Cross-Sectional and Longitudinal MRI Brain Scans Reveal Accelerated Brain Aging in Multiple Sclerosis

Abstract

Multiple sclerosis (MS) is an inflammatory disorder of the central nervous system. By combining longitudinal MRI-based brain morphometry and brain age estimation using machine learning, we tested the hypothesis that MS patients have higher brain age relative to chronological age than healthy controls (HC) and that longitudinal rate of brain aging in MS patients is associated with clinical course and severity. Seventy-six MS patients [71% females, mean age 34.8 years (range 21–49) at inclusion] were examined with brain MRI at three time points with a mean total follow up period of 4.4 years (±0.4 years). We used additional cross-sectional MRI data from 235 HC for case-control comparison. We applied a machine learning model trained on an independent set of 3,208 HC to estimate individual brain age and to calculate the difference between estimated and chronological age, termed brain age gap (BAG). We also assessed the longitudinal change rate in BAG in individuals with MS. MS patients showed significantly higher BAG (4.4 ± 6.6 years) compared to HC (Cohen's D = 0.69, p = 4.0 × 10 −6 ). Longitudinal estimates of BAG in MS patients showed high reliability and suggested an accelerated rate of brain aging corresponding to an annual increase of 0.41 (SE = 0.15) years compared to chronological aging ( p = 0.008). Multiple regression analyses revealed higher rate of brain aging in patients with more brain atrophy (Cohen's D = 0.86, p = 4.3 × 10 −15 ) and increased white matter lesion load (WMLL) (Cohen's D = 0.55, p = 0.015). On average, patients with MS had significantly higher BAG compared to HC. Progressive brain aging in patients with MS was related to brain atrophy and increased WMLL. No significant clinical associations were found in our sample, future studies are warranted on this matter. Brain age estimation is a promising method for evaluation of subtle brain changes in MS, which is important for predicting clinical outcome and guide choice of intervention.

Longitudinal Assessment of Multiple Sclerosis with the Brain‐Age Paradigm

“…The difference between an individual's chronological age and the age predicted by machine-learning analysis of voxelwise neuroimaging data, the brain-predicted age difference (brain-PAD), has been proposed as an age-adjusted index of structural brain health. Research has shown brain-PAD to be sensitive to neurological and psychiatric diseases, 15,16 including MS. 17,18 Specifically, MS was associated with between 4 and 6 years of added brain-PAD, similar in magnitude to previous work on traumatic brain injury 19 and epilepsy 20 and greater than well-treated HIV 21 or Down's syndrome. 22 Kaufmann and colleagues reported that brain-age gap (equivalent to brain-PAD) was associated with Expanded Disability Status Scale (EDSS) score.…”

Reliable longitudinal brain age prediction in stroke patients: Associations with cognitive function and response to cognitive training

“…In line with a recent implementation in patients with MS (Høgestøl et al 2019a), our results demonstrated high reliability across all timepoints for the global and regional models with ICC ranging from .70 to .86 for the right occipital and the right parietal models respectively. The brain age estimation based on the median of the 12 regional model was amongst the most reliable models with an ICC of .89. across the two baselines and .83 across all timepoints, outperforming the estimation based on all T1 features.…”

“…Briefly, combining a wide array of informative brain imaging features in a prediction model allows for an accurate prediction of the age of an unseen individual (Franke et al 2012;Franke et al 2010). The degree to which the model under-or over-estimate the individual's age has been shown to be sensitive to a variety of health-related characteristics, including cognitive function and mortality (Boyle et al 2019;Cole & Franke 2017;Cole et al 2018;Richard et al 2018), and brain age prediction using MRI data has recently been shown to be sensitive both to the clinical manifestation and polygenic risk of various brain disorders (Høgestøl et al 2019a;Kaufmann et al 2018).…”

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

“…Individual variation in delta estimations are associated with a range of cognitive and biological measures [10,11,12,13,14,15,16,17], including cardiovascular health [14], and differences in brain age have been established between patient groups and healthy controls: individuals with conditions such as Alzheimer's disease, multiple sclerosis, epilepsy, and psychiatric disorders show on average older brain age relative to their chronological age [9,18,19,20,21,22]. Longitudinal studies have documented highly reliable brain age prediction in stroke patients [23], and accelerated brain aging in patients with schizophrenia and multiple sclerosis [21,24,25].…”