Differentiating Between Alzheimer’s Disease and Frontotemporal Dementia Based on the Resting-State Multilayer EEG Network
oleh: Yajing Si, Runyang He, Lin Jiang, Dezhong Yao, Hongxing Zhang, Peng Xu, Xuntai Ma, Liang Yu, Fali Li
| Format: | Article |
|---|---|
| Diterbitkan: | IEEE 2023-01-01 |
Deskripsi
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 (<inline-formula> <tex-math notation="LaTeX">${r}$ </tex-math></inline-formula> = 0.274, <inline-formula> <tex-math notation="LaTeX">${p}$ </tex-math></inline-formula> = 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.