Emerging evidence suggests distinct abnormal activity patterns during resting state in intrinsic functional brain networks in patients with neurodegenerative diseases, including Alzheimer's disease (AD) and mild cognitive impairment (MCI). This study aimed to identify the changes in the resting-state intracortical lagged phase synchronization derived from dense array electroencephalography (EEG) in AD and MCI.
Methods:
Resting-state current source density (CSD) and lagged phase synchronization between 84 regions of interest defined by Brodmann areas (BAs) for seven EEG frequency bands were investigated between the study groups (AD, MCI, and age-matched controls) using 128-channel EEG.
Results:
Reduced CSD and connectivity (large effect size, Cohen's d > 0.8) were found in AD and MCI compared with controls at alpha frequency. However, a positive correlation (r = 0.433; p = 0.044) of mini-mental state examination scores was found with BA 32-33 connectivity values in AD only.
Conclusion:
Reduced resting-state alpha 1 source connectivity in patient groups and correlation between attenuation of resting-state alpha 1 connectivity with cognitive decline in AD could indicate the disruption of inhibitory function of alpha rhythm leading to tonic unselective cortical excitation that affects attention and controlled access to stored information.
Impact statement
Lower cortical source density and connectivity in Alzheimer's disease (AD) and mild cognitive impairment (MCI) compared with controls were found only at alpha frequency, highlighting the strong association between cholinergic dysfunction and alpha frequency. Reduced functional connectivity in AD and MCI affected distinct key hubs of large-scale networks such as default mode network. Reduced alpha connectivity and its association with cognitive decline in patients could indicate the disruption of inhibitory function of alpha rhythm due to the disease pathology. Therefore, the distinct functional connectivity pattern of AD and MCI identified in this study could be used as the topographical and pathophysiological biomarkers of these disease states.
Get full access to this article
View all access options for this article.
References
1.
AgnoliA, MartucciN, MannaV, et al.1983. Effect of cholinergic and anticholinergic drugs on short-term memory in Alzheimer's dementia: a neuropsychological and computerized electroencephalographic study. Clin Neuropharmacol, 6:311–323.
2.
AlmkvistO, JelicV, AmberlaK, et al.2001. Responder characteristics to a single oral dose of cholinesterase inhibitor: a double-blind placebo-controlled study with tacrine in Alzheimer patients. Dement Geriatr Cogn Disord, 12:22–32.
3.
Andrews-HannaJR, ReidlerJS, SepulcreJ, et al.2010. Functional-anatomic fractionation of the brain's default network. Neuron, 65:550–562.
4.
BabiloniC, Del PercioC, LizioR, et al.2017. Abnormalities of cortical neural synchronization mechanisms in patients with dementia due to Alzheimer's and Lewy body diseases: an EEG study. Neurobiol Aging, 55:143–158.
5.
BabiloniC, Del PercioC, LizioR, et al.2018. Abnormalities of resting-state functional cortical connectivity in patients with dementia due to Alzheimer's and Lewy body diseases: an EEG study. Neurobiol Aging, 65:18–40.
6.
BabiloniC, Del PercioC, PascarelliMT, et al.2019. Abnormalities of functional cortical source connectivity of resting-state electroencephalographic alpha rhythms are similar in patients with mild cognitive impairment due to Alzheimer's and Lewy body diseases. Neurobiol Aging, 77:112–127.
7.
BabiloniC, PievaniM, VecchioF, et al.2009. White-matter lesions along the cholinergic tracts are related to cortical sources of EEG rhythms in amnesic mild cognitive impairment. Hum Brain Mapp, 30:1431–1443.
8.
BadhwarA, McFallGP, SapkotaS, et al.2020. A multiomics approach to heterogeneity in Alzheimer's disease: focused review and roadmap. Brain J Neurol, 143:1315–1331.
9.
BadhwarA, TamA, DansereauC, et al.2017. Resting-state network dysfunction in Alzheimer's disease: a systematic review and meta-analysis. Alzheimers Dement Diagn Assess Dis Monit, 8:73–85.
10.
BakerCM, BurksJD, BriggsRG, et al.2018. A connectomic atlas of the human cerebrum—Chapter 5: the insula and opercular cortex. Oper Neurosurg, 15:S175–S244.
11.
BlinowskaKJ, RakowskiF, KaminskiM, et al.2017. Functional and effective brain connectivity for discrimination between Alzheimer's patients and healthy individuals: a study on resting state EEG rhythms. Clin Neurophysiol Off J Int Fed Clin Neurophysiol, 128:667–680.
12.
BrielsCT, StamCJ, ScheltensP, et al.2020. In pursuit of a sensitive EEG functional connectivity outcome measure for clinical trials in Alzheimer's disease. Clin Neurophysiol Off J Int Fed Clin Neurophysiol, 131:88–95.
13.
BrierMR, ThomasJB, SnyderAZ, et al.2012. Loss of intranetwork and internetwork resting state functional connections with Alzheimer's disease progression. J Neurosci, 32:8890–8899.
14.
BrunovskyM, MatousekM, EdmanA, et al.2003. Objective assessment of the degree of dementia by means of EEG. Neuropsychobiology, 48:19–26.
15.
BucknerRL, SnyderAZ, ShannonBJ, et al.2005. Molecular, structural, and functional characterization of Alzheimer's disease: evidence for a relationship between default activity, amyloid, and memory. J Neurosci Off J Soc Neurosci, 25:7709–7717.
16.
BuscemaM, RossiniP, BabiloniC, et al.2007. The IFAST model, a novel parallel nonlinear EEG analysis technique, distinguishes mild cognitive impairment and Alzheimer's disease patients with high degree of accuracy. Artif Intell Med, 40:127–141.
17.
CanuetL, TelladoI, CouceiroV, et al.2012. Resting-state network disruption and APOE genotype in Alzheimer's disease: a lagged functional connectivity study. PLoS One, 7:e46289.
18.
CobenLA, ChiD, SnyderAZ, et al.1990. Replication of a study of frequency analysis of the resting awake EEG in mild probable Alzheimer's disease. Electroencephalogr Clin Neurophysiol, 75:148–154.
19.
DauwelsJ, VialatteF, CichockiA. 2010. Diagnosis of Alzheimer's disease from EEG signals: where are we standing?. Curr Alzheimer Res, 7:487–505.
20.
DelormeA, MakeigS. 2004. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods, 134:9–21.
21.
FranssonP, MarrelecG. 2008. The precuneus/posterior cingulate cortex plays a pivotal role in the default mode network: evidence from a partial correlation network analysis. Neuroimage, 42:1178–1184.
22.
GouldenN, KhusnulinaA, DavisNJ, et al.2014. The salience network is responsible for switching between the default mode network and the central executive network: Replication from DCM. Neuroimage, 99:180–190.
23.
HeX, QinW, LiuY, et al.2014. Abnormal salience network in normal aging and in amnestic mild cognitive impairment and Alzheimer's disease: abnormal salience network in normal aging and AD. Hum Brain Mapp, 35:3446–3464.
24.
HsiaoF-J, WangY-J, YanS-H, et al.2013. Altered oscillation and synchronization of default-mode network activity in mild Alzheimer's Disease compared to mild cognitive impairment: an electrophysiological study. PLoS ONE, 8:e68792.
25.
IgelströmKM, WebbTW, GrazianoMSA. 2017. Functional connectivity between the temporoparietal cortex and cerebellum in autism spectrum disorder. Cereb Cortex, 27:2617–2627.
26.
JinF, ZhengP, LiuH, et al.2018. Functional and anatomical connectivity-based parcellation of human cingulate cortex. Brain Behav, 8:e01070.
27.
KlimeschW.2012. Alpha-band oscillations, attention, and controlled access to stored information. Trends Cogn Sci, 16:606–617.
28.
KouniosJ, FrymiareJL, BowdenEM, et al.2006. The prepared mind: neural activity prior to problem presentation predicts subsequent solution by sudden insight. Psychol Sci, 17:882–890.
29.
LeechR, SharpDJ. 2014. The role of the posterior cingulate cortex in cognition and disease. Brain, 137:12–32.
30.
LizioR, Del PercioC, MarzanoN, et al.2015. Neurophysiological assessment of Alzheimer's disease individuals by a single electroencephalographic marker. J Alzheimers Dis, 49:159–177.
31.
MacLeanMH, ArnellKM, CoteKA. 2012. Resting EEG in alpha and beta bands predicts individual differences in attentional blink magnitude. Brain Cogn, 78:218–229.
32.
MandrekarJN.2010. Receiver operating characteristic curve in diagnostic test assessment. J Thorac Oncol, 5:1315–1316.
33.
MenonV.2015. Large-Scale Functional Brain Organization. Brain Mapping. Elsevier. p. 449–459.
34.
NeufeldMY, RabeyMJ, ParmetY, et al.1994. Effects of a single intravenous dose of scopolamine on the quantitative EEG in Alzheimer's disease patients and age-matched controls. Electroencephalogr Clin Neurophysiol, 91:407–412.
35.
NicholsTE, HolmesAP. 2002. Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum Brain Mapp, 15:1–25.
36.
OngurD.2000. The organization of networks within the orbital and medial prefrontal cortex of rats, monkeys and humans. Cereb Cortex, 10:206–219.
37.
OttoyJ, NiemantsverdrietE, VerhaegheJ, et al.2019. Association of short-term cognitive decline and MCI-to-AD dementia conversion with CSF, MRI, amyloid- and 18F-FDG-PET imaging. NeuroImage Clin, 22:101771.
38.
Palomero-GallagherN, HoffstaedterF, MohlbergH, et al.2019. Human pregenual anterior cingulate cortex: structural, functional, and connectional heterogeneity. Cereb Cortex, 29:2552–2574.
39.
Pascual-MarquiRD, LehmannD, KoukkouM, et al.2011. Assessing interactions in the brain with exact low-resolution electromagnetic tomography. Philos Transact A Math Phys Eng Sci, 369:3768–3784.
40.
PievaniM, HaanW de, WuT, et al.2011. Functional network disruption in the degenerative dementias. Lancet Neurol, 10:829–843.
41.
RaichleME.2015. The brain's default mode network. Annu Rev Neurosci, 38:433–447.
42.
RollsET.2014. Emotion and Decision-Making Explained, 1st ed. Oxford; New York, NY: Oxford University Press; p. 710.
43.
SpuntRP, AdolphsR. 2019. The neuroscience of understanding the emotions of others. Neurosci Lett, 693:44–48.
44.
TaoH-Y, TianX. Coherence characteristics of gamma-band EEG during rest and cognitive task in MCI and AD. In 27th Annual International Conference of the IEEE Engineering in Medicine and Biology, Shanghai, China, 2005, p. 2747–2750.
45.
UddinLQ, NomiJS, Hébert-SeropianB, et al.2017. Structure and function of the human insula. J Clin Neurophysiol, 34:300–306.
46.
VaratharajahY, RamananVK, IyerR, et al.2019. Predicting short-term MCI-to-AD progression using imaging, CSF, genetic factors, cognitive resilience, and demographics. Sci Rep, 9:2235.
47.
WangZ, XiaM, DaiZ, et al.2015. Differentially disrupted functional connectivity of the subregions of the inferior parietal lobule in Alzheimer's disease. Brain Struct Funct, 220:745–762.
48.
WordenMS, FoxeJJ, WangN, et al.2000. Anticipatory biasing of visuospatial attention indexed by retinotopically specific alpha-band electroencephalography increases over occipital cortex. J Neurosci Off J Soc Neurosci, 20:RC63.
49.
WuL, WuL, ChenY, et al.2014. A promising method to distinguish vascular dementia from Alzheimer's disease with standardized low-resolution brain electromagnetic tomography and quantitative EEG. Clin EEG Neurosci, 45:152–157.
