文献类型：期刊文献

英文题名：Early detection of Alzheimer's disease via multimodal MRI and machine learning

作者：Yuan, Jin[1,2];Liu, Yingying[1,2];Jin, Juntao[1,2];Li, Siyu[1,2];Zhang, Yamin[2]

第一作者：Yuan, Jin;袁健

通信作者：Zhang, YM[1]

机构：[1]Gansu Univ Chinese Med, Clin Med Coll 1, Lanzhou, Peoples R China;[2]Gansu Prov Hosp, Dept Neurol, Lanzhou, Peoples R China

第一机构：甘肃中医药大学

通信机构：[1]corresponding author), Gansu Prov Hosp, Dept Neurol, Lanzhou, Peoples R China.

年份：2026

卷号：18

外文期刊名：FRONTIERS IN AGING NEUROSCIENCE

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001748903600001)】；

基金：The author(s) declared that financial support was received for this work and/or its publication. This study was supported by the Science and Technology Department of Gansu Province (24JRRA890).

语种：英文

外文关键词：Alzheimer's disease; diffusion tensor imaging; early diagnosis; machine learning; resting-state functional MRI

摘要：Objective This study aimed to identify effective biomarkers associated with early-stage Alzheimer's disease (AD) by integrating multimodal neuroimaging features with machine learning (ML), addressing clinical challenges posed by global population aging.Materials and methods Multimodal neuroimaging-including resting-state functional MRI (rs-fMRI), structural MRI (sMRI), and diffusion tensor imaging (DTI)-was combined with ML techniques. A total of 234 subjects [cognitively normal (CN), mild cognitive impairment (MCI), and AD] were selected from the AD Neuroimaging Initiative (ADNI) database. Brain functional, structural, and microstructural features were extracted, and nine ML models, including support vector machine (SVM), random forest (RF), and Naive Bayes, were trained and evaluated across three binary classification tasks: AD-CN, MCI-CN, and AD-MCI.Results The SVM model achieved the highest performance for AD-CN (AUC = 0.901) and MCI-CN (AUC = 0.839), while RF performed best for AD-MCI classification (AUC = 0.809). Functional analyses revealed significant abnormalities in key regions, including the anterior cingulate cortex, hippocampus, and middle frontal gyrus in AD patients. Structural analyses confirmed that hippocampal subfield atrophy was strongly associated with cognitive decline. Diffusion metrics, particularly the DTI-ALPS index, reflected microstructural white matter damage effectively.Conclusion Integrating multimodal neuroimaging with ML enhances diagnostic accuracy for AD and MCI and identifies potential neuroimaging biomarkers, providing objective evidence to support early clinical intervention.