| Autor: |
Gaubert, Sinead, Raimondo, Federico, Houot, Marion, Corsi, Marie‐Constance, Naccache, Lionel, Sitt, Jacobo Diego, Hermann, Bertrand, Oudiette, Delphine, Gagliardi, Geoffroy Pierre, Habert, Marie‐Odile, Dubois, Bruno, Fallani, Fabrizio De Vico, Bakardjian, Hovagim, Epelbaum, Stéphane |
| Zdroj: |
Alzheimer's & Dementia: The Journal of the Alzheimer's Association; Dec2020 Supplement S11, Vol. 16 Issue 11, p1-5, 5p |
| Abstrakt: |
Background: Early biomarkers are needed to identify individuals at high risk of preclinical Alzheimer's disease (AD) (Jack et al., 2018). Electroencephalography (EEG) is a non‐invasive and cheap technique that would be an interesting screening tool for the preclinical stage of AD. Method: We included participants from the INSIGHT‐preAD cohort, which is an ongoing single‐center multimodal observational study designed to identify risk factors and markers of progression to clinical AD in 318 cognitively normal individuals aged 70–85 years with a subjective memory complaint (Dubois et al., 2018). We divided the subjects into four groups, according to their amyloid status (on 18F‐florbetapir PET) and neurodegeneration status (on ¹⁸F‐fluorodeoxyglucose PET). We analysed 314 baseline 256‐channel high‐density eyes‐closed 1‐minute resting‐state EEG recordings. We extracted 10 quantitative EEG biomarkers, including spectral measures, algorithmic complexity and functional connectivity metrics. We evaluated three different classifiers (random forest, decision tree and logistic regression) to predict amyloid and neurodegeneration status from multimodal data, combining EEG, ApoE4 genotype, demographic, neuropsychological and MRI data. We divided the dataset into a training set (n=258) and validation set (n=46). We used a 5‐fold cross‐validation on the training set to identify the optimal combination of features. Result: As recently published (Gaubert et al., 2019), the most prominent effects of neurodegeneration on EEG metrics were localized in fronto‐central regions with an increase in high‐frequency oscillations (higher beta and gamma power) and a decrease in low‐frequency oscillations (lower delta power), higher spectral entropy, higher complexity and increased functional connectivity measured by weighted Symbolic Mutual Information in theta band. In the machine learning analysis, among the different features, EEG was the most strongly predictive of neurodegeneration. Similar predictive performance was obtained when reducing the number of electrodes from 256 to 8. EEG biomarkers combined with hippocampal volumetry had a high negative predictive value (91%) and high sensitivity (82%) to predict neurodegeneration. The combination of ApoE4 genotype with demographic data was most strongly predictive of amyloid status. Conclusion: EEG could be a valuable tool to screen for or rule out neurodegeneration in elderly memory complainers. This procedure has been patented under the following PCT number: PCT/EP2019/086629. [ABSTRACT FROM AUTHOR] |
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