BACKGROUND/OBJECTIVES:Characterizing spinal cord multiple sclerosis (MS) lesions in MRI is critical for diagnosis, monitoring, and treatment evaluation. However, current automated approaches for lesion detection and segmentation are typically designed for specific MRI contrasts or acquisition sites, limiting their generalizability in real-world clinical settings where imaging protocols vary widely. This work proposes a robust multi-site, multi-contrast segmentation framework for spinal cord lesions. METHODS:The segmentation model was trained and evaluated on a large-scale dataset comprising 4428 annotated images from 1849 persons with MS across 23 imaging centers, encompassing six MRI contrasts (T1w, T2w, T2*w, PSIR, STIR, and UNIT1) acquired at 1.5 tesla (T), 3 T, and 7 T. RESULTS:Likert-type assessment performed by neuroradiologist ratings demonstrated superior generalization of the model compared to existing contrast-specific pipelines (p < 0.01). Additional experiments evaluated robustness across spinal levels, acquisition resolutions, binarization thresholds, and quantitative evaluation on external labeled datasets. CONCLUSIONS:The proposed model can achieve accurate and reliable spinal cord MS lesion segmentation across heterogeneous MRI data, addressing a key barrier to clinical translation. The model is available in the Spinal Cord Toolbox v7.2 and higher.Code repository: https://github.com/ivadomed/seg-sc-ms-lesion-multicontrast.

Imaging biomarkers are essential for monitoring multiple sclerosis (MS), wand resting-state functional MRI (rs-fMRI) offers functional insights that complement structural imaging. This study investigates whether a novel co-activation pattern (CAP) approach for dynamic rs-fMRI can function as a dual-purpose biomarker in MS, aiding diagnosis and tracking disease severity. RS-fMRI scans from 25 relapsing-remitting MS patients and 41 healthy controls (HCs) were analyzed using a novel CAP-based approach. CAPs derived from individual time frames to capture dynamic brain activity patterns incorporated a bivariate similarity assessment, eigen volume-based dimensionality reduction, and consensus clustering. We evaluated the framework in two analyses: (1) a diagnostic evaluation, using dynamic CAP features—dwell time, persistence, and transition probabilities—for group comparisons and classification; and (2) a severity-prediction analysis, relating these CAP-derived measures to clinical disability (EDSS) in MS using LASSO regression. Method performance was benchmarked against standard CAP and sliding-window (SW) approaches. It revealed significant differences in brain activity between MS and HCs, within the default mode, sensorimotor, and language networks (p < 0.05), highlighting alterations relevant to motor, cognitive, and sensory functions affected in MS. Transition probabilities showed strong correlations with EDSS (r > 0.75) and yielded better classification performance than standard CAP and SW approaches in classifying MS from HCs. These results suggest that dynamic brain activity patterns are altered in MS and linked to clinical disability. The proposed CAP provided improved performance in distinguishing MS patients, offering enhanced clinical monitoring. Transition probabilities emerged as a potential biomarker for tracking MS progression, with network shifts reflecting disease severity. As MS advances, increased transitions toward sensory, motor, and executive networks suggest compensatory recruitment. Conversely, reduced transitions from default mode and salience networks to sensorimotor and frontoparietal systems were associated with greater disability and diminished adaptive reorganization.

BACKGROUND:Advanced magnetic resonance imaging (MRI) of the cerebellum remains underutilized to detect early microstructural abnormalities associated with multiple sclerosis (MS) clinical disability. OBJECTIVES:To examine associations between cerebellar magnetization transfer ratio (MTR) and clinical measures in people with radiologically isolated syndrome (RIS), early relapsing-remitting MS (RRMS), and primary progressive MS (PPMS). METHODS:MTR data were acquired at 3.0 T across four sites in 53 RIS, 202 RRMS, 46 PPMS, and 42 control participants, as part of the Canadian Prospective Cohort Study to Understand Progression in MS (CanProCo). Multiple linear regression analyses evaluated associations between cerebellar MTR and clinical measures. RESULTS:Across MS subtypes, lower cerebellar MTR was associated with greater motor disability, most notably with impaired manual dexterity (β = -1.04 to -0.67). After the false discovery rate correction, two associations remained statistically significant (p < 0.01): lower MTR in the inferior cerebellar peduncles was associated with worse cerebellar function in RRMS, and lower MTR in the anterior lobe was associated with worse manual dexterity in PPMS. CONCLUSION:This large, multi-center, hypothesis-generating study identified two statistically significant associations between cerebellar MTR and clinical disability, alongside several exploratory findings. These results suggest that cerebellar MTR may capture clinically relevant microstructural abnormalities in early MS.

Morphometric measures derived from spinal cord segmentations can serve as diagnostic and prognostic biomarkers in neurological diseases and injuries affecting the spinal cord. For instance, the spinal cord cross-sectional area can be used to monitor cord atrophy in multiple sclerosis and to characterize compression in degenerative cervical myelopathy. While robust, automatic segmentation methods to a wide variety of contrasts and pathologies have been developed over the past few years, whether their predictions are stable as the model is updated using new datasets has not been assessed. This is particularly important for deriving normative values from healthy participants. In this study, we present a spinal cord segmentation model trained on a multisite (n = 75 sites, 1,631 participants) dataset, including 9 different MRI contrasts and several spinal cord pathologies. We also introduce a lifelong learning framework to automatically monitor the morphometric drift as the model is updated using additional datasets. The framework is triggered by an automatic GitHub Actions workflow every time a new model is created, recording the morphometric values derived from the model’s predictions over time. As a real-world application of the proposed framework, we employed the spinal cord segmentation model to update a recently introduced normative database of healthy participants containing commonly used measures of spinal cord morphometry. Results showed that (i) our model performs well compared with its previous versions and existing pathology-specific models on the lumbar spinal cord, images with severe compression, and in the presence of intramedullary lesions and/or atrophy achieving an average Dice score of 0.95 ± 0.03; (ii) the automatic workflow for monitoring morphometric drift provides a quick feedback loop for developing future segmentation models; and (iii) the scaling factor required to update the database of morphometric measures is nearly constant among slices across the given vertebral levels, showing minimum drift between the current and previous versions of the model monitored by the framework. The code and model are open source and accessible via Spinal Cord Toolbox v7.0.

The spinal cord plays a central role in the pathophysiology and clinical manifestations of multiple sclerosis (MS), yet remains under-studied compared with the brain. This review summarizes key insights from the 2025 North American Imaging in MS Spinal Cord Imaging Workshop, highlighting recent advances, ongoing challenges, and future opportunities in MS spinal cord imaging. We review pathological studies and outline the clinical relevance of spinal cord lesions and atrophy for diagnosis, prognosis, and disease monitoring, highlighting emerging biomarkers of progression independent of relapse activity. Correlations between magnetic resonance imaging, histopathology, and clinical outcomes support the validation and translational potential of advanced spinal cord imaging techniques. Finally, we discuss spinal cord-specific processing pipelines and reproducibility challenges. Collectively, these insights underscore the need to integrate advanced and quantitative spinal cord imaging into clinical trials, research studies, and-when feasible-clinical care, to fully capture the extent of MS pathology, and ultimately improve patient outcomes.

Background: Multiple sclerosis (MS) slowly expanding lesions (SELs) are defined on magnetic resonance imaging (MRI) as contiguous regions of pre-existing focal non-contrast-enhancing T2 lesions with constant and concentric local expansion on conventional T1-weighted and T2-weighted images. SELs are associated with an increased risk of disability progression. Methods: Myelin-related changes detected using myelin water fraction (MWF) and magnetisation transfer ratio (MTR) in SELs and T2 lesions were measured over 192 weeks in participants with relapsing MS. Results: In participants with SELs (SEL+), SELs (MWF: 0.12 ± 0.03, MTR: 33.1 ± 3.6 pu) showed reduced myelin measures at baseline compared to T2 lesions (MWF: 0.13 ± 0.02, MTR: 35.1 ± 2.4 pu). In participants without SELs (SEL−), T2 lesions had higher myelin measures (MWF: 0.15 ± 0.02, MTR: 36.2 ± 2.0 pu) compared to T2 lesions in SEL+. Over 4 years, only SELs showed decreases in MWF (−11.4%). The percentage of abnormal voxels within normal-appearing white matter was higher in SEL+ and increased over time (SEL+ MWF Week 0: 0.56%, Week 192: 0.98%; SEL− MWF Week 0: 0.13%, Week 192: 0.25%). Conclusion: Our results indicate progressive focal and global demyelination in SEL+ participants and that the presence of SELs might be a biomarker for participants with ongoing diffuse or smouldering inflammation within the whole brain.

PURPOSE:Conventional MRI offers limited insight into specific characteristics of central nervous system tissue, whereas quantitative MRI measures can provide more detailed information about different aspects of microstructure. A multi-metric approach involving multiple quantitative measures may improve our understanding of healthy tissue and pathology. Previous work shows myelin water fraction (MWF) is related to fractional anisotropy (FA), but this relationship is complicated by confounding factors that may be resolved using tensor-valued diffusion imaging, which yields measurements of microscopic FA (μFA) and tissue heterogeneity (CMD). Our aims were to better understand how measures from myelin water and tensor-valued diffusion imaging relate to one another, and to demonstrate how these measures can be used to characterize microstructure in both healthy white matter and pathological changes. METHODS:We assessed the relationship between MWF, FA, μFA, and CMD from 25 healthy individuals through atlas comparison, correlation analysis, and tract profiling. We also applied z-score analysis and tract profiling in five people with multiple sclerosis (MS) to evaluate the multi-metric utility of these measures in assessing pathology. RESULTS:Although correlation analysis showed moderate, but potentially misleading relationships between metrics, tract profiling showed consistent tract-specific pattern differences between metrics in healthy tissue. In MS, MWF, μFA, and CMD were the most sensitive to pathological changes, showing regions of abnormality even in normal-appearing white matter and along lesional tracts, and highlighting different types of damage. CONCLUSION:Using MWF, μFA, and CMD to separately assess myelination, anisotropy, and tissue heterogeneity enhances our ability to investigate development, aging, disease, and injury.

BACKGROUND:Depression and anxiety are more prevalent in people with multiple sclerosis (pwMS) than in the general population and are thus common psychiatric comorbidities in MS. As such, screening for psychiatric comorbidities in pwMS is an important component of MS care. However, large-scale screening efforts using scales that measure depressive symptoms with the goal of identifying a subgroup of pwMS with a high probability of depression, and thus in need of further assessment, have not been successful. Machine learning algorithms using routinely collected clinical information may be able to serve the same purpose without the encumbrance of scale administration and scoring. METHODS:We used baseline clinical and demographic data collected in the Canadian Prospective Cohort Study to Understand Progression in MS (CanProCo). The Patient Health Questionnaire-9 (PHQ-9) was used to screen for symptoms of depression and the Generalized Anxiety Disorder-7 (GAD-7) for symptoms of anxiety. Machine learning with elastic net was used to develop logistic regression models to predict participant scores above traditional cut-off scores ≥10 that are indicative of clinically significant depression and anxiety. RESULTS:Machine learning with elastic net produced a model that was able to predict scores ≥10 on the PHQ-9 in participants in the testing dataset with a high area under the curve (AUC = 0.927). Prediction of scores ≥10 on the GAD-7 in participants in the testing dataset was modest (AUC = 0.813). Final multivariable logistic regression models found that increased self-reported psychosocial fatigue (OR: 1.55, p < 0.001 and OR: 1.27, p = 0.0026), increased self-reported cognitive fatigue (OR: 1.08, p < 0.001 and OR: 1.08, p < 0.001), and number of comorbidities (OR: 1.26, p = 0.0015 and OR: 1.15, p = 0.041) were predictive of scoring above the cut-off on PHQ-9 and GAD-7, respectively. CONCLUSION:The identification of psychiatric comorbidities such as depression in pwMS could be facilitated by making use of clinical variables with similar success to direct administration of rating scales.

Clinical research emphasizes the implementation of rigorous and reproducible study designs that rely on between-group matching or controlling for sources of biological variation such as subject's sex and age. However, corrections for body size (i.e., height and weight) are mostly lacking in clinical neuroimaging designs. This study investigates the importance of body size parameters in their relationship with spinal cord (SC) and brain magnetic resonance imaging (MRI) metrics. Data were derived from a cosmopolitan population of 267 healthy human adults (age 30.1 ± 6.6 years old, 125 females). We show that body height correlates with brain gray matter (GM) volume, cortical GM volume, total cerebellar volume, brainstem volume, and cross-sectional area (CSA) of cervical SC white matter (CSA-WM; 0.44 ≤ r ≤ 0.62). Intracranial volume (ICV) correlates with body height (r = 0.46) and the brain volumes and CSA-WM (0.37 ≤ r ≤ 0.77). In comparison, age correlates with cortical GM volume, precentral GM volume, and cortical thickness (-0.21 ≥ r ≥ -0.27). Body weight correlates with magnetization transfer ratio in the SC WM, dorsal columns, and lateral corticospinal tracts (-0.20 ≥ r ≥ -0.23). Body weight further correlates with the mean diffusivity derived from diffusion tensor imaging (DTI) in SC WM (r = -0.20) and dorsal columns (-0.21), but only in males. CSA-WM correlates with brain volumes (0.39 ≤ r ≤ 0.64), and with precentral gyrus thickness and DTI-based fractional anisotropy in SC dorsal columns and SC lateral corticospinal tracts (-0.22 ≥ r ≥ -0.25). Linear mixture of age, sex, or sex and age, explained 2 ± 2%, 24 ± 10%, or 26 ± 10%, of data variance in brain volumetry and SC CSA. The amount of explained variance increased to 33 ± 11%, 41 ± 17%, or 46 ± 17%, when body height, ICV, or body height and ICV were added into the mixture model. In females, the explained variances halved suggesting another unidentified biological factor(s) determining females' central nervous system (CNS) morphology. In conclusion, body size and ICV are significant biological variables. Along with sex and age, body size should therefore be included as a mandatory variable in the design of clinical neuroimaging studies examining SC and brain structure; and body size and ICV should be considered as covariates in statistical analyses. Normalization of different brain regions with ICV diminishes their correlations with body size, but simultaneously amplifies ICV-related variance (r = 0.72 ± 0.07) and suppresses volume variance of the different brain regions (r = 0.12 ± 0.19) in the normalized measurements.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is the causative agent of COVID-19 and has infected >700 million persons worldwide. Individuals infected with SARS-CoV-2 are at risk for cognitive decline and at higher risk of dementia compared with those diagnosed with other respiratory tract infections. Data from animal models suggest that SARS-CoV-2 infection triggers an overaggressive neuroinflammatory response resulting in myelin loss. Whether SARS-CoV-2 is associated with myelin loss in older individuals remains unknown. We investigated the impact of SARS-CoV-2 on myelin in older individuals from the Canadian Longitudinal Study on Aging COVID-19 Brain Health Study who underwent brain MRIs. We included SARS-CoV-2 confirmed cases at baseline (2021-2022) via positive serological testing or health care provider diagnosis. Non-infected controls had negative serological testing and reported no COVID-19 diagnosis. Myelin data were acquired via myelin water imaging using a 3D MRI gradient and spin echo sequence for T2 measurement. Myelin content was extracted from 16 regions-of-interest within the cerebral white matter. 3D T1-weighted scans were acquired for registrations and to estimate intracranial volume. T2- and PD-weighted scans were acquired for segmentation of white matter lesions. We performed cross-sectional comparisons via analysis of covariance. Exploratory analyses were conducted to assess the association of SARS-CoV-2-related symptom incidence and severity with myelin content by group. All models were adjusted for age, age 2 , sex, ethnicity, white matter lesion burden, intracranial volume, and study site. We included 352 community-dwelling individuals (SARS-CoV-cases, n= 64; controls, n=288). Their mean [SD] age was 65.26 (8.35) years, and 50.3% were female. There were no differences between SARS-CoV-2 cases and controls on myelin content across all regions-of-interest. Cases showed greater incidence ( p <0.001) and severity ( p <0.001) of symptoms compared with controls (Figure 1). Exploratory analysis revealed significant interactions between symptom incidence and severity with group after correcting for multiple comparisons (Table 1, p corrected < 0.05). Post hoc analysis showed that symptom incidence and severity were inversely associated with myelin in SARS-CoV-2 cases but not in controls across multiple regions-of-interest (Figure 2). Myelin loss may occur in older individuals who experienced greater incidence and severity of SARS-CoV-2 infection symptoms.

Very-low-field MRI (<100 mT) holds promise for Point-of-Care brain imaging applications, including stroke and multiple sclerosis, with T1 mapping emerging as a key biomarker for brain development and pathology. However, current low-field T1 mapping protocols suffer from long acquisition times and limited multi-site repeatability. This study aimed to improve T1 mapping at 64 mT using a clinically feasible 10-minute protocol and assess repeatability and reproducibility across sites. We present an analysis of the repeatability and reproducibility of rapid T1 measurements in a commercially available phantom and in 60 volunteers, scanned with a portable 64 mT MRI systems at six sites. T1 mapping was performed using an undersampled 3D inversion-recovery turbo spin-echo sequence with a 10.8-minute scan time, and reconstructed with a locally low-rank approach. Our results in phantom demonstrated high reproducibility in T1 measurements (below 3% differences from the average), with non-significant differences between sites. Longitudinal measurements demonstrated high repeatability over time both in vivo and in phantom settings in one site, with minimal variability (average Coefficient of Variation of 0.6%). Average in vivo T1 values for white matter and cortex were 290 ± 6 ms and 332 ± 8 ms, respectively and the values demonstrated high reproducibility, with differences of less than 4% from the average across sites. Our results demonstrate the feasibility of multi-site in vivo T1 mapping at 64 mT, providing normative T1 values at this field strength and supporting its use as a quantitative biomarker in clinical applications.

BACKGROUND:The cerebellum is a functionally and anatomically complex structure, which, in multiple sclerosis (MS), is affected by focal white/gray matter lesions and by secondary neurodegeneration of afferent/efferent connections to the supratentorial brain and the spinal cord. OBJECTIVES:To assess the efficacy of ocrelizumab compared with interferon β-1a (IFN β-1a)/placebo on cerebellar volume loss and the effect of switching to ocrelizumab on volume change in the Phase III trials in relapsing MS (RMS, OPERA I/II) and in primary progressive MS (PPMS, ORATORIO). METHODS:Cerebellar volume change was computed using paired Jacobian integration and analyzed using a mixed-effect repeated measurement model. RESULTS:In RMS, ocrelizumab reduced cerebellar volume loss in the double-blind period (DBP) and the difference (30% at DBP end) was maintained in the open-label extension (OLE) after control patients (IFN β-a) were switched to ocrelizumab. In PPMS, there was a small numerical difference in the DBP, but a larger (up to 22%) difference in favor of ocrelizumab in the OLE. CONCLUSIONS:In both RMS and PPMS, early treatment with ocrelizumab helps to prevent additional cerebellar volume loss compared with delayed switching to ocrelizumab. Further analysis is needed to fully understand the clinical impact of cerebellar atrophy.

Background: This study explored whether Myelin Water imaging could detect myelin injury in Anti-NMDA receptor autoimmune encephalitis (NMDAr-AIE), where traditional neuroimaging is often normal. Myelin Water Fraction (MWF) quantifies myelin content by distinguishing myelin sheath water from other brain water compartments. Methods: Adult participants with confirmed NMDAr-AIE diagnoses and healthy controls (HC) underwent 3T brain MRI including MWF mapping. Participants were recruited after discharge from the hospital. Mean MWF was calculated for 4 white matter regions of interest (ROI). Patient demographics, clinical assessments, treatment, and outcomes were collected. Results: Five participants with NMDAr-AIE (4F/1M, mean age 30, SD 7) and four HC (3F/1M, mean age 36, SD 6) were included. All NMDAr-AIE participants had normal or non-specific T2 hyperintensities on initial imaging and had received immunotherapy. The mean Modified Rankin Score (MRS) on discharge was 2. MWF (mean ± SD) for normal-appearing white matter, corpus callosum, corticospinal tract, and superior longitudinal fasciculus were 0.10±0.02, 0.12±0.02, 0.15±0.03, 0.12±0.02, which were very similar to HC at 0.09±0.02, 0.11±0.01, 0.15±0.02, and 0.11±0.02, respectively. Conclusions: Myelin Water imaging showed no myelin pathology in five NMDAr-AIE patients, with MWF values comparable to HC. This suggests that myelin pathways are relatively preserved post-recovery from AIE.