Abstract
This study presents a voxel-based multiple regression analysis of different magnetic resonance image modalities, including anatomical T1-weighted, T2∗ relaxometry, and diffusion tensor imaging. Quantitative parameters sensitive to complementary brain tissue alterations, including morphometric atrophy, mineralization, microstructural damage, and anisotropy loss, were compared in a linear physiological aging model in 140 healthy subjects (range 20-74 years). The performance of different predictors and the identification of the best biomarker of age-induced structural variation were compared without a priori anatomical knowledge. The best quantitative predictors in several brain regions were iron deposition and microstructural damage, rather than macroscopic tissue atrophy. Age variations were best resolved with a combination of markers, suggesting that multiple predictors better capture age-induced tissue alterations. The results of the linear model were used to predict apparent age in different regions of individual brain. This approach pointed to a number of novel applications that could potentially help highlighting areas particularly vulnerable to disease.
Original language | English |
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Article number | 7460883 |
Pages (from-to) | 1232-1239 |
Number of pages | 8 |
Journal | IEEE Journal of Biomedical and Health Informatics |
Volume | 20 |
Issue number | 5 |
DOIs | |
Publication status | Published - Sep 1 2016 |
ASJC Scopus subject areas
- Biotechnology
- Computer Science Applications
- Electrical and Electronic Engineering
- Health Information Management