Alpha and Theta Rhythm Abnormality in Alzheimer’s Disease: A Study Using a Computational Model

  • Basabdatta Sen BhattacharyaEmail author
  • Damien Coyle
  • Liam P. Maguire
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 718)


Electroencephalography (EEG) studies in Alzheimer’s Disease (AD) patients show an attenuation of average power within the alpha band (7.5–13 Hz) and an increase of power in the theta band (4–7 Hz). Significant body of evidence suggest that thalamocortical circuitry underpin the generation and modulation of alpha and theta rhythms. The research presented in this chapter is aimed at gaining a better understanding of the neuronal mechanisms underlying EEG band power changes in AD which may in the future provide useful biomarkers towards early detection of the disease and for neuropharmaceutical investigations. The study is based on a classic computational model of the thalamocortical circuitry which exhibits oscillation within the theta and the alpha bands. We are interested in the change in model oscillatory behaviour corresponding with changes in the connectivity parameters in the thalamocortical as well as sensory input pathways. The synaptic organisation as well as the connectivity parameter values in the model are modified based on recent experimental data from the cat thalamus. We observe that the inhibitory population in the model plays a crucial role in mediating the oscillatory behaviour of the model output. Further, increase in connectivity parameters in the afferent and efferent pathways of the inhibitory population induces a slowing of the output power spectra. These observations may have implications for extending the model for further AD research.


Dominant Frequency Alpha Rhythm Alpha Band Theta Band Thalamic Reticular Nucleus 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work is supported by the Northern Ireland Department for Education and Learning under the Strengthening the All Island Research Base programme. B. Sen Bhattacharya would like to thank Dr. David Watson for valuable comments and suggestions on the work and several useful discussions from time to time.


  1. 1.
    Basar, E., Guntekin, B.: A review of brain oscillations in cognitive disorders and the role of neurotransmitters. Brain Res. 1235, 172–193 (2008) PubMedCrossRefGoogle Scholar
  2. 2.
    Basar, E., Schurmann, M., Basar-Eroglu, C., Karakas, S.: Alpha oscillations in brain functioning: an integrative theory. Int. J. Psychophysiol. 26, 5–29 (1997) PubMedCrossRefGoogle Scholar
  3. 3.
    Bennys, K., Rondouin, G., Vergnes, C., Touchon, J.: Diagnostic value of quantitative EEG in Azheimer’s Disease. Clin. Neurophysiol. 31, 153–160 (2001) CrossRefGoogle Scholar
  4. 4.
    Bhattacharya, B.S., Coyle, D., Maguire, L.P.: A computational modelling approach to investigate alpha rhythm slowing associated with Alzheimer’s Disease. In: Proceedings of the Conference on Brain Inspired Cognitive Systems (BICS), Madrid, Spain, pp. 382–392 (2010) Google Scholar
  5. 5.
    Bhattacharya, B.S., Coyle, D., Maguire, L.P.: Intra- and inter-connectivity influences on event related changes in thalamocortical alpha rhythms. In: Proceedings of the conference on Biologically-Inspired Computation: Theories and Applications (BIC-TA), Liverpool, United Kingdom, pp. 1685–1692 (2010). ISBN 978-1-4244-6439-5 Google Scholar
  6. 6.
    Bhattacharya, B.S., Coyle, D., Maguire, L.P.: Thalamocortical circuitry and alpha rhythm slowing: an empirical study based on a classic computational model. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN), Barcelona, Spain, pp. 3912–3918 (2010) Google Scholar
  7. 7.
    Brenner, R.P., Reynolds, C.F., Ulrich, R.F.: Diagnostic efficacy of computerized spectral versus visual EEG analysis in elderly normal, demented and depressed subjects. Electroencephalogr. Clin. Neurophysiol. 69, 110–117 (1988) PubMedCrossRefGoogle Scholar
  8. 8.
    Brenner, R.P., Reynolds, C.F., Ulrich, R.F.: EEG findings in depressive pseudodementia and dementia with secondary depression. Electroencephalogr. Clin. Neurophysiol. 72, 298–304 (1989) PubMedCrossRefGoogle Scholar
  9. 9.
    Brenner, R.P., Ulrich, R.F., Spiker, D.G., Sclabassi, R.J., Reynolds, C.F., Marin, R.S., Boller, F.: Computerized EEG spectral analysis in elderly normal, demented and depressed subjects. Electroencephalogr. Clin. Neurophysiol. 64, 483–492 (1986) PubMedCrossRefGoogle Scholar
  10. 10.
    Cantero, J.L., Atienza, M., Gomez-Herrero, G., Cruz-Vadell, A., Gil-Neciga, E., Rodriguez-Romero, R., Garcia-Solis, D.: Functional integrity of thalamocortical circuits differentiates normal ageing from mild cognitive impairment. Hum. Brain Mapp. 30, 3944–3957 (2009) PubMedCrossRefGoogle Scholar
  11. 11.
    Stam, C.J., Pijn, J.P.M., Suffczyński, P., da Silva, F.H.L.: Dynamics of the human alpha rhythm: evidence for non-linearity? Clin. Neurophysiol. 110, 1801–1813 (1999) PubMedCrossRefGoogle Scholar
  12. 12.
    Cummings, J.L., Vinters, H.V., Cole, G.M., Khachaturian, Z.S.: Alzheimer’s Disease: Etiologies, pathophysiology, cognitive reserve, and treatment opportunities. Neurology 51(Suppl 1), 2–17 (1998) Google Scholar
  13. 13.
    Siemers, E.M.: Advances in biomarkers and modelling for the development of improved therapeutics: early Alzheimer’s treatment. Abstract of talk at: 1st International Congress on Alzheimer’s Disease and Advanced Neurotechnologies (2010) Google Scholar
  14. 14.
    Geula, C.: Abnormalities of neural circuitry in Alzheimer’s Disease. Neurology 51(Suppl 1), 18–29 (1998) Google Scholar
  15. 15.
    Hogan, M., Swanwick, G.R.J., Kaiser, J., Rowan, M., Lawlor, B.: Memory-related EEG power and coherence reductions in mild Alzheimer’s Disease. Int. J. Psychophysiol. 49, 147–163 (2003) PubMedCrossRefGoogle Scholar
  16. 16.
    Hughes, S.W., Crunelli, V.: Thalamocortical mechanisms in EEG alpha rhythms and their pathological implications. The Neuroscientist 11(4), 357–372 (2005) PubMedCrossRefGoogle Scholar
  17. 17.
    Hughes, S.W., Lorincz, M., Cope, D.W., Blethyn, K.L., Kekesi, K.A., Parri, H.R., Juhasz, G., Crunelli, V.: Synchronised oscillations at α and θ frequencies in the Lateral Geniculate Nucleus. Neuron 42, 253–268 (2004) PubMedCrossRefGoogle Scholar
  18. 18.
    Izhikevich, E.M., Edelman, G.M.: Large-scale model of mammalian thalamocortical systems. Proc. Natl. Acad. Sci. USA 105(9), 3593–3598 (2008) PubMedCrossRefGoogle Scholar
  19. 19.
    Jeong, J.: EEG dynamics in patients with Alzheimer’s disease. Clin. Neurophysiol. 115, 1490–1505 (2004) PubMedCrossRefGoogle Scholar
  20. 20.
    Jones, E.G.: The Thalamus, Vols. I and II, 1st edn. Cambridge University Press, Cambridge (2007) Google Scholar
  21. 21.
    Kim, U., Sanchez-Vives, M.V., McCormick, D.A.: Functional dynamics of gabaergic inhibition in the thalamus. Science 278, 130–134 (1997) PubMedCrossRefGoogle Scholar
  22. 22.
    Llinas, R.: The intrinsic electrophysiological properties of mammalian neurons: insights into central nervous system function. Science 242, 1654–1664 (1988) PubMedCrossRefGoogle Scholar
  23. 23.
    McCormick, D.A.: Acetylcholine: distribution, receptors, and action, pp. 91–101 (1989) Google Scholar
  24. 24.
    McCormick, D.A., Bal, T.: Sleep and arousal: thalamocortical mechanisms. Annu. Rev. Neurosci. 20, 185–215 (1997) PubMedCrossRefGoogle Scholar
  25. 25.
    Montez, T., Poil, S.S., Jones, B.F., Manshanden, I., Verbunt, J.P., van Dijk, B.W., Brussaard, A.B., van Ooyen, A., Stam, C.J., Scheltens, P., Linkenkaer-Hansen, K.: Altered temporal correlations in parietal alpha and prefrontal theta oscillations in early-stage Alzheimer’s Disease. Proc. Natl. Acad. Sci. USA 106(5), 1614–1619 (2009) PubMedCrossRefGoogle Scholar
  26. 26.
    Niedermeyer, E.: Alpha rhythms as physiological and abnormal phenomena. Int. J. Psychophysiol. 26, 31–49 (1997) PubMedCrossRefGoogle Scholar
  27. 27.
    Prinz, P.N., Vitiello, M.V.: Dominant occipital (alpha) rhythm frequency in early stage Alzheimer’s Disease and depression. Electroencephalogr. Clin. Neurophysiol. 73, 427–432 (1989) PubMedCrossRefGoogle Scholar
  28. 28.
    Raji, C.A., Lopez, O.L., Kuller, L.H., Carmichael, O.T., Becker, J.T.: Age, Alzheimer Disease, and brain structure. Neurology 73, 1899–1905 (2009) PubMedCrossRefGoogle Scholar
  29. 29.
    Rodrigues, S., Chizhov, A.V., Marten, F., Terry, J.R.: Mappings between a macroscopic neural-mass model and a reduced conductance-based model. NeuroImage 102, 361–371 (2010) Google Scholar
  30. 30.
    Romei, V., et al.: On the role of prestimulus alpha rhythms over occipito-parietal areas in visual input regulation: correlation or causation? J. Neurosci. 30, 8692–8697 (2010) PubMedCrossRefGoogle Scholar
  31. 31.
    Schreckenberger, M., Lange-Asschenfeld, C., Lochmann, M., Mann, K., Siessmeier, T., Buchholz, H.-G., Bartenstein, P., Gründer, G.: The thalamus as the generator and modulator of EEG alpha rhythm: a combined PET/EEG study with lorazepam challenge in humans. NeuroImage 22(2), 637–644 (2004) PubMedCrossRefGoogle Scholar
  32. 32.
    Sherman, S.M.: Thalamus. Scholarpedia 1(9), 1583 (2006) CrossRefGoogle Scholar
  33. 33.
    Sherman, S.M., Guillery, R.W.: Exploring the Thalamus, 1st edn. Academic Press, New York (2001) Google Scholar
  34. 34.
    da Silva, F.H.L.: Neural mechanisms underlying brain waves: from neural membranes to networks. Electroencephalogr. Clin. Neurophysiol. 79, 81–93 (1991) CrossRefGoogle Scholar
  35. 35.
    da Silva, F.H.L., van Lierop, T.H.M.T., Schrijer, C.F., van Leeuwen, W.S.: Essential differences between alpha rhythms and barbiturate spindles: spectra and thalamo-cortical coherences. Electroencephalogr. Clin. Neurophysiol. 35, 641–645 (1973) PubMedCrossRefGoogle Scholar
  36. 36.
    Soininen, H., Reinikainen, K., Partanen, J., Helkala, E.-L., Paljärvi, L., Riekkinen, P. Sr.: Slowing of electroencephalogram and choline acetyltransferase activity in post mortem frontal cortex in definite Alzheimer’s Disease. Neuroscience 49(3), 529–535 (1992) PubMedCrossRefGoogle Scholar
  37. 37.
    Soininen, H., Reinikainen, K., Partanen, J., Mervaala, E., Paljarvi, L., Helkala, E.-L., Riekkinen, P. Sr.: Slowing of the dominant occipital rhythm in electroencephalogram is associated with low concentration of noradrenaline in the thalamus in patients with Alzheimer’s disease. Neurosci. Lett. 137, 5–8 (1992) PubMedCrossRefGoogle Scholar
  38. 38.
    Stam, C.J.: Use of magnetoencephalography (MEG) to study functional brain networks in neurodegenerative disorders. J. Neurolog. Sci. 289, 128–134 (2010) CrossRefGoogle Scholar
  39. 39.
    Suffczyński, P.: Neural dynamics underlying brain thalamic oscillations investigated with computational models. Ph.D. thesis, Institute of Experimental Physics, University of Warsaw (October 2000) Google Scholar
  40. 40.
    Suffczyński, P., et al.: Event-related dynamics of alpha rhythms: a neuronal network model of focal ERD/surround ERS. In: Pfurtscheller, G., da Silva, F.H.L. (eds.) Handbook of Electroencephalography and Clinical Neurophysiology, Revised series, pp. 67–85. Elsevier, Amsterdam (1999) Google Scholar
  41. 41.
    Tombol, T.: Short neurons and their synaptic relations in the specific thalamic nuclei. Brain Res. 3, 307–326 (1967) PubMedCrossRefGoogle Scholar
  42. 42.
    Tourtellotte, W.G., Hoesen, G.W.V., Hyman, B.T., Tikoo, R.K., Damasio, A.R.: Afferents of the thalamic reticular nucleus are pathologically altered in Alzheimer’s Disease. J. Neuropathol. Exp. Neurol. 48(3), 336 (1989) CrossRefGoogle Scholar
  43. 43.
    Uhlhaas, P.J., Pantel, J., Lanfermann, H., Prvulovic, D., Haenschel, C., Maurer, K., Linden, D.E.J.: Visual perceptual organization deficits in Alzheimer’s dementia. Dement. Geriatr. Cogn. Disord. 25(5), 465–475 (2008) PubMedCrossRefGoogle Scholar
  44. 44.
    Ursino, M., Cona, F., Zavaglia, M.: The generation of rhythms within a cortical region: Analysis of a neural mass model. NeuroImage 52(3), 1080–1094 (2010) PubMedCrossRefGoogle Scholar
  45. 45.
    Wada, Y., Nanbu, Y., Jiang, Z.-Y., Koshino, Y., Yamaguchi, N., Hashimoto, T.: Electroencephalographic abnormalities in patients with presenile dementia of the Alzheimer type: quantitative analysis at rest and during photic stimulation. Biol. Psychiatry 41, 217–225 (1997) PubMedCrossRefGoogle Scholar
  46. 46.
    Waugh, W.H.: A call to reduce the incidence of Alzheimer’s Disease. J. Appl. Res. 10(2), 53–57 (2010) Google Scholar
  47. 47.
    Wilson, H.R., Cowan, J.D.: Excitatory and inhibitory interaction in localized populations of model neurons. J. Biophys. 12, 1–23 (1972) CrossRefGoogle Scholar
  48. 48.
    Wilson, H.R., Cowan, J.D.: A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue. Kybernetik 13, 55–80 (1973) PubMedCrossRefGoogle Scholar
  49. 49.
    Zetterberg, L.H., Kristiansson, L., Mossberg, K.: Performance of a model for a local neuron population. Biol. Cybern. 31, 15–26 (1978) PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Basabdatta Sen Bhattacharya
    • 1
    Email author
  • Damien Coyle
    • 1
  • Liam P. Maguire
    • 1
  1. 1.University of UlsterDerryUK

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