PET-validated EEG-machine learning algorithm predicts brain amyloid pathology in pre-dementia Alzheimer’s disease

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 interpretati...

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Veröffentlicht in:	Scientific Reports Jg. 13; H. 1; S. 10299 - 12
Hauptverfasser:	Kim, Nam Heon, Park, Ukeob, Yang, Dong Won, Choi, Seong Hye, Youn, Young Chul, Kang, Seung Wan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer Science and Business Media LLC 26.06.2023 Nature Publishing Group UK Nature Publishing Group Nature Portfolio
Schlagworte:	631/114 631/378 692/53 Accuracy Algorithms Alzheimer Disease Alzheimer Disease - diagnostic imaging Alzheimer Disease - pathology Alzheimer's disease Amyloid beta-Peptides Amyloid beta-Peptides - metabolism Biomarkers Brain Brain - metabolism Cognitive ability Cognitive Dysfunction Cognitive Dysfunction - diagnosis Cognitive Dysfunction - pathology Dementia Dementia disorders Disease Progression EEG Humanities and Social Sciences Humans Learning algorithms Machine Learning Medicine multidisciplinary Neurodegenerative diseases Pathology Positron-Emission Tomography Q R Science Science (multidisciplinary) Statistical analysis β-Amyloid
ISSN:	2045-2322, 2045-2322
Online-Zugang:	Volltext
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