Explore the vast range of titles available.

BackgroundSlowly expanding lesions (SELs) in multiple sclerosis (MS) are markers of chronic active lesions and seem to trigger disability. This study aimed to analyse spatial features of SELs through diffusion MRI and their clinical impact on progression independent of relapse activity (PIRA).MethodsAn observational study of MS subjects prospectively followed since 2011; inclusion required at least three longitudinal T1/T2-weighted and diffusion-weighted MRIs. Subjects followed clinical assessments, using Multiple Sclerosis Functional Composite (MSFC) and Expanded Disability Status Scale. At MRI, lesions were categorised using non-linear deformation as definite or possible SELs, and non-SELs. Fractional anisotropy (FA) was extracted from each lesion core and perilesional area. Differences in FA values across core and perilesional areas by SEL category were assessed using the Mann-Whitney test. Associations with PIRA and clinical outcomes were evaluated using mixed-effects, logistic and Cox regression models.Results130 subjects underwent MRI (median 25 months) and clinical assessments (median follow-up 9.2 years), of which 29 (22%) developed PIRA. Of 4811 lesions, 8% were definite SELs. Definite SELs exhibited FA decline over time in core and perilesional areas compared with other lesions. Longitudinal core FA reductions within definite SELs were associated with worse MSFC z-score evolution (β=0.03, 95% CI 0.01 to 0.05, p=0.003), higher odds for PIRA (OR=0.01, 95% CI 0.01 to 0.12, p=0.001) and predicted faster time to reach first PIRA event (HR=0.03, 95% CI 0 to 0.49, p=0.015).ConclusionsDefinite SELs show distinct greater microstructural damage and are associated with PIRA, making their FA signature a potential predictor of MS progression.

Spinal cord injury causes a cascade of physiological responses, which may trigger a subsequent neurotoxic increase in intracellular sodium. This can lead to neurodegeneration, both at and beyond the site of injury, causing clinical symptoms and loss of function. However, in vivo measurements of tissue sodium remain challenging. Here we utilise sodium magnetic resonance spectroscopy ( 23 Na-MRS) at 3T to measure tissue sodium concentration (TSC) and its association with microstructural measures and macromolecular MRI metrics in the cervical spinal cord, distal to the site of injury. Twenty people with cervical myelopathy and twenty healthy controls, were studied. Associations with motor and sensory impairments were explored using ASIA and jOAMEQ scores. No significant difference in TSC in the cervical myelopathy group (39 ± 10 mM) relative to healthy controls (35 ± 13 mM) was found. However, patients had a significantly lower cord-cross-sectional area than controls (70 ± 9 mm 2 vs. 82 ± 9 mm 2 , p  < 0.001). Lower-extremity function positively correlated with intracellular volume fraction ( p  = 0.031). In conclusion, using 23 Na-MRS, TSC in cervical myelopathy patients was successfully measured. Differences in TSC relative to healthy controls did not reach significance, despite a significant reduction in cord-cross-sectional area. However, lower intracellular volume fraction, indicating reduced neurite density distal to the site of injury, was associated with physical impairment.

Background Shear Wave Elastography (SWE) imaging is a novel ultrasound technique for quantifying tissue elasticity. Studies have demonstrated that SWE is able to differentiate between diseased and normal tissue in a wide range clinical applications. However its applicability to atherosclerotic carotid disease has not been established. The aim of this study was to assess the feasibility and potential clinical benefit of using SWE imaging for the assessment of carotid plaques. Methods Eighty-one patients (mean age 76 years, 51 male) underwent greyscale and SWE imaging. Elasticity was quantified by measuring mean Young’s Modulus (YM) within the plaque and within the vessel wall. Echogenicity was assessed using the Gray-Weale classification scale and the greyscale median (GSM). Results Fifty four plaques with stenosis greater than 30% were assessed. Reproducibility of YM measurements, quantified by the inter-frame coefficient of variation, was 22% within the vessel wall and 19% within the carotid plaque. Correlation with percentage stenosis was significant for plaque YM (p = 0.003), but insignificant for plaque GSM (p = 0.46). Plaques associated with focal neurological symptoms had significantly lower mean YM than plaques in asymptomatic patients (62 kPa vs 88 kPa; p = 0.01). Logistic regression and Receiver Operating Characteristic (ROC) analysis showed improvements in sensitivity and specificity when percentage stenosis was combined with the YM (area under ROC = 0.78). Conclusions Our study showed SWE is able to quantify carotid plaque elasticity and provide additional information that may be of clinical benefit to help identify the unstable carotid plaque.

Quantitative maps obtained with diffusion weighted (DW) imaging, such as fractional anisotropy (FA) -calculated by fitting the diffusion tensor (DT) model to the data,-are very useful to study neurological diseases. To fit this map accurately, acquisition times of the order of several minutes are needed because many noncollinear DW volumes must be acquired to reduce directional biases. Deep learning (DL) can be used to reduce acquisition times by reducing the number of DW volumes. We already developed a DL network named \"one-minute FA,\" which uses 10 DW volumes to obtain FA maps, maintaining the same characteristics and clinical sensitivity of the FA maps calculated with the standard method using more volumes. Recent publications have indicated that it is possible to train DL networks and obtain FA maps even with 4 DW input volumes, far less than the minimum number of directions for the mathematical estimation of the DT. Here we investigated the impact of reducing the number of DW input volumes to 4 or 7, and evaluated the performance and clinical sensitivity of the corresponding DL networks trained to calculate FA, while comparing results also with those using our one-minute FA. Each network training was performed on the human connectome project open-access dataset that has a high resolution and many DW volumes, used to fit a ground truth FA. To evaluate the generalizability of each network, they were tested on two external clinical datasets, not seen during training, and acquired on different scanners with different protocols, as previously done. Using 4 or 7 DW volumes, it was possible to train DL networks to obtain FA maps with the same range of values as ground truth - map, only when using HCP test data; pathological sensitivity was lost when tested using the external clinical datasets: indeed in both cases, no consistent differences were found between patient groups. On the contrary, our \"one-minute FA\" did not suffer from the same problem. When developing DL networks for reduced acquisition times, the ability to generalize and to generate quantitative biomarkers that provide clinical sensitivity must be addressed.

Fractional anisotropy (FA) is a quantitative map sensitive to microstructural properties of tissue in-vivo that is extensively used to study the healthy and pathological brain. This map is classically calculated by model fitting (standard method) and requires many diffusion-weighted (DW) images for data quality and unbiased readings, hence needing acquisition times of several minutes. Here we adapted the U-net architecture to be generalized and to obtain good quality FA from DW volumes acquired in one minute. Our network requires 10 input DW volumes (hence fast acquisition), is robust to the direction of application of the diffusion gradients (hence generalised) and preserves/improves maps quality (hence good quality maps). We trained the network on the human connectome project (HCP) data, using standard model fitting on the entire set of DW directions to extract FA (ground truth). We addressed the generalization problem i.e., we trained the network to be applicable, without re-training, to clinical datasets acquired on different scanners with different DW imaging protocols. The network was applied to two different clinical datasets to assess FA quality and sensitivity to pathology in temporal lobe epilepsy and multiple sclerosis, respectively. For HCP data, when compared to the ground truth FA, the FA obtained from 10 DW volumes using the network was significantly better (p<10-4) than the FA obtained using the standard pipeline. For the clinical datasets, the network FA retained the same microstructural characteristics as the FA calculated with all DW volumes using the standard method. At subject level, the comparison between white matter (WM) ground truth FA values and network FA showed the same distribution; at group level, statistical differences of WM values detected in the clinical datasets with the ground truth FA were reproduced when using values from the network FA, i.e. the network retained sensitivity to pathology. In conclusion, the proposed network provides a clinically available method to obtain FA from a generic set of 10 DW volumes acquirable in one minute, augmenting data quality compared to direct model fitting, reducing the possibility of bias from a sub-sampled data and retaining FA pathological sensitivity, which is very attractive for clinical applications.

Objective We investigated the effects of adding regions to current dissemination in space (DIS) criteria for multiple sclerosis (MS). Methods Participants underwent brain, optic nerve, and spinal cord MRI. Baseline DIS was assessed by 2017 McDonald criteria and versions including optic nerve, temporal lobe, or corpus callosum as a fifth region (requiring 2/5), a version with all regions (requiring 3/7) and optic nerve variations requiring 3/5 and 4/5 regions. Performance was evaluated against MS diagnosis (2017 McDonald criteria) during follow‐up. Results Eighty‐four participants were recruited (53F, 32.8 ± 7.1 years). 2017 McDonald DIS criteria were 87% sensitive (95% CI: 76–94), 73% specific (50–89), and 83% accurate (74–91) in identifying MS. Modified criteria with optic nerve improved sensitivity to 98% (91–100), with specificity 33% (13–59) and accuracy 84% (74–91). Criteria including temporal lobe showed sensitivity 94% (84–98), specificity 50% (28–72), and accuracy 82% (72–90); criteria including corpus callosum showed sensitivity 90% (80–96), specificity 68% (45–86), and accuracy 85% (75–91). Criteria adding all three regions (3/7 required) had sensitivity 95% (87–99), specificity 55% (32–76), and accuracy 85% (75–91). When requiring 3/5 regions (optic nerve as the fifth), sensitivity was 82% (70–91), specificity 77% (55–92), and accuracy 81% (71–89); with 4/5 regions, sensitivity was 56% (43–69), specificity 95% (77–100), and accuracy 67% (56–77). Interpretation Optic nerve inclusion increased sensitivity while lowering specificity. Increasing required regions in optic nerve criteria increased specificity and decreased sensitivity. Results suggest considering the optic nerve for DIS. An option of 3/5 or 4/5 regions preserved specificity, and criteria adding all three regions had highest accuracy.

Objective In multiple sclerosis chronic demyelination is associated with axonal loss, and ultimately contributes to irreversible progressive disability. Enhancing remyelination may slow, or even reverse, disability. We recently trialled bexarotene versus placebo in 49 people with multiple sclerosis. While the primary MRI outcome was negative, there was converging neurophysiological and MRI evidence of efficacy. Multiple factors influence lesion remyelination. In this study we undertook a systematic exploratory analysis to determine whether treatment response – measured by change in magnetisation transfer ratio – is influenced by location (tissue type and proximity to CSF) or the degree of abnormality (using baseline magnetisation transfer ratio and T1 values). Methods We examined treatment effects at the whole lesion level, the lesion component level (core, rim and perilesional tissues) and at the individual lesion voxel level. Results At the whole lesion level, significant treatment effects were seen in GM but not WM lesions. Voxel‐level analyses detected significant treatment effects in WM lesion voxels with the lowest baseline MTR, and uncovered gradients of treatment effect in both WM and CGM lesional voxels, suggesting that treatment effects were lower near CSF spaces. Finally, larger treatment effects were seen in the outer and surrounding components of GM lesions compared to inner cores. Interpretation Remyelination varies markedly within and between lesions. The greater remyelinating effect in GM lesions is congruent with neuropathological observations. For future remyelination trials, whole GM lesion measures require less complex post‐processing compared to WM lesions (which require voxel level analyses) and markedly reduce sample sizes.

Objective To assess the pathological mechanisms contributing to white matter (WM) lesion expansion or contraction and remyelination in multiple sclerosis (MS). Methods We assessed 1,613 lesions in 49 people with relapsing–remitting MS in the CCMR‐One bexarotene trial (EudraCT 2014‐003145‐99). We measured lesion orientation relative to WM tracts, surface‐in gradients and veins. Jacobian deformation was used to assess lesion expansion over 6 months, while magnetization transfer ratio (MTR) imaging was used to assess remyelination. Results At baseline, 33% of lesions were aligned with veins, 2% along WM tracts, 0% with surface‐in gradients, and 4% orthogonal to veins. No significant differences were observed in lesion shape, while lesions aligned with surface‐in gradients and with veins had lower volume compared to all remaining orientations. At follow‐up, 13% of lesions expanded and 7% contracted. The directions for both expansion and contraction were 18% and 8%, respectively, along WM tracts, 20% and 15% parallel to veins, 22% and 23% orthogonal to veins and 0% and 1% along surface‐in gradients. Bexarotene had no effect on lesion expansion or contraction, but MTR significantly increased in lesions aligned with surface‐in gradients and veins. Interpretation Lesion expansion and shrinkage are affected by venous and WM tract factors, but these do not influence bexarotene's capacity to promote remyelination. This, instead, appears to be affected by surface‐in factors. To limit lesion expansion and maximize tissue repair, multiple processes may need to be targeted. By applying the tensor model, we analysed lesion orientation and the directionality of lesion expansion/contraction in multiple sclerosis. Each lesion is summarized as an ellipsoid, and the tensor model is applied to calculate lesion anisotropy. From the top to the bottom white matter atlas, surface‐in gradient segmentation and venous atlas used in the present study are represented together with examples of lesions aligned with each orientation assessed.