Graph-based inter-subject pattern analysis of fMRI data - Archive ouverte HAL Access content directly
Journal Articles PLoS ONE Year : 2014

Graph-based inter-subject pattern analysis of fMRI data


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
Fichier principal
Vignette du fichier
takerkart_plosone2014_gsvc_halversion.pdf (2.65 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01027769 , version 1 (22-07-2014)



Sylvain Takerkart, Guillaume Auzias, Bertrand Thirion, Liva Ralaivola. Graph-based inter-subject pattern analysis of fMRI data. PLoS ONE, 2014, 10.1371/journal.pone.0104586. ⟨10.1371/journal.pone.0104586⟩. ⟨hal-01027769⟩
628 View
167 Download



Gmail Facebook X LinkedIn More