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Current Alzheimer Research

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ISSN (Print): 1567-2050
ISSN (Online): 1875-5828

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Research Article

Functional Connectivity Alterations Based on the Weighted Phase Lag Index: An Exploratory Electroencephalography Study on Alzheimer’s Disease

Author(s): Yi Yan, Aonan Zhao, Weina Ying, Yinghui Qiu, Yanfei Ding, Ying Wang, Wei Xu* and Yulei Deng*

Volume 18, Issue 6, 2021

Published on: 01 October, 2021

Page: [513 - 522] Pages: 10

DOI: 10.2174/1567205018666211001110824

Price: $65

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Abstract

Objective: Numerous electroencephalography (EEG) studies focus on the alteration of electrical activity in patients with Alzheimer’s Disease (AD), but there are no consistent results especially regarding functional connectivity. We supposed that the weighted Phase Lag Index (w- PLI), as phase-based measures of functional connectivity, may be used as an auxiliary diagnostic method for AD.

Methods: We enrolled 30 patients with AD, 30 patients with Mild Cognitive Impairment (MCI), and 30 Healthy Controls (HC). EEGs were recorded in all participants at baseline during relaxed wakefulness. Following EEG preprocessing, Power Spectral Density (PSD) and wPLI parameters were determined to further analyze whether they were correlated to cognitive scores.

Results: In the patients with AD, the increased PSD in theta band was presented compared with MCI and HC groups, which was associated with disturbances of the directional, computational, and delayed memory capacity. Furthermore, the wPLI revealed a distinctly lower connection strength between frontal and distant areas in the delta band and a higher connection strength of the central and temporo-occipital region in the theta band for AD patients. Moreover,we found a significant negative correlation between theta functional connectivity and cognitive scores.

Conclusion: Increased theta PSD and decreased delta wPLI may be one of the earliest changes in AD and associated with disease severity. The parameter wPLI is a novel measurement of phase synchronization and has potentials in understanding underlying functional connectivity and aiding in the diagnostics of AD.

Keywords: Alzheimer's disease, mild cognitive impairment, EEG, power spectral density, weighted phase lag index, cognitive scores.

« Previous Next »
[1]
Alzheimer’s Association. 2015 Alzheimer’s disease facts and figures. Alzheimers Dement 2015; 11(3): 332-84.
[http://dx.doi.org/10.1016/j.jalz.2015.02.003] [PMID: 25984581]
[2]
Bahar-Fuchs A, Martyr A, Goh AM, Sabates J, Clare L. Cognitive training for people with mild to moderate dementia. Cochrane Database Syst Rev 2019; 3: CD013069.
[http://dx.doi.org/10.1002/14651858.CD013069.pub2] [PMID: 30909318]
[3]
Ganguli M, Dodge HH, Shen C, DeKosky ST. Mild cognitive impairment, amnestic type: an epidemiologic study. Neurology 2004; 63(1): 115-21.
[http://dx.doi.org/10.1212/01.WNL.0000132523.27540.81] [PMID: 15249620]
[4]
Langa KM, Levine DA. The diagnosis and management of mild cognitive impairment: a clinical review. JAMA 2014; 312(23): 2551-61.
[http://dx.doi.org/10.1001/jama.2014.13806] [PMID: 25514304]
[5]
Roberts R, Knopman DS. Classification and epidemiology of MCI. Clin Geriatr Med 2013; 29(4): 753-72.
[http://dx.doi.org/10.1016/j.cger.2013.07.003] [PMID: 24094295]
[6]
Ruiz-Gómez SJ, Gómez C, Poza J, et al. Measuring alterations of spontaneous EEG neural coupling in Alzheimer’s disease and mild cognitive impairment by means of cross-entropy metrics. Front Neuroinform 2018; 12: 76.
[http://dx.doi.org/10.3389/fninf.2018.00076] [PMID: 30459586]
[7]
Kinnunen KM, Cash DM, Poole T, et al. Presymptomatic atrophy in autosomal dominant Alzheimer’s disease: A serial magnetic resonance imaging study. Alzheimers Dement 2018; 14(1): 43-53.
[http://dx.doi.org/10.1016/j.jalz.2017.06.2268] [PMID: 28738187]
[8]
Whitwell JL. Alzheimer’s disease neuroimaging. Curr Opin Neurol 2018; 31(4): 396-404.
[http://dx.doi.org/10.1097/WCO.0000000000000570] [PMID: 29762152]
[9]
Fan M, Chou CA. Detecting abnormal pattern of epileptic seizures via temporal synchronization of EEG signals. IEEE Trans Biomed Eng 2019; 66(3): 601-8.
[http://dx.doi.org/10.1109/TBME.2018.2850959] [PMID: 29993518]
[10]
Engels MM, Stam CJ, van der Flier WM, Scheltens P, de Waal H, van Straaten EC. Declining functional connectivity and changing hub locations in Alzheimer’s disease: an EEG study. BMC Neurol 2015; 15: 145.
[http://dx.doi.org/10.1186/s12883-015-0400-7] [PMID: 26289045]
[11]
Park YM, Che HJ, Im CH, Jung HT, Bae SM, Lee SH. Decreased EEG synchronization and its correlation with symptom severity in Alzheimer’s disease. Neurosci Res 2008; 62(2): 112-7.
[http://dx.doi.org/10.1016/j.neures.2008.06.009] [PMID: 18672010]
[12]
Soininen H, Partanen J, Pääkkönen A, Koivisto E, Riekkinen PJ. Changes in absolute power values of EEG spectra in the follow-up of Alzheimer’s disease. Acta Neurol Scand 1991; 83(2): 133-6.
[http://dx.doi.org/10.1111/j.1600-0404.1991.tb04662.x] [PMID: 2017898]
[13]
Jelic V, Shigeta M, Julin P, Almkvist O, Winblad B, Wahlund LO. Quantitative electroencephalography power and coherence in Alzheimer’s disease and mild cognitive impairment. Dementia 1996; 7(6): 314-23.
[PMID: 8915037]
[14]
Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage 2010; 52(3): 1059-69.
[http://dx.doi.org/10.1016/j.neuroimage.2009.10.003] [PMID: 19819337]
[15]
Sankari Z, Adeli H, Adeli A. Intrahemispheric, interhemispheric, and distal EEG coherence in Alzheimer’s disease. Clin Neurophysiol 2011; 122(5): 897-906.
[http://dx.doi.org/10.1016/j.clinph.2010.09.008] [PMID: 21056936]
[16]
Babiloni C, Del Percio C, Lizio R, et al. Abnormalities of resting-state functional cortical connectivity in patients with dementia due to Alzheimer’s and Lewy body diseases: an EEG study. Neurobiol Aging 2018; 65: 18-40.
[http://dx.doi.org/10.1016/j.neurobiolaging.2017.12.023] [PMID: 29407464]
[17]
Adler G, Brassen S, Jajcevic A. EEG coherence in Alzheimer’s dementia. J Neural Transm (Vienna) 2003; 110(9): 1051-8.
[http://dx.doi.org/10.1007/s00702-003-0024-8] [PMID: 12928837]
[18]
Musaeus CS, Nielsen MS, Høgh P. Altered low-frequency EEG connectivity in mild cognitive impairment as a sign of clinical progression. J Alzheimers Dis 2019; 68(3): 947-60.
[http://dx.doi.org/10.3233/JAD-181081] [PMID: 30883355]
[19]
Wang R, Wang J, Yu H, Wei X, Yang C, Deng B. Decreased coherence and functional connectivity of electroencephalograph in Alzheimer’s disease. Chaos 2014; 24(3): 033136.
[http://dx.doi.org/10.1063/1.4896095] [PMID: 25273216]
[20]
Vinck M, Oostenveld R, van Wingerden M, Battaglia F, Pennartz CM. An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias. Neuroimage 2011; 55(4): 1548-65.
[http://dx.doi.org/10.1016/j.neuroimage.2011.01.055] [PMID: 21276857]
[21]
Musaeus CS, Engedal K, Høgh P, et al. Oscillatory connectivity as a diagnostic marker of dementia due to Alzheimer’s disease. Clin Neurophysiol 2019; 130(10): 1889-99.
[http://dx.doi.org/10.1016/j.clinph.2019.07.016] [PMID: 31408790]
[22]
Briels CT, Schoonhoven DN, Stam CJ, de Waal H, Scheltens P, Gouw AA. Reproducibility of EEG functional connectivity in Alzheimer’s disease. Alzheimers Res Ther 2020; 12(1): 68.
[http://dx.doi.org/10.1186/s13195-020-00632-3] [PMID: 32493476]
[23]
McKhann GM, Knopman DS, Chertkow H, et al. The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 2011; 7(3): 263-9.
[http://dx.doi.org/10.1016/j.jalz.2011.03.005] [PMID: 21514250]
[24]
Knopman DS, Petersen RC. Mild cognitive impairment and mild dementia: a clinical perspective. Mayo Clin Proc 2014; 89(10): 1452-9.
[http://dx.doi.org/10.1016/j.mayocp.2014.06.019] [PMID: 25282431]
[25]
Petersen RC. Mild cognitive impairment as a diagnostic entity. J Intern Med 2004; 256(3): 183-94.
[http://dx.doi.org/10.1111/j.1365-2796.2004.01388.x] [PMID: 15324362]
[26]
Stam CJ, Nolte G, Daffertshofer A. Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Hum Brain Mapp 2007; 28(11): 1178-93.
[http://dx.doi.org/10.1002/hbm.20346] [PMID: 17266107]
[27]
Sunwoo JS, Lee S, Kim JH, et al. Altered functional connectivity in idiopathic rapid eye movement sleep behavior disorder: A resting-state EEG study. Sleep 2017; 40(6)
[http://dx.doi.org/10.1093/sleep/zsx058] [PMID: 28431177]
[28]
Benjamini Y, Hochberg Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J R Stat Soc B 1995; 57(1): 289-300.
[http://dx.doi.org/10.1111/j.2517-6161.1995.tb02031.x]
[29]
Zalesky A, Fornito A, Bullmore ET. Network-based statistic: identifying differences in brain networks. Neuroimage 2010; 53(4): 1197-207.
[http://dx.doi.org/10.1016/j.neuroimage.2010.06.041] [PMID: 20600983]
[30]
Babiloni C, Lizio R, Del Percio C, et al. Cortical sources of resting state EEG rhythms are sensitive to the progression of early stage Alzheimer’s disease. J Alzheimers Dis 2013; 34(4): 1015-35.
[http://dx.doi.org/10.3233/JAD-121750] [PMID: 23340039]
[31]
Gutiérrez-de Pablo V, Gómez C, Poza J, et al. Relationship between the presence of the ApoE ε4 allele and EEG complexity along the Alzheimer’s disease continuum. Sensors (Basel) 2020; 20(14): E3849.
[http://dx.doi.org/10.3390/s20143849] [PMID: 32664228]
[32]
Benwell CSY, Davila-Pérez P, Fried PJ, et al. EEG spectral power abnormalities and their relationship with cognitive dysfunction in patients with Alzheimer’s disease and type 2 diabetes. Neurobiol Aging 2020; 85: 83-95.
[http://dx.doi.org/10.1016/j.neurobiolaging.2019.10.004] [PMID: 31727363]
[33]
Musaeus CS, Engedal K, Høgh P, et al. EEG theta power is an early marker of cognitive decline in dementia due to Alzheimer’s disease. J Alzheimers Dis 2018; 64(4): 1359-71.
[http://dx.doi.org/10.3233/JAD-180300] [PMID: 29991135]
[34]
Chen AC, Feng W, Zhao H, Yin Y, Wang P. EEG default mode network in the human brain: spectral regional field powers. Neuroimage 2008; 41(2): 561-74.
[http://dx.doi.org/10.1016/j.neuroimage.2007.12.064] [PMID: 18403217]
[35]
Kim JS, Lee SH, Park G, et al. Clinical implications of quantitative electroencephalography and current source density in patients with Alzheimer’s disease. Brain Topogr 2012; 25(4): 461-74.
[http://dx.doi.org/10.1007/s10548-012-0234-1] [PMID: 22736322]
[36]
Fox SE, Wolfson S, Ranck JB Jr. Hippocampal theta rhythm and the firing of neurons in walking and urethane anesthetized rats. Exp Brain Res 1986; 62(3): 495-508.
[http://dx.doi.org/10.1007/BF00236028] [PMID: 3720881]
[37]
Zhang H, Jacobs J. Traveling theta waves in the human hippocampus. J Neurosci 2015; 35(36): 12477-87.
[http://dx.doi.org/10.1523/JNEUROSCI.5102-14.2015] [PMID: 26354915]
[38]
Jeong J. EEG dynamics in patients with Alzheimer’s disease. Clin Neurophysiol 2004; 115(7): 1490-505.
[http://dx.doi.org/10.1016/j.clinph.2004.01.001] [PMID: 15203050]
[39]
Musaeus CS, Nielsen MS, Østerbye NN, Høgh P. Decreased parietal beta power as a sign of disease progression in patients with mild cognitive impairment. J Alzheimers Dis 2018; 65(2): 475-87.
[http://dx.doi.org/10.3233/JAD-180384] [PMID: 30056426]
[40]
Marshall AC, Cooper NR. The association between high levels of cumulative life stress and aberrant resting state EEG dynamics in old age. Biol Psychol 2017; 127: 64-73.
[http://dx.doi.org/10.1016/j.biopsycho.2017.05.005] [PMID: 28501607]
[41]
Mierau A, Klimesch W, Lefebvre J. State-dependent alpha peak frequency shifts: Experimental evidence, potential mechanisms and functional implications. Neuroscience 2017; 360: 146-54.
[http://dx.doi.org/10.1016/j.neuroscience.2017.07.037] [PMID: 28739525]
[42]
Badhwar A, Tam A, Dansereau C, Orban P, Hoffstaedter F, Bellec P. Resting-state network dysfunction in Alzheimer’s disease: A systematic review and meta-analysis. Alzheimers Dement (Amst) 2017; 8: 73-85.
[http://dx.doi.org/10.1016/j.dadm.2017.03.007] [PMID: 28560308]
[43]
Vecchio F, Miraglia F, Alù F, et al. Classification of Alzheimer’s disease with respect to physiological aging with innovative EEG biomarkers in a machine learning implementation. J Alzheimers Dis 2020; 75(4): 1253-61.
[http://dx.doi.org/10.3233/JAD-200171] [PMID: 32417784]
[44]
Minati L, Chan D, Mastropasqua C, et al. Widespread alterations in functional brain network architecture in amnestic mild cognitive impairment. J Alzheimers Dis 2014; 40(1): 213-20.
[http://dx.doi.org/10.3233/JAD-131766] [PMID: 24366921]
[45]
Tran XA, Mcdonald N, Dickinson A, et al. Functional connectivity during language processing in 3-month-old infants at familial risk for autism spectrum disorder. Eur J Neurosci 2021; 53(5): 1621-37.
[46]
Raeisi K, Mohebbi M, Khazaei M, Seraji M, Yoonessi A. Phase-synchrony evaluation of EEG signals for Multiple Sclerosis diagnosis based on bivariate empirical mode decomposition during a visual task. Comput Biol Med 2020; 117: 103596.
[http://dx.doi.org/10.1016/j.compbiomed.2019.103596] [PMID: 32072973]
[47]
Racz FS, Stylianou O, Mukli P, Eke A. Multifractal dynamic functional connectivity in the resting-state brain. Front Physiol 2018; 9: 1704.
[http://dx.doi.org/10.3389/fphys.2018.01704] [PMID: 30555345]
[48]
Das S, Puthankattil SD. Complex network analysis of MCI-AD EEG signals under cognitive and resting state. Brain Res 2020; 1735: 146743.
[http://dx.doi.org/10.1016/j.brainres.2020.146743] [PMID: 32114060]
[49]
Miraglia F, Vecchio F, Bramanti P, Rossini PM. EEG characteristics in “eyes-open” versus “eyes-closed” conditions: Small-world network architecture in healthy aging and age-related brain degeneration. Clin Neurophysiol 2016; 127(2): 1261-8.
[http://dx.doi.org/10.1016/j.clinph.2015.07.040] [PMID: 26603651]
[50]
Moretti DV, Frisoni GB, Pievani M, et al. Cerebrovascular disease and hippocampal atrophy are differently linked to functional coupling of brain areas: an EEG coherence study in MCI subjects. J Alzheimers Dis 2008; 14(3): 285-99.
[http://dx.doi.org/10.3233/JAD-2008-14303] [PMID: 18599955]
[51]
Buckner RL, Snyder AZ, Shannon BJ, et al. Molecular, structural, and functional characterization of Alzheimer’s disease: evidence for a relationship between default activity, amyloid, and memory. J Neurosci 2005; 25(34): 7709-17.
[http://dx.doi.org/10.1523/JNEUROSCI.2177-05.2005] [PMID: 16120771]
[52]
Colgin LL. Mechanisms and functions of theta rhythms. Annu Rev Neurosci 2013; 36: 295-312.
[http://dx.doi.org/10.1146/annurev-neuro-062012-170330] [PMID: 23724998]
[53]
Guderian S, Düzel E. Induced theta oscillations mediate large-scale synchrony with mediotemporal areas during recollection in humans. Hippocampus 2005; 15(7): 901-12.
[http://dx.doi.org/10.1002/hipo.20125] [PMID: 16161060]
[54]
Henneman WJ, Sluimer JD, Barnes J, et al. Hippocampal atrophy rates in Alzheimer disease: added value over whole brain volume measures. Neurology 2009; 72(11): 999-1007.
[http://dx.doi.org/10.1212/01.wnl.0000344568.09360.31] [PMID: 19289740]
[55]
Babiloni C, Ferri R, Noce G, et al. Resting-state electroencephalographic delta rhythms may reflect global cortical arousal in healthy old seniors and patients with Alzheimer’s disease dementia. Int J Psychophysiol 2020; 158: 259-70.
[http://dx.doi.org/10.1016/j.ijpsycho.2020.08.012] [PMID: 33080295]
[56]
Das N, Ren J, Spence JS, Rackley A, Chapman SB. Relationship of parieto-occipital brain energy phosphate metabolism and cognition using 31P MRS at 7-tesla in amnestic mild cognitive impairment. Front Aging Neurosci 2020; 12: 222.
[http://dx.doi.org/10.3389/fnagi.2020.00222] [PMID: 33005142]
[57]
Han Y, Wang K, Jia J, Wu W. Changes of EEG spectra and functional connectivity during an object-location memory task in Alzheimer’s disease. Front Behav Neurosci 2017; 11: 107.
[http://dx.doi.org/10.3389/fnbeh.2017.00107] [PMID: 28620287]
[58]
Hidasi Z, Czigler B, Salacz P, Csibri E, Molnár M. Changes of EEG spectra and coherence following performance in a cognitive task in Alzheimer’s disease. Int J Psychophysiol 2007; 65(3): 252-60.
[http://dx.doi.org/10.1016/j.ijpsycho.2007.05.002] [PMID: 17586077]

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