ArticleScientific reports2026
Quantitative EEG signatures of power and functional connectivity alterations in Alzheimer's disease and frontotemporal dementia.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Dementia is a common neurodegenerative disease in the elderly, which affects the structural and functional connectivity of the brain. Recent studies indicate that electrophysiological measures, such as power spectral features and functional connectivity (FC), show promise for the diagnosis and classification of dementia. However, findings across studies remain inconsistent, and distinct electrophysiological patterns separating dementia subtypes, as well as Frontotemporal Dementia (FTD) and cognitively normal (CN) individuals, are not yet well established. This study focuses on spectral power and functional connectivity (FC) analyses of the Electroencephalography (EEG) frequency bands (delta, theta, alpha, beta, and gamma) in Alzheimer's Disease (AD) and FTD. A publicly available eyes-closed (EC), resting-state (RS) EEG dataset comprising 88 age-matched participants, 36 with AD, 29 CN, and 23 with FTD, was used in this study. Absolute power was computed using Welch's method, while FC within each frequency band was assessed using Inter-Site Phase Clustering (ISPC) and network-based statistics, edge and node strength. The global power analysis revealed a significantly higher alpha power in CN compared to both AD and FTD. Regional analysis revealed a significantly lower temporal and parietal alpha in AD relative to CN and a significantly lower occipital alpha and beta in both AD and FTD compared to CN. Topographical power analysis showed unique significant differences within lobes in delta, theta, alpha, and gamma bands in AD and FTD, with AD illustrating a relatively more heterogeneous power distribution than FTD. Furthermore, FC analysis indicated that compared with CN, AD exhibited significantly lower edge strength in delta, theta, beta, and gamma bands, while significantly lower node strength in delta, theta, and gamma bands. Likewise, compared with CN, FTD showed significantly lower edge and node strength in the delta and theta bands, while significantly higher in the beta band. Furthermore, when compared to FTD, AD revealed a significantly lower edge and node strength in the delta, beta, and gamma bands. In conclusion, AD was associated with widespread FC disruptions, while FTD retained partially preserved connectivity, with the temporal lobe more affected than the frontal lobe. These findings suggest that band power and FC alterations may serve as potential biomarkers for diagnosing and classifying dementia into AD and FTD.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.