Study on Brain Functional Connectivity Using Resting State Electroencephalogram Based on Synchronization Likelihood in Alzheimer's Disease_Journal of Biomedical Engineering

Authors：

LILi , CHENJingjing ,  ZHENGXuyuan

School of Biomedical Engineering amd Technology, Tianjin Medical University, Tianjin 300070, China;

Corresponding author：

ZHENGXuyuan, Email: zhengxuyuan@tijmu.edu.cn

Keywords：

Alzheimer's disease; synchronization likelihood; electroencephalogram; alpha-band; brain functional connectivity

DOI：

10.7507/1001-5515.20150170

Video：

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Alzheimer's disease (AD) is the most common type of dementia and a neurodegenerative disease with progressive cognitive dysfunction as the main feature. How to identify the early changes of cognitive dysfunction and give appropriate treatments is of great significance to delay the onset of dementia. Some other researches have shown that AD is associated with abnormal changes of brain networks. To study human brain functional connectivity characteristics in AD, 16 channels electroencephalogram (EEG) were recorded under resting and eyes-closed condition in 15 AD patients and 15 subjects in the control group. The synchronization likelihood of the full-band and alpha-band (8-13 Hz) data were evaluated, which resulted in the synchronization likelihood coefficient matrices. Considering a threshold T, the matrices were converted into binary graphs. Then the graphs of two groups were measured by topological parameters including the clustering coefficient and global efficiency. The results showed that the global efficiency of the network in full-band EEG was significantly smaller in AD group for the values of T=0.06 and T=0.07, but there was no statistically significant difference in the clustering coefficients between the two groups for the values of T (0.05-0.07). However, the clustering coefficient and global efficiency were significantly lower in AD patients at alpha-band for the same threshold range than those of subjects in the control group. It suggests that there may be decreases of the brain connectivity strength in AD patients at alpha-band of the resting-state EEG. This study provides a support for quantifying functional brain state of AD from the brain network perspective.

Citation： LILi, CHENJingjing, ZHENGXuyuan. Study on Brain Functional Connectivity Using Resting State Electroencephalogram Based on Synchronization Likelihood in Alzheimer's Disease. Journal of Biomedical Engineering, 2015, 32(5): 952-957. doi: 10.7507/1001-5515.20150170 Copy

1.	STERN P. Connection, connection, connection…[J]. Science, 2013, 342(6158):577.
2.	STAM C J. Functional connectivity patterns of human magnetoencephalographic recordings:a ‘small-world’ network?[J]. Neurosci Lett, 2004, 355(1/2):25-28.
3.	GREICIUS M D, SRIVASTAVA G, REISS A L, et al. Default-mode network activity distinguishes Alzheimer's disease from healthy aging:Evidence from functional MRI[J]. Proc Natl Acad Sci U S A, 2004, 101(13):4637-4642.
4.	WANG Kun, LIANG Meng, WANG Liang, et al. Altered functional connectivity in early Alzheimer's disease:a resting-state fMRI study[J]. Hum Brain Mapp, 2007, 28(10):967-978.
5.	ZHANG Hongying, WANG Shijie, XING Jiong, et al. Detection of PCC functional connectivity characteristics in resting-state fMRI in mild Alzheimer's disease[J]. Behav Brain Res, 2009, 197(1):103-108.
6.	SMIT D J, BOERSMA M, SCHNACK H G, et al. The brain matures with stronger functional connectivity and decreased randomness of its network[J]. PLoS One, 2012, 7(5):e36896.
7.	YAO Zhijun, ZHANG Yuanchao, LIN Lei, et al. Abnormal cortical networks in mild cognitive impairment and Alzheimer's disease[J]. PLoS Comput Biol, 2010, 6(11):e1001006.
8.	SRINIVASAN R, WINTER W R, DING Jian, et al. EEG and MEG coherence:measures of functional connectivity at distinct spatial scales of neocortical dynamics[J]. J Neurosci Methods, 2007, 166(1):41-52.
9.	SANZ-ARIGITA E J, SCHOONHEIM M M, DAMOISEAUX J S, et al. Loss of ‘small-world’ networks in Alzheimer's disease:graph analysis of FMRI resting-state functional connectivity[J]. PLoS One, 2010, 5(11):e13788.
10.	SUPEKAR K, MENON V, RUBIN D, et al. Network analysis of intrinsic functional brain connectivity in Alzheimer's disease[J]. PLoS Comput Biol, 2008, 4(6):e1000100.
11.	STAM C J, JONES B F, NOLTE G, et al. Small-world networks and functional connectivity in Alzheimer's disease[J]. Cereb Cortex, 2007, 17(1):92-99.
12.	STAM C J, DE HAAN W, DAFFERTSHOFER A, et al. Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer's disease[J]. Brain, 2009, 132(Pt 1):213-224.
13.	DE HAAN W, PIJNENBURG Y A, STRIJERS R L, et al. Functional neural network analysis in frontotemporal dementia and Alzheimer's disease using EEG and graph theory[J]. BMC Neurosci, 2009, 10:101.
14.	GUSNARD D A, RAICHLE M E, RAICHLE M E. Searching for a baseline:functional imaging and the resting human brain[J]. Nat Rev Neurosci, 2001, 2(10):685-694.
15.	KLIMESCH W. EEG alpha and theta oscillations reflect cognitive and memory performance:a review and analysis[J]. Brain Res Brain Res Rev, 1999, 29(2/3):169-195.
16.	NUNEZ P L, WINGEIER B M, SILBERSTEIN R B. Spatial-temporal structures of human alpha rhythms:theory, microcurrent sources, multiscale measurements, and global binding of local networks[J]. Hum Brain Mapp, 2001, 13(3):125-164.
17.	STAM C J, VAN DIJK B W. Sychronization likelihood:an unbiased measure of generalized synchronization in multivariate data sets[J]. Physica D, 2002, 163:236-241.
18.	MONTEZ T, LINKENKAER-HANSEN K, VAN DIJK B W, et al. Synchronization likelihood with explicit time-frequency priors[J]. Neuroimage, 2006, 33(4):1117-1125.
19.	SPORNS O, CHIALVO D R, KAISER M, et al. Organization, development and function of complex brain networks[J]. Trends Cogn Sci, 2004, 8(9):418-425.
20.	SPORNS O. Structure and function of complex brain networks[J]. Dialogues Clin Neurosci, 2013, 15(3):247-262.
21.	ACHARD S, BULLMORE E. Efficiency and cost of economical brain functional networks[J]. PLoS Comput Biol, 2007, 3(2):e17.

1. STERN P. Connection, connection, connection…[J]. Science, 2013, 342(6158):577.
2. STAM C J. Functional connectivity patterns of human magnetoencephalographic recordings:a ‘small-world’ network?[J]. Neurosci Lett, 2004, 355(1/2):25-28.
3. GREICIUS M D, SRIVASTAVA G, REISS A L, et al. Default-mode network activity distinguishes Alzheimer's disease from healthy aging:Evidence from functional MRI[J]. Proc Natl Acad Sci U S A, 2004, 101(13):4637-4642.
4. WANG Kun, LIANG Meng, WANG Liang, et al. Altered functional connectivity in early Alzheimer's disease:a resting-state fMRI study[J]. Hum Brain Mapp, 2007, 28(10):967-978.
5. ZHANG Hongying, WANG Shijie, XING Jiong, et al. Detection of PCC functional connectivity characteristics in resting-state fMRI in mild Alzheimer's disease[J]. Behav Brain Res, 2009, 197(1):103-108.
6. SMIT D J, BOERSMA M, SCHNACK H G, et al. The brain matures with stronger functional connectivity and decreased randomness of its network[J]. PLoS One, 2012, 7(5):e36896.
7. YAO Zhijun, ZHANG Yuanchao, LIN Lei, et al. Abnormal cortical networks in mild cognitive impairment and Alzheimer's disease[J]. PLoS Comput Biol, 2010, 6(11):e1001006.
8. SRINIVASAN R, WINTER W R, DING Jian, et al. EEG and MEG coherence:measures of functional connectivity at distinct spatial scales of neocortical dynamics[J]. J Neurosci Methods, 2007, 166(1):41-52.
9. SANZ-ARIGITA E J, SCHOONHEIM M M, DAMOISEAUX J S, et al. Loss of ‘small-world’ networks in Alzheimer's disease:graph analysis of FMRI resting-state functional connectivity[J]. PLoS One, 2010, 5(11):e13788.
10. SUPEKAR K, MENON V, RUBIN D, et al. Network analysis of intrinsic functional brain connectivity in Alzheimer's disease[J]. PLoS Comput Biol, 2008, 4(6):e1000100.
11. STAM C J, JONES B F, NOLTE G, et al. Small-world networks and functional connectivity in Alzheimer's disease[J]. Cereb Cortex, 2007, 17(1):92-99.
12. STAM C J, DE HAAN W, DAFFERTSHOFER A, et al. Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer's disease[J]. Brain, 2009, 132(Pt 1):213-224.
13. DE HAAN W, PIJNENBURG Y A, STRIJERS R L, et al. Functional neural network analysis in frontotemporal dementia and Alzheimer's disease using EEG and graph theory[J]. BMC Neurosci, 2009, 10:101.
14. GUSNARD D A, RAICHLE M E, RAICHLE M E. Searching for a baseline:functional imaging and the resting human brain[J]. Nat Rev Neurosci, 2001, 2(10):685-694.
15. KLIMESCH W. EEG alpha and theta oscillations reflect cognitive and memory performance:a review and analysis[J]. Brain Res Brain Res Rev, 1999, 29(2/3):169-195.
16. NUNEZ P L, WINGEIER B M, SILBERSTEIN R B. Spatial-temporal structures of human alpha rhythms:theory, microcurrent sources, multiscale measurements, and global binding of local networks[J]. Hum Brain Mapp, 2001, 13(3):125-164.
17. STAM C J, VAN DIJK B W. Sychronization likelihood:an unbiased measure of generalized synchronization in multivariate data sets[J]. Physica D, 2002, 163:236-241.
18. MONTEZ T, LINKENKAER-HANSEN K, VAN DIJK B W, et al. Synchronization likelihood with explicit time-frequency priors[J]. Neuroimage, 2006, 33(4):1117-1125.
19. SPORNS O, CHIALVO D R, KAISER M, et al. Organization, development and function of complex brain networks[J]. Trends Cogn Sci, 2004, 8(9):418-425.
20. SPORNS O. Structure and function of complex brain networks[J]. Dialogues Clin Neurosci, 2013, 15(3):247-262.
21. ACHARD S, BULLMORE E. Efficiency and cost of economical brain functional networks[J]. PLoS Comput Biol, 2007, 3(2):e17.

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