Age-Related Alterations in EEG Network Connectivity in Healthy Aging.

paper Cited Public

Authors: Javaid, Hamad; Kumarnsit, Ekkasit; Chatpun, Surapong
Year: 2022
Journal: Brain sciences
PMID: 35203981
DOI: 10.3390/brainsci12020218
PMCID: PMC8870284

Figure 1

Illustration of experimental protocol.

Figure 2

Eyes-open in middle-aged vs. elderly subjects’ network features analysis (Wilcoxon’s test; * p < 0.05): (a) global efficiency, (b) local efficiency, (c) clustering coefficient, (d) node betweenness centrality, (e) assortativity, (f) characteristic path length, and (g) node strength.

Figure 3

Eyes-closed in middle-aged vs. elderly individuals’ network features analysis (Wilcoxon’s test; * p < 0.05): (a) global efficiency, (b) local efficiency, (c) characteristic path length, (d) clustering coefficient, (e) assortativity, (f) node strength, and (g) node betweenness centrality.

Figure 4

WM task in middle-aged vs. elderly individuals’ network features analysis (Wilcoxon’s test; * p < 0.05): (a) global efficiency, (b) local efficiency, (c) characteristic path length, (d) clustering coefficient, (e) assortativity, (f) node strength, and (g) node betweenness centrality.

Figure 5

Topographic map of EEG network features for middle-aged vs. elderly in eyes-open state: (a) mean global efficiency, (b) mean local efficiency, and (c) mean clustering coefficient.

Figure 6

Topographic map of EEG network features for middle-aged vs. elderly in eyes-closed state: (a) mean global efficiency, (b) mean local efficiency, and (c) mean clustering coefficient.

Figure 7

Topographic map of EEG network features for middle-aged vs. elderly in the visual WM task state: (a) mean global efficiency, (b) mean local efficiency, and (c) mean clustering coefficient.

Figure 8

Confusion matrices of all classifiers in eyes-open state.

Figure 9

Confusion matrices of all classifiers in eyes-closed state.

Figure 10

Confusion matrices of all classifiers in WM task state.

Figure 11

ROC plot of all classifiers in eyes-open state.

Figure 12

ROC plot of all classifiers in eyes-closed state.

Figure 13

ROC plot of all classifiers in WM task state.

#	Section	Preview
40	4. Discussion	The measure of assortativity showed significant differences in functional networks between healthy…
41	4. Discussion	It has been suggested that the band works as a communication mechanism in cortical areas [71]. Beta…
42	4. Discussion	The graph indices show significant differences in the efficiency and clustering of networks in the…
43	4. Discussion	Table 5 shows previous work based on graph theory features and the application of machine learning…
44	4. Discussion	the KNN classifier performed better compared to SVM and RF. The best sensitivity and specificity…
45	5. Conclusions	This study presents a method for investigating the differences in elderly and middle-aged EEG…
46	5. Conclusions	significant results in the eyes-open and eyes-closed states, as well as under a WM task condition.…

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