Amyloid-β prediction machine learning model using source-based morphometry across neurocognitive disorders

Amyloid-β prediction machine learning model using source-based morphometry across neurocognitive disorders

Abstract Previous studies have developed and explored magnetic resonance imaging (MRI)-based machine learning models for predicting Alzheimer’s disease (AD). However, limited research has focused on models incorporating diverse patient populations. This study aimed to build a clinically useful predi...

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Bibliographic Details
Main Authors: Yuki Momota, Shogyoku Bun, Jinichi Hirano, Kei Kamiya, Ryo Ueda, Yu Iwabuchi, Keisuke Takahata, Yasuharu Yamamoto, Toshiki Tezuka, Masahito Kubota, Morinobu Seki, Ryo Shikimoto, Yu Mimura, Taishiro Kishimoto, Hajime Tabuchi, Masahiro Jinzaki, Daisuke Ito, Masaru Mimura
Format: Article
Language:English
Published: Nature Portfolio 2024-04-01
Series:Scientific Reports
Subjects:
Alzheimer’s disease
Amyloid-β
Machine learning
Magnetic resonance imaging
Source-based morphometry
Online Access:https://doi.org/10.1038/s41598-024-58223-3

Internet

https://doi.org/10.1038/s41598-024-58223-3

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