Functional magnetic resonance imaging (fMRI) is a noninvasive technique allowing the study of brain activity via the measurement of hemodynamic changes. Recently, a joint detection-estimation (JDE) framework was developed and relies on both (1) the brain activity detection and (2) the hemodynamic response function estimation, two steps that are generally addressed in a separate way. The JDE approach is a parcel-based model that alternates (1) and (2) on each parcel successively. The JDE analysis assumes that all delivered stimuli (e.g. visual, auditory, etc.) possibly generate a response everywhere in the brain although activation is likely to be induced by only some of them in specific brain areas. Inclusion of irrelevant events may degrade the results. Since the relevant conditions or stimulus types can change between different brain areas, a model selection procedure will be computationally expensive. Furthermore, criteria are not always available to select the relevant conditions prior to activation detection, especially in pathological cases. The goal of this work is to develop a JDE extension allowing an automatic selection of the relevant conditions according to the brain activity they elicit. This condition selection is done simultaneously to the analysis and adaptively through the different brain areas. Analysis on simulated and real datasets illustrate the ability of our model to select the relevant conditions and its interest compare to the standard JDE analysis.
