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February 17, 2026Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring1 citationsOpen Access

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

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ABAlberto BenussiPCPaola CaroppoKing's College LondonFPFederica PalacinoUniversity of Trieste

Key Points

  • This research aims to understand how EEG network organization differs across Alzheimer's disease, frontotemporal dementia, and dementia with Lewy bodies.
  • Analyzed resting-state EEG from 173 participants: 56 AD, 59 FTD, 26 DLB, and 32 healthy controls.
  • Quantified spectral power and amplitude-envelope correlation-based connectivity across different frequency bands.
  • Identified syndrome-specific patterns of network reorganization based on EEG metrics.
  • AD exhibited significant slowing with increases in delta/theta waves and loss of posterior alpha activity.
  • FTD showed preserved alpha activity but reductions in frontal beta waves.
  • DLB had excess delta/theta activity, attenuation in posterior alpha, and notably reduced gamma waves.

Abstract

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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Cite This Study

Benussi et al. (2026) studied this question.

synapsesocial.com/papers/6994058c4e9c9e835dfd6828https://doi.org/10.1002/dad2.70275
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