The International journal of neuroscience · 2025
EEG spectral power differences in Alzheimer's disease and frontotemporal dementia.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Pan Yue, Zhu Li, Wang Zi-Liang
Аннотация
BACKGROUND: Differentiating between Alzheimer's disease (AD) and frontotemporal dementia (FTD) based on clinical symptoms alone can be challenging. This study investigates the utility of resting-state EEG spectral power as a tool to distinguish between these two neurodegenerative conditions.
METHODS: We analyzed a publicly available dataset containing EEG recordings from 36 AD patients, 23 FTD patients, and 29 age-matched healthy controls (HC). Spectral power across delta, theta, alpha, and beta frequency bands was computed for both eyes-closed and eyes-open conditions. Rigorous statistical analysis with FDR correction was employed to identify group differences. To further investigate the relationship between EEG spectral alterations and clinical cognitive status, a correlation analysis was conducted.
RESULTS: Both patient groups showed significant deviations from HC, but with distinct patterns. AD was characterized by a classic pattern of posterior alpha power decrease and frontal theta power increase. In contrast, FTD showed a more focused reduction of alpha power at frontal and central sites. These patterns were robust across both eyes-closed and eyes-open states, suggesting their potential as stable biomarkers. The spectral features showed limited correlation with MMSE scores, indicating they may capture unique aspects of neuropathology not reflected in standard cognitive screening.
CONCLUSION: Resting-state EEG reveals distinct spectral signatures for AD and FTD, supporting its potential as a low-cost, non-invasive adjunctive tool for differential diagnosis. The replication of these findings in an independent, open-access dataset underscores their reliability and provides a foundation for developing automated diagnostic algorithms.
METHODS: We analyzed a publicly available dataset containing EEG recordings from 36 AD patients, 23 FTD patients, and 29 age-matched healthy controls (HC). Spectral power across delta, theta, alpha, and beta frequency bands was computed for both eyes-closed and eyes-open conditions. Rigorous statistical analysis with FDR correction was employed to identify group differences. To further investigate the relationship between EEG spectral alterations and clinical cognitive status, a correlation analysis was conducted.
RESULTS: Both patient groups showed significant deviations from HC, but with distinct patterns. AD was characterized by a classic pattern of posterior alpha power decrease and frontal theta power increase. In contrast, FTD showed a more focused reduction of alpha power at frontal and central sites. These patterns were robust across both eyes-closed and eyes-open states, suggesting their potential as stable biomarkers. The spectral features showed limited correlation with MMSE scores, indicating they may capture unique aspects of neuropathology not reflected in standard cognitive screening.
CONCLUSION: Resting-state EEG reveals distinct spectral signatures for AD and FTD, supporting its potential as a low-cost, non-invasive adjunctive tool for differential diagnosis. The replication of these findings in an independent, open-access dataset underscores their reliability and provides a foundation for developing automated diagnostic algorithms.