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

• Published in: Scientific reports Vol. 14; no. 1; pp. 7633 - 12
• Main Authors: Momota, Yuki, Bun, Shogyoku, Hirano, Jinichi, Kamiya, Kei, Ueda, Ryo, Iwabuchi, Yu, Takahata, Keisuke, Yamamoto, Yasuharu, Tezuka, Toshiki, Kubota, Masahito, Seki, Morinobu, Shikimoto, Ryo, Mimura, Yu, Kishimoto, Taishiro, Tabuchi, Hajime, Jinzaki, Masahiro, Ito, Daisuke, Mimura, Masaru
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.04.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 692/617/375/132; 692/699/375/365; 692/699/375/365/1283; 692/700/1421/65; Accuracy; Alzheimer Disease - diagnostic imaging; Alzheimer Disease - pathology; Alzheimer's disease; Amyloid beta-Peptides; Amyloid-β; Apolipoprotein E; Apolipoproteins; Brain - pathology; Cognition; Cognitive ability; Cognitive Dysfunction - diagnostic imaging; Cognitive Dysfunction - pathology; Frontotemporal dementia; Humanities and Social Sciences; Humans; Learning algorithms; Machine Learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Mental disorders; Morphometry; multidisciplinary; Neurodegenerative diseases; Neurological diseases; Population studies; Prediction models; Progressive supranuclear palsy; Science; Science (multidisciplinary); Source-based morphometry; Substantia grisea; β-Amyloid; Amyloid-β; Alzheimer’s disease; Magnetic resonance imaging; Source-based morphometry; Machine learning
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-58223-3

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 prediction model for amyloid-beta (Aβ) deposition using source-based morphometry, using a data-driven algorithm based on independent component analyses. Additionally, we assessed how the predictive accuracies varied with the feature combinations. Data from 118 participants clinically diagnosed with various conditions such as AD, mild cognitive impairment, frontotemporal lobar degeneration, corticobasal syndrome, progressive supranuclear palsy, and psychiatric disorders, as well as healthy controls were used for the development of the model. We used structural MR images, cognitive test results, and apolipoprotein E status for feature selection. Three-dimensional T1-weighted images were preprocessed into voxel-based gray matter images and then subjected to source-based morphometry. We used a support vector machine as a classifier. We applied SHapley Additive exPlanations, a game-theoretical approach, to ensure model accountability. The final model that was based on MR-images, cognitive test results, and apolipoprotein E status yielded 89.8% accuracy and a receiver operating characteristic curve of 0.888. The model based on MR-images alone showed 84.7% accuracy. Aβ-positivity was correctly detected in non-AD patients. One of the seven independent components derived from source-based morphometry was considered to represent an AD-related gray matter volume pattern and showed the strongest impact on the model output. Aβ-positivity across neurological and psychiatric disorders was predicted with moderate-to-high accuracy and was associated with a probable AD-related gray matter volume pattern. An MRI-based data-driven machine learning approach can be beneficial as a diagnostic aid.