Niamh Cawley

Reduced gamma-aminobutyric acid concentration is associated with physical disability in progressive multiple sclerosis.

Neurodegeneration is thought to be the major cause of ongoing, irreversible disability in progressive stages of multiple sclerosis. Gamma-aminobutyric acid is the principle inhibitory neurotransmitter in the brain. The aims of this study were to investigate if gamma-aminobutyric acid levels (i) are abnormal in patients with secondary progressive multiple sclerosis compared with healthy controls; and (ii) correlate with physical and cognitive performance in this patient population. Thirty patients with secondary progressive multiple sclerosis and 17 healthy control subjects underwent single-voxel MEGA-PRESS (MEscher-GArwood Point RESolved Spectroscopy) magnetic resonance spectroscopy at 3 T, to quantify gamma-aminobutyric acid levels in the prefrontal cortex, right hippocampus and left sensorimotor cortex. All subjects were assessed clinically and underwent a cognitive assessment. Multiple linear regression models were used to compare differences in gamma-aminobutyric acid concentrations between patients and controls adjusting for age, gender and tissue fractions within each spectroscopic voxel. Regression was used to examine the relationships between the cognitive function and physical disability scores specific for these regions with gamma-aminobuytric acid levels, adjusting for age, gender, and total N-acetyl-aspartate and glutamine-glutamate complex levels. When compared with controls, patients performed significantly worse on all motor and sensory tests, and were cognitively impaired in processing speed and verbal memory. Patients had significantly lower gamma-aminobutyric acid levels in the hippocampus (adjusted difference = -0.403 mM, 95% confidence intervals -0.792, -0.014, P = 0.043) and sensorimotor cortex (adjusted difference = -0.385 mM, 95% confidence intervals -0.667, -0.104, P = 0.009) compared with controls. In patients, reduced motor function in the right upper and lower limb was associated with lower gamma-aminobutyric acid concentration in the sensorimotor cortex. Specifically for each unit decrease in gamma-aminobutyric acid levels (in mM), there was a predicted -10.86 (95% confidence intervals -16.786 to -4.482) decrease in grip strength (kg force) (P < 0.001) and -8.74 (95% confidence intervals -13.943 to -3.015) decrease in muscle strength (P < 0.006). This study suggests that reduced gamma-aminobutyric acid levels reflect pathological abnormalities that may play a role in determining physical disability. These abnormalities may include decreases in the pre- and postsynaptic components of gamma-aminobutyric acid neurotransmission and in the density of inhibitory neurons. Additionally, the reduced gamma-aminobutyric acid concentration may contribute to the neurodegenerative process, resulting in increased firing of axons, with consequent increased energy demands, which may lead to neuroaxonal degeneration and loss of the compensatory mechanisms that maintain motor function. This study supports the idea that modulation of gamma-aminobutyric acid neurotransmission may be an important target for neuroprotection in multiple sclerosis.See De Stefano and Giorgio (doi:10.1093/brain/awv213) for a scientific commentary on this article.

Longitudinal multiple sclerosis lesion segmentation: Resource and challenge

Abstract In conjunction with the ISBI 2015 conference, we organized a longitudinal lesion segmentation challenge providing training and test data to registered participants. The training data consisted of five subjects with a mean of 4.4 time‐points, and test data of fourteen subjects with a mean of 4.4 time‐points. All 82 data sets had the white matter lesions associated with multiple sclerosis delineated by two human expert raters. Eleven teams submitted results using state‐of‐the‐art lesion segmentation algorithms to the challenge, with ten teams presenting their results at the conference. We present a quantitative evaluation comparing the consistency of the two raters as well as exploring the performance of the eleven submitted results in addition to three other lesion segmentation algorithms. The challenge presented three unique opportunities: (1) the sharing of a rich data set; (2) collaboration and comparison of the various avenues of research being pursued in the community; and (3) a review and refinement of the evaluation metrics currently in use. We report on the performance of the challenge participants, as well as the construction and evaluation of a consensus delineation. The image data and manual delineations will continue to be available for download, through an evaluation website2 as a resource for future researchers in the area. This data resource provides a platform to compare existing methods in a fair and consistent manner to each other and multiple manual raters. HighlightsPublic lesion data base of 21 training data sets and 61 testing data sets.Fully automated evaluation website.Comparison between 14 state‐of‐the‐art algorithms and 2 manual delineators.

Machine learning based compartment models with permeability for white matter microstructure imaging

Abstract Some microstructure parameters, such as permeability, remain elusive because mathematical models that express their relationship to the MR signal accurately are intractable. Here, we propose to use computational models learned from simulations to estimate these parameters. We demonstrate the approach in an example which estimates water residence time in brain white matter. The residence time &tgr;i of water inside axons is a potentially important biomarker for white matter pathologies of the human central nervous system, as myelin damage is hypothesised to affect axonal permeability, and thus &tgr;i. We construct a computational model using Monte Carlo simulations and machine learning (specifically here a random forest regressor) in order to learn a mapping between features derived from diffusion weighted MR signals and ground truth microstructure parameters, including &tgr;i. We test our numerical model using simulated and in vivo human brain data. Simulation results show that estimated parameters have strong correlations with the ground truth parameters (Symbol) for volume fraction, residence time, axon radius and diffusivity respectively), and provide a marked improvement over the most widely used Kärger model (Symbol). The trained model also estimates sensible microstructure parameters from in vivo human brain data acquired from healthy controls, matching values found in literature, and provides better reproducibility than the Kärger model on both the voxel and ROI level. Finally, we acquire data from two Multiple Sclerosis (MS) patients and compare to the values in healthy subjects. We find that in the splenium of corpus callosum (CC‐S) the estimate of the residence time is 0.57±0.05 s for the healthy subjects, while in the MS patient with a lesion in CC‐S it is 0.33±0.12 s in the normal appearing white matter (NAWM) and 0.19±0.11 s in the lesion. In the corticospinal tracts (CST) the estimate of the residence time is 0.52±0.09 s for the healthy subjects, while in the MS patient with a lesion in CST it is 0.56±0.05 s in the NAWM and 0.13±0.09 s in the lesion. These results agree with our expectations that the residence time in lesions would be lower than in NAWM because the loss of myelin should increase permeability. Overall, we find parameter estimates in the two MS patients consistent with expectations from the pathology of MS lesions demonstrating the clinical potential of this new technique. Symbol. No caption available. Symbol. No caption available. HighlightsSome tissue parameters remain elusive because mathematical models are intractable.We propose to use machine learning to estimate these parameters, here permeability.Simulation results show an excellent agreement between estimations and ground truth.New technique performs better than the standard Karger Model.In‐vivo results consistent with pathology of MS lesions showing clinical potential.

Spinal cord atrophy as a primary outcome measure in phase II trials of progressive multiple sclerosis

Objectives: To measure the development of spinal cord (SC) atrophy over 1 year in patients with progressive multiple sclerosis (PMS) and determine the sample sizes required to demonstrate a reduction in spinal cord cross-sectional area (SC-CSA) as an outcome measure in clinical trials. Methods: In total, 44 PMS patients (26 primary progressive multiple sclerosis (PPMS), 18 secondary progressive multiple sclerosis (SPMS)) and 29 healthy controls (HCs) were studied at baseline and 12 months. SC-CSA was measured using the three-dimensional (3D) fast field echo sequences acquired at 3T and the active surface model. Multiple linear regressions were used to investigate changes in imaging measurements. Results: PPMS patients had shorter disease duration, lower Expanded Disability Status Scale (EDSS) and larger SC-CSA than SPMS patients. All patients together showed a significantly greater decrease in percentage SC-CSA change than HCs, which was driven by the PPMS. All patients deteriorated over 1 year, but no association was found between percentage SC-CSA change and clinical changes. The sample size per arm required to detect a 50% treatment effect over 1 year, at 80% power, was 57 for PPMS and 546 for SPMS. Conclusion: SC-CSA may become an outcome measure in trials of PPMS patients, when they are at an early stage of the disease, have moderate disability and modest SC atrophy.

The pathophysiology of symptomatic propriospinal myoclonus

Propriospinal myoclonus (PSM) is the most common form of spinal myoclonus. It was first described in 1991 by Brown et al. as a movement disorder characterized by the occurrence of repetitive, nonrhythmic, usually flexor, jerks of the trunk, which could also involve the neck and the upper or the lower limbs. On the basis of neurophysiological studies, it was hypothesized to arise from a spinal generator, most commonly at the thoracic level, abnormally recruiting in a constant order the axial muscles up and down the spinal cord via slow intrinsic propriospinal pathways. Patients with PSM have been classified into two broad categories: those affected with idiopathic PSM (ie, primary, in which no underlying cause is found), and those affected with symptomatic PSM. However, the exact pathophysiology underlying both idiopathic and symptomatic PSM is unknown. Recently, independent groups have produced evidence that most, if not all, patients presumed to be affected with idiopathic PSM have a functional (psychogenic) origin for their jerks. Up to 85% of those patients had a Bereitschafts-potential (BP; from German, “readiness potential,” also called the premotor potential) before their jerk, and even in those who did not, the subsequent clinical course was strongly suggestive of a functional movement disorder (FMD). Moreover, the “typical” polymyographic pattern of PSM can be mimicked voluntarily. Patients with symptomatic PSM represent a potentially good model to better understand the pathophysiology of PSM. Unfortunately, literature on symptomatic PSM has been so far limited to a few anecdotal reports. Symptomatic PSM has been associated with a broad range of conditions (putatively considered responsible for the appearance of the jerks), including presence of spinal structural lesions (SL), vitamin deficiency, general and spinal anesthesia, infective disorders, and use of cannabis. The seemingly uncontroversial link that has been made between PSM and such different conditions hides instead considerable ambiguity about the pathophysiological mechanism underlying PSM. Here, we critically reevaluate the reported cases of symptomatic PSM and the suggested hypotheses regarding the presence of the propriospinal pathway in humans, aiming to provide some clarity on the pathophysiology of PSM.

Deep gray matter volume loss drives disability worsening in multiple sclerosis

Gray matter (GM) atrophy occurs in all multiple sclerosis (MS) phenotypes. We investigated whether there is a spatiotemporal pattern of GM atrophy that is associated with faster disability accumulation in MS.

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

Background: 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. Objectives: The purpose of this study was to investigate how 3 T MRI affects the agreement between raters on lesion detection and diagnosis. Methods: We selected 30 patients and 10 healthy controls from our ongoing prospective multicentre cohort. All subjects received baseline 1.5 and 3 T brain and spinal cord MRI. Patients also received follow-up brain MRI at 3–6 months. Four experienced neuroradiologists and four less-experienced raters scored the number of lesions per anatomical region and determined dissemination in space and time (McDonald 2010). Results: In controls, the mean number of lesions per rater was 0.16 at 1.5 T and 0.38 at 3 T (p = 0.005). For patients, this was 4.18 and 4.40, respectively (p = 0.657). Inter-rater agreement on involvement per anatomical region and dissemination in space and time was moderate to good for both field strengths. 3 T slightly improved agreement between experienced raters, but slightly decreased agreement between less-experienced raters. Conclusion: Overall, the interobserver agreement was moderate to good. 3 T appears to improve the reading for experienced readers, underlining the benefit of additional training.

Progression of regional grey matter atrophy in multiple sclerosis

See Stankoff and Louapre (doi:10.1093/brain/awy114) for a scientific commentary on this article. Grey matter atrophy in multiple sclerosis affects certain areas preferentially. Eshaghi et al. use a data-driven computational model to predict the order in which regions atrophy, and use this sequence to stage patients. Atrophy begins in deep grey matter nuclei and posterior cortical regions, before spreading to other cortical areas.

Three-Tesla MRI does not improve the diagnosis of multiple sclerosis: A multicenter study

Objective In the work-up of patients presenting with a clinically isolated syndrome (CIS), 3T MRI might offer a higher lesion detection than 1.5T, but it remains unclear whether this affects the fulfilment of the diagnostic criteria for multiple sclerosis (MS). Methods We recruited 66 patients with CIS within 6 months from symptom onset and 26 healthy controls in 6 MS centers. All participants underwent 1.5T and 3T brain and spinal cord MRI at baseline according to local optimized protocols and the MAGNIMS guidelines. Patients who had not converted to MS during follow-up received repeat brain MRI at 3–6 months and 12–15 months. The number of lesions per anatomical region was scored by 3 raters in consensus. Criteria for dissemination in space (DIS) and dissemination in time (DIT) were determined according to the 2017 revisions of the McDonald criteria. Results Three-Tesla MRI detected 15% more T2 brain lesions compared to 1.5T (p < 0.001), which was driven by an increase in baseline detection of periventricular (12%, p = 0.015), (juxta)cortical (21%, p = 0.005), and deep white matter lesions (21%, p < 0.001). The detection rate of spinal cord lesions and gadolinium-enhancing lesions did not differ between field strengths. Three-Tesla MRI did not lead to a higher number of patients fulfilling the criteria for DIS or DIT, or subsequent diagnosis of MS, at any of the 3 time points. Conclusion Scanning at 3T does not influence the diagnosis of MS according to McDonald diagnostic criteria.

Fully Automated Patch-Based Image Restoration: Application to Pathology Inpainting

Pathology can have an important impact on MRI analysis. Specifically, white matter hyper-intensities, tumours, infarcts, etc., can influence the results of various image analysis techniques such as segmentation and registration. Several algorithms have been proposed for image inpainting and restoration, mainly in the context of Multiple Sclerosis lesions. These techniques commonly rely on a set of manually segmented pathological regions for inpainting. Rather than relying on prior segmentations for image restoration, we present a combined segmentation and inpainting algorithm for multimodal images. The proposed method is based on an iterative collaboration between two patch-based techniques, PatchMatch and Non-Local Means, where the former is used to estimate the most probable location of the pathological outliers and the latter to gradually fill the segmented areas with the most plausible multimodal texture. We demonstrate that the proposed method is able to automatically restore multimodal intensities in pathological regions within the context of Multiple Sclerosis.