Screening of Aβ and phosphorylated tau status in the cerebrospinal fluid through machine learning analysis of portable electroencephalography data

Screening of Aβ and phosphorylated tau status in the cerebrospinal fluid through machine learning analysis of portable electroencephalography data

Abstract Diagnosing Alzheimer’s disease (AD) through pathological markers is typically costly and invasive. This study aims to find a noninvasive, cost-effective method using portable electroencephalography (EEG) to detect changes in AD-related biomarkers in cerebrospinal fluid (CSF). A total of 102...

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Main Authors: Masahiro Hata, Yuki Miyazaki, Kohji Mori, Kenji Yoshiyama, Shoshin Akamine, Hideki Kanemoto, Shiho Gotoh, Hisaki Omori, Atsuya Hirashima, Yuto Satake, Takashi Suehiro, Shun Takahashi, Manabu Ikeda
Format: Article
Language:English
Published: Nature Portfolio 2025-01-01
Series:Scientific Reports
Subjects:
Dementia
EEG
Alzheimer’s disease
Amyloid beta
Phosphorylated tau
Machine learning
Online Access:https://doi.org/10.1038/s41598-025-86449-2
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Summary:Abstract Diagnosing Alzheimer’s disease (AD) through pathological markers is typically costly and invasive. This study aims to find a noninvasive, cost-effective method using portable electroencephalography (EEG) to detect changes in AD-related biomarkers in cerebrospinal fluid (CSF). A total of 102 patients, both with and without AD-related biomarker changes (amyloid beta and phosphorylated tau), were recorded using a 2-minute resting-state portable EEG. A machine-learning algorithm then analyzed the EEG data to identify these biomarker changes. The results showed that the machine learning model could distinguish patients with AD-related biomarker changes, achieving 68.1% accuracy (AUROC 0.75) for amyloid beta and 71.2% accuracy (AUROC 0.77) for phosphorylated tau, with gamma activities being key features. When excluding cases with idiopathic normal pressure hydrocephalus, accuracy improved to 74.1% (AUROC 0.80) for amyloid beta and 73.1% (AUROC 0.80) for phosphorylated tau. This study suggests that portable EEG combined with machine learning is a promising noninvasive and cost-effective tool for early AD-related pathological marker screening, which could enhance neurophysiological understanding and diagnostic accessibility.
ISSN:2045-2322

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