title: Group-wise parcellation of the cortex through multi-scale spectral clustering.
abstract: The delineation of functionally and structurally distinct regions as well as their connectivity can provide key knowledge towards understanding the brain's behaviour and function. Cytoarchitecture has long been the gold standard for such parcellation tasks, but has poor scalability and cannot be mapped in vivo. Functional and diffusion magnetic resonance imaging allow in vivo mapping of brain's connectivity and the parcellation of the brain based on local connectivity information. Several methods have been developed for single subject connectivity driven parcellation, but very few have tackled the task of group-wise parcellation, which is essential for uncovering group specific behaviours. In this paper, we propose a group-wise connectivity-driven parcellation method based on spectral clustering that captures local connectivity information at multiple scales and directly enforces correspondences between subjects. The method is applied to diffusion Magnetic Resonance Imaging driven parcellation on two independent groups of 50 subjects from the Human Connectome Project. Promising quantitative and qualitative results in terms of information loss, modality comparisons, group consistency and inter-group similarities demonstrate the potential of the method.
authors.affiliation: Biomedical Image Analysis Group, Department of Computing, Imperial College London, 180 Queens Gate, London SW7 2AZ, UK. Electronic address: s.parisot@imperial.ac.uk.
authors.forename: Sarah
authors.initials: S
authors.lastname: Parisot
authors.affiliation: Biomedical Image Analysis Group, Department of Computing, Imperial College London, 180 Queens Gate, London SW7 2AZ, UK.
authors.forename: Salim
authors.initials: S
authors.lastname: Arslan
authors.affiliation: Biomedical Image Analysis Group, Department of Computing, Imperial College London, 180 Queens Gate, London SW7 2AZ, UK.
authors.forename: Jonathan
authors.initials: J
authors.lastname: Passerat-Palmbach
authors.affiliation: Surgical Planning Laboratory, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St., Boston, MA 02115, USA.
authors.forename: William M
authors.initials: WM
authors.lastname: Wells
authors.affiliation: Biomedical Image Analysis Group, Department of Computing, Imperial College London, 180 Queens Gate, London SW7 2AZ, UK.
authors.forename: Daniel
authors.initials: D
authors.lastname: Rueckert
mesh_terms: D000465:Algorithms; D002540:Cerebral Cortex / Q000033:anatomy & histology* / Q000502:physiology*; D063132:Connectome / Q000379:methods*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D009415:Nerve Net / Q000033:anatomy & histology* / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique
keywords: Connectomics; Cortex parcellation; Diffusion Magnetic Resonance Imaging; Group-wise analysis; Spectral clustering