Eva M. M. Strijbis

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.

Background: Returning research results to participants is increasingly recognised as part of participant‐centred health research. How this is implemented in longitudinal cohort studies is not yet well characterised. This study aims to provide a descriptive overview of current result return practices across diverse cohort studies in the Netherlands. Methods: Drawing on the Amsterdam Cohort Hub, an infrastructure of population‐based and patient‐based cohorts in the Netherlands, representatives of 24 affiliated cohort studies completed a structured questionnaire administered via individual interviews. The questionnaire covered what results are returned, to whom, when and how. Individual‐level and group‐level return were distinguished. Cohorts also reported on motivations for returning results and barriers encountered in doing so. Data were summarised descriptively using frequencies. Results: Individual‐level results beyond incidental findings were returned by 15 cohorts (65.2%), while group‐level results were returned by 23 cohorts (96%). Individual‐level return primarily involved physical and laboratory measures or was embedded in routine clinical care. Group‐level results were mainly shared through newsletters. Practices varied by cohort type and were shaped by ethical considerations and practical constraints. Concerns about participant misinterpretation and limited resources were the most reported barriers. Conclusion: Return of research results is widely practised across cohort studies, but varies considerably in approach, shaped by ethical considerations, perceived communication risks, and financial and practical constraints. This descriptive overview of current practices may serve as a starting point for cohorts seeking to reflect on or develop their own result return procedures. Future research should examine the effects of various forms of result return on participant engagement and retention, as well as the role of participant preferences in shaping these practices. Patient or Public Contribution: This study includes a descriptive inventory of patient and public involvement in longitudinal cohort studies, in the form of result return practices. The findings are directly relevant to research participants, as they concern how cohort studies communicate research results to the people who contribute their data. The results may inform how cohorts develop and improve their participant communication strategies in the future.

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.

Human leukocyte antigen ( HLA ) class II variation is implicated in Alzheimer’s disease (AD) and longevity, but its mechanisms within the major histocompatibility complex remain unclear. We fine-mapped six independent HLA-II haplotypes in 6,053 individuals (443 cognitively healthy centenarians [CHCs], 3,219 population controls, 2,391 AD patients). Three haplotypes overlapped previous AD and lifespan signals. Hap-B ( DRB1*04 subtypes) was protective, enriched in CHCs and controls versus AD, with its neuroprotective association with tau pathology replicated in an independent Netherlands Brain Bank cohort. Hap-R ( DRB1*01:01 ) increased AD risk and reduced healthy longevity, with nominally reduced microglial HLA-II activation. Hap-Y ( DRB1*15:01 ) showed increased microglial HLA-II activation in post-mortem brain tissue, independent of quantitative AD neuropathology. These findings indicate distinct HLA-II haplotypes shape AD susceptibility and healthy longevity through separate mechanisms, linking HLA architecture to brain immune states beyond classical neuropathology.

Background and ObjectivesCurrent guidelines recommend annual MRI scans during treatment with disease-modifying therapies, including ocrelizumab, in patients with multiple sclerosis (MS) to monitor ...

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...