Multi-subject Bayesian Joint Detection and Estimation in fMRI

• Published in: 2014 International Workshop on Pattern Recognition in Neuroimaging pp. 1 - 4
• Main Authors: Badillo, Solveig, Desmidt, Severine, Ginisty, Chantal, Ciuciu, Philippe
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2014
• Subjects: Bayesian inference; Estimation; Hemodynamic estimation; Hemodynamics; Hidden Markov models; Joint Detection Estimation; multi-subject fMRI studies; Robustness; Senior citizens; Shape; Signal to noise ratio
• DOI: 10.1109/PRNI.2014.6858508

Modern cognitive experiments in functional Magnetic Resonance Imaging (fMRI) rely on a cohort of subjects sampled from a population of interest to study characteristics of the healthy brain or to identify biomarkers on a specific pathology (e.g., Alzheimer's disease) or disorder (e.g., ageing). Group-level studies usually proceed in two steps by making random-effect analysis on top of intra-subject analyses, to localize activated regions in response to stimulations or to estimate brain dynamics. Here, we focus on improving the accuracy of group-level inference of the hemodynamic response function (HRF). We rest on a existing Joint Detection-Estimation (JDE) framework which aims at detecting evoked activity and estimating HRF shapes jointly. So far, region-specific group-level HRFs have been captured by averaging intra-subject HRF profiles. Here, our approach extends the JDE formalism to the multi-subject context by proposing a hierarchical Bayesian modeling that includes an additional layer for describing the link between subject-specific and group-level HRFs. This extension outperforms the original approach both on artificial and real multi-subject datasets.