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 measure 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.

Tractometry, also known as tract profiling, is a powerful technique for probing microstructural properties along white matter (WM) tracts. A prerequisite for tractography-based tractometry is bundle parcellation-the subdivision of WM bundles into smaller segments where microstructural measures can be computed. However, existing parcellation methods lack consistency across bundles and timepoints, which reduces reproducibility and limits their utility for both longitudinal and cross-sectional studies. Moreover, these methods typically depend on tractography and bundle segmentation, two processes that are computationally expensive and often highly variable. In this work, we introduce BundleParc, a consistent and tractography-free bundle parcellation method. Instead of relying on streamline generation, BundleParc maps fiber orientation distribution function (fODF) volumes directly to label maps. Rigorous evaluation on research and clinical cohorts show that BundleParc is not only much simpler than state-of-the-art tract-based profiling methods, it is also consistently more accurate, robust and reproducible. With these results, BundleParc is a new solution for fast, easy-to-use, and off-the-shelf bundle segmentation and parcellation.

Highlights • Deep learning improved thalamus segmentation in multiple sclerosis brain scans.• Atlas based methods overestimated thalamus volume despite spatial overlap.• Voxel overlap and volume accuracy diverged across segmentation tools.• Quantitative magnetic resonance maps modestly improved disability associations.

Multiple sclerosis (MS) is a chronic inflammatory neurodegenerative disorder that typically affects young adults and is primarily characterized by demyelinating lesions in the central nervous system (CNS). According to the Revised McDonald Criteria, the clinical diagnosis of MS can be established based on a combination of clinical observations, the presence of focal lesions in at least two distinct CNS areas on magnetic resonance imaging (MRI) and the detection of specific oligoclonal bands in the cerebrospinal fluid. Conventional MRI remains a cornerstone of MS diagnosis and disease monitoring, providing high-resolution assessments of lesion burden and brain atrophy. In addition, advanced MRI methods are increasingly applied in research settings to probe myelin integrity, iron deposition, and biochemical changes, with the potential to complement established diagnostic workflows in the future. Despite remarkable advances in the management of MS over the past two decades, complex differential diagnoses and the lack of effective imaging tools for therapy monitoring remain major obstacles, thus channeling the development of innovative molecular imaging probes that can be harnessed in clinical practice. Indeed, positron emission tomography (PET) has a significant potential to advance the contemporary diagnosis and management of MS. Given the solid body of evidence implicating myelin dysfunction in the pathophysiology of MS, myelin-targeted imaging probes have been developed, and are currently under clinical evaluation for MS diagnosis and therapy monitoring. In parallel, ligands for the 18 kDa translocator protein (TSPO) and the cannabinoid receptor type 2 (CB2R) have been employed to capture neuroinflammatory processes by visualizing microglial activation, while other tracers allow the assessment of synaptic integrity across various disease stages of MS. Further, PET probes have been employed to delineate the role of activated microglia and facilitate the assessment of synaptic dysfunction across all disease stages of MS. This review discusses the challenges and opportunities of translational molecular imaging by highlighting key molecular concepts that are currently leveraged for diagnostic imaging, patient stratification, therapy monitoring and drug development in MS. Moreover, we shed light on potential future developments that hold promise to advance our understanding of MS pathophysiology, with the ultimate goal to provide the best possible patient care for every individual MS patient.

In this work we investigate the feasibility of a correction method for removing the orientation dependence of magnetization transfer (MT) measures in the context of tractometry. Following previous work on the track-based characterization of such orientation dependence using diffusion MRI, a correction method was developed. It uses polynomial fits to extrapolate the single-fiber characterizations and allows the MT measures across all white matter tracks to be shifted towards a chosen reference value, effectively removing the bias of fiber orientation with respect to the main magnetic field. Three different references were tested on a dataset of one hundred acquisitions and the performance was accessed by evaluating the removal of the orientation dependence and the reduction of variance between acquisitions, while also exploring the effects on tractometry results. Throughout these experiments, various challenges and pitfalls of an empirical correction method were laid out, like the absence of ground truth or the lack of knowledge about the complex behavior of the phenomenon in crossing-fiber voxels. Nonetheless, a solution was presented, paving the way towards a fully validated correction method for MT measures.

Background and Objectives Progression independent of relapse activity (PIRA) is associated with worse outcomes in people with multiple sclerosis (pwMS). Although previous research has linked PIRA to accelerated brain and spinal cord atrophy and compartmentalized chronic inflammation, the role of white matter (WM) tract degeneration remains unclear. This study aimed to explore the relationship between PIRA and the integrity of major WM tracts using diffusion tensor imaging (DTI). Methods A cohort of 258 pwMS was stratified based on the presence or absence of PIRA over a 4-year follow-up period. At the end of follow-up, DTI metrics were compared between groups using propensity score–weighted linear regression models to account for potential confounders. Results PwMS with ≥1 PIRA event (n = 39) exhibited significant reductions in fractional anisotropy and increases in radial, axial, and mean diffusivity within the corpus callosum and motor tracts (false discovery rate–adjusted p ≤ 0.04) compared with those without PIRA, indicating more pronounced WM damage. Discussion Our findings highlight an association between PIRA and microstructural damage in key WM tracts. The observed DTI changes likely reflect processes such as Wallerian degeneration and contribute to the growing evidence linking PIRA to neurodegeneration.

Remyelination of cortical lesions in people with multiple sclerosis (pwMS) has been shown to be extensive. In this work, we aimed to assess whether postmortem quantitative MRI (qMRI) can help detect those areas. We imaged six fixed whole brains of deceased pwMS by 3T‐MRI using magnetization transfer ratio (MTR, 570 μm isotropic), myelin water fraction (MWF, 1000 μm isotropic), quantitative T1 (qT1, 670 μm isotropic), quantitative susceptibility mapping (QSM, 330 μm isotropic) and radial diffusivity (RD, 1300 or 1400 μm isotropic) maps. Immunohistochemistry for myelin proteins was performed in 129 tissue blocks including the cortex and enabled the detection of cortical demyelination (DM), cortical remyelination (RM), and normal‐appearing cortex (NAC). We identified 25 DM, 25 RM, and for each of these areas, a corresponding NAC near the lesion. Wilcoxon paired tests showed that: (a) qT1 and RD were higher and QSM lower in DM versus NAC (all p < 0.001), whereas RD was higher and QSM lower in RM versus NAC (p = 0.048 and p < 0.01 respectively); (b) mean qT1 in RM did not differ from mean qT1 in NAC (p = 0.074); (c) MWF and MTR were not different between DM and RM. We compared the delta between DM versus NAC (∆DM) and the delta between RM versus NAC (∆RM) using a Mann–Whitney test, in which RM showed a partial recovery of qT1 only (∆qT1 DM > ∆qT1 RM, p = 0.045). Mixed‐effect models confirmed the findings obtained using univariate analyses. qT1 and QSM, but not RD, correlated with MBP intensity (r = −0.28, p < 0.01 and r = 0.29, p < 0.01 respectively). A Bonferroni correction was performed for multiple testing. Our data show that qT1 is altered in demyelinated but not in remyelinated cortical areas, while QSM and RD are affected by any cortical abnormalities. Accordingly, qT1 might be considered a potential imaging biomarker of cortical RM.

We report biomarker treatment effects in the GRADUATE I and II phase 3 studies of gantenerumab in early Alzheimer's disease (AD).