Leveraging centrality metrics for the early detection of dementias.

BACKGROUND

Alzheimer's disease (AD) and frontotemporal dementia (FTD) are neurodegenerative syndromes associated with progressive disruption of functional brain networks. This pilot study evaluated whether a stacked set of resting-state EEG-derived centrality measures could characterize functional network alterations across AD, FTD, and healthy control groups.

METHODOLOGY: Scalp EEG data from 45 participants, comprising 15 AD, 15 FTD, and 15 controls, were analyzed using a 19-channel 10-20 montage. After standardized preprocessing, functional connectivity networks were constructed for each participant, with the 19 EEG electrodes treated as network nodes. Four centrality measures were computed: leverage centrality, weighted leverage measure, betweenness centrality, and closeness centrality. Group differences were assessed across anatomical regions, hemispheres, and anterior-posterior-central divisions, with Benjamini-Hochberg false discovery rate correction applied.

RESULTS

Closeness centrality provided the strongest corrected evidence of disease-related functional network alteration, with FDR-significant regional effects retained in frontal, occipital, and temporal regions. Hemispheric analyses showed bilateral closeness-centrality alterations, particularly in AD, while betweenness centrality showed a corrected left-hemisphere effect for AD versus controls. Temporal-region findings across multiple metrics were biologically consistent with known AD and FTD alteration patterns but remained partly exploratory after correction. Leverage-based measures showed a strong negative association with age in FTD, whereas betweenness and closeness centrality demonstrated moderate associations with MMSE.

CONCLUSION

EEG-derived stacked centrality analysis may provide a useful pilot framework for characterizing dementia-related functional network disruption in AD and FTD, with closeness centrality showing the strongest corrected evidence of disease-related network alteration. Larger, longitudinal, and clinically stratified studies are required to validate its diagnostic, prognostic, and disease-monitoring utility.