A joint detection-estimation framework for analysing within-subject fMRI data

Ciuciu, Philippe; Vincent, Thomas; Risser, Laurent; Donnet, Sophie

Journal de la société française de statistique, Tome 151 (2010) no. 1, pp. 58-89.

Dans cet article, nous synthétisons la méthodologie usuelle et des variantes plus élaborées pour analyser des données individuelles d’Imagerie par Résonance Magnétique fonctionnelle (IRMf). De telles données sont acquises au cours d’expériences sensorielles ou cognitives dont le but est de stimuler le sujet dans le scanner afin d’évoquer une activité cérébrale typique. À partir de données quadri-dimensionnelles (trois dans l’espace et le temps), le but est double : d’abord, détecter les régions impliquées dans les processus sensoriels ou cognitifs que le protocole expérimental manipule ; ensuite, estimer la dynamique cérébrale sous-jacente. La première question est généralement abordée dans le contexte du Modèle Linéaire Général (MLG) qui suppose a priori connue la réponse impulsionnelle du filtre hémodynamique. La seconde question traite de l’estimation de la forme de cette réponse qui fait sens uniquement dans les régions activées. Durant ces cinq dernières années, un nouveau cadre de détection-estimation conjointe traitant ces deux questions simultanément a été proposé successivement dans [ 59 , 60 , 102 ]. Nous montrons ici jusqu’à quel point cette méthodologie étend les approches à base de MLG, les améliore en termes de sensibilité et spécificité statistique, quelles questions supplémentaires ce cadre permet d’investiguer théoriquement et comment il fournit un moyen aux utilisateurs de traiter de façon adaptée leur données non-lissées spatialement, aussi bien dans le répère 3D de l’acquisition que sur la surface corticale.

In this paper, we review classical and advanced methodologies for analysing within-subject functional Magnetic Resonance Imaging (fMRI) data. Such data are usually acquired during sensory or cognitive experiments that aims at stimulating the subject in the scanner and eliciting evoked brain activity. From such four-dimensional datasets (three in space, one in time), the goal is twofold: first, detecting brain regions involved in the sensory or cognitives processes that the experimental design manipulates; second, estimating the underlying activation dynamics. The first issue is usually addressed in the context of the General Linear Model (GLM), which a priori assumes a X form for the impulse response of the hemodynamic filter. The second question aims at estimating this shape which makes sense in activating regions only. In the last five years, a novel Joint Detection-Estimation (JDE) framework addressing these two questions simultaneously has been proposed in [ 59 , 60 , 102 ]. We show to which extent this methodology outperforms the GLM approach in terms of statistical sensitivity and specificity, which additional questions it allows us to investigate theoretically and how it provides us with a well-adapted framework to treat spatially unsmoothed real fMRI data both in the 3D acquisition volume and on the cortical surface.

Keywords: fMRI, neuroimaging, nonparametric hemodynamics, Bayesian inference, MCMC, model selection, Markov random fields, partition function, Potts fields
Mot clés : IRMf, neuro-imagerie, hémodynamique non-paramétrique, inférence bayésienne, MCMC, sélection de modèles, champs de Markov, fonction de partition, champs de Pots

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