Nancy Sicotte

Estriol combined with glatiramer acetate for women with relapsing-remitting multiple sclerosis: a randomised, placebo-controlled, phase 2 trial

BACKGROUNDnRelapses of multiple sclerosis decrease during pregnancy, when the hormone estriol is increased. Estriol treatment is anti-inflammatory and neuroprotective in preclinical studies. In a small single-arm study of people with multiple sclerosis estriol reduced gadolinium-enhancing lesions and was favourably immunomodulatory. We assessed whether estriol treatment reduces multiple sclerosis relapses in women.nnnMETHODSnWe did a randomised, double-blind, placebo-controlled phase 2 trial at 16 academic neurology centres in the USA, between June 28, 2007, and Jan 9, 2014. Women aged 18-50 years with relapsing-remitting multiple sclerosis were randomly assigned (1:1) with a random permuted block design to either daily oral estriol (8 mg) or placebo, each in combination with injectable glatiramer acetate 20 mg daily. Patients and all study personnel, except for pharmacists and statisticians, were masked to treatment assignment. The primary endpoint was annualised relapse rate after 24 months, with a significance level of p=0.10. Relapses were confirmed by an increase in Expanded Disability Status Scale score assessed by an independent physician. Analysis was by intention to treat. The trial is registered with ClinicalTrials.gov, number NCT00451204.nnnFINDINGSnWe enrolled 164 patients: 83 were allocated to the estriol group and 81 were allocated to the placebo group. The annualised confirmed relapse rate was 0.25 relapses per year (95% CI 0.17-0.37) in the estriol group versus 0.37 relapses per year (0.25-0.53) in the placebo group (adjusted rate ratio 0.63, 95% CI 0.37-1.05; p=0.077). The proportion of patients with serious adverse events did not differ substantially between the estriol group and the placebo group (eight [10%] of 82 patients vs ten [13%] of 76 patients). Irregular menses were more common in the estriol group than in the placebo group (19 [23%] vs three [4%], p=0.0005), but vaginal infections were less common (one [1%] vs eight [11%], p=0.0117). There were no differences in breast fibrocystic disease, uterine fibroids, or endometrial lining thickness as assessed by clinical examination, mammogram, uterine ultrasound, or endometrial lining biopsy.nnnINTERPRETATIONnEstriol plus glatiramer acetate met our criteria for reducing relapse rates, and treatment was well tolerated over 24 months. These results warrant further investigation in a phase 3 trial.nnnFUNDINGnNational Institutes of Health, National Multiple Sclerosis Society, Conrad N Hilton Foundation, Jack H Skirball Foundation, Sherak Family Foundation, and the California Community Foundation.

Metric Optimization for Surface Analysis in the Laplace-Beltrami Embedding Space

In this paper, we present a novel approach for the intrinsic mapping of anatomical surfaces and its application in brain mapping research. Using the Laplace-Beltrami eigen-system, we represent each surface with an isometry invariant embedding in a high dimensional space. The key idea in our system is that we realize surface deformation in the embedding space via the iterative optimization of a conformal metric without explicitly perturbing the surface or its embedding. By minimizing a distance measure in the embedding space with metric optimization, our method generates a conformal map directly between surfaces with highly uniform metric distortion and the ability of aligning salient geometric features. Besides pairwise surface maps, we also extend the metric optimization approach for group-wise atlas construction and multi-atlas cortical label fusion. In experimental results, we demonstrate the robustness and generality of our method by applying it to map both cortical and hippocampal surfaces in population studies. For cortical labeling, our method achieves excellent performance in a cross-validation experiment with 40 manually labeled surfaces, and successfully models localized brain development in a pediatric study of 80 subjects. For hippocampal mapping, our method produces much more significant results than two popular tools on a multiple sclerosis study of 109 subjects.

Conformal metric optimization on surface (CMOS) for deformation and mapping in laplace-beltrami embedding space

In this paper we develop a novel technique for surface deformation and mapping in the high-dimensional Laplace-Beltrami embedding space. The key idea of our work is to realize surface deformation in the embedding space via optimization of a conformal metric on the surface. Numerical techniques are developed for computing derivatives of the eigenvalues and eigenfunctions with respect to the conformal metric, which is then applied to compute surface maps in the embedding space by minimizing an energy function. In our experiments, we demonstrate the robustness of our method by applying it to map hippocampal atrophy of multiple sclerosis patients with depression on a data set of 109 subjects. Statistically significant results have been obtained that show excellent correlation with clinical variables. A comparison with the popular SPHARM tool has also been performed to demonstrate that our method achieves more significant results.

Automated corpus callosum extraction via Laplace-Beltrami nodal parcellation and intrinsic geodesic curvature flows on surfaces

Corpus callosum (CC) is an important structure in human brain anatomy. In this work, we propose a fully automated and robust approach to extract corpus callosum from T1-weighted structural MR images. The novelty of our method is composed of two key steps. In the first step, we find an initial guess for the curve representation of CC by using the zero level set of the first nontrivial Laplace-Beltrami (LB) eigenfunction on the white matter surface. In the second step, the initial curve is deformed toward the final solution with a geodesic curvature flow on the white matter surface. For numerical solution of the geodesic curvature flow on surfaces, we represent the contour implicitly on a triangular mesh and develop efficient numerical schemes based on finite element method. Because our method depends only on the intrinsic geometry of the white matter surface, it is robust to orientation differences of the brain across population. In our experiments, we validate the proposed algorithm on 32 brains from a clinical study of multiple sclerosis disease and demonstrate that the accuracy of our results.

Volumetric Analysis from a Harmonized Multisite Brain MRI Study of a Single Subject with Multiple Sclerosis

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. They assessed intersite variability in scan data, by imaging a volunteer with relapsing-remitting MS with a scan-rescan at each site. In multicenter studies with consistent scanner field strength and manufacturer after protocol harmonization, systematic differences can lead to severe biases in volumetric analyses. BACKGROUND AND PURPOSE: MR 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 METHODS: All 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. RESULTS: Systematic 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. CONCLUSIONS: Even in multicenter studies with consistent scanner field strength and manufacturer after protocol harmonization, systematic differences can lead to severe biases in volumetric analyses.

Adult-onset glutaric aciduria type I presenting with white matter abnormalities and subependymal nodules

A 55-year-old female presented with a 6-year history of paresthesias, incontinence, spasticity, and gait abnormalities. Neuroimaging revealed white matter abnormalities associated with subependymal nodules. Biochemical evaluation noted increased serum C5-DC glutarylcarnitines and urine glutaric and 3-hydroxyglutaric acids. Evaluation of the glutaryl-CoA dehydrogenase (GCDH) gene revealed compound heterozygosity consisting of a novel variant (c.1219C>G; p.Leu407Val) and pathogenic mutation (c.848delT; p.L283fs). Together, these results were consistent with a diagnosis of adult-onset type I glutaric aciduria.

Gradient nonlinearity effects on upper cervical spinal cord area measurement from 3D T1‐weighted brain MRI acquisitions

To explore (i) the variability of upper cervical cord area (UCCA) measurements from volumetric brain 3D T1‐weighted scans related to gradient nonlinearity (GNL) and subject positioning; (ii) the effect of vendor‐implemented GNL corrections; and (iii) easily applicable methods that can be used to retrospectively correct data.

An Automated Statistical Technique for Counting Distinct Multiple Sclerosis Lesions

BACKGROUND AND PURPOSE: Lesion 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 METHODS: MR 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. RESULTS: The 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. CONCLUSIONS: This 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.

An automated statistical technique for counting distinct multiple sclerosis lesions can recover aspects of lesion history and provide relevant disease information

Background Lesion load is a common biomarker in multiple sclerosis, yet it has historically shown modest associations with clinical outcomes. 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 (i.e. spatially overlapping) lesions. We introduce a statistical technique for cross-sectionally counting pathologically distinct lesions. Methods MRI is used to assess the probability of lesion at each location. The texture of this map is quantified using a novel technique, and clusters resembling the center of a lesion are counted. Results Validity was demonstrated by comparing the proposed count to a gold-standard count in 60 subjects observed longitudinally. The counts were highly correlated (r = .97, p < .001) and not significantly different (t59 = −0.83, p > .40). Reliability was determined using 14 scans of a clinically stable subject acquired at 7 sites, and variability of lesion count was equivalent to that of lesion load. Accounting for lesion load and age, lesion count was negatively associated (t58 = −2.73, p < .01) with the Expanded Disability Status Scale (EDSS). Average lesion size had a higher association with EDSS (r =.35, p < .01) than lesion load (r = .10, p > .40) or lesion count (r = −.12, p > .30) alone. Conclusion These findings demonstrate that it is possible to recover important aspects of the natural history of lesion formation without longitudinal data, and suggest that lesion size provides complementary information about disease. Grant Support The project described was supported in part by the NIH grants R01 NS085211, R21 NS093349, and R01 NS094456 from the National Institute of Neurological Disorders and Stroke (NINDS). The study was also supported by the Intramural Research Program of NINDS and the Race to Erase MS Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.

Cultivating the Multiple Sclerosis Workforce of the Future

Multiple sclerosis (MS) is a complex neurologic disorder that affects people with ever-changing needs. The MS health-care field has entered an era of exponential knowledge growth in which better understanding of the immunologic dysregulation of the disease has translated into an expanding array of treatment options. It is estimated that, if it has not already, within the next decade the demands of a growing MS patient population will outstrip the number of professionals dedicated to the management of this chronic, lifelong disease. Therefore, there is a pressing need to attract and retain clinicians in this dynamic field. In response to this need, the Foundation of the Consortium of Multiple Sclerosis Centers organized a 2-day colloquium, a Mentorship Forum, on January 23-24, 2015, bringing together talented internal medicine and neurology trainees from across North America with an interest in MS and neuroimmunology. This article highlights the rationale for the MS Mentorship Forum, its structure and content, and its outcomes. We believe that the stage has been set to interest young, promising clinicians in learning more about MS and to encourage them to consider a career in this field. In so doing, we hope to contribute to the development of the next generation of MS experts to make a palpable difference in the lives of those affected by MS.