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 |
|---|---|
| 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 |
Fingerprint
ASJC Scopus subject areas
- Biotechnology
- Computer Science Applications
- Electrical and Electronic Engineering
- Health Information Management
Cite this
Importance of Multimodal MRI in Characterizing Brain Tissue and Its Potential Application for Individual Age Prediction. / Cherubini, Andrea; Caligiuri, Maria Eugenia; Peran, Patrice; Sabatini, Umberto; Cosentino, Carlo; Amato, Francesco.
In: IEEE Journal of Biomedical and Health Informatics, Vol. 20, No. 5, 7460883, 01.09.2016, p. 1232-1239.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Importance of Multimodal MRI in Characterizing Brain Tissue and Its Potential Application for Individual Age Prediction
AU - Cherubini, Andrea
AU - Caligiuri, Maria Eugenia
AU - Peran, Patrice
AU - Sabatini, Umberto
AU - Cosentino, Carlo
AU - Amato, Francesco
PY - 2016/9/1
Y1 - 2016/9/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84987739747&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84987739747&partnerID=8YFLogxK
U2 - 10.1109/JBHI.2016.2559938
DO - 10.1109/JBHI.2016.2559938
M3 - Article
AN - SCOPUS:84987739747
VL - 20
SP - 1232
EP - 1239
JO - IEEE Journal of Biomedical and Health Informatics
JF - IEEE Journal of Biomedical and Health Informatics
SN - 2168-2194
IS - 5
M1 - 7460883
ER -