A hierarchical model for simultaneous detection and estimation in multi-subject fMRI studies

• Published in: NeuroImage (Orlando, Fla.) Vol. 98; pp. 61 - 72
• Main Authors: Degras, David, Lindquist, Martin A.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.09.2014; Elsevier Limited
• Subjects: Algorithms; Behavior; Brain - physiology; Brain Mapping; Computer Simulation; Derivatives; Economic models; Humans; Image Interpretation, Computer-Assisted; Magnetic Resonance Imaging; Medical imaging; Models, Neurological; Models, Statistical; NMR; Noise; Nuclear magnetic resonance; Parameter estimation; Population
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2014.04.052

In this paper we introduce a new hierarchical model for the simultaneous detection of brain activation and estimation of the shape of the hemodynamic response in multi-subject fMRI studies. The proposed approach circumvents a major stumbling block in standard multi-subject fMRI data analysis, in that it both allows the shape of the hemodynamic response function to vary across region and subjects, while still providing a straightforward way to estimate population-level activation. An efficient estimation algorithm is presented, as is an inferential framework that allows for not only tests of activation, but also tests for deviations from some canonical shape. The model is validated through simulations and application to a multi-subject fMRI study of thermal pain. •New model for joint detection of activation and estimation of HRF in fMRI.•Allows HRF to vary across regions/subjects, while allowing group-level inference.•Allows for both tests of activation and deviations from canonical HRF shape.•Validated through simulations and application to fMRI study of thermal pain.