What question did this study set out to answer?

The study aims to differentiate between Alzheimer’s disease (AD) and frontotemporal dementia (FTD) using EEG graph-theory analysis.

January 25, 2026

Graph-Theoretical Analysis of Resting-State EEG Networks Differentiates Alzheimer’s Disease and Frontotemporal Dementia

Puntos clave

The study aims to differentiate between Alzheimer’s disease (AD) and frontotemporal dementia (FTD) using EEG graph-theory analysis.
Conducted EEG graph-theoretical analysis of resting-state networks.
Compared alterations in network architecture between AD and FTD.
Identified specific disruptions associated with each dementia subtype.
AD showed impaired global integration and loss of small-world architecture.
FTD demonstrated region-specific disruptions in network connectivity.
EEG graph metrics indicate potential as cost-effective biomarkers for dementia differentiation.

Resumen

EEG graph-theory analysis revealed distinct network alterations in AD and FTD. AD showed impaired global integration and loss of small-world architecture, while FTD demonstrated region-specific disruptions. These findings suggest that EEG graph metrics may serve as cost-effective biomarkers for differentiating dementia subtypes and understanding disease-specific network dysfunction.

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