PET-validated EEG-machine learning algorithm predicts brain amyloid pathology in pre-dementia Alzheimer’s disease
Abstract Developing reliable biomarkers is important for screening Alzheimer’s disease (AD) and monitoring its progression. Although EEG is non-invasive direct measurement of brain neural activity and has potentials for various neurologic disorders, vulnerability to noise, difficulty in clinical int...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-06-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-36713-0 |
| _version_ | 1797789843965607936 |
|---|---|
| author | Nam Heon Kim Ukeob Park Dong Won Yang Seong Hye Choi Young Chul Youn Seung Wan Kang |
| author_facet | Nam Heon Kim Ukeob Park Dong Won Yang Seong Hye Choi Young Chul Youn Seung Wan Kang |
| author_sort | Nam Heon Kim |
| collection | DOAJ |
| description | Abstract Developing reliable biomarkers is important for screening Alzheimer’s disease (AD) and monitoring its progression. Although EEG is non-invasive direct measurement of brain neural activity and has potentials for various neurologic disorders, vulnerability to noise, difficulty in clinical interpretation and quantification of signal information have limited its clinical application. There have been many research about machine learning (ML) adoption with EEG, but the accuracy of detecting AD is not so high or not validated with Aβ PET scan. We developed EEG-ML algorithm to detect brain Aβ pathology among subjective cognitive decline (SCD) or mild cognitive impairment (MCI) population, and validated it with Aβ PET. 19-channel resting-state EEG and Aβ PET were collected from 311 subjects: 196 SCD(36 Aβ +, 160 Aβ −), 115 MCI(54 Aβ +, 61Aβ −). 235 EEG data were used for training ML, and 76 for validation. EEG features were standardized for age and sex. Multiple important features sets were selected by 6 statistics analysis. Then, we trained 8 multiple machine learning for each important features set. Meanwhile, we conducted paired t-test to find statistically different features between amyloid positive and negative group. The best model showed 90.9% sensitivity, 76.7% specificity and 82.9% accuracy in MCI + SCD (33 Aβ +, 43 Aβ −). Limited to SCD, 92.3% sensitivity, 75.0% specificity, 81.1% accuracy (13 Aβ +, 24 Aβ −). 90% sensitivity, 78.9% specificity and 84.6% accuracy for MCI (20 Aβ +, 19 Aβ −). Similar trends of EEG power have been observed from the group comparison between Aβ + and Aβ −, and between MCI and SCD: enhancement of frontal/ frontotemporal theta; attenuation of mid-beta in centroparietal areas. The present findings suggest that accurate classification for beta-amyloid accumulation in the brain based on QEEG alone could be possible, which implies that QEEG is a promising biomarker for beta-amyloid. Since QEEG is more accessible, cost-effective, and safer than amyloid PET, QEEG-based biomarkers may play an important role in the diagnosis and treatment of AD. We expect specific patterns in QEEG could play an important role to predict future progression of cognitive impairment in the preclinical stage of AD. Further feature engineering and validation with larger dataset is recommended. |
| first_indexed | 2024-03-13T01:56:19Z |
| format | Article |
| id | doaj.art-5942928ac74f470ebf573b9945920c49 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-13T01:56:19Z |
| publishDate | 2023-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-5942928ac74f470ebf573b9945920c492023-07-02T11:12:55ZengNature PortfolioScientific Reports2045-23222023-06-0113111210.1038/s41598-023-36713-0PET-validated EEG-machine learning algorithm predicts brain amyloid pathology in pre-dementia Alzheimer’s diseaseNam Heon Kim0Ukeob Park1Dong Won Yang2Seong Hye Choi3Young Chul Youn4Seung Wan Kang5iMediSync InciMediSync IncDepartment of Neurology, St. Mary’s Hospital, The Catholic University of KoreaDepartment of Neurology, Inha University School of MedicineDepartment of Neurology, Chung-Ang University College of MedicineiMediSync IncAbstract Developing reliable biomarkers is important for screening Alzheimer’s disease (AD) and monitoring its progression. Although EEG is non-invasive direct measurement of brain neural activity and has potentials for various neurologic disorders, vulnerability to noise, difficulty in clinical interpretation and quantification of signal information have limited its clinical application. There have been many research about machine learning (ML) adoption with EEG, but the accuracy of detecting AD is not so high or not validated with Aβ PET scan. We developed EEG-ML algorithm to detect brain Aβ pathology among subjective cognitive decline (SCD) or mild cognitive impairment (MCI) population, and validated it with Aβ PET. 19-channel resting-state EEG and Aβ PET were collected from 311 subjects: 196 SCD(36 Aβ +, 160 Aβ −), 115 MCI(54 Aβ +, 61Aβ −). 235 EEG data were used for training ML, and 76 for validation. EEG features were standardized for age and sex. Multiple important features sets were selected by 6 statistics analysis. Then, we trained 8 multiple machine learning for each important features set. Meanwhile, we conducted paired t-test to find statistically different features between amyloid positive and negative group. The best model showed 90.9% sensitivity, 76.7% specificity and 82.9% accuracy in MCI + SCD (33 Aβ +, 43 Aβ −). Limited to SCD, 92.3% sensitivity, 75.0% specificity, 81.1% accuracy (13 Aβ +, 24 Aβ −). 90% sensitivity, 78.9% specificity and 84.6% accuracy for MCI (20 Aβ +, 19 Aβ −). Similar trends of EEG power have been observed from the group comparison between Aβ + and Aβ −, and between MCI and SCD: enhancement of frontal/ frontotemporal theta; attenuation of mid-beta in centroparietal areas. The present findings suggest that accurate classification for beta-amyloid accumulation in the brain based on QEEG alone could be possible, which implies that QEEG is a promising biomarker for beta-amyloid. Since QEEG is more accessible, cost-effective, and safer than amyloid PET, QEEG-based biomarkers may play an important role in the diagnosis and treatment of AD. We expect specific patterns in QEEG could play an important role to predict future progression of cognitive impairment in the preclinical stage of AD. Further feature engineering and validation with larger dataset is recommended.https://doi.org/10.1038/s41598-023-36713-0 |
| spellingShingle | Nam Heon Kim Ukeob Park Dong Won Yang Seong Hye Choi Young Chul Youn Seung Wan Kang PET-validated EEG-machine learning algorithm predicts brain amyloid pathology in pre-dementia Alzheimer’s disease Scientific Reports |
| title | PET-validated EEG-machine learning algorithm predicts brain amyloid pathology in pre-dementia Alzheimer’s disease |
| title_full | PET-validated EEG-machine learning algorithm predicts brain amyloid pathology in pre-dementia Alzheimer’s disease |
| title_fullStr | PET-validated EEG-machine learning algorithm predicts brain amyloid pathology in pre-dementia Alzheimer’s disease |
| title_full_unstemmed | PET-validated EEG-machine learning algorithm predicts brain amyloid pathology in pre-dementia Alzheimer’s disease |
| title_short | PET-validated EEG-machine learning algorithm predicts brain amyloid pathology in pre-dementia Alzheimer’s disease |
| title_sort | pet validated eeg machine learning algorithm predicts brain amyloid pathology in pre dementia alzheimer s disease |
| url | https://doi.org/10.1038/s41598-023-36713-0 |
| work_keys_str_mv | AT namheonkim petvalidatedeegmachinelearningalgorithmpredictsbrainamyloidpathologyinpredementiaalzheimersdisease AT ukeobpark petvalidatedeegmachinelearningalgorithmpredictsbrainamyloidpathologyinpredementiaalzheimersdisease AT dongwonyang petvalidatedeegmachinelearningalgorithmpredictsbrainamyloidpathologyinpredementiaalzheimersdisease AT seonghyechoi petvalidatedeegmachinelearningalgorithmpredictsbrainamyloidpathologyinpredementiaalzheimersdisease AT youngchulyoun petvalidatedeegmachinelearningalgorithmpredictsbrainamyloidpathologyinpredementiaalzheimersdisease AT seungwankang petvalidatedeegmachinelearningalgorithmpredictsbrainamyloidpathologyinpredementiaalzheimersdisease |