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Learning brain alterations in multiple sclerosis from multimodal neuroimaging data

Abstract : Multiple Sclerosis (MS) is the most common progressive neurological disease of young adults worldwide and thus represents a major public health issue with about 90,000 patients in France and more than 500,000 people affected with MS in Europe. In order to optimize treatments, it is essential to be able to measure and track brain alterations in MS patients. In fact, MS is a multi-faceted disease which involves different types of alterations, such as myelin damage and repair. Under this observation, multimodal neuroimaging are needed to fully characterize the disease. Magnetic resonance imaging (MRI) has emerged as a fundamental imaging biomarker for multiple sclerosis because of its high sensitivity to reveal macroscopic tissue abnormalities in patients with MS. Conventional MR scanning provides a direct way to detect MS lesions and their changes, and plays a dominant role in the diagnostic criteria of MS. Moreover, positron emission tomography (PET) imaging, an alternative imaging modality, can provide functional information and detect target tissue changes at the cellular and molecular level by using various radiotracers. For example, by using the radiotracer [11C]PIB, PET allows a direct pathological measure of myelin alteration. However, in clinical settings, not all the modalities are available because of various reasons. In this thesis, we therefore focus on learning and predicting missing-modality-derived brain alterations in MS from multimodal neuroimaging data.
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Submitted on : Monday, February 8, 2021 - 7:11:34 PM
Last modification on : Wednesday, November 3, 2021 - 5:44:46 AM

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Wen Wei. Learning brain alterations in multiple sclerosis from multimodal neuroimaging data. Bioengineering. Université Côte d'Azur, 2020. English. ⟨NNT : 2020COAZ4021⟩. ⟨tel-02862395v2⟩

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