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

Abstract

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.

Resting-state electroencephalographic biomarkers of Alzheimer’s disease

“…Previous EEG studies exploring AD brain connectomics showed discordant results, basically due to different methodological approaches, which complicates the comparability of obtained results ( Vecchio et al, 2017 , Xie and He, 2011 , Lazarou et al, 2020 , Tijms et al, 2013 ). Among others, for instance, one study applied linear lagged coherence to eLORETA solutions extracted from a population of AD, MCI and healthy subjects, describing network reconfiguration profiles which are only partially in line with our findings ( Vecchio et al, 2014 , Vecchio et al, 2015 ); in parallel with an increased normalized clustering coefficient in theta band in MCI and AD patients, indeed, they also described an increased normalized path length.…”

Differential Expression of miR-381-3p in Alzheimer’s Disease Patients and Its Role in Beta-Amyloid-Induced Neurotoxicity and Inflammation

“…Ninety-eight AD patients were included, who met the National Institute of Neurological and Communication Disorders and Stroke/Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA) criteria for probable AD [13]. Patients with chronic kidney disease, heart disease, diabetes, vascular dementia, and other neurodegenerative dementia, previous history of depression or schizophrenia, receiving drugs to improve neurocognitive function, hearing loss, blindness, and severe speech disorders were excluded from the study.…”

Exploring Network Properties Across Preclinical Stages of Alzheimer’s Disease Using a Visual Short-Term Memory and Attention Task with High-Density Electroencephalography: A Brain-Connectome Neurophysiological Study

“…Our recent review [ 94 ] suggested that SCD may actually demonstrate intermediate changes between healthy elders and MCI at a network level of the brain and implies that brain connectome could be considered as a potential biomarker of predicting future cognitive deterioration associated with AD. However very limited studies have so far explored the brain network in SCD using EEG [ 93, 94 ]. In the majority of the included studies, SCD exhibited lower values compared to healthy controls with regards to local efficiency, network strength, and shortest path length but preserved global network properties such as small-world and rich-club.…”