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Background and purposeLesion load is a common biomarker in multiple sclerosis, yet it has historically shown modest association with clinical outcome. Lesion count, which encapsulates the natural history of lesion formation and is thought to provide complementary information, is difficult to assess in patients with confluent (ie, spatially overlapping) lesions. We introduce a statistical technique for cross-sectionally counting pathologically distinct lesions.Materials and methodsMR imaging was used to assess the probability of a lesion at each location. The texture of this map was quantified using a novel technique, and clusters resembling the center of a lesion were counted. Validity compared with a criterion standard count was demonstrated in 60 subjects observed longitudinally, and reliability was determined using 14 scans of a clinically stable subject acquired at 7 sites.ResultsThe proposed count and the criterion standard count were highly correlated (r = 0.97, P < .001) and not significantly different (t59 = -.83, P = .41), and the variability of the proposed count across repeat scans was equivalent to that of lesion load. After accounting for lesion load and age, lesion count was negatively associated (t58 = -2.73, P < .01) with the Expanded Disability Status Scale. Average lesion size had a higher association with the Expanded Disability Status Scale (r = 0.35, P < .01) than lesion load (r = 0.10, P = .44) or lesion count (r = -.12, P = .36) alone.ConclusionsThis study introduces a novel technique for counting pathologically distinct lesions using cross-sectional data and demonstrates its ability to recover obscured longitudinal information. The proposed count allows more accurate estimation of lesion size, which correlated more closely with disability scores than either lesion load or lesion count alone.

Background and purposeMR imaging can be used to measure structural changes in the brains of individuals with multiple sclerosis and is essential for diagnosis, longitudinal monitoring, and therapy evaluation. The North American Imaging in Multiple Sclerosis Cooperative steering committee developed a uniform high-resolution 3T MR imaging protocol relevant to the quantification of cerebral lesions and atrophy and implemented it at 7 sites across the United States. To assess intersite variability in scan data, we imaged a volunteer with relapsing-remitting MS with a scan-rescan at each site.Materials and methodsAll imaging was acquired on Siemens scanners (4 Skyra, 2 Tim Trio, and 1 Verio). Expert segmentations were manually obtained for T1-hypointense and T2 (FLAIR) hyperintense lesions. Several automated lesion-detection and whole-brain, cortical, and deep gray matter volumetric pipelines were applied. Statistical analyses were conducted to assess variability across sites, as well as systematic biases in the volumetric measurements that were site-related.ResultsSystematic biases due to site differences in expert-traced lesion measurements were significant (P < .01 for both T1 and T2 lesion volumes), with site explaining >90% of the variation (range, 13.0-16.4 mL in T1 and 15.9-20.1 mL in T2) in lesion volumes. Site also explained >80% of the variation in most automated volumetric measurements. Output measures clustered according to scanner models, with similar results from the Skyra versus the other 2 units.ConclusionsEven in multicenter studies with consistent scanner field strength and manufacturer after protocol harmonization, systematic differences can lead to severe biases in volumetric analyses.

Multiple sclerosis (MS) is characterized by temporal and spatial dissemination of demyelinating lesions in the central nervous system. Associated neurodegenerative changes contributing to disability have been recognized even at early disease stages. Recent studies show the importance of gray matter damage for the accrual of clinical disability rather than white matter where demyelination is easily visualized by magnetic resonance imaging (MRI). The susceptibility to MS is influenced by genetic risk, but genetic factors associated with the disability are not known. We used MRI data to determine cortical thickness in 557 MS cases and 75 controls and in another cohort of 219 cases. We identified nine areas showing different thickness between cases and controls (regions of interest, ROI) (eight of them were negatively correlated with Kurtzke's expanded disability status scale, EDSS) and conducted genome-wide association studies (GWAS) in 464 and 211 cases available from the two data sets. No marker exceeded genome-wide significance in the discovery cohort. We next combined nominal statistical evidence of association with physical evidence of interaction from a curated human protein interaction network, and searched for subnetworks enriched with nominally associated genes and for commonalities between the two data sets. This network-based pathway analysis of GWAS detected gene sets involved in glutamate signaling, neural development and an adjustment of intracellular calcium concentration. We report here for the first time gene sets associated with cortical thinning of MS. These genes are potentially correlated with disability of MS. Common genetic variation is associated with cortical thickness in multiple sclerosis (MS) patients. Here we performed GWA on two independent cohorts of patients (n=675 in total). While no marker exceeded genome-wide significance, protein interaction based network analysis identified pathways involved in glutamate signaling, neural development and intracellular calcium concentration as significantly associated with cortical thickness in MS.

Objective There is increasing evidence that altered glutamate (Glu) homeostasis is involved in the pathophysiology of multiple sclerosis (MS). The aim of this study was to evaluate the in vivo effects of excess brain Glu on neuroaxonal integrity measured by N-acetylaspartate (NAA), brain volume, and clinical outcomes in a large, prospectively followed cohort of MS subjects. Methods We used multivoxel spectroscopy at 3T to longitudinally estimate Glu and NAA concentrations from large areas of normal-appearing white and gray matter (NAWM and GM) in MS patients (n = 343) with a mean follow-up time of 5 years. Using linear mixed-effects models, Glu was examined as a predictor of NAA decline, annualized percentage brain volume change, and evolution of clinical outcomes (Multiple Sclerosis Functional Composite [MSFC], Paced Auditory Serial Addition Test-3 [PASAT], and Expanded Disability Status Scale). Glu/NAA ratio was tested as a predictor of brain volume loss and clinical outcomes. Results Baseline Glu[NAWM] was predictive of accelerated longitudinal decline in NAA[GM] (-0.06mM change in NAA[GM]/yr for each unit increase in Glu; p = 0.004). The sustained elevation of Glu[NAWM] was predictive of a loss of 0.28mM/yr in NAA[NAWM] (p < 0.001) and 0.15mM/yr in NAA[GM] (p = 0.056). Each 10% increase in Glu/NAA [NAWM] was associated with a loss of 0.33% brain volume/yr (p = 0.001), 0.009 standard deviations/yr in MSFC z-score (p < 0.001), and 0.17 points/yr on the PASAT (p < 0.001). Interpretation These results indicate that higher Glu concentrations increase the rate of NAA decline, and higher Glu/NAA[NAWM] ratio increases the rate of decline of brain volume, MSFC, and PASAT. This provides evidence of a relationship between brain Glu and markers of disease progression in MS. © 2014 American Neurological Association.

ObjectA novel method of estimating metabolite T1 relaxation times using MR spectroscopic imaging (MRSI) is proposed. As opposed to conventional single-voxel metabolite T1 estimation methods, this method investigates regional and gray matter (GM)/white matter (WM) differences in metabolite T1 by taking advantage of the spatial distribution information provided by MRSI.Material and methodsThe method, validated by Monte Carlo studies, involves a voxel averaging to preserve the GM/WM distribution, a non-linear least squares fit of the metabolite T1 and an estimation of its standard error by bootstrapping. It was applied in vivo to estimate the T1 of N-acetyl compounds (NAA), choline, creatine and myo-inositol in eight normal volunteers, at 1.5 T, using a short echo time 2D-MRSI slice located above the ventricles.ResultsWM-T 1,NAA was significantly (P < 0.05) longer in anterior regions compared to posterior regions of the brain. The anterior region showed a trend of a longer WM T1 compared to GM for NAA, creatine and myo-Inositol. Lastly, accounting for the bootstrapped standard error estimate in a group mean T1 calculation yielded a more accurate T1 estimation.ConclusionThe method successfully measured in vivo metabolite T1 using MRSI and can now be applied to diseased brain.

In this paper, we discuss issues in verification, validation, and uncertainty prediction in computational fluid dynamics (CFD). Mesh adaptation is used to perform the systematic grid refinement studies required for code and simulation verification. We demonstrate the method of manufactured solutions (MMS) for code verification. Examples of successful and failed validation show that reliable prediction can only occur with an appropriate mathematical model of physics along with accurate numerical solution of the differential equations. The paper emphasizes good CFD practices to achieve systematic verification and put validation studies on solid grounds. Finally, the sensitivity equation method (SEM) is used to cascade input data uncertainty through the CFD code for estimating the uncertainty of the flow response. Key words: adaptive finite elements, verification, validation, uncertainty, Navier-Stokes. On discute des enjeux de verification, validation et de prediction d'incertitudes en CFD (<< computational fluid dynamics >>). On utilise des maillages adaptatifs lors des etudes systematiques de raffinement de maillages requises pour la verification de code et de simulation. On presente la methodes des solutions manufacturees pour la verification de code. Des exemples de verification reussie et echouee demontrent que des predictions fiables s'obtiennent uniquement avec un modele mathematique approprie et des solutions numeriques precises des equations differentielles. L'article met l'accent sur les bonnes pratiques en CFD pour assurer une verification systematique et la rigueur de la validation. Finalement, on utilise la methode de l'equation des sensibilites pour obtenir des estimes des incertitudes des predictions induites par celles des inputs. Mots-cles: elements finis adaptatifs, verification, validation, incertitude, Navier-Stokes. [Traduit par la Redaction], 1. Introduction The accurate and reliable prediction of turbulent flows has been the subject of much research by the computational fluid dynamics (CFD) community for many years. A review of [...]

Three issues related to good computational fluid dynamics (CFD) practice are discussed. First, adaptive methods are shown to be a simple tool to perform systematic grid refinement studies needed to achieve solutions with controlled accuracy (verification of simulations). Second, it is shown that the sensitivity equation method provides insights about which parameters critically affect the flow response. Finally, flow sensitivities are used to propagate model parameter uncertainties through the CFD code to yield uncertainty estimates of the CFD predictions. This provides a rigorous framework for comparing predictions to measurements (validation of predictions). These combined approaches help to build confidence in CFD predictions and develop good CFD practice. The resulting uncertainty bars put CFD on par with experimental techniques. The approaches are demonstrated on two-dimensional problems: a k-[epsilon] model of the flow in an annular turn-around duct and conjugate free convection with variable fluid properties. Taken together, these approaches offer a good prospect for developing families of computing methods that can be viewed as standards of good practice in CFD, ensuring that verification and validation studies are performed on solid grounds.