Neuropsychologia · 2026
Leveraging centrality metrics for the early detection of dementias.
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Sinumol S, Suvish, Ghamari Mehrdad, Sheela Priyalakshmi, Das Surya, Sunil Kumar R
Аннотация
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.
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.