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Your search keyword '"Stefano, Magon"' showing total 3 results
Start Over Author "Stefano, Magon" Publisher springer nature
3 results on '"Stefano, Magon"'

1. EHMTI-0393. Abnormal ictal large-scale network connectivity in migraine without aura: a resting-state functional connectivity study

Author

Messoud Ashina, Asghar, Till Sprenger, Egill Rostrup, A Hougaard, Norsiah Ahmad, Stefano Magon, and Faisal Mohammad Amin

Subjects
medicine.medical_specialty, Neurology, Resting state fMRI, business.industry, Aura, Functional connectivity, Large scale network, Clinical Neurology, General Medicine, medicine.disease, Anesthesiology and Pain Medicine, Migraine, Meeting Abstract, medicine, Ictal, sense organs, Neurology (clinical), business, Neuroscience, Default mode network
Abstract

Alterations in cerebral resting-state functional connectivity (RSFC) have been reported outside of migraine attacks. To date, no studies studied possible changes in RSFC during migraine attacks.

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2. EHMTI-0186. Multi-center 3T MRI study of cortical thickness abnormalities in migraine

Author

Stefano Magon, Arne May, M. Mallar Chakravarty, Faisal Mohammad Amin, Jannis Müller, Messoud Ashina, Till Sprenger, Peter J. Goadsby, Christian L. Seifert, Christoph J. Schankin, and Anne Stankewitz

Subjects
Pathology, medicine.medical_specialty, Study groups, Neurology, business.industry, Clinical Neurology, Small sample, General Medicine, medicine.disease, Anesthesiology and Pain Medicine, Migraine, Meeting Abstract, Medicine, In patient, Neurology (clinical), Radiology, business
Abstract

A few studies have investigated CTh in patients with migraine showing highly variable and inconsistent results. The relatively small sample sizes of the previous studies and unbalanced study groups may partially explain these inconsistencies.

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3. White matter lesion filling improves the accuracy of cortical thickness measurements in multiple sclerosis patients: a longitudinal study

Author

Laura Gaetano, Ludwig Kappos, Ernst-Wilhelm Radue, Stefano Magon, Yvonne Naegelin, Till Sprenger, M. Mallar Chakravarty, Jason P. Lerch, and Christoph Stippich

Subjects
Adult, Male, Pathology, medicine.medical_specialty, Longitudinal study, Multiple Sclerosis, Time Factors, White matter lesion, computer.software_genre, Pattern Recognition, Automated, Cortical thickness, Lesion, White matter, Young Adult, Cellular and Molecular Neuroscience, Voxel, medicine, Humans, Longitudinal Studies, Gray Matter, Lesion filling, Retrospective Studies, Cerebral Cortex, medicine.diagnostic_test, business.industry, General Neuroscience, Multiple sclerosis, Methodology Article, Longitudinal analysis, Magnetic resonance imaging, Signal Processing, Computer-Assisted, Organ Size, Middle Aged, medicine.disease, Magnetic Resonance Imaging, White Matter, Intensity (physics), medicine.anatomical_structure, Cross-Sectional Studies, Female, medicine.symptom, Nuclear medicine, business, computer, Follow-Up Studies
Abstract

Background Previous studies have demonstrated that white matter (WM) lesions bias automated brain tissue classifications and cerebral volume measurements. However, filling WM lesions using the intensity of neighbouring normal-appearing WM has been shown to increase the accuracy of automated volume measurements in the brain. In the present study, we investigate the influence of WM lesions on cortical thickness (CTh) measures and assessed the impact of lesion filling on both cross-sectional/longitudinal and global/regional measurements of CTh in multiple sclerosis (MS) patients. Methods Fifty MS patients were studied at baseline as well as after three and six years of follow-up. CTh was estimated using a fully automated pipeline (CIVET) on T1-weighted magnetic resonance images data acquired at 1.5 Tesla without (original) and with WM lesion filling (filled). WM lesions were semi-automatically segmented and then filled with the mean intensity of the neighbouring voxels. For both original and filled T1 images we investigated and compared the main CIVET’s steps: tissue classification, surfaces generation and CTh measurement. Results On the original T1 images, the majority of WM lesion volume (72%) was wrongly classified as gray matter (GM). After lesion filling the accuracy of WM lesions classification improved significantly (p

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