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A Novel Connectome-based Electrophysiological Study of Subjective Cognitive Decline Related to Alzheimer’s Disease by Using Resting-state High-density EEG EGI GES 300

1
Information Technologies Institute, Centre for Research and Technology Hellas (CERTH-ITI), 57001 Thessaloniki, Greece
2
1st Department of Neurology, G.H. “AHEPA”, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), 54124 Thessaloniki, Greece
3
Informatics Department, Aristotle University of Thessaloniki (AUTH), 54124 Thessaloniki, Greece
4
Greek Association of Alzheimer’s Disease and Related Disorders (GAADRD), 54643 Thessaloniki, Greece
5
Department of Electrical and Computer Engineering, Faculty of Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
*
Author to whom correspondence should be addressed.
Brain Sci. 2020, 10(6), 392; https://doi.org/10.3390/brainsci10060392
Received: 25 May 2020 / Revised: 17 June 2020 / Accepted: 18 June 2020 / Published: 19 June 2020
(This article belongs to the Special Issue Human Brain Dynamics: Latest Advances and Prospects)
Aim: To investigate for the first time the brain network in the Alzheimer’s disease (AD) spectrum by implementing a high-density electroencephalography (HD-EEG - EGI GES 300) study with 256 channels in order to seek if the brain connectome can be effectively used to distinguish cognitive impairment in preclinical stages. Methods: Twenty participants with AD, 30 with mild cognitive impairment (MCI), 20 with subjective cognitive decline (SCD) and 22 healthy controls (HC) were examined with a detailed neuropsychological battery and 10 min resting state HD-EEG. We extracted correlation matrices by using Pearson correlation coefficients for each subject and constructed weighted undirected networks for calculating clustering coefficient (CC), strength (S) and betweenness centrality (BC) at global (256 electrodes) and local levels (29 parietal electrodes). Results: One-way ANOVA presented a statistically significant difference among the four groups at local level in CC [F (3, 88) = 4.76, p = 0.004] and S [F (3, 88) = 4.69, p = 0.004]. However, no statistically significant difference was found at a global level. According to the independent sample t-test, local CC was higher for HC [M (SD) = 0.79 (0.07)] compared with SCD [M (SD) = 0.72 (0.09)]; t (40) = 2.39, p = 0.02, MCI [M (SD) = 0.71 (0.09)]; t (50) = 0.41, p = 0.004 and AD [M (SD) = 0.68 (0.11)]; t (40) = 3.62, p = 0.001 as well, while BC showed an increase at a local level but a decrease at a global level as the disease progresses. These findings provide evidence that disruptions in brain networks in parietal organization may potentially represent a key factor in the ability to distinguish people at early stages of the AD continuum. Conclusions: The above findings reveal a dynamically disrupted network organization of preclinical stages, showing that SCD exhibits network disorganization with intermediate values between MCI and HC. Additionally, these pieces of evidence provide information on the usefulness of the 256 HD-EEG in network construction. View Full-Text
Keywords: electroencephalography; subjective cognitive decline; brain connectivity; mild cognitive impairment; Alzheimer’s disease; resting state; network analysis electroencephalography; subjective cognitive decline; brain connectivity; mild cognitive impairment; Alzheimer’s disease; resting state; network analysis
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Lazarou, I.; Georgiadis, K.; Nikolopoulos, S.; Oikonomou, V.P.; Tsolaki, A.; Kompatsiaris, I.; Tsolaki, M.; Kugiumtzis, D. A Novel Connectome-based Electrophysiological Study of Subjective Cognitive Decline Related to Alzheimer’s Disease by Using Resting-state High-density EEG EGI GES 300. Brain Sci. 2020, 10, 392.

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