Linear and Nonlinear EEG Synchronization in Alzheimer’s Disease

Oldřich Vyšata1,2, Martin Vališ1, Aleš Procházka2, Robert Rusina3,4, Ladislav Pazdera5
1Department of Neurology, Faculty of Medicine in Hradec Králové, Charles University, Hradec Králové, Czech Republic
2Department of Computing and Control Engineering, Institute of Chemical Technology, Prague, Czech Republic
3Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University in Prague, and General University Hospital in Prague, Prague, Czech Republic
4Department of Neurology, Thomayer Hospital and Institute for Postgraduate Education in Medicine, Prague, Czech Republic
5Neurocentre Caregroup Ltd., Rychnov nad Kneznou, Czech Republic

Tóm tắt

Từ khóa


Tài liệu tham khảo

Z. Sankari, H. Adeli, and A. Adeli, “Wavelet coherence model for diagnosis of Alzheimer disease,” Clin. EEG Neurosci., 43, No. 4, 268-227 (2012).

V. S. Afraimovich, N. N. Verichev, and M. I. Rabinovich, “Stochastic synchronization of oscillation in dissipative systems,” Radiophys. Quantium Electron., 29, No. 9, 795-803 (1986).

M. G. Rosenblum, A. S. Pikovsky, and J. Kurths, “Phase synchronization of chaotic oscillators,” Phys. Rev. Lett., 76, No. 3-4, 1804-1807 (1996).

J. Dauwels, F. Vialatte, and A. Cichocki, “Diagnosis of Alzheimer’s disease from EEG signals: where are we standing?,” Curr. Alzheimer Res., 7, No. 6, 487-505 (2010).

J. P. Lachaux, A. Lutz, D. Rudrauf, et al., “Estimating the time course of coherence between single-trial brain signals: an introduction to wavelet coherence,” Clin. Neurophys., 32, No. 3, 157–174 (2002).

A. Klein, T. Sauer, A. Jedynak, and W. Skrandies, “Conventional and wavelet coherence applied to sensory-evoked electrical brain activity,” IEEE Trans. Biomed. Eng., 53, No. 2, 266–272 (2006).

V. Sakkalis, T. Oikonomou, E. Pachou, et al., “Time-significant wavelet coherence for the evaluation of schizophrenic brain activity using a graph theory approach,” 28th Annu. Int. Conf. IEEE, New York, EMBS’06 (2006).

S. Aviyente, “A measure of mutual information on the time-frequency plane,” Proc. ICASSP, 2005, 481–484 (2005).

J. A. Gray, “Brain systems that mediate both emotion and cognition,” Cogn. Emot., 4, No. 3, 269–288 (1990).

J. Jeong, J. C. Gore, and B. S. Peterson, “Mutual information analysis of the EEG in patients with Alzheimer’s disease,” Clin. Neurophys., 112, No. 5, 827-835 (2001).

T. Schreiber and A. Schmitz, “Surrogate time series,” Physica D, 142, No. 3-4, 346–382 (2000).

C. W. J. Granger, “Investigating causal relations by econometric models and cross-spectral methods,” Econometrica, 37, No. 3, 424-438 (1969).

J. Arnhold, K. Lehnertz, P. Grassberger, and C. E. Elger, “A robust method for detecting interdependences: application to intracranially recorded EEG,” Physica D, 134, No 1, 419–430 (1999).

Y. He, Z. Chen, G. Gong, and A. Evans, “Neuronal networks in Alzheimer’s disease,” Neuroscientist, 15, No. 4, 333-350 (2009).

J. H. Morrison, S. Scherr, D. A. Lewis, et al., “The laminar and regional distribution of neocortical somatostatin and neuritic plaques: implications for Alzheimer’s disease as a global neocortical disconnection syndrome,” In: A. B. Scheibel and A. F. Wechsler, eds., The Biological Substrates of Alzheimer’s Disease, Academic Press, Orlando, pp. 115–131 (1986).

C. J. Stam, B. F. Jones, G. Nolte, et al., “Small-world networks and functional connectivity in Alzheimer’s disease,” Cerebr. Cort., 17, No 1, 92–99 (2007).

C. Sorg, V. Riedl, M. Muhlau, et al., “Selective changes of resting-state networks in individuals at risk for Alzheimer’s disease,” Proc. Natl. Acad. Sci. USA, 104, No 47, 18760–18765 (2007).

Y. He, Z. Chen and A. Evans, “Structural insights into aberrant topological patterns of large-scale cortical networks in Alzheimer’s disease,” J. Neurosci., 18, No 28, 4756-4766 (2008).