Altered Network Organization in Mild Alzheimer's Disease Compared to Mild Cognitive Impairment Using Resting-State EEG
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Altered Network Organization in Mild Alzheimer's Disease Compared to Mild Cognitive Impairment Using Resting-State EEG

Authors: Chia-Feng Lu, Yuh-Jen Wang, Shin Teng, Yu-Te Wu, Sui-Hing Yan

Abstract:

Brain functional networks based on resting-state EEG data were compared between patients with mild Alzheimer’s disease (mAD) and matched patients with amnestic subtype of mild cognitive impairment (aMCI). We integrated the time–frequency cross mutual information (TFCMI) method to estimate the EEG functional connectivity between cortical regions and the network analysis based on graph theory to further investigate the alterations of functional networks in mAD compared with aMCI group. We aimed at investigating the changes of network integrity, local clustering, information processing efficiency, and fault tolerance in mAD brain networks for different frequency bands based on several topological properties, including degree, strength, clustering coefficient, shortest path length, and efficiency. Results showed that the disruptions of network integrity and reductions of network efficiency in mAD characterized by lower degree, decreased clustering coefficient, higher shortest path length, and reduced global and local efficiencies in the delta, theta, beta2, and gamma bands were evident. The significant changes in network organization can be used in assisting discrimination of mAD from aMCI in clinical.

Keywords: EEG, functional connectivity, graph theory, TFCMI.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1096863

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References:


[1] R. C. Petersen, "Early diagnosis of Alzheimer’s disease: is MCI too late?," Current Alzheimer Research, vol. 6, p. 324, 2009.
[2] A. Pozueta, E. Rodríguez-Rodríguez, J. L. Vazquez-Higuera, I. Mateo, P. Sánchez-Juan, S. González-Perez, J. Berciano, and O. Combarros, "Detection of early Alzheimer's disease in MCI patients by the combination of MMSE and an episodic memory test," BMC neurology, vol. 11, p. 78, 2011.
[3] S. Landau, D. Harvey, C. Madison, E. Reiman, N. Foster, P. Aisen, R. Petersen, L. Shaw, J. Trojanowski, and C. Jack, "Comparing predictors of conversion and decline in mild cognitive impairment," Neurology, vol. 75, pp. 230-238, 2010.
[4] X. Delbeuck, M. Van der Linden, and F. Collette, "Alzheimer'disease as a disconnection syndrome?," Neuropsychology review, vol. 13, pp. 79-92, 2003.
[5] M. Bozzali, G. J. Parker, L. Serra, K. Embleton, T. Gili, R. Perri, C. Caltagirone, and M. Cercignani, "Anatomical connectivity mapping: a new tool to assess brain disconnection in Alzheimer's disease," Neuroimage, vol. 54, pp. 2045-2051, 2011.
[6] D. Popivanov, J. Dushanova, “Non-linear EEG dynamic changes and their probable relation to voluntary movement organization,” Neuroreport, vol. 10, pp. 1397–401, 1999.
[7] C. Andrew, G. Pfurtscheller, “Lack of bilateral coherence of post-movement central beta oscillations in the human electroencephalogram,” Neurosci. Lett., vol. 273, pp. 89–92, 1999.
[8] P.L. Nunez, R. Srinivasan, A.F. Westdorp, R.S. Wijesinghe, D.M. Tucker, R.B. Silberstein et al., “EEG coherency: I: statistics, reference electrode, volume conduction, Laplacians, cortical imaging, and interpretation at multiple scales,” Electroencephalogr. Clin. Neurophysiol., vol. 103, pp. 499–515, 1997.
[9] C.F. Lu, S. Teng, C.I. Hung, P.J. Tseng, L.T. Lin, P.L. Lee, Y.T. Wu, “Reorganization of functional connectivity during the motor task using EEG time–frequency cross mutual information analysis,” Clinical Neurophysiology, vol. 122, no. 8, pp. 1569-1579, 2011.
[10] C. Shannon, “A mathematical theory of communication,” Bell Syst Tech J, vol. 27, pp. 379–426, 1948.
[11] Y. Liu, C. Yu, X. Zhang, J. Liu, Y. Duan, A. F. Alexander-Bloch, B. Liu, T. Jiang, and E. Bullmore, "Impaired long distance functional connectivity and weighted network architecture in Alzheimer's disease," Cerebral Cortex, vol. 24, pp. 1422-1435, 2014.
[12] C. Stam, B. Jones, G. Nolte, M. Breakspear, and P. Scheltens, "Small-world networks and functional connectivity in Alzheimer's disease," Cerebral Cortex, vol. 17, pp. 92-99, 2007.
[13] M.F. Folstein, S.E. Folstein, P.R. McHugh, “Mini-mental state. A practical method for grading the cognitive state of patients for the clinician,” J. Psychiatr. Res., vol. 12, pp. 189–198, 1975.
[14] J.C. Morris, “The Clinical Dementia Rating (CDR): current version and scoring rules,” Neurology, vol. 43, pp. 2412–2414, 1993.
[15] R.C. Petersen, “Mild cognitive impairment as a diagnostic entity,” J. Intern. Med., vol. 256, pp. 183–194, 2004.
[16] G. McKhann, D. Drachman, M. Folstein, R. Katzman, D. Price et al,. “Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease,” Neurology, vol. 34, pp. 939–944, 1984.
[17] A. Delorme, S. Makeig, “EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis.” J. Neurosci. Methods., vol. 134, pp. 9–21, 2004.
[18] A. Gramfort, T. Papadopoulo, E. Olivi, M. Clerc, “OpenMEEG: opensource software for quasistatic bioelectromagnetics,” BioMedical Engineering OnLine, vol. 9, no. 1, pp.45, 2010.
[19] M.S. Hamalainen, R.J. Ilmoniemi, “Interpreting magnetic fields of the brain: minimum norm estimates,” Med. Biol. Eng. Comput., vol. 32, pp. 35–42, 1994.
[20] F.H. Lin, T. Witzel, S.P. Ahlfors, S.M. Stufflebeam, J.W. Belliveau et al, “Assessing and improving the spatial accuracy in MEG source localization by depth-weighted minimum-norm estimates,” Neuroimage, vol. 31, pp. 160–171, 2006.
[21] F. Tadel, S. Baillet, J.C. Mosher, D. Pantazis, R.M. Leahy, “Brainstorm: a user-friendly application for MEG/EEG analysis,” Comput. Intell. Neurosci., 879716, 2011..
[22] D.L. Collins, A.P. Zijdenbos, V. Kollokian et al., “Design and construction of a realistic digital brain phantom,” IEEE Transactions on Medical Imaging, vol. 17, no. 3, pp. 463–468, 1998.
[23] A. Klein, J. Tourville, “101 labeled brain images and a consistent human cortical labeling protocol,” Frontiers in neuroscience, vol. 6, article 171, 2012.
[24] C.C. Chen, J.C. Hsieh, Y.Z. Wu, P.L. Lee, S.S. Chen, D.M. Niddam, T.C. Yeh, Y.T. Wu, “Mutual-information-based approach for neural connectivity during self-paced finger lifting task,” Human brain mapping, vol. 29, no. 3, pp. 265-280, 2008.
[25] M. Rubinov, O. Sporns, “Complex network measures of brain connectivity: Uses and interpretations,” Neuroimage, vol. 52, pp. 1059–1069, 2010.
[26] M.D. Humphries, K. Gurney, “Network ‘Small-world-ness’: a quantitative method for determining canonical network equivalence,” PLoS One, vol. 3, article e0002051, 2008.
[27] V. Latora, M. Marchiori, “Efficient behavior of small-world networks,” Phys. Rev. Lett., vol. 87, article 198701, 2001.
[28] E. Bullmore, O. Sporns, “Complex brain networks: graph theoretical analysis of structural and functional systems,” Nat. Rev. Neurosci., vol. 10, pp. 186–198, 2009.
[29] S. Achard, E. Bullmore, “Efficiency and cost of economical brain functional networks,” PLoS Comput. Biol., vol. 3, no. 2, article e17, 2007.
[30] Y. Benjamini, Y. Hochberg, “Controlling the false discovery rate: a practical and powerful approach to multiple testing,” J. R. Stat. Soc. Series B Stat. Methodol., vol. 57, pp. 289–300, 1995.
[31] Y. He, Z. Chen, G. Gong, A. Evans, “Neuronal Networks in Alzheimer’s Disease,” Neuroscientist, vol. 15, pp. 333-350, 2009.
[32] Y. Liu, C. Yu, X. Zhang, J. Liu, Y. Duan, A.F. Alexander-Bloch, B. Liu, T. Jiang, E. Bullmore, “Impaired long distance functional connectivity and weighted network architecture in Alzheimer's disease,” Cerebral Cortex, vol. 24, no. 6, pp.1422-1435, 2014.
[33] R.P. Vertes, "Hippocampal theta rhythm: a tag for short-term memory," Hippocampus, vol. 15, no. 7, pp. 923–935, 2005.
[34] Y. Kubota, W. Sato, M. Toichi, T. Murai, T. Okada, A. Hayashi, A. Sengoku, “Frontal midline theta rhythm is correlated with cardiac autonomic activities during the performance of an attention demanding meditation procedure,” Cognitive Brain Research, vol. 11, no. 2, pp. 281-287, 2001.
[35] R.E. Mistlberger, B.M. Bergmann, A. Rechtschaffen, “Relationships among wake episode lengths, contiguous sleep episode lengths, and electroencephalographic delta waves in rats with suprachiasmatic nuclei lesions,” Sleep, vol. 10, no. 1, pp. 12-24, 1987.