A scalp-EEG network-based analysis of Alzheimer's disease patients at rest

Abstract

Abstract-Most brain disorders including Alzheimer's disease (AD) are related to alterations in the normal brain network organization and function. Exploring these network alterations using non-invasive and easy to use technique is a topic of great interest. In this paper, we collected EEG resting-state data from AD patients and healthy control subjects. Functional connectivity between scalp EEG signals was quantified using the phase locking value (PLV) for 6 frequency bands, θ (4-8 Hz), α1(8-10 Hz), α2(10-13 Hz), ß(13-30 Hz), γ(30-45 Hz), and broad band (0.2-45 Hz). To assess the differences in network properties, graph-theoretical analysis was performed. AD patients showed decrease of mean connectivity, average clustering and global efficiency in the lower alpha band. Positive correlation between the cognitive score and the extracted graph measures was obtained, suggesting that EEG could be a promising technique to derive new biomarkers of AD diagnosis.

Ultra-high-resolution time-frequency analysis of EEG to characterise brain functional connectivity with the application in Alzheimer’s disease

“…To demonstrate the superior performance of cross-spectrum RHHT, in distinguishing between AD and HCs, we compared our findings against those obtained by implementing other popular linear and non-linear brain connectivity methods. Phase locking value (PLV) (Lachaux et al 1999, Hassan et al 2017 Figure 9 illustrates the comparison between the proposed methods and conventional techniques in terms of the top ten channel classification accuracies. The average, highest and lowest accuracy are represented by the bar chart.…”

“…That is to say, those techniques are trying to obtain independent features from each EEG channel. However, there is evidence suggesting brain disorders affect information exchange between multiple brain areas, namely brain connectivity (Varotto et al 2014, McBride et al 2015, Hassan et al 2017, Cao et al 2021a. To be more specific, brain connectivity is divided into three well-accepted categories: neuroanatomical brain connectivity, functional brain connectivity, and effective brain connectivity (Abbasvandi andNasrabadi 2019, Cao et al 2021b).…”

Machine Learning on Visibility Graph Features Discriminates the Cognitive Event-Related Potentials of Patients with Early Alzheimer’s Disease from Healthy Aging

“…In total, we used 12 features to classify the Alzheimer's (AD) and the robust normal elderly (RNE) groups. Six of these features have been tested in previous EEG graph theory studies of AD, namely clustering coefficient sequence similarity [15], average clustering coefficient [38,39], global efficiency [15,38,40], local efficiency [15,41], small-worldness [15] and graph index complexity [15,16]. The other six are graph features heavily studied in the field of computer science that, to the best of our knowledge, have not yet been considered in EEG graph theory studies.…”

“…It is interpreted as sum of all inverse shortest path distances divided by the number of shortest path distances counted. A higher global efficiency corresponds to a network that is more efficient at transmitting/combining information and relates to the small-worldness of the network [15,38,[47][48][49][50]. In context, a higher global efficiency in a VG means that there are likely more EEG time points that are visible from other points which are relatively farther away in time.…”