An update on the role of magnetic resonance imaging in predicting and monitoring multiple sclerosis progression.

INTRODUCTION: While magnetic resonance imaging (MRI) is established in diagnosing and monitoring disease activity in multiple sclerosis (MS), its utility in predicting and monitoring disease progression is less clear. AREAS COVERED: The authors consider changing concepts in the phenotypic classification of MS, including progression independent of relapses; pathological processes underpinning progression; advances in MRI measures to assess them; how well MRI features explain and predict clinical outcomes, including models that assess disease effects on neural networks, and the potential role for machine learning. EXPERT OPINION: Relapsing-remitting and progressive MS have evolved from being viewed as mutually exclusive to having considerable overlap. Progression is likely the consequence of several pathological elements, each important in building more holistic prognostic models beyond conventional phenotypes. MRI is well placed to assess pathogenic processes underpinning progression, but we need to bridge the gap between MRI measures and clinical outcomes. Mapping pathological effects on specific neural networks may help and machine learning methods may be able to optimize predictive markers while identifying new, or previously overlooked, clinically relevant features. The ever-increasing ability to measure features on MRI raises the dilemma of what to measure and when, and the challenge of translating research methods into clinically useable tools.

Early-stage volume losses in the corpus callosum and thalamus predict the progression of brain atrophy in patients with multiple sclerosis.

BACKGROUND: A method that can be used in the early stage of multiple sclerosis (MS) to predict the progression of brain volume loss (BVL) has not been fully established. METHODS: To develop a method of predicting progressive BVL in patients with MS (pwMS), eighty-two consecutive Japanese pwMS-with either relapsing-remitting MS (86%) or secondary progressive MS (14%)-and 41 healthy controls were included in this longitudinal retrospective analysis over an observational period of approximately 3.5 years. Using a hierarchical cluster analysis with multivariate imaging data obtained by FreeSurfer analysis, we classified the pwMS into clusters. RESULTS: At baseline and follow-up, pwMS were cross-sectionally classified into three major clusters (Clusters 1, 2, and 3) in ascending order by disability and BVL. Among the patients included in Cluster 1 at baseline, approximately one-third of patients (12/52) transitioned into Cluster 2 at follow-up. The volumes of the corpus callosum, the thalamus, and the whole brain excluding the ventricles were significantly decreased in the transition group compared with the nontransition group and were found to be the most important predictors of transition. CONCLUSION: Decreased volumes of the corpus callosum and thalamus in the relatively early stage of MS may predict the development of BVL.

Vitamin D did not reduce multiple sclerosis disease activity after a clinically isolated syndrome.

Low serum levels of 25-hydroxyvitamin D [25(OH)D] and low sunlight exposure are known risk factors for the development of multiple sclerosis. Add-on vitamin D supplementation trials in established multiple sclerosis have been inconclusive. The effects of vitamin D supplementation to prevent multiple sclerosis is unknown. We aimed to test the hypothesis that oral vitamin D3 supplementation in high-risk clinically isolated syndrome (abnormal MRI, at least three T2 brain and/or spinal cord lesions), delays time to conversion to definite multiple sclerosis, that the therapeutic effect is dose-dependent, and that all doses are safe and well tolerated. We conducted a double-blind trial in Australia and New Zealand. Eligible participants were randomized 1:1:1:1 to placebo, 1000, 5000 or 10 000 international units (IU) of oral vitamin D3 daily within each study centre (n = 23) and followed for up to 48 weeks. Between 2013 and 2021, we enrolled 204 participants. Brain MRI scans were performed at baseline, 24 and 48 weeks. The main study outcome was conversion to clinically definite multiple sclerosis based on the 2010 McDonald criteria defined as either a clinical relapse or new brain MRI T2 lesion development. We included 199 cases in the intention-to-treat analysis based on assigned dose. Of these, 116 converted to multiple sclerosis by 48 weeks (58%). Compared to placebo, the hazard ratios (95% confidence interval) for conversion were 1000 IU 0.87 (0.50, 1.50); 5000 IU 1.37 (0.82, 2.29); and 10 000 IU 1.28 (0.76, 2.14). In an adjusted model including age, sex, latitude, study centre and baseline symptom number, clinically isolated syndrome onset site, presence of infratentorial lesions and use of steroids, the hazard ratios (versus placebo) were 1000 IU 0.80 (0.45, 1.44); 5000 IU 1.36 (0.78, 2.38); and 10 000 IU 1.07 (0.62, 1.85). Vitamin D3 supplementation was safe and well tolerated. We did not demonstrate reduction in multiple sclerosis disease activity by vitamin D3 supplementation after a high-risk clinically isolated syndrome.

Thalamic asymmetry in Multiple Sclerosis.

BACKGROUND: Multiple Sclerosis (MS) is a chronic neuroinflammatory disease that affects the central nervous system. Asymmetry is one of the finding in brain MRI of these patients, which is related to the debilitating symptoms of the disease. This study aimed to investigate and compare the thalamic asymmetry in MS patients and its relationship with other MRI and clinical findings of these patients. METHODS: This cross-sectional study conducted on 83 patients with relapse-remitting MS (RRMS), 43 patients with secondary progressive MS (SPMS), and 89 healthy controls. The volumes of total intracranial, total gray matter, total white matter, lesions, thalamus, and also the thalamic asymmetry indices were calculated. The 9-hole peg test (9-HPT) and Expanded Disability Status Scale (EDSS) were assessed as clinical findings. RESULTS: We showed that the normalized whole thalamic volume in healthy subjects was higher than MS patients (both RRMS and SPMS). Thalamic asymmetry index (TAI) was significantly different between RRMS patients and SPMS patients (p = 0.011). The absolute value of TAI was significantly lower in healthy subjects than in RRMS (p < 0.001) and SPMS patients (p < 0.001), and SPMS patients had a higher absolute TAI compared to RRMS patients (p = 0.037). CONCLUSIONS: In this cross-sectional study we showed a relationship between normalized whole thalamic volume and MS subtype. Also, we showed that the asymmetric indices of the thalamus can be related to the progression of the disease. Eventually, we showed that thalamic asymmetry can be related to the disease progression and subtype changes in MS.

Clinical and Imaging Outcomes after Vitamin D Supplementation in Patients with Multiple Sclerosis: A Systematic Review.

The link between vitamin D and multiple sclerosis (MS) has been suggested in epidemiological, genetic, immunological, and clinical studies. The aim of the present systematic review of the literature was to assess the effects of vitamin D supplementation on clinical and imaging outcomes in patients with MS. The outcomes we assessed included relapse events, disability progression, and magnetic resonance imaging (MRI) lesions. The search was conducted using PubMed, ClinicalTrials.gov, and EudraCT databases, and it included records published up until 28 February 2023. The systematic review was reported according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 guidelines. Nineteen independent clinical studies (corresponding to 24 records) were included in the systematic review. The risk of bias in randomized controlled trials (RCTs) was analyzed using the Cochrane risk-of-bias tool. Fifteen trials investigated relapse events, and most of them reported no significant effect of vitamin D supplementation. Eight of 13 RCTs found that vitamin D supplementation had no effect on disability [assessed by Expanded Disability Status Scale (EDSS) scores] compared to controls. Interestingly, recent RCTs reported a significant reduction in new MRI lesions in the central nervous system of MS patients during supplementation with vitamin D3.

Automatic segmentation of the thalamus using a massively trained 3D convolutional neural network: higher sensitivity for the detection of reduced thalamus volume by improved inter-scanner stability.

OBJECTIVES: To develop an automatic method for accurate and robust thalamus segmentation in T1w-MRI for widespread clinical use without the need for strict harmonization of acquisition protocols and/or scanner-specific normal databases. METHODS: A three-dimensional convolutional neural network (3D-CNN) was trained on 1975 T1w volumes from 170 MRI scanners using thalamus masks generated with FSL-FIRST as ground truth. Accuracy was evaluated with 18 manually labeled expert masks. Intra- and inter-scanner test-retest stability were assessed with 477 T1w volumes of a single healthy subject scanned on 123 MRI scanners. The sensitivity of 3D-CNN-based volume estimates for the detection of thalamus atrophy was tested with 127 multiple sclerosis (MS) patients and a normal database comprising 4872 T1w volumes from 160 scanners. The 3D-CNN was compared with a publicly available 2D-CNN (FastSurfer) and FSL. RESULTS: The Dice similarity coefficient of the automatic thalamus segmentation with manual expert delineation was similar for all tested methods (3D-CNN and FastSurfer 0.86 ± 0.02, FSL 0.87 ± 0.02). The standard deviation of the single healthy subject's thalamus volume estimates was lowest with 3D-CNN for repeat scans on the same MRI scanner (0.08 mL, FastSurfer 0.09 mL, FSL 0.15 mL) and for repeat scans on different scanners (0.28 mL, FastSurfer 0.62 mL, FSL 0.63 mL). The proportion of MS patients with significantly reduced thalamus volume was highest for 3D-CNN (24%, FastSurfer 16%, FSL 11%). CONCLUSION: The novel 3D-CNN allows accurate thalamus segmentation, similar to state-of-the-art methods, with considerably improved robustness with respect to scanner-related variability of image characteristics. This might result in higher sensitivity for the detection of disease-related thalamus atrophy. KEY POINTS: • A three-dimensional convolutional neural network was trained for automatic segmentation of the thalamus with a heterogeneous sample of T1w-MRI from 1975 patients scanned on 170 different scanners. • The network provided high accuracy for thalamus segmentation with manual segmentation by experts as ground truth. • Inter-scanner variability of thalamus volume estimates across different MRI scanners was reduced by more than 50%, resulting in increased sensitivity for the detection of thalamus atrophy.

χ-Separation Imaging for Diagnosis of Multiple Sclerosis versus Neuromyelitis Optica Spectrum Disorder.

Background Use of χ-separation imaging can provide surrogates for iron and myelin that relate closely to abnormal changes in multiple sclerosis (MS) lesions. Purpose To evaluate the appearances of MS and neuromyelitis optica spectrum disorder (NMOSD) brain lesions on χ-separation maps and explore their diagnostic value in differentiating the two diseases in comparison with previously reported diagnostic criteria. Materials and Methods This prospective study included individuals with MS or NMOSD who underwent χ-separation imaging from October 2017 to October 2020. Positive (χpos) and negative (χneg) susceptibility were estimated separately by using local frequency shifts and calculating R2' (R2' = R2* - R2). R2 mapping was performed with a machine learning approach. For each lesion, presence of the central vein sign (CVS) and paramagnetic rim sign (PRS) and signal characteristics on χneg and χpos maps were assessed and compared. For each participant, the proportion of lesions with CVS, PRS, and hypodiamagnetism was calculated. Diagnostic performances were assessed using receiver operating characteristic (ROC) curve analysis. Results A total of 32 participants with MS (mean age, 34 years ± 10 [SD]; 25 women, seven men) and 15 with NMOSD (mean age, 52 years ± 17; 14 women, one man) were evaluated, with a total of 611 MS and 225 NMOSD brain lesions. On the χneg maps, 80.2% (490 of 611) of MS lesions were categorized as hypodiamagnetic versus 13.8% (31 of 225) of NMOSD lesions (P < .001). Lesion appearances on the χpos maps showed no evidence of a difference between the two diseases. In per-participant analysis, participants with MS showed a higher proportion of hypodiamagnetic lesions (83%; IQR, 72-93) than those with NMOSD (6%; IQR, 0-14; P < .001). The proportion of hypodiamagnetic lesions achieved excellent diagnostic performance (area under the ROC curve, 0.96; 95% CI: 0.91, 1.00). Conclusion On χ-separation maps, multiple sclerosis (MS) lesions tend to be hypodiamagnetic, which can serve as an important hallmark to differentiate MS from neuromyelitis optica spectrum disorder. © RSNA, 2022 Supplemental material is available for this article.

Differential vulnerability of thalamic nuclei in multiple sclerosis.

OBJECTIVES: Investigating differential vulnerability of thalamic nuclei in multiple sclerosis (MS). METHODS: In a secondary analysis of prospectively collected datasets, we pooled 136 patients with MS or clinically isolated syndrome and 71 healthy controls all scanned with conventional 3D-T1 and white-matter-nulled magnetization-prepared rapid gradient echo (WMn-MPRAGE) and tested for cognitive performance. T1-based thalamic segmentation was compared with the reference WMn-MPRAGE method. Volumes of thalamic nuclei were compared according to clinical phenotypes and cognitive profile. RESULTS: T1- and WMn-MPRAGE provided comparable segmentations (0.84 ± 0.13 < volume-similarity-index < 0.95 ± 0.03). Medial and posterior thalamic groups were significantly more affected than anterior and lateral groups. Cognitive impairment related to volume loss of the anterior group. CONCLUSION: Thalamic nuclei closest to the third ventricle are more affected, with cognitive consequences.

Exploring the Association of HLA Genetic Risk Burden on Thalamic and Hippocampal Atrophy in Multiple Sclerosis Patients.

Multiple sclerosis (MS) is a complex disease of the central nervous system for which human leukocyte antigen (HLA) alleles are major contributors to susceptibility. Several investigations have focused on the relationship between HLA and clinical parameters, while few studies have evaluated its correlation with brain magnetic resonance imaging (MRI) measures. We investigated the association between the HLA genetic burden (HLAGB), originating from the most updated HLA alleles associated with MS, and neuroimaging endophenotypes, with a specific focus on brain atrophy metrics. A monocentric Italian cohort of 334 MS patients with imputed HLA alleles and cross-sectional volumetric measures of white matter (WM), gray matter (GM), hippocampus, thalamus and T2-hyperintense lesions was investigated. Linear regression models with covariate adjustment were fitted for each metric. We detected no effect of HLAGB on WM and GM volumes. Interestingly, we found a marginal correlation between higher HLAGB and lower hippocampal volume (ß = -0.142, p = 0.063) and a nominal association between higher HLAGB and lower thalamic volume (ß = -0.299, p = 0.047). No association was found with T2 lesion volumes. The putative impact of higher HLAGB on hippocampus and thalamus suggests, if replicated in independent cohorts, a possible cumulative contribution of HLA risk loci on brain volumetric traits linked to clinical deficits in MS.

The role of machine learning in developing non-magnetic resonance imaging based biomarkers for multiple sclerosis: a systematic review.

BACKGROUND: Multiple sclerosis (MS) is a neurological condition whose symptoms, severity, and progression over time vary enormously among individuals. Ideally, each person living with MS should be provided with an accurate prognosis at the time of diagnosis, precision in initial and subsequent treatment decisions, and improved timeliness in detecting the need to reassess treatment regimens. To manage these three components, discovering an accurate, objective measure of overall disease severity is essential. Machine learning (ML) algorithms can contribute to finding such a clinically useful biomarker of MS through their ability to search and analyze datasets about potential biomarkers at scale. Our aim was to conduct a systematic review to determine how, and in what way, ML has been applied to the study of MS biomarkers on data from sources other than magnetic resonance imaging. METHODS: Systematic searches through eight databases were conducted for literature published in 2014-2020 on MS and specified ML algorithms. RESULTS: Of the 1, 052 returned papers, 66 met the inclusion criteria. All included papers addressed developing classifiers for MS identification or measuring its progression, typically, using hold-out evaluation on subsets of fewer than 200 participants with MS. These classifiers focused on biomarkers of MS, ranging from those derived from omics and phenotypical data (34.5% clinical, 33.3% biological, 23.0% physiological, and 9.2% drug response). Algorithmic choices were dependent on both the amount of data available for supervised ML (91.5%; 49.2% classification and 42.3% regression) and the requirement to be able to justify the resulting decision-making principles in healthcare settings. Therefore, algorithms based on decision trees and support vector machines were commonly used, and the maximum average performance of 89.9% AUC was found in random forests comparing with other ML algorithms. CONCLUSIONS: ML is applicable to determining how candidate biomarkers perform in the assessment of disease severity. However, applying ML research to develop decision aids to help clinicians optimize treatment strategies and analyze treatment responses in individual patients calls for creating appropriate data resources and shared experimental protocols. They should target proceeding from segregated classification of signals or natural language to both holistic analyses across data modalities and clinically-meaningful differentiation of disease.

Design of an Incremental Music Teaching and Assisted Therapy System Based on Artificial Intelligence Attention Mechanism.

With the continuous updating and advancement of artificial intelligence technology, it gradually begins to shine in various industries, especially playing an increasingly important role in incremental music teaching and assisted therapy systems. This study designs artificial intelligence models from the perspectives of attention mechanism, contextual information guidance, and distant dependencies combined with incremental music teaching for the segmentation of MS (multiple sclerosis) lesions and achieves the automatic and accurate segmentation of MS lesions through the multidimensional analysis of multimodal magnetic resonance imaging data, which provides a basis for physicians to quantitatively analyze MS lesions, thus assisting them in the diagnosis and treatment of MS. To address the highly variable characteristics of MS lesion location, size, number, and shape, this paper firstly designs a 3D context-guided module based on Kronecker convolution to integrate lesion information from different fields of view, starting from lesion contextual information capture. Then, a 3D spatial attention module is introduced to enhance the representation of lesion features in MRI images. The experiments in this paper confirm that the context-guided module, cross-dimensional cross-attention module, and multidimensional feature similarity module designed for the characteristics of MS lesions are effective, and the proposed attentional context U-Net and multidimensional cross-attention U-Net have greater advantages in the objective evaluation index of lesion segmentation, while being combined with the incremental music teaching approach to assist treatment, which provides a new idea for the intelligent assisted treatment approach. In this paper, from algorithm design to experimental validation, both in terms of accuracy, the operational difficulty of the experiment, consumption of arithmetic power, and time cost, the unique superiority of the artificial intelligence attention-based combined with incremental music teaching adjunctive therapy system proposed in this paper can be seen in the MS lesion segmentation task.

Hearing abnormalities in multiple sclerosis: clinical semiology and pathophysiologic mechanisms.

Auditory manifestations from multiple sclerosis (MS) are not as common as the well-recognized sentinel exacerbations of optic neuritis, partial myelitis, motor weakness, vertiginous episodes, heat intolerance, and eye movement abnormalities. This paper discusses four cases of auditory changes, secondary to MS, and describes the first case, to our knowledge, of palinacousis, the perseveration of hearing, despite cessation of the sound stimulus. For each we characterize the initial complaint, the diagnostic work up, and ultimately, underscore the individualized treatment interventions, that allowed us to achieve a remission in all four cases. Individually codifying the treatment regimens served to mitigate, if not to abolish, the clinical derangements in hearing. Special attention is focused upon examination of the clinical manifestations and the pathophysiologic mechanisms which are responsible for them. We further emphasize the differential diagnostic considerations, and physical exam findings, along with the results of laboratory testing, neuro-imaging sequences, and lesion localization. Taken together, such information is germane to organizing cogently coherent strategic treatment plan(s). We believe that this small case series represents a clinically pragmatic example of 'precision medicine'; a principal theme and goal throughout this paper, the achievement of such in MS, but also as an illustration for the assessment and management schema for neuroimmunologic disorders in general.

Disability in multiple sclerosis is related to thalamic connectivity and cortical network atrophy.

BACKGROUND: Thalamic atrophy is proposed to be a major predictor of disability progression in multiple sclerosis (MS), while thalamic function remains understudied. OBJECTIVES: To study how thalamic functional connectivity (FC) is related to disability and thalamic or cortical network atrophy in two large MS cohorts. METHODS: Structural and resting-state functional magnetic resonance imaging (fMRI) was obtained in 673 subjects from Amsterdam (MS: N = 332, healthy controls (HC): N = 96) and Graz (MS: N = 180, HC: N = 65) with comparable protocols, including disability measurements in MS (Expanded Disability Status Scale, EDSS). Atrophy was measured for the thalamus and seven well-recognized resting-state networks. Static and dynamic thalamic FC with these networks was correlated with disability. Significant correlates were included in a backward multivariate regression model. RESULTS: Disability was most strongly related (adjusted R2 = 0.57, p < 0.001) to higher age, a progressive phenotype, thalamic atrophy and increased static thalamic FC with the sensorimotor network (SMN). Static thalamus-SMN FC was significantly higher in patients with high disability (EDSS ⩾ 4) and related to network atrophy but not thalamic atrophy or lesion volumes. CONCLUSION: The severity of disability in MS was related to increased static thalamic FC with the SMN. Thalamic FC changes were only related to cortical network atrophy, but not to thalamic atrophy.

MRI-based thalamic volumetry in multiple sclerosis using FSL-FIRST: Systematic assessment of common error modes.

BACKGROUND AND PURPOSE: FSL's FMRIB's Integrated Registration and Segmentation Tool (FSL-FIRST) is a widely used and well-validated tool. Automated thalamic segmentation is a common application and an important longitudinal measure for multiple sclerosis (MS). However, FSL-FIRST's algorithm is based on shape models derived from non-MS groups. As such, the present study sought to systematically assess common thalamic segmentation errors made by FSL-FIRST on MRIs from people with multiple sclerosis (PwMS). METHODS: FSL-FIRST was applied to generate thalamic segmentation masks for 890 MR images in PwMS. Images and masks were reviewed systematically to classify and quantify errors, as well as associated anatomical variations and MRI abnormalities. For cases with overt errors (n = 362), thalamic masks were corrected and quantitative volumetric differences were calculated. RESULTS: In the entire quantitative volumetric group, the mean volumetric error of FSL-FIRST was 2.74% (0.360 ml): among only corrected cases, the mean volumetric error was 6.79% (0.894 ml). The average percent volumetric error associated with seven error types, two anatomical variants, and motions artifacts are reported. Additional analyses showed that the presence of motion artifacts or anatomical variations significantly increased the probability of error (χ2  = 18.14, p < .01 and χ2  = 64.89, p < .001, respectively). Finally, thalamus volume error was negatively associated with degree of atrophy, such that smaller thalami were systematically overestimated (r = -.28, p < .001). CONCLUSIONS: In PwMS, FSL-FIRST thalamic segmentation miscalculates thalamic volumetry in a predictable fashion, and may be biased to overestimate highly atrophic thalami. As such, it is recommended that segmentations be reviewed and corrected manually when appropriate for specific studies.

White matter tracts that overlap with the thalamus and the putamen are protected against multiple sclerosis pathology.

BACKGROUND: The thalamus and the putamen are highly connected hubs implicated in multiple sclerosis (MS) pathology. It remains unclear if white matter (WM) tracts, which pass through them, have a different susceptibility to MS pathology, and if so, if their impact on disability predominates over that exerted by disease in other WM tracts. We hypothesized that WM tracts connected to and passing through these hubs (subsequently termed hub+ tracts) would be more susceptible to MS-related pathology than tracts that do not pass through them (hub- tracts) due to retrograde and anterograde distant degeneration. Thus, we compared the lesion load and neurite orientation dispersion and density imaging (NODDI) derived metrics between hub+ and hub- tracts and assessed the relationship between these MRI metrics and those of physical impairment. METHODS: Eighteen patients (mean age of 45.5 years, 12 females) had 3 Tesla MRI consisting of T1-weighted and T2-weighted Fluid Attenuated Inversion Recovery (FLAIR), and NODDI from which the orientation dispersion index (ODI), neurite density index (NDI), and isotropic volume fraction (IVF) were derived. Forty-nine WM tracts, i.e., 12 hub+ and 37 hub- tracts, were segmented out. Exploratory analyses of the differences in lesion burden, whole tract and normal appearing WM (NAWM) NODDI metrics were carried out between the two types of tracts using a Mann-Whitney U test. Correlations with physical impairment, quantified using the expanded disability status scale (EDSS) and timed 25-foot walk (T25FW) test were assessed using Spearman correlation analyses. RESULTS: Hub- tracts had larger T1- (p<0.001) and T2-lesion (p<0.001) volumes; lower ODI (p<0.001), NDI (p<0.001) and higher IVF (p = 0.020) in comparison to hub+ tracts. Measures of tissue injury in hub+ tracts correlated with those of clinical disability, though less strongly than in hub- tracts. CONCLUSIONS: Contrary to our hypothesis, our exploratory pilot study results suggest that WM tracts that overlap with the thalamus and the putamen have a lower degree of lesional and non-lesional tissue injury, suggesting a protective role of the hubs against MS pathology or a higher degree of vulnerability of those not passing through hub stations. We also show a weaker association between disability impairment and hub+ pathology, compared to that in hub- tracts. Our findings point to a potential role of disease location in relation to hubs as guidance for treatment personalization in MS.

Correlation between vitamin D and alterations in MRI among patients with multiple sclerosis.

INTRODUCTION: Multiple sclerosis (MS) is a disease of unknown etiology. Diagnosis of MS is primarily based on detection of myelin damage by magnetic resonance imaging (MRI) and classification of demyelination according to the McDonald Criteria. Cholecalciferol (vitamin D3) has been shown to affect the onset and progression of MS via its immunomodulating properties. The role of vitamin D in MS pathogenesis and treatment deserves further investigation, as there is sufficient evidence to suggest a correlation between vitamin D blood level and brain MRI lesion load. STATE OF KNOWLEDGE: Elevated blood vitamin D concentration is linked with demyelination, as determined by T2-weighted and gadolinium-enhanced MRI. Blood vitamin D blood levels are affected by sun exposure, among other factors; however, there is no evident connection between abnormalities in myelination and seasonality. Vitamin D supplementation among MS patients has been associated with a lower probability of new lesions and loss of existing lesion volume, as observed seen in T1-weighted MRI scans (p=0.03). An increase in TGF-beta levels was noted among patients using vitamin D supplementation, which may suggest a mechanism by which cholecalciferol may improve MS prognosis. Patients with clinically isolated syndrome (CIS) exhibited an inverse correlation between vitamin D concentration and risk of new lesions as seen in T2-weighted MRI scans. Moreover, vitamin D intake among these patients lowered the risk of progression to clinically definite multiple sclerosis (CDMS). Daily intake of vitamin D during fingolimod treatment correlated strongly with lower numbers of new lesions. High dose vitamin D supplementation during interferon beta-1a treatment was linked to lower mean percentage of lesions compared with volume pre-treatment seen by T2-weighted MRI. RESULTS: Recent findings advocate for the monitoring of vitamin D blood levels in MS patients. Vitamin D supplementation should be considered in both MS patients and patients with CIS, where other signs of disease may be delayed. Moreover, vitamin D supplementation appears to lower the likelihood of new demyelination changes apparent in MRI examinations.

Proportions of circulating transitional B cells associate with MRI activity in interferon beta-treated multiple sclerosis patients.

B-cells contribute to MS pathogenesis. The association of circulating B-cell phenotypes with combined unique active lesions (CUA) on MRI at 48 weeks follow-up was investigated in 50 interferon beta-treated MS patients. Transitional B-cell proportions were lower in participants with CUA at week 0 and 48 [p = 0.004, p = 0.002]. A decrease in circulating anti-EBNA-1 IgG levels between week 0 and 48 associated with absence of CUA [p = 0.047], but not with B-cell profiles. In a multi-factor model for CUA-risk, transitional B-cell proportions contributed independent from NK/T-cell ratio, change in anti-EBNA-1 IgG, and vitamin D supplementation. Transitional B-cells may predict treatment response in MS.

Imaging multiple sclerosis pathology at 160 µm isotropic resolution by human whole-brain ex vivo magnetic resonance imaging at 3 T.

Postmortem magnetic resonance imaging (MRI) of the fixed healthy and diseased human brain facilitates spatial resolutions and image quality that is not achievable with in vivo MRI scans. Though challenging-and almost exclusively performed at 7 T field strength-depicting the tissue architecture of the entire brain in fine detail is invaluable since it enables the study of neuroanatomy and uncovers important pathological features in neurological disorders. The objectives of the present work were (1) to develop a 3D isotropic ultra-high-resolution imaging approach for human whole-brain ex vivo acquisitions working on a standard clinical 3 T MRI system; and (2) to explore the sensitivity and specificity of this concept for specific pathoanatomical features of multiple sclerosis. The reconstructed images demonstrate unprecedented resolution and soft tissue contrast of the diseased human brain at 3 T, thus allowing visualization of sub-millimetric lesions in the different cortical layers and in the cerebellar cortex, as well as unique cortical lesion characteristics such as the presence of incomplete/complete iron rims, and patterns of iron accumulation. Further details such as the subpial molecular layer, the line of Gennari, and some intrathalamic nuclei are also well distinguishable.

Dietary factors and MRI metrics in early Multiple Sclerosis.

BACKGROUND: Despite significant interest in diet by the MS community, research on this topic is limited; there are no published studies evaluating associations between diet and neuroimaging in MS. METHODS: We utilized baseline data from the RADIEMS cohort of early MS (diagnosed <5.0 years, n=180). Participants underwent brain MRIs to derive normalized total gray and thalamic volumes, T2 lesion volume, and white matter microstructural integrity of normal appearing white matter (NAWM). Participants completed food frequency questionnaires (FFQ) from which we calculated adherence scores to pre-specified dietary patterns including the Mediterranean-DASH Intervention for Neurodegenerative Delay (MIND) diet. We evaluated intake of the following pre-specified dietary components: fruits, vegetables, legumes, nuts, whole grains, dairy, fried foods, processed meats, and fat intake. We used multivariable-adjusted linear regression to evaluate MRI metrics versus dietary measures. RESULTS: MIND diet score was associated with thalamic volume; individuals in the highest quartile of MIND diet scores had greater thalamic volumes versus those in the lowest quartile (Q4 vs. Q1: 1.03mL; 95%CI: 0.26mL, 1.79mL; p<0.01). For individual food/nutrients, higher intakes of full-fat dairy were associated with lower T2 lesion volumes (Q4 vs. Q1: -0.93mL; 95%CI: -1.51mL, -0.35ml; p<0.01). Higher intakes of marine omega-3 fatty acids were associated with greater NAWM microstructural integrity (Q4 vs. Q1: 0.40; 95%CI: 0.03, 0.76; p=0.04). Other foods/nutrients were not associated with MRI outcomes. CONCLUSIONS: In this first study focused on neuroimaging and diet in MS, we note significant associations in a cross-sectional early MS cohort. Longitudinal follow-up of imaging/clinical outcomes will provide additional insights.

Development and evaluation of a manual segmentation protocol for deep grey matter in multiple sclerosis: Towards accelerated semi-automated references.

BACKGROUND: Deep grey matter (dGM) structures, particularly the thalamus, are clinically relevant in multiple sclerosis (MS). However, segmentation of dGM in MS is challenging; labeled MS-specific reference sets are needed for objective evaluation and training of new methods. OBJECTIVES: This study aimed to (i) create a standardized protocol for manual delineations of dGM; (ii) evaluate the reliability of the protocol with multiple raters; and (iii) evaluate the accuracy of a fast-semi-automated segmentation approach (FASTSURF). METHODS: A standardized manual segmentation protocol for caudate nucleus, putamen, and thalamus was created, and applied by three raters on multi-center 3D T1-weighted MRI scans of 23 MS patients and 12 controls. Intra- and inter-rater agreement was assessed through intra-class correlation coefficient (ICC); spatial overlap through Jaccard Index (JI) and generalized conformity index (CIgen). From sparse delineations, FASTSURF reconstructed full segmentations; accuracy was assessed both volumetrically and spatially. RESULTS: All structures showed excellent agreement on expert manual outlines: intra-rater JI > 0.83; inter-rater ICC ≥ 0.76 and CIgen ≥ 0.74. FASTSURF reproduced manual references excellently, with ICC ≥ 0.97 and JI ≥ 0.92. CONCLUSIONS: The manual dGM segmentation protocol showed excellent reproducibility within and between raters. Moreover, combined with FASTSURF a reliable reference set of dGM segmentations can be produced with lower workload.