Statistical models to learn the structural organisation of neural circuits from multimodal brain images : application to Gilles de la Tourette syndrome

Abstract : We propose a statistical framework to analyse morphological and organisational anomalies altering the anatomy of neural circuits in patients with Gilles de la Tourette syndrome. The components of each circuit, from both white and grey matter, are represented as 3D meshes and integrated in a single shape complex. This allows to study their organisation and in particular the structural connectivity. The proposed methodology is based on a statistical approach called atlas construction which results in a template complex, capturing the invariants of the population and in template-to-subject deformations, describing the morphological variability. First, we embed the atlas construction in a Bayesian framework which allows to automatically estimate important parameters otherwise fixed by the user. Second, we reduce the important computational resources required to process fiber bundles by defining an approximation scheme based on a new computational model called weighted currents. Third, we describe a new deformation scheme for the atlas construction which permits to model variations in structural connectivity and at the same time to capture global anatomical changes. We show the effectiveness of the proposed method by comparing two groups of 44 patients and 22 controls respectively. Results highlight anomalies about both the shape of grey matter structures and structural connectivity.
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Pietro Gori. Statistical models to learn the structural organisation of neural circuits from multimodal brain images : application to Gilles de la Tourette syndrome. Bioinformatics [q-bio.QM]. Université Pierre et Marie Curie - Paris VI, 2016. English. ⟨NNT : 2016PA066057⟩. ⟨tel-01368191⟩

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