Estrogen treatment decreases matrix metalloproteinase (MMP)-9 in autoimmune demyelinating disease through estrogen receptor alpha (ERalpha).

Matrix metalloproteinases (MMPs) have a crucial function in migration of inflammatory cells into the central nervous system (CNS). Levels of MMP-9 are elevated in multiple sclerosis (MS) and predict the occurrence of new active lesions on magnetic resonance imaging (MRI). This translational study aims to determine whether in vivo treatment with the pregnancy hormone estriol affects MMP-9 levels from immune cells in patients with MS and mice with experimental autoimmune encephalomyelitis (EAE). Peripheral blood mononuclear cells (PBMCs) collected from three female MS patients treated with estriol and splenocytes from EAE mice treated with estriol, estrogen receptor (ER) alpha ligand, ERbeta ligand or vehicle were stimulated ex vivo and analyzed for levels of MMP-9. Markers of CNS infiltration were assessed using MRI in patients and immunohistochemistry in mice. Supernatants from PBMCs obtained during estriol treatment in female MS patients showed significantly decreased MMP-9 compared with pretreatment. Decreases in MMP-9 coincided with a decrease in enhancing lesion volume on MRI. Estriol treatment of mice with EAE reduced MMP-9 in supernatants from autoantigen-stimulated splenocytes, coinciding with decreased CNS infiltration by T cells and monocytes. Experiments with selective ER ligands showed that this effect was mediated through ERalpha. In conclusion, estriol acting through ERalpha to reduce MMP-9 from immune cells is one mechanism potentially underlying the estriol-mediated reduction in enhancing lesions in MS and inflammatory lesions in EAE

Collaborative International Research in Clinical and Longitudinal Experience Study in NMOSD.

Objective: To develop a resource of systematically collected, longitudinal clinical data and biospecimens for assisting in the investigation into neuromyelitis optica spectrum disorder (NMOSD) epidemiology, pathogenesis, and treatment. Methods: To illustrate its research-enabling purpose, epidemiologic patterns and disease phenotypes were assessed among enrolled subjects, including age at disease onset, annualized relapse rate (ARR), and time between the first and second attacks. Results: As of December 2017, the Collaborative International Research in Clinical and Longitudinal Experience Study (CIRCLES) had enrolled more than 1,000 participants, of whom 77.5% of the NMOSD cases and 71.7% of the controls continue in active follow-up. Consanguineous relatives of patients with NMOSD represented 43.6% of the control cohort. Of the 599 active cases with complete data, 84% were female, and 76% were anti-AQP4 seropositive. The majority were white/Caucasian (52.6%), whereas blacks/African Americans accounted for 23.5%, Hispanics/Latinos 12.4%, and Asians accounted for 9.0%. The median age at disease onset was 38.4 years, with a median ARR of 0.5. Seropositive cases were older at disease onset, more likely to be black/African American or Hispanic/Latino, and more likely to be female. Conclusions: Collectively, the CIRCLES experience to date demonstrates this study to be a useful and readily accessible resource to facilitate accelerating solutions for patients with NMOSD

W.: Detection and Segmentation of Pathological Structures by the Extended Graph-Shifts Algorithm

Abstract. We propose an extended graph-shifts algorithm for image segmentation and labeling. This algorithm performs energy minimization by manipulating a dynamic hierarchical representation of the image. It consists of a set of moves occurring at different levels of the hierarchy where the types of move, and the level of the hierarchy, are chosen automatically so as to maximally decrease the energy. Extended graph-shifts can be applied to a broad range of problems in medical imaging. In this paper, we apply extended graph-shifts to the detection of pathological brain structures: (i) segmentation of brain tumors, and (ii) detection of multiple sclerosis lesions. The energy terms in these tasks are learned from training data by statistical learning algorithms. We demonstrate accurate results, precision and recall in the order of 93%, and also show that the algorithm is computationally efficient, segmenting a full 3D volume in about one minute.

Detection and Segmentation of Pathological Structures by the Extended Graph-Shifts Algorithm

We propose an extended graph-shifts algorithm for image segmentation and labeling. This algorithm performs energy minimization by manipulating a dynamic hierarchical representation of the image. It consists of a set of moves occurring at different levels of the hierarchy where the types of move, and the level of the hierarchy, are chosen automatically so as to maximally decrease the energy. Extended graph-shifts can be applied to a broad range of problems in medical imaging. In this paper, we apply extended graph-shifts to the detection of pathological brain structures: (i) segmentation of brain tumors, and (ii) detection of multiple sclerosis lesions. The energy terms in these tasks are learned from training data by statistical learning algorithms.We demonstrate accurate results, precision and recall in the order of 93%, and also show that the algorithm is computationally efficient, segmenting a full 3D volume in about one minute

Detection and Segmentation of Pathological Structures by the Extended Graph-Shifts Algorithm

We propose an extended graph-shifts algorithm for image segmentation and labeling. This algorithm performs energy minimization by manipulating a dynamic hierarchical representation of the image. It consists of a set of moves occurring at different levels of the hierarchy where the types of move, and the level of the hierarchy, are chosen automatically so as to maximally decrease the energy. Extended graph-shifts can be applied to a broad range of problems in medical imaging. In this paper, we apply extended graph-shifts to the detection of pathological brain structures: (i) segmentation of brain tumors, and (ii) detection of multiple sclerosis lesions. The energy terms in these tasks are learned from training data by statistical learning algorithms.We demonstrate accurate results, precision and recall in the order of 93%, and also show that the algorithm is computationally efficient, segmenting a full 3D volume in about one minute

W.: Detection and Segmentation of Pathological Structures by the Extended Graph-Shifts Algorithm

Abstract. We propose an extended graph-shifts algorithm for image segmentation and labeling. This algorithm performs energy minimization by manipulating a dynamic hierarchical representation of the image. It consists of a set of moves occurring at different levels of the hierarchy where the types of move, and the level of the hierarchy, are chosen automatically so as to maximally decrease the energy. Extended graph-shifts can be applied to a broad range of problems in medical imaging. In this paper, we apply extended graph-shifts to the detection of pathological brain structures: (i) segmentation of brain tumors, and (ii) detection of multiple sclerosis lesions. The energy terms in these tasks are learned from training data by statistical learning algorithms. We demonstrate accurate results, precision and recall in the order of 93%, and also show that the algorithm is computationally efficient, segmenting a full 3D volume in about one minute.

Three‐dimensional whole‐brain simultaneous T1, T2, and T1ρ quantification using MR Multitasking: Method and initial clinical experience in tissue characterization of multiple sclerosis

PurposeTo develop a 3D whole-brain simultaneous T1/T2/T1ρ quantification method with MR Multitasking that provides high quality, co-registered multiparametric maps in 9 min.MethodsMR Multitasking conceptualizes T1/T2/T1ρ relaxations as different time dimensions, simultaneously resolving all three dimensions with a low-rank tensor image model. The proposed method was validated on a phantom and in healthy volunteers, comparing quantitative measurements against corresponding reference methods and evaluating the scan-rescan repeatability. Initial clinical validation was performed in age-matched relapsing-remitting multiple sclerosis (RRMS) patients to examine the feasibility of quantitative tissue characterization and to compare with the healthy control cohort. The feasibility of synthesizing six contrast-weighted images was also examined.ResultsOur framework produced high quality, co-registered T1/T2/T1ρ maps that closely resemble the reference maps. Multitasking T1/T2/T1ρ measurements showed substantial agreement with reference measurements on the phantom and in healthy controls. Bland-Altman analysis indicated good in vivo repeatability of all three parameters. In RRMS patients, lesions were conspicuously delineated on all three maps and on four synthetic weighted images (T2-weighted, T2-FLAIR, double inversion recovery, and a novel "T1ρ-FLAIR" contrast). T1 and T2 showed significant differences for normal appearing white matter between patients and controls, while T1ρ showed significant differences for normal appearing white matter, cortical gray matter, and deep gray matter. The combination of three parameters significantly improved the differentiation between RRMS patients and healthy controls, compared to using any single parameter alone.ConclusionMR Multitasking simultaneously quantifies whole-brain T1/T2/T1ρ and is clinically promising for quantitative tissue characterization of neurological diseases, such as MS

Three‐dimensional whole‐brain simultaneous T1, T2, and T1 ρ

PURPOSE: To develop a 3D whole-brain simultaneous T1/T2/T1ρ quantification method with MR Multitasking that provides high quality, co-registered multiparametric maps in 9min. METHODS: MR Multitasking conceptualizes T1/T2/T1ρ relaxations as different time dimensions, simultaneously resolving all three dimensions with a low-rank tensor image model. The proposed method was validated on a phantom and in healthy volunteers, comparing quantitative measurements against corresponding reference methods and evaluating the scan-rescan repeatability. Initial clinical validation was performed in age-matched relapsing-remitting multiple sclerosis (RRMS) patients to examine the feasibility of quantitative tissue characterization and to compare with the healthy control cohort. The feasibility of synthesizing six contrast-weighted images was also examined. RESULTS: Our framework produced high quality, co-registered T1/T2/T1ρ maps that closely resemble the reference maps. Multitasking T1/T2/T1ρ measurements showed substantial agreement with reference measurements on the phantom and in healthy controls. Bland-Altman analysis indicated good in vivo repeatability of all three parameters. In RRMS patients, lesions were conspicuously delineated on all three maps and on four synthetic weighted images (T2-weighted, T2-FLAIR, double inversion recovery, and a novel “T1ρ-FLAIR” contrast). T1 and T2 showed significant differences for normal appearing white matter between patients and controls, while T1ρ showed significant differences for normal appearing white matter, cortical gray matter, and deep gray matter. The combination of three parameters significantly improved the differentiation between RRMS patients and healthy controls, as compared to using any single parameter alone. CONCLUSION: MR Multitasking simultaneously quantifies whole-brain T1/T2/T1ρ and is clinically promising for quantitative tissue characterization of neurological diseases such as MS

Thalamic–hippocampal–prefrontal disruption in relapsing–remitting multiple sclerosis

Background: Cortical, thalamic and hippocampal gray matter atrophy in relapsing–remitting MS (RRMS) is associated cognitive deficits. However, the role of interconnecting white matter pathways including the fornix, cingulum, and uncinate fasciculus (UF) is less well studied. Objective: To assess MS damage to a hippocampal–thalamic–prefrontal network and the relative contributions of its components to specific cognitive domains. Methods: We calculated diffusion tensor fractional anisotropy (FA) in the fornix, cingulum and UF as well as thalamic and hippocampal volumes in 27 RRMS patients and 20 healthy controls. A neuropsychological battery was administered and 4 core tests known to be sensitive to MS changes were used to assess cognitive impairment. To determine the relationships between structure and cognition, all tests were grouped into 4 domains: attention/executive function, processing speed, verbal memory, and spatial memory. Univariate correlations with structural measures and depressive symptoms identified potential contributors to cognitive performance and subsequent linear regression determined their relative effects on performance in each domain. For significant predictors, we also explored the effects of laterality and axial versus radial diffusivity. Results: RRMS patients had worse performance on the Symbol Digit Modalities Test, but no significant impairment in the 4 cognitive domains. RRMS had reduced mean FA of all 3 pathways and reduced thalamic and hippocampal volumes compared to controls. In RRMS we found that thalamic volume and BDI predicted attention/executive function, UF FA predicted processing speed, thalamic volume predicted verbal memory, and UF FA and BDI predicted spatial memory. Conclusions: Hippocampal–thalamic–prefrontal disruption affects cognitive performance in early RRMS with mild to minimal cognitive impairment, confirming both white and gray matter involvement in MS and demonstrating utility in assessing functional networks to monitor cognition