Author(s): Liu, X (Liu, Xia); Beheshti, I (Beheshti, Iman); Zheng, WH (Zheng, Weihao); Li, YC (Li, Yongchao); Li, S (Li, Shan); Zhao, ZY (Zhao, Ziyang); Yao, ZJ (Yao, Zhijun); Hu, B (Hu, Bin)

Source: COMPUTERS IN BIOLOGY AND MEDICINE Volume: 143 Article Number: 105285 DOI: 10.1016/j.compbiomed.2022.105285 Published: APR 2022

Abstract: Studying brain aging improves our understanding in differentiating typical and atypical aging. Directly utilizing traditional morphological features for brain age estimation did not show significant performance in healthy controls (HCs), which may be due to the negligence of the information of structural similarities among cortical regions. For this issue, the multi-feature-based network (MFN) built upon morphological features can be employed to describe these similarities. Based on this, we hypothesized that the MFN is more efficient and robust than traditional morphological features in brain age estimating. In this work, we used six different types of morphological features (i.e., cortical volume, cortical thickness, curvature index, folding index, local gyrification index, and surface area) to build individual MFN for brain age estimation. The efficacy of MFN was estimated on 2501 HCs with T1-weighted structural magnetic resonance imaging (sMRI) data and compared with traditional morphological features. We attained a mean absolute error (MAE) of 3.73 years using the proposed method on an independent test set, whereas a mean absolute error of 5.30 years was derived from morphological features. Our experimental results demonstrated that the MFN is an efficient and robust metric for estimating brain age.

Accession Number: WOS:000788100500004

PubMed ID: 35158116

Author Identifiers:

Author        Web of Science ResearcherID        ORCID Number

Zheng, Weihao                  0000-0003-2996-5909

ISSN: 0010-4825

eISSN: 1879-0534

Full Text: https://www.sciencedirect.com/science/article/pii/S0010482522000774?via%3Dihub