Altered Network Organization in Mild Alzheimer's Disease Compared to Mild Cognitive Impairment Using Resting-State EEG
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.
[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.
[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.
@article{"International Journal of Medical, Medicine and Health Sciences:68290", author = "Chia-Feng Lu and Yuh-Jen Wang and Shin Teng and Yu-Te Wu and Sui-Hing Yan", title = "Altered Network Organization in Mild Alzheimer's Disease Compared to Mild Cognitive Impairment Using Resting-State EEG", 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.", volume = "8", number = "11", pages = "781-7", }
