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Bayesian joint detection-estimation of brain activity in fMRI

Within-subject analysis in fMRI essentially addresses two problems, the /detection/ of brain regions eliciting evoked activity and the /estimation/ of the underlying dynamics. These issues are usually tackled sequentially while there are intrinsically connected one another. To this end, we have proposed a joint detection-estimation framework to address these problems in the Bayesian formalism. Detection is achieved by modeling activating and non-activating voxels through two-class spatial mixture models (SMM) within each region while hemodynamic response estimation is performed at a regional scale in a nonparametric way. The proposed method is /unsupervised/ and /spatially adaptive/ in the sense that the amount of spatial correlation is automatically tuned from the data and this setting automatically varies across brain regions. In addition, the level of regularization is specific to each experimental condition since both the signal-to-noise ratio and the activation pattern may vary across stimulus types in a given brain region. These aspects require the precise estimation of multiple partition functions of underlying Ising fields. This is addressed efficiently using first /path sampling/ for a small subset of fields and then using a recently developed fast extrapolation technique for the large remaining set. Simulation results emphasize that detection relying on unsupervised spatial mixture models achieve similar results to supervised SMMs without any hand-tuning of the correlation parameter. On real datasets, the gain is illustrated in a localizer fMRI experiment : brain activations appear more spatially resolved using SMM in comparison with classical General Linear Model(GLM)-based approaches, while estimating a specific parcel-based HRF shape. Our approach therefore validates the treatment of /unsmoothed/ fMRI data without fixed GLM definition /feasible/ at the subject level and makes also the classical strategy of spatial Gaussian filtering deprecated.

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