Hugo Vrenken

Clinical progression is strongly linked to gray matter atrophy in multiple sclerosis (MS), detectable early on MRI and progressing nonrandomly across the brain. However, the mechanisms driving its spatiotemporal progression and individual variability remain unclear. Using MRIs from 2187 participants, alongside normative data, we systematically investigated network-based mechanisms underlying MS-related atrophy. Regional atrophy was associated with functional cortical hubness, supporting the nodal stress hypothesis, and was constrained by anatomical and functional connections, consistent with transneuronal degeneration. Lesional disconnection and transcriptomic vulnerability played marginal roles. Patient- and subgroup-level analyses revealed that network-based mechanisms are specifically linked to MS-related neurodegeneration and may operate differently in distinct subtypes or disease phases. Atrophy patterns were anchored to the connectivity profiles of disease epicenters involving the visual, sensorimotor, and temporal cortices, as well as the hippocampi, amygdalae, and thalami. Network-based measures enhanced the explanation of future atrophy progression in people with MS, providing a mechanistic framework to understand neurodegeneration in MS.

Background: Long-term studies on multiple sclerosis progression often face dropout, possibly among those with more aggressive disease, biasing the cohort. Objectives: We assessed potential survivorship bias in a long-term observational multiple sclerosis cohort. Methods: Three hundred sixty-one patients with multiple sclerosis were followed for 10 years, with clinical assessments, blood biomarkers and magnetic resonance imaging at baseline, follow-up 1 and follow-up 2. Progression was defined as an Expanded Disability Status Scale increase. Dropout was analysed and predicted; estimated Expanded Disability Status Scale was calculated for dropouts to assess bias. Brain volumes were normalized. Results: One hundred and sixty-two patients with multiple sclerosis completed all follow-ups; 81 showed progression. Dropout rates were 97/361 at follow-up 1 and 102/264 at follow-up 2. Dropouts had worse Expanded Disability Status Scale, Nine-Hole Peg test, Timed 25-Foot Walk, lower brain volume and higher lesion volume before dropping out. Blood biomarkers did not differ. Main dropout predictors were higher baseline Expanded Disability Status Scale, lesion volume, worse Nine-Hole Peg test and shorter disease duration. Median estimated Expanded Disability Status Scale at follow-up 2 for dropouts was 5.5 versus 3.5 for non-dropouts. Discussion: Fewer than half completed the study. High lesion load and worse clinical status predicted dropout. This may cause underestimation of progression and reduced statistical power. Similar bias may affect other long-term studies in chronic diseases.

Objective: This study investigated the predictive value of neurofilament light chain (NfL) levels beyond MRI measures of disease activity in multiple sclerosis (MS). Methods: This study is a retrospective post hoc analysis of the early interferon treatment group of the combined 5-year REFLEX/REFLEXION studies in clinically isolated syndrome. Baseline z-scored NfL (zNfL) and changes in zNfL during the first two years were related to brain atrophy, lesion activity, and clinical progression. Results: This study included 262 patients (61.83% female, mean age 31.68 ± 8.43, median Expanded Disability Status Scale 1.5). Mean zNfL levels were highest at baseline and higher in patients with gadolinium-enhancing lesions or high T2 lesion counts ( p < 0.001). Baseline zNfL correlated with higher volumes of T2 lesions at baseline and new/enlarging/shrinking/disappearing lesions in years 1 and 2 ( r _s = 0.20–0.68, p = 0.002– r = 0.16, p = 0.035), and an increased risk of and faster conversion to clinically definite MS (OR = 1.18, p = 0.035; HR = 1.16, p = 0.021). Baseline zNfL was no longer predictive when including baseline MRI variables and demographics. Longitudinal zNfL changes correlated especially with shrinking lesions during year 1 ( r _s = − 0.42, p < 0.001) and new/enlarging lesions during year 2 ( r _s = 0.26 and 0.23, p < 0.001). Subsequent global and central atrophy ( r = −0.18, p = 0.025; r = 0.20, p = 0.015) were only related to zNfL changes during year 2, even after including baseline MRI variables and demographics. Discussion: Cross-sectional zNfL was related to active disease and predictive of disease progression, but not after taking MRI measures into account. The longitudinal zNfL changes were related to lesion activity and subsequent atrophy, even after including MRI measures.

Progressive structural brain changes are a hallmark of neurodegenerative conditions like Alzheimer’s disease (AD), frontotemporal dementia (FTD), multiple sclerosis (MS), and Parkinson’s disease (PD). The brain-predicted age difference (brain-PAD) has emerged as a promising biomarker to quantify these alterations, yet its unique clinical contribution relative to conventional measures of global brain atrophy such as the brain parenchymal fraction (BPF) remains underexplored. In this transdiagnostic study across AD, FTD, MS, and PD, we systematically evaluated brain-PAD’s capacity to distinguish patients from controls, its cross-sectional and longitudinal associations with cognition, and its voxel-wise structural correlates. We benchmarked brain-PAD against BPF to determine its added explanatory value. Brain-PAD successfully distinguished patients from controls, adding to BPF alone, in AD, FTD, and MS, but not PD. Across disorders, higher brain-PAD correlated with worse cognition, showing clear added value beyond BPF particularly in AD and MS. Baseline brain-PAD also independently predicted subsequent cognitive changes in AD, FTD, and MS, over and above BPF. Voxel-wise analyses revealed spatial features underlying brain-PAD including, beyond global tissue loss, specific regional atrophy matching each disease’s characteristic pattern. Collectively, these findings demonstrate that brain-PAD is a clinically meaningful, transdiagnostic biomarker of neurodegeneration that complements conventional volumetric measures like the BPF.

Background and ObjectivesThere is an unmet need for neuroregenerative therapies in multiple sclerosis (MS). Mesenchymal stem cells (MSCs) have shown immunomodulatory and regenerative effects in pre...

Clinical progression is strongly linked to grey matter atrophy in multiple sclerosis (MS), detectable early on MRI and progressing non-randomly across the brain. However, the mechanisms driving its spatio-temporal progression and individual variability remain unclear. Using MRIs from 2,187 participants, alongside normative data, we systematically investigated network-based mechanisms underlying MS-related atrophy. Regional atrophy colocalised with functional cortical hubs, supporting the nodal stress hypothesis, and propagated along anatomical and functional connections, consistent with transneuronal degeneration. Lesional disconnection and transcriptomic vulnerability played marginal roles. Patient- and subgroup-level analyses revealed that network-based mechanisms are specifically linked to MS-related neurodegeneration and may operate differently in distinct subtypes or disease phases. Atrophy patterns were anchored to the connectivity profiles of disease epicentres involving the visual, sensorimotor, and temporal cortices, and the hippocampi and thalami. Network-based measures enhanced the prediction of future atrophy progression in individual with MS, providing a mechanistic framework to understand neurodegeneration in MS.

The brain-predicted age difference (brain-PAD) is a novel marker of neurodegeneration in multiple sclerosis (MS). Brain-PAD has been associated with clinical disability in heterogeneous MS patient cohorts of varying ages and disease durations. In this study, we investigate the relation between clinical disability and brain-PAD in a unique birth-year cohort of people with MS (pwMS) and healthy controls (HCs) of the same age all born in 1966, eliminating age as a confounding factor. This was a cross-sectional cohort study conducted in the Netherlands. Disability was quantified using the expanded disability status scale (EDSS), 9-hole peg test (9HPT), and the timed 25-foot walk test (T25FWT). Cognition was assessed using the Minimal Assessment of Cognitive Function in MS battery. The brain-PAD was calculated by subtracting the person's chronological age from the predicted brain age derived from 3-dimensional T1-weighted brain MRI scans using machine learning (brainageR software). Brain-PAD for HCs and MS subtypes (relapsing remitting, secondary progressive, and primary progressive) were compared using a generalized linear model. The relation between brain-PAD and disease duration and disability and cognitive measures were tested using univariate linear regression. In addition, the clinical explanatory value added by brain-PAD to those of brain parenchymal fraction (BPF) and T2 lesion volume was investigated. The study included 116 HC (mean age 52.9 ± 1.1 years, 61% female) and 237 pwMS (mean age 52.9 ± 0.9 years, median disease duration 16.3 years [interquartile range 8.2-24.4] and a median EDSS of 3.5 [interquartile range 2.5-4.0]). Brain-PAD was higher in pwMS compared with HC by 9.7 years (SE = 0.82, p < 0.0001). Longer disease duration was associated with a higher brain-PAD (β = 0.21, p < 0.001). A higher brain-PAD was associated with worse performance on the T25FWT (β = 0.0063, p < 0.05), 9HPT (β = 0.0074, p < 0.001), and EDSS (β = 0.028, p < 0.05). Brain-PAD was higher for cognitively impaired people with MS, compared with cognitively preserved pwMS and HC (p < 0.0001). Brain-PAD had added explanatory value over BPF in clinical outcome measures. In a cohort unbiased by age differences, greater brain ageing was associated with worse performance on disability and cognitive tests, underscoring the potential of brain-PAD as a marker for neurodegeneration and disease severity in MS.

Brain atrophy is relevant for understanding disease progression and treatment response in people with multiple sclerosis (pwMS). Automatic brain volume-reporting tools often rely on healthy control (HC) reference curves to interpret brain volumes, whereas brain volume loss is different in pwMS. This observational study aimed to develop an MS-specific reference model for brain volumes and evaluate its performance compared with HC-based curves, as a proof-of-concept. Participants, pwMS and HCs, from the Amsterdam MS cohort were included based on the availability of T1-weighted MR scans. Normalized brain volumes (NBVs) were obtained using commercially available software. The software program also provides NBV percentiles, based on age-specific and sex-specific HC curves, grouped into NBV quartiles, describing deviation from expected NBVs. Disease severity was determined with the MS severity score (MSSS), Symbol Digit Modalities Test (SDMT), and 9-Hole Peg Test (9HPT). An MS-specific model was developed by regressing NBVs against age, sex, disease duration, and MS phenotype. The resulting MS model was also used to classify pwMS into quartiles describing deviation from expected NBV, given the modeled patient characteristics, with leave-one-out predictions. Quartile classification from HC-based and MS-based reference curves was compared with MSSS using analysis of variance (ANOVA). Regressions for NBVs from 713 pwMS and 259 HCs (mean age: 49.1 ± 9.7 and 48.3 ± 10.1, %female: 70.4% and 67.2%, respectively) were significant for age, sex, disease duration, and phenotype, which were included in the MS-specific model. MS-specific model quartile designations significantly improved associations with MSSS values (p = 2.2*10^-9, η² = 0.06) compared with HC-based quartiles. MSSS values worsened with lower NBV quartiles in the MS-specific model (difference between quartiles 1-4 = -0.84, p = 6.1*10^-3, 95% CI [-1.5 to -0.18])), which was not observed for HC-based quartiles (p = 0.98). Quartile group differences were observed for 9HPT (MS: p = 3.5*10^-3, η² = 0.02, HC: p = 6.6*10^-3, η² = 0.02) and SDMT (MS: p = 3.1*10^-4, η² = 0.05, HC: p = 5.4*10^-4, η² = 0.04) values, but MS-specific quartiles again improved quartile associations (p = 0.036, η² = 0.01 and p = 0.02, η² = 0.01, respectively). NBV values derived from an MS-specific reference model offer improved relevance for assessing disease severity compared with curves derived from age-specific and sex-specific HC reference models. Improving the model toward application in individual people could enhance clinical implementation.