INTRODUCTION: Electroencephalography (EEG) provides a temporally precise index of neural dysfunction, capturing changes in oscillatory activity, connectivity, and network organization. While spectral slowing is well documented in Alzheimer's disease (AD), frontotemporal dementia (FTD), and dementia with Lewy bodies (DLB), less is known about how these alterations extend to large-scale networks. METHODS: We studied 173 participants: 56 AD, 59 FTD, 26 DLB, and 32 healthy controls (HC). Resting-state EEG was analyzed to quantify spectral power and amplitude-envelope correlation-based connectivity across frequency bands. RESULTS: AD showed canonical slowing with delta/theta increases and posterior alpha loss. FTD exhibited preserved alpha but frontal beta reductions, while DLB displayed delta/theta excess, posterior alpha attenuation, and uniquely reduced gamma. Connectivity analyses revealed syndrome-specific patterns of network reorganization with distinct frequency-dependent signatures. DISCUSSION: EEG network metrics capture distinct disease signatures and may inform mechanistic models of dementia.

EEG network reorganization across Alzheimer's disease, frontotemporal dementia, and dementia with Lewy bodies

Benussi, Alberto
Primo
;
Palacino, Federica;Manganotti, Paolo
Penultimo
;
2026-01-01

Abstract

INTRODUCTION: Electroencephalography (EEG) provides a temporally precise index of neural dysfunction, capturing changes in oscillatory activity, connectivity, and network organization. While spectral slowing is well documented in Alzheimer's disease (AD), frontotemporal dementia (FTD), and dementia with Lewy bodies (DLB), less is known about how these alterations extend to large-scale networks. METHODS: We studied 173 participants: 56 AD, 59 FTD, 26 DLB, and 32 healthy controls (HC). Resting-state EEG was analyzed to quantify spectral power and amplitude-envelope correlation-based connectivity across frequency bands. RESULTS: AD showed canonical slowing with delta/theta increases and posterior alpha loss. FTD exhibited preserved alpha but frontal beta reductions, while DLB displayed delta/theta excess, posterior alpha attenuation, and uniquely reduced gamma. Connectivity analyses revealed syndrome-specific patterns of network reorganization with distinct frequency-dependent signatures. DISCUSSION: EEG network metrics capture distinct disease signatures and may inform mechanistic models of dementia.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/3126800
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