Description
Date depot: 30 mars 2018
Titre: Unsupervised learning from neuroimaging data to identify disease subtypes in Alzheimer’s disease and related disorders
Directeur de thèse:
Didier DORMONT (ICM)
Directrice de thèse:
Ninon BURGOS (ICM)
Domaine scientifique: Sciences et technologies de l'information et de la communication
Thématique CNRS : Non defini
Resumé:
Neurodegenerative diseases are a major public health concern for our societies. In particular, neurodegenerative dementias, such as Alzheimer’s disease (AD), affect over 20 million people world-wide and this number is expected to reach 80 million by 2040. The development of new treatments is hampered by the heterogeneity of these diseases. For example, an Alzheimer’s pathology may correspond to a typical profile of prominent memory loss and medial temporal lobe alterations, but also to more atypical posterior, language or visual presentations. While the best characterized and most typical phenotypes have been known by clinicians for a long time, the heterogeneity of diseases remains inadequately characterized. In particular, it is unclear if additional clusters exist beyond those that can be identified clinically and to which extend the putative clusters overlap. In the past decade, large datasets of patients explored with multiple modalities (clinical data, MRI, PET, genetics…) have been gathered. This opens the possibility to study the heterogeneity of neurodegenerative diseases in a data-driven manner, by learning the main components and clusters. To that purpose, it is necessary to design new unsupervised learning approaches.
Doctorant.e: Thibeau--Sutre Elina