Exploring Brain Age Calculation Models Available for Alzheimer’s Disease

Journal of Beijing Institute of Technology ›› 2023, Vol. 32 ›› Issue (2) : 181 -187.

PDF (6067KB)
Journal of Beijing Institute of Technology ›› 2023, Vol. 32 ›› Issue (2) : 181 -187. DOI: 10.15918/j.jbit1004-0579.2023.011

Exploring Brain Age Calculation Models Available for Alzheimer’s Disease

Author information +
History +
PDF (6067KB)

Abstract

The advantages of structural magnetic resonance imaging (sMRI)-based multidimensional tensor morphological features in brain disease research are the high sensitivity and resolution of sMRI to comprehensively capture the key structural information and quantify the structural deformation. However, its direct application to regression analysis of high-dimensional small-sample data for brain age prediction may cause “dimensional catastrophe”. Therefore, this paper develops a brain age prediction method for high-dimensional small-sample data based on sMRI multidimensional morphological features and constructs brain age gap estimation (BrainAGE) biomarkers to quantify abnormal aging of key subcortical structures by extracting subcortical structural features for brain age prediction, which can then establish statistical analysis models to help diagnose Alzheimer’s disease and monitor health conditions, intervening at the preclinical stage.

Keywords

brain age gap estimation (BrainAGE); Alzheimer’s disease (AD) / structural magnetic resonance imaging (sMRI)

Cite this article

Download citation ▾
null. Exploring Brain Age Calculation Models Available for Alzheimer’s Disease. Journal of Beijing Institute of Technology, 2023, 32(2): 181-187 DOI:10.15918/j.jbit1004-0579.2023.011

登录浏览全文

4963

注册一个新账户 忘记密码

References

AI Summary AI Mindmap
PDF (6067KB)

620

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/