Non-lesional cerebellar damage in patients with clinically isolated syndrome: DTI measures predict early conversion into clinically definite multiple sclerosis

Patterns of cerebral damage in multiple sclerosis and aquaporin-4 antibody-positive neuromyelitis optica spectrum disorders-major differences revealed by non-conventional imaging

Multiple sclerosis and aquaporin-4 antibody neuromyelitis optica spectrum disorders are distinct autoimmune CNS disorders with overlapping clinical features but differing pathology. Multiple sclerosis is primarily a demyelinating disease with the presence of widespread axonal damage, while neuromyelitis optica spectrum disorders is characterized by astrocyte injury with secondary demyelination. Diagnosis is typically based on lesion characteristics observed on standard MRI imaging and antibody testing but can be challenging in patients with in-between clinical presentations. Non-conventional MRI techniques can provide valuable diagnostic information by measuring disease processes at the microstructural level. We used non-conventional MRI to measure markers of axonal loss in specific white matter tracts in multiple sclerosis and neuromyelitis optica spectrum disorders, depending on their relationship with focal lesions. Patients with relapsing-remitting multiple sclerosis (n = 20), aquaporin-4 antibody-associated neuromyelitis optica spectrum disorders (n = 20) and healthy controls (n = 20) underwent a 3T brain MRI, including T1-, T2- and diffusion-weighted sequences, quantitative susceptibility mapping and phase-sensitive inversion recovery sequence. Tractometry was used to differentiate tract fibres traversing through white matter lesions from those that did not. Neurite density index was assessed using neurite orientation dispersion and density imaging model. Cortical damage was evaluated using T1 relaxation rates. Cortical lesions and paramagnetic rim lesions were identified using phase-sensitive inversion recovery and quantitative susceptibility mapping. In tracts traversing lesions, only one out of 50 tracts showed a decreased neurite density index in multiple sclerosis compared with neuromyelitis optica spectrum disorders. Among 50 tracts not traversing lesions, six showed reduced neurite density in multiple sclerosis (including three in the cerebellum and brainstem) compared to neuromyelitis optica spectrum disorders. In multiple sclerosis, reduced neurite density was found in the majority of fibres traversing (40/50) and not traversing (37/50) white matter lesions when compared to healthy controls. A negative correlation between neurite density in lesion-free fibres and cortical lesions, but not paramagnetic rim lesions, was observed in multiple sclerosis (39/50 tracts). In neuromyelitis optica spectrum disorders compared to healthy controls, decreased neurite density was observed in a subset of fibres traversing white matter lesions, but not in lesion-free fibres. In conclusion, we identified significant differences between multiple sclerosis and neuromyelitis optica spectrum disorders corresponding to their distinct pathologies. Specifically, in multiple sclerosis, neurite density reduction was widespread across fibres, regardless of their relationship to white matter lesions, while in neuromyelitis optica spectrum disorders, this reduction was limited to fibres passing through white matter lesions. Further studies are needed to evaluate the discriminatory potential of neurite density measures in white matter tracts for differentiating multiple sclerosis from neuromyelitis optica spectrum disorders.

Patterns of brain atrophy in recently-diagnosed relapsing-remitting multiple sclerosis

Recurrent neuroinflammation in relapsing-remitting MS (RRMS) is thought to lead to neurodegeneration, resulting in progressive disability. Repeated magnetic resonance imaging (MRI) of the brain provides non-invasive measures of atrophy over time, a key marker of neurodegeneration. This study investigates regional neurodegeneration of the brain in recently-diagnosed RRMS using volumetry and voxel-based morphometry (VBM). RRMS patients (N = 354) underwent 3T structural MRI <6 months after diagnosis and 1-year follow-up, as part of the Scottish multicentre 'FutureMS' study. MRI data were processed using FreeSurfer to derive volumetrics, and FSL for VBM (grey matter (GM) only), to establish regional patterns of change in GM and normal-appearing white matter (NAWM) over time throughout the brain. Volumetric analyses showed a decrease over time (q<0.05) in bilateral cortical GM and NAWM, cerebellar GM, brainstem, amygdala, basal ganglia, hippocampus, accumbens, thalamus and ventral diencephalon. Additionally, NAWM and GM volume decreased respectively in the following cortical regions, frontal: 14 out of 26 regions and 16/26; temporal: 18/18 and 15/18; parietal: 14/14 and 11/14; occipital: 7/8 and 8/8. Left GM and NAWM asymmetry was observed in the frontal lobe. GM VBM analysis showed three major clusters of decrease over time: 1) temporal and subcortical areas, 2) cerebellum, 3) anterior cingulum and supplementary motor cortex; and four smaller clusters within the occipital lobe. Widespread GM and NAWM atrophy was observed in this large recently-diagnosed RRMS cohort, particularly in the brainstem, cerebellar GM, and subcortical and occipital-temporal regions; indicative of neurodegeneration across tissue types, and in accord with limited previous studies in early disease. Volumetric and VBM results emphasise different features of longitudinal lobar and loco-regional change, however identify consistent atrophy patterns across individuals. Atrophy measures targeted to specific brain regions may provide improved markers of neurodegeneration, and potential future imaging stratifiers and endpoints for clinical decision making and therapeutic trials.

Evaluating Magnetic Resonance Diffusion Properties Together with Brain Volumetry May Predict Progression to Multiple Sclerosis

RATIONALE AND OBJECTIVES

Although the gold standard in predicting future progression from clinically isolated syndrome (CIS) to clinically definite multiple sclerosis (CDMS) consists in the McDonald criteria, efforts are being made to employ various advanced MRI techniques for predicting clinical progression. This study's main aim was to evaluate the predictive power of diffusion tensor imaging (DTI) of the brain and brain volumetry to distinguish between patients having CIS with future progression to CDMS from those without progression during the following 2 years and to compare those parameters with conventional MRI evaluation.

MATERIALS AND METHODS

All participants underwent an MRI scan of the brain. DTI and volumetric data were processed and various parameters were compared between the study groups.

RESULTS

We found significant differences between the subgroups of patients differing by future progression to CDMS in most of those DTI and volumetric parameters measured. Fractional anisotropy of water diffusion proved to be the strongest predictor of clinical conversion among all parameters evaluated, demonstrating also higher specificity compared to evaluation of conventional MRI images according to McDonald criteria.

CONCLUSION

Conclusion: Our results provide evidence that the evaluation of DTI parameters together with brain volumetry in patients with early-stage CIS may be useful in predicting conversion to CDMS within the following 2 years of the disease course.

Cerebellar and/or Brainstem Lesions Indicate Poor Prognosis in Multiple Sclerosis: A Systematic Review

Multiple sclerosis is a serious neurological disease that affects millions of people worldwide. Cerebellar and brainstem symptoms are common in the course of multiple sclerosis, but their prognostic value is unclear. This systematic review aimed to determine the relationship between the location of lesions in the cerebellum and/or brainstem and the prognosis in multiple sclerosis. In this systematic review, we searched and comprehensively read articles related to this research topic in Chinese and English electronic databases (PubMed, Embase, Cochrane Library, CNKI, and CBM) using search terms “multiple sclerosis,” “cerebellum,” “brainstem,” “prognosis,” and others. Cerebellar and brainstem clinically isolated syndromes and clinically definite multiple sclerosis were important predictors of transformation (hazard ratio, 2.58; 95% confidence interval, 1.58–4.22). Cerebellar and/or brainstem lesions indicate a poor overall prognosis in multiple sclerosis, but because of inconsistency, more clinical data are needed.

Peripapillary Vessel Density as Early Biomarker in Multiple Sclerosis

Background: To evaluate retinal vessel density (VD) in macular and in peripapillary regions in patients with recent onset of multiple sclerosis, at initial demyelinating event (IDE) and in matched relapsing-remitting multiple sclerosis (RRMS) patients.

Methods: We evaluated VD in superficial capillary plexus, deep capillary plexus, choriocapillaris and radial peripapillary capillary plexus in IDE, RRMS patients and in matched healthy controls (HCs) through Optical Coherence Tomography Angiography (OCT-A). Clinical history, including history of optic neuritis, Expanded Disability Status scale and disease duration of patients were collected.

Results: Thirty patients (20 with IDE and 10 with RRMS) and 15 HCs were enrolled. IDE patients showed a lower VD in radial peripapillary capillary plexus compared with controls (coeff. β = −3.578; p = 0.002). RRMS patients displayed a lower VD in both superficial capillary plexus and radial peripapillary capillary plexus compared with HCs (coeff. β = −4.955; p = 0.002, and coeff. β = −7.446; p < 0.001, respectively). Furthermore, RRMS patients showed a decreased VD in radial peripapillary capillary plexus compared with IDE patients (coeff. β = −3.868; p = 0.003).

Conclusions: Peripapillary region vessel density reduction, revealed through OCT-A, might be considered as an early event in MS, and might be relevant as a biomarker of disease pathology.

What do we currently know about the clinically isolated syndrome suggestive of multiple sclerosis? An update

Multiple sclerosis (MS) is a chronic, demyelinating, not fully understood disease of the central nervous system. The first demyelinating clinical episode is called clinically isolated syndrome (CIS) suggestive of MS. Although the most common manifestations of CIS are long tracts dysfunction and unilateral optic neuritis, it can also include isolated brainstem syndromes, cerebellar involvement, and polysymptomatic clinical image. Recently, the frequency of CIS diagnosis has decreased due to the more sensitive and less specific 2017 McDonald criteria compared with the revisions from 2010. Not all patients with CIS develop MS. The risk of conversion can be estimated based on many predictive factors including epidemiological, ethnical, clinical, biochemical, radiological, immunogenetic, and other markers. The management of CIS is nowadays widely discussed among clinicians and neuroscientists. To date, interferons, glatiramer acetate, teriflunomide, cladribine, and some other agents have been evaluated in randomized, placebo-controlled, double-blind studies relying on large groups of patients with the first demyelinating event. All of these drugs were shown to have beneficial effects in patients with CIS and might be used routinely in the future. The goal of this article is to explore the most relevant topics regarding CIS as well as to provide the most recent information in the field. The review presents CIS definition, classification, clinical image, predictive factors, and management. What is more, this is one of very few reviews summarizing the topic in the light of the 2017 McDonald criteria.

Classification of Multiple Sclerosis Clinical Profiles via Graph Convolutional Neural Networks

Recent advances in image acquisition and processing techniques, along with the success of novel deep learning architectures, have given the opportunity to develop innovative algorithms capable to provide a better characterization of neurological related diseases. In this work, we introduce a neural network based approach to classify Multiple Sclerosis (MS) patients into four clinical profiles. Starting from their structural connectivity information, obtained by diffusion tensor imaging and represented as a graph, we evaluate the classification performances using unweighted and weighted connectivity matrices. Furthermore, we investigate the role of graph-based features for a better characterization and classification of the pathology. Ninety MS patients (12 clinically isolated syndrome, 30 relapsing-remitting, 28 secondary-progressive, and 20 primary-progressive) along with 24 healthy controls, were considered in this study. This work shows the great performances achieved by neural networks methods in the classification of the clinical profiles. Furthermore, it shows local graph metrics do not improve the classification results suggesting that the latent features created by the neural network in its layers have a much important informative content. Finally, we observe that graph weights representation of brain connections preserve important information to discriminate between clinical forms.