Double diffeomorphism: combining morphometry and structural connectivity analysis

Abstract : The brain is composed of several neural circuits which may be seen as anatomical complexes composed of grey matter structures interconnected by white matter tracts. Grey and white matter components may be modelled as 3D surfaces and curves respectively. Neurodevelopmental disorders involve morphological and organizational alterations which can not be jointly captured by usual shape analysis techniques based on single diffeomorphisms. We propose a new deformation scheme, called double diffeomorphism, which is a combination of two diffeomorphisms. The first one captures changes in structural connectivity, whereas the second one recovers the global morphological variations of both grey and white matter structures. This deformation model is integrated into a Bayesian framework for atlas construction. We evaluate it on a data-set of 3D structures representing the neural circuits of patients with Gilles de la Tourette syndrome (GTS). We show that this approach makes it possible to localise, quantify and easily visualise the pathological anomalies altering the morphology and organization of the neural circuits. Furthermore, results also indicate that the proposed deformation model better discriminates between controls and GTS patients than a single diffeomorphism.
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Pietro Gori, Olivier Colliot, Linda Kacem, Yulia Worbe, Alexandre Routier, et al.. Double diffeomorphism: combining morphometry and structural connectivity analysis. IEEE Transactions on Medical Imaging, Institute of Electrical and Electronics Engineers, 2018, 37 (9), pp.2033-2043. ⟨10.1109/TMI.2018.2813062⟩. ⟨hal-01709847v2⟩

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