive problems. Int. J. Alzheimer Dis. 2010:163065, 2010.

    Google Scholar 

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Acknowledgments

We thank A. Lawson, J. Howe, E. Walsh, J. Lianekhammy, S. Kaiser, C. Black, K. Tran, and L. Broster at UK for their assistance in data acquisition and database management. Research was sponsored in part by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the US Department of Energy, and in part by the NSF under grant number CMMI-0845753; DOE OR-22725 to NM, NIH AG000986 to YJ, NCRRUL1RR033173 to UK CTS, P30AG028383 to UK Sanders-Brown Center on Aging.

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Authors and Affiliations

  1. Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, TN, 37996, USA

    Joseph McBride & Xiaopeng Zhao

  2. National Institute for Mathematical and Biological Synthesis, University of Tennessee, Knoxville, TN, 37996, USA

    Xiaopeng Zhao

  3. Oak Ridge National Laboratory, Oak Ridge, TN, 37831-6418, USA

    Nancy Munro

  4. Department of Neurology, University of Kentucky, Lexington, KY, 40536, USA

    Charles Smith & Gregory Jicha

  5. Department of Behavioral Science, University of Kentucky, Lexington, KY, 40536, USA

    Yang Jiang

  6. Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, 40536, USA

    Charles Smith, Gregory Jicha & Yang Jiang

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  1. Joseph McBride
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  2. Xiaopeng Zhao
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  3. Nancy Munro
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  4. Charles Smith
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  5. Gregory Jicha
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Correspondence to Xiaopeng Zhao.

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Associate Editor Ioannis A. Kakadiaris oversaw the review of this article.

Nancy Munro is retired.

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McBride, J., Zhao, X., Munro, N. et al. Resting EEG Discrimination of Early Stage Alzheimer’s Disease from Normal Aging Using Inter-Channel Coherence Network Graphs. Ann Biomed Eng 41, 1233–1242 (2013). https://doi.org/10.1007/s10439-013-0788-4

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  • DOI: https://doi.org/10.1007/s10439-013-0788-4

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