| Autor: |
Lefèvre J; Centre National de la Recherche Scientifique, ENSAM, LSIS, Aix Marseille University, University of Toulon, Marseille, France.; Centre National de la Recherche Scientifique, Institut de Neurosciences de la Timone, Aix Marseille University, Marseille, France., Pepe A; Centre National de la Recherche Scientifique, ENSAM, LSIS, Aix Marseille University, University of Toulon, Marseille, France.; Centre National de la Recherche Scientifique, Institut de Neurosciences de la Timone, Aix Marseille University, Marseille, France., Muscato J; Centre National de la Recherche Scientifique, ENSAM, LSIS, Aix Marseille University, University of Toulon, Marseille, France., De Guio F; Institut National de la Santé et de la Recherche Médicale, University Paris Diderot, Sorbonne Paris Cité, UMR-S 1161, Paris, France., Girard N; Centre National de la Recherche Scientifique, CRMBM, Aix Marseille University, Marseille, France.; APHM, Hopital de la Timone, Service de Neuroradiologie, Marseille, France., Auzias G; Centre National de la Recherche Scientifique, Institut de Neurosciences de la Timone, Aix Marseille University, Marseille, France., Germanaud D; CEA, Neurospin, UNIACT, Equipe Neuropédiatrie, Gif-sur-Yvette, France.; Institut National de la Santé et de la Recherche Médicale, Université Sorbonne Paris Cité, CEA, UMR 1129, Paris, France.; APHP, Hopital Robert-Debré, DHU Protect, Service de Neurologie Pédiatrique et des Maladies Métaboliques, Université Paris Diderot, Paris, France. |
| Abstrakt: |
Understanding the link between structure, function and development in the brain is a key topic in neuroimaging that benefits from the tremendous progress of multi-modal MRI and its computational analysis. It implies, inter alia , to be able to parcellate the brain volume or cortical surface into biologically relevant regions. These parcellations may be inferred from existing atlases (e.g., Desikan) or sets of rules, as would do a neuroanatomist for lobes, but also directly driven from the data (e.g., functional or structural connectivity) with minimum a priori. In the present work, we aimed at using the intrinsic geometric information contained in the eigenfunctions of Laplace-Beltrami Operator to obtain parcellations of the cortical surface based only on its description by triangular meshes. We proposed a framework adapted from spectral clustering, which is general in scope and suitable for the co-parcellation of a group of subjects. We applied it to a dataset of 62 adults, optimized it and revealed a striking agreement between parcels produced by this unsupervised clustering and Freesurfer lobes (Desikan atlas), which cannot be explained by chance. Constituting the first reported attempt of spectral-based fully unsupervised segmentation of neuroanatomical regions such as lobes, spectral analysis of lobes (Spanol) could conveniently be fitted into a multimodal pipeline to ease, optimize or speed-up lobar or sub-lobar segmentation. In addition, we showed promising results of Spanol on smoother brains and notably on a dataset of 15 fetuses, with an interest for both the understanding of cortical ontogeny and the applicative field of perinatal computational neuroanatomy. |
