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Bayesian Gaussian Process Classification from Event-Related Brain Potentials in Alzheimer’s Disease

  • Wolfgang Fruehwirt
  • Pengfei Zhang
  • Matthias Gerstgrasser
  • Dieter Grossegger
  • Reinhold Schmidt
  • Thomas Benke
  • Peter Dal-Bianco
  • Gerhard Ransmayr
  • Leonard Weydemann
  • Heinrich Garn
  • Markus Waser
  • Michael Osborne
  • Georg Dorffner
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10259)

Abstract

Event-related potentials (ERPs) have been shown to reflect neurodegenerative processes in Alzheimer’s disease (AD) and might qualify as non-invasive and cost-effective markers to facilitate the objectivization of AD assessment in daily clinical practice. Lately, the combination of multivariate pattern analysis (MVPA) and Gaussian process classification (GPC) has gained interest in the neuroscientific community. Here, we demonstrate how a MVPA-GPC approach can be applied to electrophysiological data. Furthermore, in order to account for the temporal information of ERPs, we develop a novel method that integrates interregional synchrony of ERP time signatures. By using real-life ERP recordings of a prospective AD cohort study (PRODEM), we empirically investigate the usefulness of the proposed framework to build neurophysiological markers for single subject classification tasks. GPC outperforms the probabilistic reference method in both tasks, with the highest AUC overall (0.802) being achieved using the new spatiotemporal method in the prediction of rapid cognitive decline.

Keywords

Machine learning Gaussian process classification Event-related potentials Alzheimer’s disease Single subject classification 

Notes

Acknowledgment

The PRODEM study has been supported by the Austrian Research Promotion Agency FFG, project no. 827462, including financial contributions from Dr. Grossegger and Drbal GmbH, Vienna, Austria.

References

  1. 1.
    Howe, A.S., Bani-Fatemi, A., De Luca, V.: The clinical utility of the auditory P300 latency subcomponent event-related potential in preclinical diagnosis of patients with mild cognitive impairment and Alzheimer’s disease. Brain Cogn. 86, 64–74 (2014)CrossRefGoogle Scholar
  2. 2.
    Howe, A.S.: Meta-analysis of the endogenous N200 latency event-related potential subcomponent in patients with Alzheimer’s disease and mild cognitive impairment. Clin. Neurophysiol. 125, 1145–1151 (2014)CrossRefGoogle Scholar
  3. 3.
    Olichney, J.M., Yang, J.C., Taylor, J., Kutas, M.: Cognitive event-related potentials: biomarkers of synaptic dysfunction across the stages of Alzheimer’s disease. J. Alzheimer’s Dis. 26(Suppl. 3), 215–228 (2011)Google Scholar
  4. 4.
    Challis, E., Hurley, P., Serra, L., Bozzali, M., Oliver, S., Cercignani, M.: Gaussian process classification of Alzheimer’s disease and mild cognitive impairment from resting-state fMRI. NeuroImage 112, 232–243 (2015)CrossRefGoogle Scholar
  5. 5.
    Hui, J.S., Wilson, R.S., Bennett, D.A., Bienias, J.L., Gilley, D.W., Evans, D.A.: Rate of cognitive decline and mortality in Alzheimer’s disease. Neurology 61, 1356–1361 (2003)CrossRefGoogle Scholar
  6. 6.
    Liu, C.C., Kanekiyo, T., Xu, H., Bu, G.: Apolipoprotein E and Alzheimer disease: risk, mechanisms and therapy. Nat. Rev. Neurol. 9, 106–118 (2013)CrossRefGoogle Scholar
  7. 7.
    Rosengarten, B., Paulsen, S., Burr, O., Kaps, M.: Effect of ApoE ε4 allele on visual evoked potentials and resultant flow coupling in patients with Alzheimer. J. Geriatr. Psychiatry Neurol. 23, 165–170 (2010)CrossRefGoogle Scholar
  8. 8.
    Green, J., Levey, A.I.: Event-related potential changes in groups at increased risk for Alzheimer disease. Arch. Neurol. 56, 1398–1403 (1999)CrossRefGoogle Scholar
  9. 9.
    Lee, T.-W., Yu, Y.W.-Y., Hong, C.-J., Tsai, S.-J., Wu, H.-C., Chen, T.-J.: The influence of apolipoprotein E Epsilon4 polymorphism on qEEG profiles in healthy young females: a resting EEG study. Brain Topogr. 25, 431–442 (2012)CrossRefGoogle Scholar
  10. 10.
    Canuet, L., Tellado, I., Couceiro, V., Fraile, C., Fernandez-Novoa, L., Ishii, R., Takeda, M., Cacabelos, R.: Resting-state network disruption and APOE genotype in Alzheimer’s disease: a lagged functional connectivity study. PLoS ONE 7, e46289 (2012)CrossRefGoogle Scholar
  11. 11.
    Folstein, M.F., Folstein, S.E., McHugh, P.R.: “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J. Psychiatr. Res. 12, 189–198 (1975)CrossRefGoogle Scholar
  12. 12.
    Carcaillon, L., Pérès, K., Péré, J.J., Helmer, C., Orgogozo, J.M., Dartigues, J.F.: Fast cognitive decline at the time of dementia diagnosis: a major prognostic factor for survival in the community. Dement. Geriatr. Cogn. Disord. 23, 439–445 (2007)CrossRefGoogle Scholar
  13. 13.
    Puglielli, L., Tanzi, R.E., Kovacs, D.M.: Alzheimer’s disease: the cholesterol connection. Nat. Neurosci. 6, 345–351 (2003)CrossRefGoogle Scholar
  14. 14.
    Anderer, P., Semlitsch, H.V., Saletu, B., Barbanoj, M.J.: Artifact processing in topographic mapping of electroencephalographic activity in neuropsychopharmacology. Psychiatry Res.: Neuroimaging 45, 79–93 (1992)CrossRefGoogle Scholar
  15. 15.
    Delorme, A., Makeig, S.: EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134, 9–21 (2004)CrossRefGoogle Scholar
  16. 16.
    Barachant, A., Congedo, M.: A Plug&Play P300 BCI Using Information Geometry. arXiv preprint arXiv:1409.0107 (2014)
  17. 17.
    Congedo, M., Barachant, A., Andreev, A.: A New Generation of Brain-Computer Interface Based on Riemannian Geometry arXiv:1310.8115 (2013)
  18. 18.
    Zeng, L.-L., Shen, H., Liu, L., Wang, L., Li, B., Fang, P., Zhou, Z., Li, Y., Hu, D.: Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis. Brain 135, 1498–1507 (2012)CrossRefGoogle Scholar
  19. 19.
    Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning. The MIT Press, Cambridge (2006)MATHGoogle Scholar
  20. 20.
    Adler, R.J., Taylor, J.E.: Random Fields and Geometry. Springer, New York (2007)MATHGoogle Scholar
  21. 21.
    Rasmussen, C.E., Nickisch, H.: Gaussian processes for machine learning (GPML) toolbox. J. Mach. Learn. Res. 11, 3011–3015 (2010)MathSciNetMATHGoogle Scholar
  22. 22.
    Chen, M.: Pattern Recognition and Machine Learning Toolbox. MATLAB Central File Exchange (2016)Google Scholar
  23. 23.
    Brodersen, K.H., Ong, C.S., Stephan, K.E., Buhmann, J.M.: The balanced accuracy and its posterior distribution. In: Proceedings of the 2010 20th International Conference on Pattern Recognition, pp. 3121–3124. IEEE Computer Society (2010)Google Scholar
  24. 24.
    Hanley, J.A., McNeil, B.J.: A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 148, 839–843 (1983)CrossRefGoogle Scholar
  25. 25.
    Stahl, D., Pickles, A., Elsabbagh, M., Johnson, M.H., The, B.T.: Novel machine learning methods for ERP analysis: a validation from research on infants at risk for autism. Dev. Neuropsychol. 37, 274–298 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Wolfgang Fruehwirt
    • 1
    • 2
  • Pengfei Zhang
    • 2
  • Matthias Gerstgrasser
    • 3
  • Dieter Grossegger
    • 4
  • Reinhold Schmidt
    • 5
  • Thomas Benke
    • 6
  • Peter Dal-Bianco
    • 7
  • Gerhard Ransmayr
    • 8
  • Leonard Weydemann
    • 1
  • Heinrich Garn
    • 9
  • Markus Waser
    • 9
  • Michael Osborne
    • 2
  • Georg Dorffner
    • 1
  1. 1.Section for AI and Decision SupportMedical University of ViennaViennaAustria
  2. 2.Department of Engineering ScienceUniversity of OxfordOxfordUK
  3. 3.Department of Computer ScienceUniversity of OxfordOxfordUK
  4. 4.Dr. Grossegger & Drbal GmbHViennaAustria
  5. 5.Department of NeurologyMedical University of GrazGrazAustria
  6. 6.Department of NeurologyMedical University of InnsbruckInnsbruckAustria
  7. 7.Department of NeurologyMedical University of ViennaViennaAustria
  8. 8.Department of NeurologyLinz General HospitalLinzAustria
  9. 9.AIT Austrian Institute of Technology GmbHViennaAustria

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