Sensitivity analysis of parcellation in the joint detection-estimation of brain activity in fMRI

Within-subject analysis in fMRI relies on both (i) a detection step to localize which parts of the brain are activated by a given stimulus type, and on (ii) an estimation step to recover the underlying brain dynamics. In [1], a Bayesian detection- estimation approach that jointly addresses (i)-(ii) has been proposed. In the latter, a functionally homogeneous parcel- lation of the brain is required prior to this analysis. If tools exist to produce suitable parcellations [2], the question remains open of its impact on both activation detection and dynamics estimation. Here, we present a sensitivity analysis of this Bayesian model regarding the parcellation. We show that some activating clusters are stable regarding parcellation while others are highly variable. The overall procedure is quite sensitive to the input parcellation as the uncertainty of the estimated effect is correlated to its size. The perspective is to extend our model with an adaptive parcellation combined with the detection-estimation.