Shannon Kolind

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: Magnetic resonance spectroscopy (MRS) offers non-invasive assessments of neuron–oligodendrocyte coupling and neuroinflammation to monitor treatment response in multiple sclerosis (MS). Objective: To track changes in N-acetylaspartate and myo-inositol in relapsing MS (RMS) and primary progressive MS (PPMS) patients treated with ocrelizumab over 2 years. Methods: Single-voxel MRS at 3T was acquired at baseline in 10 healthy controls (HCs), and weeks 0, 12, 24, 52, and 96 in MS participants at a single center. Results: Baseline myo-inositol was higher in PPMS than RMS ( p = 0.047) and HC ( p = 0.001), and correlated with disability across both MS groups ( r = 0.57, p = 0.0006). Following treatment with ocrelizumab, both RMS and PPMS demonstrated declines in myo-inositol over time, returning toward HC levels (RMS p = 0.016; PPMS p = 0.004). Conversely, N-acetylaspartate was not different between groups and remained stable over time. Conclusion: Ocrelizumab treatment is associated with declining myo-inositol levels measured by MRS in both RMS and PPMS. Myo-inositol offers a unique biomarker to track resolution of gliosis and reactive microglia with treatment. Furthermore, the relationship between a higher concentration of myo-inositol and greater disability across both MS subtypes at baseline supports the presence of “smouldering inflammation” as a disease process across the spectrum of MS. Clinical Trial: Sub-study of the Ocrelizumab Biomarker Outcome Evaluation (OBOE; ML29966) trial: https://clinicaltrials.gov/study/NCT02688985

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.

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/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

Background: Cognitive impairment (CI) is a common feature of multiple sclerosis (MS). Radiologically isolated syndrome (RIS) can demonstrate CI patterns similar to MS, providing an opportunity to explore the associations between CI and early stage disease pathology. Objectives: To investigate myelin damage in RIS normal appearing white matter (NAWM) using the myelin heterogeneity index (MHI) and to determine the relationship between MHI and processing speed. Methods: A total of 28 people with RIS and 22 controls completed an MRI, including multi-component T ₂ mapping to calculate MHI for whole brain (WB), corpus callosum (CC), superior longitudinal fasciculus (SLF), and cingulum (CING) NAWM. The RIS cohort additionally completed the Processing Speed Test (PST), a measure of processing speed. Results: CC NAWM MHI was higher in RIS versus controls (indicative of myelin damage, p = 0.0140), with similar trends seen in WB, SLF, and CING ( p > 0.05). A moderate correlation was found between CING NAWM MHI and PST scores ( ρ = –0.39, p = 0.040), with similar trends seen in WB, CC, and SLF ( p > 0.05). Conclusion: Diffuse myelin damage was detected in RIS NAWM, along with associations between increased damage and slower processing speed, suggesting a potential pathological mechanism for RIS-related CI.

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.

The lower infrastructure requirements of portable ultra‐low field MRI (ULF‐MRI) systems have enabled their use in diverse settings such as intensive care units and remote medical facilities. The UNITY Project is an international neuroimaging network harnessing this technology, deploying portable ULF‐MRI systems globally to expand access to MRI for studies into brain development. Given the wide range of environments where ULF‐MRI systems may operate, there are external factors that might influence image quality. This work aims to introduce the quality control (QC) framework used by the UNITY Project to investigate how robust the systems are and how QC metrics compare between sites and over time. We present a QC framework using a commercially available phantom, scanned with 64 mT portable MRI systems at 17 sites across 12 countries on four continents. Using automated, open‐source analysis tools, we quantify signal‐to‐noise, image contrast, and geometric distortions. Our results demonstrated that the image quality is robust to the varying operational environment, for example, electromagnetic noise interference and temperature. The Larmor frequency was significantly correlated to room temperature, as was image noise and contrast. Image distortions were less than 2.5 mm, with high robustness over time. Similar to studies at higher field, we found that changes in pulse sequence parameters from software updates had an impact on QC metrics. This study demonstrates that portable ULF‐MRI systems can be deployed in a variety of environments for multi‐center neuroimaging studies and produce robust results.

Purpose of review: To review recent clinical uses of low-field magnetic resonance imaging (MRI) to guide incorporation into neurological practice. Recent findings: Use of low-field MRI has been demonstrated in applications including tumours, vascular pathologies, multiple sclerosis, brain injury, and paediatrics. Safety, workflow, and image quality have also been evaluated. Summary: Low-field MRI has the potential to increase access to critical brain imaging for patients who otherwise may not obtain imaging in a timely manner. This includes areas such as the intensive care unit and emergency room, where patients could be imaged at the point of care rather than be transported to the MRI scanner. Such systems are often more affordable than conventional systems, allowing them to be more easily deployed in resource constrained settings. A variety of systems are available on the market or in a research setting and are currently being used to determine clinical uses for these devices. The utility of such devices must be fully evaluated in clinical scenarios before adoption into standard practice can be achieved. This review summarizes recent clinical uses of low-field MR as well as safety, workflows, and image quality to aid practitioners in assessing this new technology.

Background and purpose: Magnetic resonance imaging (MRI) measures of tissue microstructure are important for monitoring brain white matter (WM) disorders like leukodystrophies and multiple sclerosis. They should be sensitive to underlying pathological changes. Three whole‐brain isotropic quantitative methods were applied and compared within a cohort of controls and leukodystrophy patients: two novel myelin water imaging (MWI) techniques (multi‐compartment relaxometry diffusion‐informed MWI: MCR‐DIMWI, and multi‐echo T2 relaxation imaging with compressed sensing: METRICS) and neurite orientation dispersion and density imaging (NODDI).Methods: For 9 patients with different leukodystrophies (age range 0.4‐62.4 years) and 15 control subjects (2.3‐61.3 years), T1‐weighted MRI, fluid‐attenuated inversion recovery, multi‐echo gradient echo with variable flip angles, METRICS, and multi‐shell diffusion‐weighted imaging were acquired on 3 Tesla. MCR‐DIMWI, METRICS, NODDI, and quality control measures were extracted to evaluate differences between patients and controls in WM and deep gray matter (GM) regions of interest (ROIs). Pearson correlations, effect size calculations, and multi‐level analyses were performed.Results: MCR‐DIMWI and METRICS‐derived myelin water fractions (MWFs) were lower and relaxation times were higher in patients than in controls. Effect sizes of MWF values and relaxation times were large for both techniques. Differences between patients and controls were more pronounced in WM ROIs than in deep GM. MCR‐DIMWI‐MWFs were more homogeneous within ROIs and more bilaterally symmetrical than METRICS‐MWFs. The neurite density index was more sensitive in detecting differences between patients and controls than fractional anisotropy. Most measures obtained from MCR‐DIMWI, METRICS, NODDI, and diffusion tensor imaging correlated strongly with each other.Conclusion: This proof‐of‐concept study shows that MCR‐DIMWI, METRICS, and NODDI are sensitive techniques to detect changes in tissue microstructure in WM disorders.