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Differentiating Between Alzheimer's Disease and Frontotemporal Dementia Based on the Resting-State Multilayer EEG Network.
- Source :
-
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society [IEEE Trans Neural Syst Rehabil Eng] 2023; Vol. 31, pp. 4521-4527. Date of Electronic Publication: 2023 Nov 16. - Publication Year :
- 2023
-
Abstract
- Frontotemporal dementia (FTD) is frequently misdiagnosed as Alzheimer's disease (AD) due to similar clinical symptoms. In this study, we constructed frequency-based multilayer resting-state electroencephalogram (EEG) networks and extracted representative network features to improve the differentiation between AD and FTD. When compared with healthy controls (HC), AD showed primarily stronger delta-alpha cross-couplings and weaker theta-sigma cross-couplings. Notably, when comparing the AD and FTD groups, we found that the AD exhibited stronger delta-alpha and delta-beta connectivity than the FTD. Thereafter, by extracting the representative network features and then applying these features in the classification between AD and FTD, an accuracy of 81.1% was achieved. Finally, a multivariable linear regressive model was built, based on the differential topologies, and then adopted to predict the scores of the Mini-Mental State Examination (MMSE) scale. Accordingly, the predicted and actual measured scores were indeed significantly correlated with each other ( r = 0.274, p = 0.036). These findings consistently suggest that frequency-based multilayer resting-state networks can be utilized for classifying AD and FTD and have potential applications for clinical diagnosis.
Details
- Language :
- English
- ISSN :
- 1558-0210
- Volume :
- 31
- Database :
- MEDLINE
- Journal :
- IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
- Publication Type :
- Academic Journal
- Accession number :
- 37922187
- Full Text :
- https://doi.org/10.1109/TNSRE.2023.3329174