title: Graph-based inter-subject pattern analysis of FMRI data.
abstract: In brain imaging, solving learning problems in multi-subjects settings is difficult because of the differences that exist across individuals. Here we introduce a novel classification framework based on group-invariant graphical representations, allowing to overcome the inter-subject variability present in functional magnetic resonance imaging (fMRI) data and to perform multivariate pattern analysis across subjects. Our contribution is twofold: first, we propose an unsupervised representation learning scheme that encodes all relevant characteristics of distributed fMRI patterns into attributed graphs; second, we introduce a custom-designed graph kernel that exploits all these characteristics and makes it possible to perform supervised learning (here, classification) directly in graph space. The well-foundedness of our technique and the robustness of the performance to the parameter setting are demonstrated through inter-subject classification experiments conducted on both artificial data and a real fMRI experiment aimed at characterizing local cortical representations. Our results show that our framework produces accurate inter-subject predictions and that it outperforms a wide range of state-of-the-art vector- and parcel-based classification methods. Moreover, the genericity of our method makes it is easily adaptable to a wide range of potential applications. The dataset used in this study and an implementation of our framework are available at http://dx.doi.org/10.6084/m9.figshare.1086317.
authors.affiliation: Institut de Neurosciences de la Timone UMR 7289, Aix Marseille Université, CNRS, Marseille, France; Laboratoire d'Informatique Fondamentale UMR 7279, Aix Marseille Université, CNRS, Marseille, France.
authors.forename: Sylvain
authors.initials: S
authors.lastname: Takerkart
authors.affiliation: Institut de Neurosciences de la Timone UMR 7289, Aix Marseille Université, CNRS, Marseille, France.
authors.forename: Guillaume
authors.initials: G
authors.lastname: Auzias
authors.affiliation: Parietal Team, INRIA Saclay - Ile-de-France, Saclay, France.
authors.forename: Bertrand
authors.initials: B
authors.lastname: Thirion
authors.affiliation: Laboratoire d'Informatique Fondamentale UMR 7279, Aix Marseille Université, CNRS, Marseille, France.
authors.forename: Liva
authors.initials: L
authors.lastname: Ralaivola
mesh_terms: D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging*; D001931:Brain Mapping / Q000379:methods*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods; D059906:Neuroimaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D011859:Radiography