Menno Michiel Schoonheim

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.

Cognitive impairment (CI) is common in multiple sclerosis (MS), yet the network-level mechanisms underlying CI remain poorly understood. This study aimed to clarify how the joint organization of structural and functional brain networks contributes to CI in MS, and how network-derived features change when clinical information and MRI-derived measures are added. We analyzed neuropsychological and multimodal MRI data from the Amsterdam MS cohort (N = 330) and assessed generalizability externally (N = 27). Graph-theoretical measures of brain connectivity were extracted from diffusion-weighted and resting-state functional MRI. Machine learning models classified cognitively impaired versus preserved patients. Classification performance was quantified using the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. Feature importance was evaluated using Shapley values. Network-derived features improved discrimination over demographic information alone (AUROC = 0.77; p < 0.001). Adding MRI-derived measures significantly improved performance (AUROC = 0.81; p < 0.001), whereas clinical variables added little additional information (AUROC = 0.79; p = 0.65). External validation demonstrated good discriminative ability (AUROC = 0.76) but low sensitivity. Structural connectivity within the dorsal attention network emerged as an informative feature. These findings suggest that network-derived features help classify cognitive status in MS and remain relevant alongside conventional features. Structural connectivity, especially within the dorsal attention network, may provide insight into systems-level correlates of CI 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.

The brain-predicted age difference (brain-PAD) is considered a marker of neurodegeneration in people with multiple sclerosis (pwMS) and is associated with greater disability and cognitive impairment. However, the impact of disease-modifying factors (DMFs) on brain-PAD remains unknown, as does the extent to which their effect on disability and cognition is mediated through brain-PAD. The goal of this study was to investigate this in pwMS using a same-age cohort to eliminate calendar age as a confounding factor. Brain age was determined using brainageR from 3-dimensional T1-weighted MRI scans, and brain-PAD was calculated as the difference between predicted and chronological age. Disability was evaluated with the Expanded Disability Status Scale (EDSS), the 9-hole peg test (9HPT), and timed 25-foot walk test (T25FWT). Cognitive function was assessed using the Minimal Assessment of Cognitive Function in MS battery and converted to Z-scores. DMFs included lifetime smoking, alcohol consumption, physical activity, diet, leisure, and educational level, as well as body mass index (BMI) at 18 years. The effect of DMFs on brain-PAD was examined using linear regression, adjusting for sex. Mediation analyses were performed to investigate to what extent brain-PAD is a mediator of DMF effects on disability and cognition. The study included 117 healthy controls (mean age 52.8 ± 1.1 years, 74.3% female) and 242 pwMS (mean age 52.8 ± 0.9 years, 70.6% female), with a median disease duration of 15.2 years (interquartile range [IQR] 8.2-24.5) and a median EDSS score of 3.5 (IQR 2.5-4.0). Smoking (β = 0.20, p = 0.002), alcohol consumption (β = 0.18, p = 0.007), and BMI at 18 years (β = 0.15, p = 0.021) were associated with a higher brain-PAD, whereas higher physical activity (β = -0.13, p = 0.041) was linked to a lower brain-PAD. Mediation analysis demonstrated an indirect effect of smoking and alcohol on EDSS score (β = 0.039, p = 0.003; β = 0.039, p = 0.009), 9HPT (β = 0.057, p < 0.0001; β = 0.055, p = 0.004), T25FWT score (β = 0.045, p = 0.009; β = 0.044, p = 0.013), and cognitive performance (β = -0.066, p = 0.0004; β = -0.060, p = 0.016). Physical activity demonstrated an indirect effect of better performance on EDSS score (β = -0.027, p = 0.032) and cognitive performance (β = 0.042, p = 0.034). DMFs unrelated to MS, particularly smoking, alcohol use, and BMI at 18 years, accelerate brain aging. Brain-PAD mediates these effects and contributes to worsening disability and cognition, underscoring the potential of lifestyle interventions to mitigate neurodegeneration and preserve function in pwMS.

Background and ObjectivesThe cost and complexity of phase 2 randomized-controlled trials (RCTs) hinder further development of promising treatment candidates for Alzheimer disease (AD). The Simon Tw...

Background Multiple sclerosis (MS) is a chronic neurological disease affecting both white and gray matter of the central nervous system. Despite the well-established involvement of cortical lesions in MS, feasibility limitations in their visualization on typical magnetic resonance imaging (MRI) protocols prevent their evaluation in nearly all clinical trials. Recently, several post-processing methods, including synthetic contrasts and artificial intelligence (AI)-based approaches, have shown potential for enhancing cortical lesion detection on conventional MRI data. These methods have the potential to reanalyze existing clinical-trial data to answer key mechanistic questions about both MS development and about treatment effects. Methods We sought to evaluate the feasibility of combining and extending existing methods into a unified framework for analysis using the data from the large, multicenter, phase 3 ORATORIO trial (full n = 732, age=44.6 ± 8.0; development subset n = 80, age=46.6 ± 7.1). We specifically evaluated three of the most promising of them – fluid-attenuated inversion recovery squared (FLAIR²), T1/T2 ratio, and artificial intelligence-derived double inversion recovery (AI-DIR) – and introduced a new combined contrast called multi-modal cortical lesion enhanced (MMCLE). We also harnessed transformer-based semantic segmentation to improve automated detection and delineation of these lesions. Results At baseline, we detected 14.8 + /−20.72 lesions per participant, with 86.0% true positive rate and 8.4% false positive rate across subjects for blinded MMCLE, using simultaneous review of all contrasts as the reference. High reproducibility was observed across field strengths and acquisition types (ICC 88.8-92.5%). Conclusions We confirmed that cortical lesions can be clearly visualized and quantified with these methods. Using deep learning, we also confirmed that the simultaneous use of multiple contrasts improves quantification.

Objective: The objective of this study was to investigate the ability of patient‐reported/administered outcomes to capture disability worsening and progression independent of relapse activity (PIRA) in multiple sclerosis (MS). Methods: We included patients from the longitudinal multicenter MS PATHS cohort with ≥3 assessments and >6 months follow‐up. PIRA was defined on the patient‐determined disease steps (PDDS) and self‐administered tests assessing walking speed, manual dexterity, and processing speed, in the absence of self‐reported relapses. We assessed (i) prevalence of PDDS/self‐administered test‐based disability worsening and PIRA definitions; (ii) their concurrent validity, through the association with worsening and PIRA definitions based on the expanded disability status scale (EDSS; Barcelona subcohort); and (iii) their clinical meaningfulness, through the correlation with brain magnetic resonance imaging (MRI) and quality‐of‐life (QoL) data trajectories. Results: We included 9,088 patients (73% female patients; age = 46.8 years; disease duration = 14.2 years). Over a 3‐year follow‐up, 4,066 (45%) patients worsened on ≥1 patient‐reported/administered outcome, of which 2,357 (58%) developed PIRA. In the Barcelona subcohort (N = 279), 68 (24%) patients developed EDSS‐based worsening, of which 30 (44%) developed EDSS‐based PIRA; 99 of 279 (35%) worsened on ≥1 patient‐reported/administered test, of which 78 of 99 (79%) had PIRA. Worsening (but not PIRA) on PDDS was associated with EDSS‐based worsening (odds ratio = 3.03 [1.42; 6.43], p = 0.004). PDDS/self‐administered‐test‐based worsening or PIRA strongly correlated with accelerated brain atrophy and T2 lesion volume increase compared to non‐worseners ( p < 0.05), and showed worse QoL ( p < 0.05). Interpretation: Despite only partial agreement with EDSS‐based definitions, PDDS/self‐administered test‐based worsening and PIRA definitions were clinically meaningful, by identifying patients with greater brain damage and poorer QoL, supporting their use in clinical practice. ANN NEUROL 2026;100:525–546

Background: Pathophysiological drivers of sex differences in multiple sclerosis (MS) remain unclear. Potential candidates include bioactive lipid mediators (LMs), which are pivotal regulators of neuro-inflammatory responses. Objective: To investigate sex-dependent LM differences in MS. Methods: Plasma lipids were measured using targeted HPLC-MS/MS in a birth-year MS cohort (N = 410; n = 285 MS, n = 169 RRMS, n = 80 SPMS and n = 36 PPMS). Lipid profiles were compared between sexes and correlated with disability (Expanded Disability Status Scale (EDSS)), neuroaxonal damage (neurofilament light, NfL), whole brain volume (WBV) and T2 lesion volume (LV). Results: In healthy controls, 9-HOTrE levels were higher in women than men. In individuals with MS, women had higher 9-HOTrE, 18-HEPE and 9-HODE levels than men, while men had higher 15-HEPE, 17-HDHA and 11,12-DiHET levels than women. Among these LMs, increased 11,12-DiHET levels were associated with higher EDSS and NfL levels, while higher levels of the remaining LMs, such as 9-HOTrE, were associated with lower EDSS and lower LV. Conclusion: These findings provide insights into potential sex-dependent lipid-mediated pathophysiological mechanisms in MS patients. Our findings suggest that certain LMs, such as 9-HOTrE, may display neuroprotective effects in MS.