Anti-CD20 therapy depletes activated myelin-specific CD8+ T cells in multiple sclerosis.

CD8+ T cells are believed to play an important role in multiple sclerosis (MS), yet their role in MS pathogenesis remains poorly defined. Although myelin proteins are considered potential autoantigenic targets, prior studies of myelin-reactive CD8+ T cells in MS have relied on in vitro stimulation, thereby limiting accurate measurement of their ex vivo precursor frequencies and phenotypes. Peptide:MHC I tetramers were used to identify and validate 5 myelin CD8+ T cell epitopes, including 2 newly described determinants in humans. The validated tetramers were used to measure the ex vivo precursor frequencies and phenotypes of myelin-specific CD8+ T cells in the peripheral blood of untreated MS patients and HLA allele-matched healthy controls. In parallel, CD8+ T cell responses against immunodominant influenza epitopes were also measured. There were no differences in ex vivo frequencies of tetramer-positive myelin-specific CD8+ T cells between MS patients and control subjects. An increased proportion of myelin-specific CD8+ T cells in MS patients exhibited a memory phenotype and expressed CD20 compared to control subjects, while there were no phenotypic differences observed among influenza-specific CD8+ T cells. Longitudinal assessments were also measured in a subset of MS patients subsequently treated with anti-CD20 monoclonal antibody therapy. The proportion of memory and CD20+ CD8+ T cells specific for certain myelin but not influenza epitopes was significantly reduced following anti-CD20 treatment. This study, representing a characterization of unmanipulated myelin-reactive CD8+ T cells in MS, indicates these cells may be attractive targets in MS therapy

Optical coherence tomography in multiple sclerosis: A 3-year prospective multicenter study

Prospective multicenter study; Multiple sclerosis; TomographyEstudi prospectiu multicèntric; Esclerosi múltiple; TomografiaEstudio multicéntrico prospectivo; Esclerosis múltiple; TomografíaObjective To evaluate changes over 3 years in the thickness of inner retinal layers including the peripapillary retinal nerve fiber layer (pRNFL), and combined macular ganglion cell and inner plexiform layers (mGCIPL), in individuals with relapsing-remitting multiple sclerosis (RRMS) versus healthy controls; to determine whether optical coherence tomography (OCT) is sufficiently sensitive and reproducible to detect small degrees of neuroaxonal loss over time that correlate with changes in brain volume and disability progression as measured by the Expanded Disability Status Scale (EDSS). Methods Individuals with RRMS from 28 centers (n = 333) were matched with 64 healthy participants. OCT scans were performed on Heidelberg Spectralis machines (at baseline; 1 month; 6 months; 6-monthly thereafter). Results OCT measurements were highly reproducible between baseline and 1 month (intraclass correlation coefficient >0.98). Significant inner retinal layer thinning was observed in individuals with multiple sclerosis (MS) compared with controls regardless of previous MS-associated optic neuritis––group differences (95% CI) over 3 years: pRNFL: −1.86 (−2.54, −1.17) µm; mGCIPL: −2.03 (−2.78, −1.28) µm (both p 5 years (pRNFL: p < 0.05; mGCIPL: p < 0.01). Brain volume decreased by 1.3% in individuals with MS over 3 years compared to 0.5% in control subjects (effect size 0.76). mGCIPL atrophy correlated with brain atrophy (p < 0.0001). There was no correlation of OCT data with disability progression. Interpretation OCT has potential to estimate rates of neurodegeneration in the retina and brain. The effect size for OCT, smaller than for magnetic resonance imaging based on Heidelberg Spectralis data acquired in this study, was increased in early disease.The authors wish to thank Carolyn M. Ervin for her substantial contribution in the data analyses, as well as Mark Kirby, Aisling Towell, and Marie-Catherine Mousseau (Novartis Ireland Ltd.) for their writing support, funded by Novartis Pharma AG, Basel, Switzerland. FB is supported by the NIHR biomedical research center at UCLH

Intensity Inhomogeneity Correction of SD-OCT Data Using Macular Flatspace

Images of the retina acquired using optical coherence tomography (OCT) often suffer from intensity inhomogeneity problems that degrade both the quality of the images and the performance of automated algorithms utilized to measure structural changes. This intensity variation has many causes, including off-axis acquisition, signal attenuation, multi-frame averaging, and vignetting, making it difficult to correct the data in a fundamental way. This paper presents a method for inhomogeneity correction by acting to reduce the variability of intensities within each layer. In particular, the N3 algorithm, which is popular in neuroimage analysis, is adapted to work for OCT data. N3 works by sharpening the intensity histogram, which reduces the variation of intensities within different classes. To apply it here, the data are first converted to a standardized space called macular flat space (MFS). MFS allows the intensities within each layer to be more easily normalized by removing the natural curvature of the retina. N3 is then run on the MFS data using a modified smoothing model, which improves the efficiency of the original algorithm. We show that our method more accurately corrects gain fields on synthetic OCT data when compared to running N3 on non-flattened data. It also reduces the overall variability of the intensities within each layer, without sacrificing contrast between layers, and improves the performance of registration between OCT images

Applying an Open-Source Segmentation Algorithm to Different OCT Devices in Multiple Sclerosis Patients and Healthy Controls: Implications for Clinical Trials

Background. The lack of segmentation algorithms operative across optical coherence tomography (OCT) platforms hinders utility of retinal layer measures in MS trials. Objective. To determine cross-sectional and longitudinal agreement of retinal layer thicknesses derived from an open-source, fully-automated, segmentation algorithm, applied to two spectral-domain OCT devices. Methods. Cirrus HD-OCT and Spectralis OCT macular scans from 68 MS patients and 22 healthy controls were segmented. A longitudinal cohort comprising 51 subjects (mean follow-up: 1.4 ± 0.9 years) was also examined. Bland-Altman analyses and interscanner agreement indices were utilized to assess agreement between scanners. Results. Low mean differences (−2.16 to 0.26 μm) and narrow limits of agreement (LOA) were noted for ganglion cell and inner and outer nuclear layer thicknesses cross-sectionally. Longitudinally we found low mean differences (−0.195 to 0.21 μm) for changes in all layers, with wider LOA. Comparisons of rate of change in layer thicknesses over time revealed consistent results between the platforms. Conclusions. Retinal thickness measures for the majority of the retinal layers agree well cross-sectionally and longitudinally between the two scanners at the cohort level, with greater variability at the individual level. This open-source segmentation algorithm enables combining data from different OCT platforms, broadening utilization of OCT as an outcome measure in MS trials

Peginterferon beta-1a improves MRI measures and increases the proportion of patients with no evidence of disease activity in relapsing-remitting multiple sclerosis: 2-year results from the ADVANCE randomized controlled trial

Summary of MRI endpoints over 2 years by original randomisation group [6]. (DOC 53kb

OASIS is Automated Statistical Inference for Segmentation, with applications to multiple sclerosis lesion segmentation in MRI☆

Magnetic resonance imaging (MRI) can be used to detect lesions in the brains of multiple sclerosis (MS) patients and is essential for diagnosing the disease and monitoring its progression. In practice, lesion load is often quantified by either manual or semi-automated segmentation of MRI, which is time-consuming, costly, and associated with large inter- and intra-observer variability. We propose OASIS is Automated Statistical Inference for Segmentation (OASIS), an automated statistical method for segmenting MS lesions in MRI studies. We use logistic regression models incorporating multiple MRI modalities to estimate voxel-level probabilities of lesion presence. Intensity-normalized T1-weighted, T2-weighted, fluid-attenuated inversion recovery and proton density volumes from 131 MRI studies (98 MS subjects, 33 healthy subjects) with manual lesion segmentations were used to train and validate our model. Within this set, OASIS detected lesions with a partial area under the receiver operating characteristic curve for clinically relevant false positive rates of 1% and below of 0.59% (95% CI; [0.50%, 0.67%]) at the voxel level. An experienced MS neuroradiologist compared these segmentations to those produced by LesionTOADS, an image segmentation software that provides segmentation of both lesions and normal brain structures. For lesions, OASIS out-performed LesionTOADS in 74% (95% CI: [65%, 82%]) of cases for the 98 MS subjects. To further validate the method, we applied OASIS to 169 MRI studies acquired at a separate center. The neuroradiologist again compared the OASIS segmentations to those from LesionTOADS. For lesions, OASIS ranked higher than LesionTOADS in 77% (95% CI: [71%, 83%]) of cases. For a randomly selected subset of 50 of these studies, one additional radiologist and one neurologist also scored the images. Within this set, the neuroradiologist ranked OASIS higher than LesionTOADS in 76% (95% CI: [64%, 88%]) of cases, the neurologist 66% (95% CI: [52%, 78%]) and the radiologist 52% (95% CI: [38%, 66%]). OASIS obtains the estimated probability for each voxel to be part of a lesion by weighting each imaging modality with coefficient weights. These coefficients are explicit, obtained using standard model fitting techniques, and can be reused in other imaging studies. This fully automated method allows sensitive and specific detection of lesion presence and may be rapidly applied to large collections of images

Rapid Brain Meninges Surface Reconstruction with Layer Topology Guarantee

The meninges, located between the skull and brain, are composed of three membrane layers: the pia, the arachnoid, and the dura. Reconstruction of these layers can aid in studying volume differences between patients with neurodegenerative diseases and normal aging subjects. In this work, we use convolutional neural networks (CNNs) to reconstruct surfaces representing meningeal layer boundaries from magnetic resonance (MR) images. We first use the CNNs to predict the signed distance functions (SDFs) representing these surfaces while preserving their anatomical ordering. The marching cubes algorithm is then used to generate continuous surface representations; both the subarachnoid space (SAS) and the intracranial volume (ICV) are computed from these surfaces. The proposed method is compared to a state-of-the-art deformable model-based reconstruction method, and we show that our method can reconstruct smoother and more accurate surfaces using less computation time. Finally, we conduct experiments with volumetric analysis on both subjects with multiple sclerosis and healthy controls. For healthy and MS subjects, ICVs and SAS volumes are found to be significantly correlated to sex (p<0.01) and age (p<0.03) changes, respectively.Comment: ISBI 2023 Ora

Normalization Techniques for Statistical Inference from Magnetic Resonance Imaging

While computed tomography and other imaging techniques are measured in absolute units with physical meaning, magnetic resonance images are expressed in arbitrary units that are difficult to interpret and differ between study visits and subjects. Much work in the image processing literature on intensity normalization has focused on histogram matching and other histogram mapping techniques, with little emphasis on normalizing images to have biologically interpretable units. Furthermore, there are no formalized principles or goals for the crucial comparability of image intensities within and across subjects. To address this, we propose a set of criteria necessary for the normalization of images. We further propose simple and robust biologically motivated normalization techniques for multisequence brain imaging that have the same interpretation across acquisitions and satisfy the proposed criteria. We compare the performance of different normalization methods in thousands of images of patients with Alzheimer\u27s Disease, hundreds of patients with multiple sclerosis, and hundreds of healthy subjects obtained in several different studies at dozens of imaging centers

Statistical normalization techniques for magnetic resonance imaging☆☆☆

While computed tomography and other imaging techniques are measured in absolute units with physical meaning, magnetic resonance images are expressed in arbitrary units that are difficult to interpret and differ between study visits and subjects. Much work in the image processing literature on intensity normalization has focused on histogram matching and other histogram mapping techniques, with little emphasis on normalizing images to have biologically interpretable units. Furthermore, there are no formalized principles or goals for the crucial comparability of image intensities within and across subjects. To address this, we propose a set of criteria necessary for the normalization of images. We further propose simple and robust biologically motivated normalization techniques for multisequence brain imaging that have the same interpretation across acquisitions and satisfy the proposed criteria. We compare the performance of different normalization methods in thousands of images of patients with Alzheimer's disease, hundreds of patients with multiple sclerosis, and hundreds of healthy subjects obtained in several different studies at dozens of imaging centers