Importance of Multimodal MRI in Characterizing Brain Tissue and Its Potential Application for Individual Age Prediction

Andrea Cherubini, Maria Eugenia Caligiuri, Patrice Peran, Umberto Sabatini, Carlo Cosentino, Francesco Amato

Research output: Contribution to journalArticlepeer-review


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 languageEnglish
Article number7460883
Pages (from-to)1232-1239
Number of pages8
JournalIEEE Journal of Biomedical and Health Informatics
Issue number5
Publication statusPublished - Sep 1 2016

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management


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