Cross-Sectional and Longitudinal 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.Seventy-six MS patients, 71 % females and 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. A machine learning model was applied on an independent training set of 3208 HC, estimating individual brain age and calculating the difference between estimated brain age and chronological age, termed brain age gap (BAG). We also assessed the longitudinal change rate in BAG in MS individuals. We used additional cross-sectional MRI data from 235 HC for case-control comparison.MS patients showed increased BAG (4.4 ±6.6 years) compared to HC (Cohen's D = 0.69, p = 4.0 x 10 -6 ). Longitudinal estimates of BAG in MS patients suggested an accelerated rate of brain aging corresponding to an annual increase of 0.41 (±1.23) years compared to chronological aging for the MS patients (p = 0.008).On average, patients with MS have significantly higher BAG compared to HC and accelerated rate of brain aging compared to chronological aging. Brain age estimation represents a promising method for evaluation of brain changes in MS, with potential for predicting future outcome and guide treatment.

Adipose tissue distribution from body MRI is associated with cross-sectional and longitudinal brain age in adults

“…Several processing steps, such as skull stripping, Talairach transforms, atlas registration as well as spherical surface maps and parcellations are then initialized with common information from the within-subject template, significantly increasing reliability and statistical power (Reuter et al, 2012). Due to the longitudinal stream in FreeSurfer influencing the thickness estimates, and subsequently having an impact on brain age prediction (Høgestøl et al, 2019), both cross-sectional and longitudinal data in the test set were processed with the longitudinal stream. All reconstructions were visually assessed and edited by trained research personnel.…”

“…The difference between the brain-predicted age and an individual's chronological age, also referred to as the brain age gap (BAG), can be used to assess deviations from expected age trajectories, with potential utility in studies of brain disorders and ageing (Cole et al, 2017;. This has clear clinical implications for patient groups, where studies have reported larger brain age gaps in patients with various neurological and psychiatric disorders (Han et al, 2020;Høgestøl et al, 2019;Pardoe et al, 2017;Sone et al, 2019;Tønnesen et al, 2020).…”

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

“…A preprint of this manuscript was published at bioRxiv (https://www.biorxiv.org/content/10.1101/440412v1) on October 10th 28 (34).…”

Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: A review

“…Unfortunately, there are currently no symptoms, physical findings, or laboratory tests to accurately diagnose MS [34][35]. For this reason, several methods are used to diagnose MS that include reviewing the patient's medical history [36][37], medical imaging techniques such as MRI [38][39][40], spinal fluid analysis [41][42], and blood tests [43][44]. Currently, MRI modalities are the best non-invasive method used for the diagnosis of MS [45][46][47].…”