scholarly journals Contrasting the brain imaging features of MOG-antibody disease, with AQP4-antibody NMOSD and multiple sclerosis

2021 ◽  
pp. 135245852110189
Author(s):  
Silvia Messina ◽  
Romina Mariano ◽  
Adriana Roca-Fernandez ◽  
Ana Cavey ◽  
Maciej Jurynczyk ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Grey Matter ◽  
Diffusion Imaging ◽  
White Matter Lesions ◽  
Lesion Volume ◽  
Imaging Features ◽  
Strongly Correlated ◽  
Deep Grey Matter ◽  
Magnetic Resonance Imaging Mri ◽  
Aqp4 Antibody

Background: Identifying magnetic resonance imaging (MRI) markers in myelin-oligodendrocytes-glycoprotein antibody-associated disease (MOGAD), neuromyelitis optica spectrum disorder-aquaporin-4 positive (NMOSD-AQP4) and multiple sclerosis (MS) is essential for establishing objective outcome measures. Objectives: To quantify imaging patterns of central nervous system (CNS) damage in MOGAD during the remission stage, and to compare it with NMOSD-AQP4 and MS. Methods: 20 MOGAD, 19 NMOSD-AQP4, 18 MS in remission with brain or spinal cord involvement and 18 healthy controls (HC) were recruited. Volumetrics, lesions and cortical lesions, diffusion-imaging measures, were analysed. Results: Deep grey matter volumes were lower in MOGAD ( p = 0.02) and MS ( p = 0.0001), compared to HC and were strongly correlated with current lesion volume (MOGAD R = −0.93, p < 0.001, MS R = −0.65, p = 0.0034). Cortical/juxtacortical lesions were seen in a minority of MOGAD, in a majority of MS and in none of NMOSD-AQP4. Non-lesional tissue fractional anisotropy (FA) was only reduced in MS ( p = 0.01), although focal reductions were noted in NMOSD-AQP4, reflecting mainly optic nerve and corticospinal tract pathways. Conclusion: MOGAD patients are left with grey matter damage, and this may be related to persistent white matter lesions. NMOSD-AQP4 patients showed a relative sparing of deep grey matter volumes, but reduced non-lesional tissue FA. Observations from our study can be used to identify new markers of damage for future multicentre studies.

scholarly journals Contrasting the brain imaging features of MOG-Antibody disease, with AQP4-Antibody NMOSD and Multiple Sclerosis

2020 ◽  
Author(s):  
Silvia Messina ◽  
Romina Mariano ◽  
Adriana Roca-Fernandez ◽  
Ana Cavey ◽  
Maciej Jurynczyk ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Visual Acuity ◽  
White Matter ◽  
Grey Matter ◽  
Optic Chiasm ◽  
Lesion Volume ◽  
Imaging Features ◽  
Conventional Imaging ◽  
Cortical Lesions ◽  
Deep Grey Matter

Neuromyelitis optica associated with aquaporin-4-antibodies (NMOSD-AQP4) and myelin oligodentrocyte-glycoprotein antibody-associated disorder (MOGAD) have been recently recognised as different from multiple sclerosis. Although conventional MRI may help distinguish multiple sclerosis from antibody-mediated diseases, the use of quantitative and non-conventional imaging may give more pathological information and explain the clinical differences. We compared, using non-conventional imaging, brain MRI findings in 75 subjects in remission with NMOSD-AQP4, MOGAD, multiple sclerosis or healthy controls (HC). Volumetrics, white matter and cortical lesions, and tissue integrity measures using diffusion imaging, were analysed in the four groups along with their association with disability (expanded disability status scale [EDSS] and visual acuity). The volumetric analysis showed that, deep grey matter volumes were significantly lower in multiple sclerosis (p=0.0001) and MOGAD (p=0.02), compared to HC. Relapsing MOGAD had lower white matter, pallidus and hippocampus volumes than in monophasic (p<0.05). Optic chiasm volume was reduced only in NMOSD-AQP4 who had at least one episode of optic neuritis (ON) (NMOSD-AQP4-ON vs NMOSD-AQP4 p<0.001, HC p<0.001, MOGAD-ON p=0.04, multiple sclerosis-ON p=0.02) likely reflecting the recognised posterior location of NMOSD-AQP4-ON and its severity. Lesion volume was greatest in multiple sclerosis followed by MOGAD and in these two diseases, the lesion volume correlated with disease duration (multiple sclerosis R=0.46, p=0.05, MOGAD R=0.81, p<0.001), cortical thickness (multiple sclerosis R=-0.64, p=0.0042, MOGAD=-0.71, p=0.005) and deep grey matter volumes (multiple sclerosis R=-0.65, p=0.0034, MOGAD R=-0.93, p<0.001). Lesional-fractional anisotropy (FA) was reduced and mean diffusivity increased in all patients, but overall, FA was only reduced in the non-lesional tissue in multiple sclerosis (p=0.01), although focal reductions were noted in NMOSD-AQP4, reflecting mainly optic nerve and corticospinal tract pathways. Cortical/juxtacortical lesions were seen in a minority of MOGAD, while cortical/juxtacortical and purely cortical lesions were identified in the majority of multiple sclerosis and in none of the NMOSD-AQP4. Non-lesional FA in NMOSD-AQP4, lower white-matter volume and female sex in multiple sclerosis, and lower brainstem volume in MOGAD were the best predictors of EDSS disability (accounting for 46%, 49% and 19% respectively). Worse visual acuity associated with lower optic chiasm volume in NMOSD-AQP4 and lower thalamus volume in MOGAD (accounting for 58% and 35% respectively). Although MOGAD patients had good outcomes, deep grey matter atrophy was present. In contrast, NMOSD-AQP4 patients showed a relative sparing of deep grey matter volumes, despite greater residual disability as compared with MOGAD patients. NMOSD-AQP4 but not MOGAD patients showed reduced FA in non-lesional tissue.

scholarly journals Automated quantification of deep grey matter structures and white matter lesions using magnetic resonance imaging in relapsing remission multiple sclerosis

2021 ◽  
Vol 52 (1) ◽  
Author(s):  
Mina Rizkallah ◽  
Mohamed Hefida ◽  
Mohamed Khalil ◽  
Rasha Mahmoud Dawoud
Keyword(s):  
Multiple Sclerosis ◽  
Statistical Analysis ◽  
White Matter ◽  
Grey Matter ◽  
Brain Volume ◽  
Correlation Coefficients ◽  
White Matter Lesions ◽  
The Other ◽  
Automated Segmentation ◽  
Deep Grey Matter

Abstract Background Brain volume loss (BVL) is widespread in MS and occurs throughout the disease course at a rate considerably greater than in the general population. In MS, brain volume correlates with and predicts future disability, making BVL a relevant measure of diffuse CNS damage leading to clinical disease progression, as well as serving as a useful outcome in evaluating MS therapies. The aim of our study was to evaluate the role of automated segmentation and quantification of deep grey matter structures and white matter lesions in Relapsing Remitting Multiple Sclerosis patients using MR images and to correlate the volumetric results with different degrees of disability based on expanded disability status scale (EDSS) scores. Results All the patients in our study showed relative atrophy of the thalamus and the putamen bilaterally when compared with the normal control group. Statistical analysis was significant for the thalamus and the putamen atrophy (P value < 0.05). On the other hand, statistical analysis was not significant for the caudate and the hippocampus (P value > 0.05); there was a significant positive correlation between the white matter lesions volume and EDSS scores (correlation coefficient of 0.7505). On the other hand, there was a significant negative correlation between the thalamus and putamen volumes, and EDSS scores (correlation coefficients < − 0.9), while the volumes of the caudate and the hippocampus had a very weak and non-significant correlation with the EDSS scores (correlation coefficients > − 0.35). Conclusions The automated segmentation and quantification tools have a great role in the assessment of brain structural changes in RRMS patients, and that it became essential to integrate these tools in the daily medical practice for the great value they add to the current evaluation measures.

scholarly journals Simultaneous lesion and brain segmentation in multiple sclerosis using deep neural networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Richard McKinley ◽  
Rik Wepfer ◽  
Fabian Aschwanden ◽  
Lorenz Grunder ◽  
Raphaela Muri ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Neural Networks ◽  
Grey Matter ◽  
White Matter Lesions ◽  
Brain Structures ◽  
Training Dataset ◽  
Single Centre ◽  
Deep Grey Matter ◽  
Reference Implementation ◽  
Dataset Shift

AbstractSegmentation of white matter lesions and deep grey matter structures is an important task in the quantification of magnetic resonance imaging in multiple sclerosis. In this paper we explore segmentation solutions based on convolutional neural networks (CNNs) for providing fast, reliable segmentations of lesions and grey-matter structures in multi-modal MR imaging, and the performance of these methods when applied to out-of-centre data. We trained two state-of-the-art fully convolutional CNN architectures on the 2016 MSSEG training dataset, which was annotated by seven independent human raters: a reference implementation of a 3D Unet, and a more recently proposed 3D-to-2D architecture (DeepSCAN). We then retrained those methods on a larger dataset from a single centre, with and without labels for other brain structures. We quantified changes in performance owing to dataset shift, and changes in performance by adding the additional brain-structure labels. We also compared performance with freely available reference methods. Both fully-convolutional CNN methods substantially outperform other approaches in the literature when trained and evaluated in cross-validation on the MSSEG dataset, showing agreement with human raters in the range of human inter-rater variability. Both architectures showed drops in performance when trained on single-centre data and tested on the MSSEG dataset. When trained with the addition of weak anatomical labels derived from Freesurfer, the performance of the 3D Unet degraded, while the performance of the DeepSCAN net improved. Overall, the DeepSCAN network predicting both lesion and anatomical labels was the best-performing network examined.

scholarly journals Reduced accuracy of MRI deep grey matter segmentation in multiple sclerosis: an evaluation of four automated methods against manual reference segmentations in a multi-center cohort

2020 ◽  
Vol 267 (12) ◽  
pp. 3541-3554 ◽  
Author(s):  
Alexandra de Sitter ◽  
◽  
Tom Verhoeven ◽  
Jessica Burggraaff ◽  
Yaou Liu ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Grey Matter ◽  
Majority Voting ◽  
Dice Similarity Coefficient ◽  
Lesion Volume ◽  
Impaired Performance ◽  
Segmentation Methods ◽  
Deep Grey Matter ◽  
Global And Local ◽  
Automated Methods

Abstract Background Deep grey matter (DGM) atrophy in multiple sclerosis (MS) and its relation to cognitive and clinical decline requires accurate measurements. MS pathology may deteriorate the performance of automated segmentation methods. Accuracy of DGM segmentation methods is compared between MS and controls, and the relation of performance with lesions and atrophy is studied. Methods On images of 21 MS subjects and 11 controls, three raters manually outlined caudate nucleus, putamen and thalamus; outlines were combined by majority voting. FSL-FIRST, FreeSurfer, Geodesic Information Flow and volBrain were evaluated. Performance was evaluated volumetrically (intra-class correlation coefficient (ICC)) and spatially (Dice similarity coefficient (DSC)). Spearman's correlations of DSC with global and local lesion volume, structure of interest volume (ROIV), and normalized brain volume (NBV) were assessed. Results ICC with manual volumes was mostly good and spatial agreement was high. MS exhibited significantly lower DSC than controls for thalamus and putamen. For some combinations of structure and method, DSC correlated negatively with lesion volume or positively with NBV or ROIV. Lesion-filling did not substantially change segmentations. Conclusions Automated methods have impaired performance in patients. Performance generally deteriorated with higher lesion volume and lower NBV and ROIV, suggesting that these may contribute to the impaired performance.

Deep grey matter T2 hypo-intensity in patients with paediatric multiple sclerosis

2011 ◽  
Vol 17 (6) ◽  
pp. 702-707 ◽  
Author(s):  
Antonia Ceccarelli ◽  
Maria A Rocca ◽  
Elisabetta Perego ◽  
Lucia Moiola ◽  
Angelo Ghezzi ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Caudate Nucleus ◽  
Grey Matter ◽  
Lesion Volume ◽  
Longitudinal Assessment ◽  
Paediatric Patients ◽  
Relapsing Remitting ◽  
Left Caudate ◽  
Deep Grey Matter ◽  
Dual Echo

Objective: T2 hypo-intensity on magnetic resonance imaging scans is thought to reflect pathological iron deposition in the presence of disease. In this pilot study, we evaluated the utility of the quantification of T2 hypo-intensities in paediatric patients by estimating deep grey matter (DGM) T2 hypo-intensities in paediatric patients with multiple sclerosis (MS) or clinically isolated syndromes (CIS), and their changes over 1 year. Methods: A dual-echo sequence was obtained from 45 paediatric patients (10 with CIS, 35 with relapsing–remitting MS, 8 with an onset of the disease before the age of 10 and 37 during adolescence) and 14 age-matched healthy controls (HC). Eleven patients were reassessed both clinically and with MRI after 1 year. Normalized T2 intensity in the basal ganglia and thalamus was quantified. Results: At baseline, DGM T2 intensity was similar between paediatric patients and HC in all the structures analysed, except for the head of the left caudate nucleus ( p = 0.001). DGM T2 intensity of the head of the left caudate nucleus was similar between paediatric CIS and RRMS patients, but it was reduced in adolescent-onset paediatric patients versus HC ( p = 0.002). In all patients, DGM T2 intensity of the head of the left caudate nucleus was correlated with T2 lesion volume ( r = −0.39, p = 0.007). DGM T2 intensity in all the structures analysed with longitudinal assessment remained stable over the follow-up in the cohort of patients. Conclusions: The quantification of DGM T2 intensity in paediatric patients may provide surrogate markers of neurodegeneration. In paediatric MS, DGM is likely to be affected by iron-related changes, which are likely to be, at least partially, secondary to WM damage.

scholarly journals Validating atlas-based lesion disconnectomics in multiple sclerosis: a retrospective multi-centric study

2021 ◽  
Author(s):  
Veronica Ravano ◽  
Michaela Andelova ◽  
Mario Joao Fartaria ◽  
Mazen Fouad A-Wali Mahdi ◽  
Benedicte Marechal ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Clinical Symptoms ◽  
Brain Connectivity ◽  
Diffusion Imaging ◽  
White Matter Lesions ◽  
Acquisition Time ◽  
Lesion Volume ◽  
Topological Graph ◽  
Embedding Technique ◽  
Graph Properties

The translational potential of MR-based connectivity modelling is limited by the need for advanced diffusion imaging, which is not part of clinical protocols for many diseases. In addition, where diffusion data is available, brain connectivity analyses rely on tractography algorithms which imply two major limitations. First, tracking algorithms are known to be sensitive to the presence of white matter lesions and therefore leading to interpretation pitfalls and poor inter-subject comparability in clinical applications such as multiple sclerosis. Second, tractography quality is highly dependent on the acquisition parameters of diffusion sequences, leading to a trade-off between acquisition time and tractography precision. Here, we propose an atlas-based approach to study the interplay between structural disconnectivity and lesions without requiring individual diffusion imaging. In a multi-centric setting involving three distinct multiple sclerosis datasets (containing both 1.5T and 3T data), we compare our atlas-based structural disconnectome computation pipeline to disconnectomes extracted from individual tractography and explore its clinical utility for reducing the gap between radiological findings and clinical symptoms in multiple sclerosis. Results using topological graph properties showed that overall, our atlas-based disconnectomes were suitable approximations of individual disconnectomes from diffusion imaging. Small-worldness was found to decrease for larger total lesion volumes thereby suggesting a loss of efficiency in brain connectivity of MS patients. A graph embedding technique followed by dimensionality reduction found a topological organization that mirrored disability. Finally, the global efficiency of the created brain graph, combined with total lesion volume, allowed to stratify patients into subgroups with different clinical scores in all three cohorts.

Heterogeneous pathological processes account for thalamic degeneration in multiple sclerosis: Insights from 7 T imaging

2017 ◽  
Vol 24 (11) ◽  
pp. 1433-1444 ◽  
Author(s):  
Céline Louapre ◽  
Sindhuja T Govindarajan ◽  
Costanza Giannì ◽  
Nancy Madigan ◽  
Jacob A Sloane ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Progressive Multiple Sclerosis ◽  
Lesion Volume ◽  
Thalamic Lesion ◽  
Cortical Lesions ◽  
Focal Lesions ◽  
Thalamic Degeneration ◽  
Relapsing Remitting ◽  
Thalamic Lesions ◽  
Magnetic Resonance Imaging Mri

Background: Thalamic degeneration impacts multiple sclerosis (MS) prognosis. Objective: To investigate heterogeneous thalamic pathology, its correlation with white matter (WM), cortical lesions and thickness, and as function of distance from cerebrospinal fluid (CSF). Methods: In 41 MS subjects and 17 controls, using 3 and 7 T imaging, we tested for (1) differences in thalamic volume and quantitative T2* (q-T2*) (2) globally and (3) within concentric bands originating from the CSF/thalamus interface; (4) the relation between thalamic, cortical, and WM metrics; and (5) the contribution of magnetic resonance imaging (MRI) metrics to clinical scores. We also assessed MS thalamic lesion distribution as a function of distance from CSF. Results: Thalamic lesions were mainly located next to the ventricles. Thalamic volume was decreased in MS versus controls ( p < 10−2); global q-T2* was longer in secondary progressive multiple sclerosis (SPMS) only ( p < 10−2), indicating myelin and/or iron loss. Thalamic atrophy and longer q-T2* correlated with WM lesion volume ( p < 0.01). In relapsing-remitting MS, q-T2* thalamic abnormalities were located next to the WM ( p < 0.01 (uncorrected), p = 0.09 (corrected)), while they were homogeneously distributed in SPMS. Cortical MRI metrics were the strongest predictors of clinical outcome. Conclusion: Heterogeneous pathological processes affect the thalamus in MS. While focal lesions are likely mainly driven by CSF-mediated factors, overall thalamic degeneration develops in association with WM lesions.

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