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

Abstract: 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 functional 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.