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

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.

Age prediction on the basis of brain anatomical measures

“…The absolute prediction errors (difference between predicted and chronological age) we obtained in our analyses are quite similar to those reported in previous studies [Brown et al, ; Cherubini et al, ; Franke et al, ]. However, it is worth mentioning that our study cannot be compared entirely with these studies, either because they only studied relatively young subjects [Brown et al, ] or they employed a much smaller sample than we used in our study [Cherubini et al, ]. However, the results are astonishingly similar in showing that the prediction error increases with increasing age [Brown et al, ; Franke et al, ].…”

Multimodal brain-age prediction and cardiovascular risk: The Whitehall II MRI sub-study

“…In line with recent findings from UK Biobank [14,15], positive associations were found between brain-age deltas and diastolic blood pressure, alcohol intake, and stroke risk, concurring with previous WHII studies [32,50], and demonstrating that the brain age-delta measure reflects individual variation in neural aging processes [30]. The associations with biomedical variables were consistent across models (see Figure 5), indicating that while modality-specific brain age models may be informative in patient groups where tissue types are differently affected by disease [26,27,54,67,68], such models may be more closely related in healthy cohorts [14]. It is possible that regional modelling of modality-specific brain aging patterns may be more suitable to detect specific associations with biomedical and clinical measures [19], which could get lost in machine learning models that summarise aging across the whole brain to produce a single global prediction [27].…”

Multi-modality neuroimaging brain-age in UK Biobank: relationship to biomedical, lifestyle and cognitive factors

“…The majority of extant brain-age studies use T1-weighted MRI alone (Cole, et al, 2019b), though previous multi-modality studies have used two or three modalities (Cherubini, et al, 2016, Groves, et al, 2012, Liem, et al, 2017, Richard, et al, 2018. Thanks to UK Biobank, I was able to combine and compared six different modalities As anticipated, T1-weighted MRI proved important for brain-age prediction here, with normalised grey matter volume being the most informative neuroimaging phenotype.…”

“…Several previous brain-age studies have used two or three modalities (Cherubini, et al, 2016, Groves, et al, 2012, Liem, et al, 2017, Richard, et al, 2018. Liem and colleagues (2017) studied n=2354 people, finding improved accuracy when combining T1-weighted structural MRI with resting-state fMRI (MAE = 4.29 years).…”