A longitudinal study of abnormalities on MRI and disability from multiple sclerosis

Background: In patients with isolated syndromes that are clinically suggestive of multiple sclerosis, such as optic neuritis or brain-stem or spinal cord syndromes, the presence of lesions as determined by T2-weighted magnetic resonance imaging (MRI) of the brain increases the likelihood that multiple sclerosis will develop. We sought to determine the relation between early lesion volume, changes in volume, and long-term disability. Methods: Seventy-one patients in a serial MRI study of patients with isolated syndromes were reassessed after a mean of 14.1 years. Disability was measured with the use of Kurtzke's Expanded Disability Status Scale (EDSS; possible range, 0 to 10, with a higher score indicating a greater degree of disability). Results: Clinically definite multiple sclerosis developed in 44 of the 50 patients (88 percent) with abnormal results on MRI at presentation and in 4 of 21 patients (19 percent) with normal results on MRI. The median EDSS score at follow-up for those with multiple sclerosis was 3.25 (range, 0 to 10); 31 percent had an EDSS score of 6 or more (including three patients whose deaths were due to multiple sclerosis). The EDSS score at 14 years correlated moderately with lesion volume on MRI at 5 years (r=0.60) and with the increase in lesion volume over the first 5 years (r=0.61). Conclusions: In patients who first present with isolated syndromes suggestive of multiple sclerosis, the increases in the volume of the lesions seen on magnetic resonance imaging of the brain in the first five years correlate with the degree of long-term disability from multiple sclerosis. This relation is only moderate, so the volume of the lesions alone may not be an adequate basis for decisions about the use of disease-modifying treatment

Grey matter involvement by focal cervical spinal cord lesions is associated with progressive multiple sclerosis

BACKGROUND: The in vivo relationship of spinal cord lesion features with clinical course and function in multiple sclerosis (MS) is poorly defined. OBJECTIVE: The objective of this paper is to investigate the associations of spinal cord lesion features on MRI with MS subgroup and disability. METHODS: We recruited 120 people: 25 clinically isolated syndrome, 35 relapsing-remitting (RR), 30 secondary progressive (SP), and 30 primary progressive (PP) MS. Disability was measured using the Expanded Disability Status Scale. We performed 3T axial cervical cord MRI, using 3D-fast-field-echo and phase-sensitive-inversion-recovery sequences. Both focal lesions and diffuse abnormalities were recorded. Focal lesions were classified according to the number of white matter (WM) columns involved and whether they extended to grey matter (GM). RESULTS: The proportion of patients with focal lesions involving at least two WM columns and extending to GM was higher in SPMS than in RRMS (p = 0.03) and PPMS (p = 0.015). Diffuse abnormalities were more common in both PPMS and SPMS, compared with RRMS (OR 6.1 (p = 0.002) and 5.7 (p = 0.003), respectively). The number of lesions per patient involving both the lateral column and extending to GM was independently associated with disability (p < 0.001). CONCLUSIONS: More extensive focal cord lesions, extension of lesions to GM, and diffuse abnormalities are associated with progressive MS and disability

Spatial variability and changes of metabolite concentrations in the cortico-spinal tract in multiple sclerosis using coronal CSI

We characterized metabolic changes along the cortico-spinal tract (CST) in multiple sclerosis (MS) patients using a novel application of chemical shift imaging (CSI) and considering the spatial variation of metabolite levels. Thirteen relapsing-remitting (RR) and 13 primary-progressive (PP) MS patients and 16 controls underwent (1)H-MR CSI, which was applied to coronal-oblique scans to sample the entire CST. The concentrations of the main metabolites, i.e., N-acetyl-aspartate, myo-Inositol (Ins), choline containing compounds (Cho) and creatine and phosphocreatine (Cr), were calculated within voxels placed in regions where the CST is located, from cerebral peduncle to corona radiata. Differences in metabolite concentrations between groups and associations between metabolite concentrations and disability were investigated, allowing for the spatial variability of metabolite concentrations in the statistical model. RRMS patients showed higher CST Cho concentration than controls, and higher CST Ins concentration than PPMS, suggesting greater inflammation and glial proliferation in the RR than in the PP course. In RRMS, a significant, albeit modest, association between greater Ins concentration and greater disability suggested that gliosis may be relevant to disability. In PPMS, lower CST Cho and Cr concentrations correlated with greater disability, suggesting that in the progressive stage of the disease, inflammation declines and energy metabolism reduces. Attention to the spatial variation of metabolite concentrations made it possible to detect in patients a greater increase in Cr concentration towards the superior voxels as compared to controls and a stronger association between Cho and disability, suggesting that this step improves our ability to identify clinically relevant metabolic changes

Prediction of time between CIS onset and clinical conversion to MS using Random Forests

CIS is diagnosed after a first neurological attack and can be considered an early stage of MS as ~80% of all CIS patients will have a second relapse within 20 years. The prediction of this second clinical relapse which marks the clinical conversion to MS (i.e., clinically-definite MS, CDMS) is very challenging, and many clinical and radiological predictors of CDMS have been identified. Machine learning techniques such as support vector machines (SVMs) have been widely applied to neuroimaging data in order to associate MRI features with binary clinical outcomes. A single-centre study has shown that it is possible to predict short-time conversion after 1 and 3 years with an accuracy of ~75 % using a priori defined features from baseline MRI measures and clinical characteristics, which were applied to support vector machines (SVMs). Random forests are another type of machine learning techniques that can easily be applied to regression problems, and consist of an ensemble of decision trees for regression where each tree is created from independent bootstraps from the input data. The present study shows the feasibility of using random forests with European multi-centre MRI data (obtained at CIS onset) to predict the actual date of conversion to CDMS rather than just a binary outcome at a fixed time point

Setting a research agenda for progressive multiple sclerosis: The International Collaborative on Progressive MS

Despite significant progress in the development of therapies for relapsing MS, progressive MS remains comparatively disappointing. Our objective, in this paper, is to review the current challenges in developing therapies for progressive MS and identify key priority areas for research. A collaborative was convened by volunteer and staff leaders from several MS societies with the mission to expedite the development of effective disease-modifying and symptom management therapies for progressive forms of multiple sclerosis. Through a series of scientific and strategic planning meetings, the collaborative identified and developed new perspectives on five key priority areas for research: experimental models, identification and validation of targets and repurposing opportunities, proof-of-concept clinical trial strategies, clinical outcome measures, and symptom management and rehabilitation. Our conclusions, tackling the impediments in developing therapies for progressive MS will require an integrated, multi-disciplinary approach to enable effective translation of research into therapies for progressive MS. Engagement of the MS research community through an international effort is needed to address and fund these research priorities with the ultimate goal of expediting the development of disease-modifying and symptom-relief treatments for progressive MS

Thickness dependence study of current-driven ferromagnetic resonance in Y3Fe5O12/heavy metal bilayers

We use ferromagnetic resonance to study the current-induced torques in YIG/heavy metal bilayers. YIG samples with thickness varying from 14.8 nm to 80 nm, with the Pt or Ta thin film on top, are measured by applying a microwave current into the heavy metals and measuring the longitudinal DC voltage generated by both spin rectification and spin pumping. From a symmetry analysis of the FMR lineshape and its dependence on YIG thickness, we deduce that the Oersted field dominates over spin-transfer torque in driving magnetization dynamics

Impact of 3 Tesla MRI on interobserver agreement in clinically isolated syndrome: A MAGNIMS multicentre study

Compared to 1.5 T, 3 T magnetic resonance imaging (MRI) increases signal-to-noise ratio leading to improved image quality. However, its clinical relevance in clinically isolated syndrome suggestive of multiple sclerosis remains uncertain