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This  research is focused on the application of statistical methods (eg, Bayesian inference) to. The Joint Detection-Estimation (JDE) framework I have promoted gives access the neuroscientist with region-specific HRFs and detection maps of brain activity in response to given experimental task or stimulus or to a combination of those from functional brain imaging (fMRI) data. This novel modelling  takes place in the Bayesian formalism and is flexible enough to account for some putative deactivations (eg epilepsy)  or neuronal habituation generating the so-called repetition-suppression effect. Moreover, spatial dependency is accounted for through the use of Markov random fields that avoids us to spatially filter the data sets.

Currently, we are working on the following directions:

  1. The impact of the JDE framework on group-level studies in fMRI (see this paper).
  2. a Variational Bayes alternative to the sampling-based strategy for computing the pointwise estimates of HRF shapes and activation maps (see this paper).
  3. The extension of the JDE framework to functional ASL data; see this MICCAI conference paper
  4. The extension of the JDE framework to embody a supplementary parcellation step into the algorithm so as to uncover homogeneous hemodynamic territories; see this submitted manuscript. This gives us the JPDE approach at the subject-level.
  5. The nonparametric Bayesian extension of the JPDE approach to perform model selection regarding the number of parcels.
  6. The multisubject hemodynamic parcellation.