Relations of Progression in Cognitive Decline with Initial EEG Resting-State Functional Network in Mild Cognitive Impairment
This study aimed at investigating whether the
functional brain networks constructed using the initial EEG (obtained
when patients first visited hospital) can be correlated with the
progression of cognitive decline calculated as the changes of
mini-mental state examination (MMSE) scores between the latest and
initial examinations. 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 investigate the organization of functional networks
in aMCI. Our finding suggested that higher integrated functional
network with sufficient connection strengths, dense connection
between local regions, and high network efficiency in processing
information at the initial stage may result in a better prognosis of the
subsequent cognitive functions for aMCI. In conclusion, the functional
connectivity can be a useful biomarker to assist in prediction of
cognitive declines in aMCI.
[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] R.C. Petersen, “Mild cognitive impairment as a diagnostic entity,” J.
Intern. Med., vol. 256, pp. 183–194, 2004.
[5] P. Missonnier, M. P. Deiber, G. Gold, F.R. Herrmann, P. Millet, A.
Michon, L. Fazio-Costa, V. Ibañez, P. Giannakopoulos, “Working
memory load–related electroencephalographic parameters can
differentiate progressive from stable mild cognitive impairment,”
Neuroscience, vol. 150, Issue 2, pp. 346-356, 2007.
[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.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.
[11] C. Shannon, “A mathematical theory of communication,” Bell Syst Tech J,
vol. 27, pp. 379–426, 1948.
[12] 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.
[13] J.C. Morris, “The Clinical Dementia Rating (CDR): current version and
scoring rules,” Neurology, vol. 43, pp. 2412–2414, 1993.
[14] 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.
[15] A. Gramfort, T. Papadopoulo, E. Olivi, M. Clerc, “OpenMEEG:
opensource software for quasistatic bioelectromagnetics,” BioMedical
Engineering OnLine, vol. 9, no. 1, pp.45, 2010.
[16] 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.
[17] 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.
[18] 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..
[19] 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.
[20] A. Klein, J. Tourville, “101 labeled brain images and a consistent human
cortical labeling protocol,” Frontiers in neuroscience, vol. 6, article 171,
2012.
[21] M.D. Humphries, K. Gurney, “Network ‘Small-world-ness’: a
quantitative method for determining canonical network equivalence,”
PLoS One, vol. 3, article e0002051, 2008.
[22] V. Latora, M. Marchiori, “Efficient behavior of small-world networks,”
Phys. Rev. Lett., vol. 87, article 198701, 2001.
[23] E. Bullmore, O. Sporns, “Complex brain networks: graph theoretical
analysis of structural and functional systems,” Nat. Rev. Neurosci., vol.
10, pp. 186–198, 2009.
[24] S. Achard, E. Bullmore, “Efficiency and cost of economical brain
functional networks,” PLoS Comput. Biol., vol. 3, no. 2, article e17, 2007.
[25] 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.
[26] Y. He, Z. Chen, G. Gong, A. Evans, “Neuronal Networks in Alzheimer’s
Disease,” Neuroscientist, vol. 15, pp. 333-350, 2009.
[27] 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.
[28] B. Tóth, B. File, R. Boha, Z. Kardos, Z. Hidasi, Z.A. Gaál, E. Csibri, P.
Salacz, C.J. Stam, M. Molnár, “EEG network connectivity changes in
mild cognitive impairment — Preliminary results,” International Journal
of Psychophysiology, vol. 92, Issue 1, pp. 1-7, 2014.
[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] R.C. Petersen, “Mild cognitive impairment as a diagnostic entity,” J.
Intern. Med., vol. 256, pp. 183–194, 2004.
[5] P. Missonnier, M. P. Deiber, G. Gold, F.R. Herrmann, P. Millet, A.
Michon, L. Fazio-Costa, V. Ibañez, P. Giannakopoulos, “Working
memory load–related electroencephalographic parameters can
differentiate progressive from stable mild cognitive impairment,”
Neuroscience, vol. 150, Issue 2, pp. 346-356, 2007.
[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.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.
[11] C. Shannon, “A mathematical theory of communication,” Bell Syst Tech J,
vol. 27, pp. 379–426, 1948.
[12] 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.
[13] J.C. Morris, “The Clinical Dementia Rating (CDR): current version and
scoring rules,” Neurology, vol. 43, pp. 2412–2414, 1993.
[14] 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.
[15] A. Gramfort, T. Papadopoulo, E. Olivi, M. Clerc, “OpenMEEG:
opensource software for quasistatic bioelectromagnetics,” BioMedical
Engineering OnLine, vol. 9, no. 1, pp.45, 2010.
[16] 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.
[17] 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.
[18] 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..
[19] 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.
[20] A. Klein, J. Tourville, “101 labeled brain images and a consistent human
cortical labeling protocol,” Frontiers in neuroscience, vol. 6, article 171,
2012.
[21] M.D. Humphries, K. Gurney, “Network ‘Small-world-ness’: a
quantitative method for determining canonical network equivalence,”
PLoS One, vol. 3, article e0002051, 2008.
[22] V. Latora, M. Marchiori, “Efficient behavior of small-world networks,”
Phys. Rev. Lett., vol. 87, article 198701, 2001.
[23] E. Bullmore, O. Sporns, “Complex brain networks: graph theoretical
analysis of structural and functional systems,” Nat. Rev. Neurosci., vol.
10, pp. 186–198, 2009.
[24] S. Achard, E. Bullmore, “Efficiency and cost of economical brain
functional networks,” PLoS Comput. Biol., vol. 3, no. 2, article e17, 2007.
[25] 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.
[26] Y. He, Z. Chen, G. Gong, A. Evans, “Neuronal Networks in Alzheimer’s
Disease,” Neuroscientist, vol. 15, pp. 333-350, 2009.
[27] 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.
[28] B. Tóth, B. File, R. Boha, Z. Kardos, Z. Hidasi, Z.A. Gaál, E. Csibri, P.
Salacz, C.J. Stam, M. Molnár, “EEG network connectivity changes in
mild cognitive impairment — Preliminary results,” International Journal
of Psychophysiology, vol. 92, Issue 1, pp. 1-7, 2014.
@article{"International Journal of Medical, Medicine and Health Sciences:68291", author = "Chia-Feng Lu and Yuh-Jen Wang and Yu-Te Wu and Sui-Hing Yan", title = "Relations of Progression in Cognitive Decline with Initial EEG Resting-State Functional Network in Mild Cognitive Impairment", abstract = "This study aimed at investigating whether the
functional brain networks constructed using the initial EEG (obtained
when patients first visited hospital) can be correlated with the
progression of cognitive decline calculated as the changes of
mini-mental state examination (MMSE) scores between the latest and
initial examinations. 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 investigate the organization of functional networks
in aMCI. Our finding suggested that higher integrated functional
network with sufficient connection strengths, dense connection
between local regions, and high network efficiency in processing
information at the initial stage may result in a better prognosis of the
subsequent cognitive functions for aMCI. In conclusion, the functional
connectivity can be a useful biomarker to assist in prediction of
cognitive declines in aMCI.
", keywords = "Cognitive decline, functional connectivity, MCI,
MMSE.", volume = "8", number = "11", pages = "788-7", }
