Bayesian hierarchical multi-subject multiscale analysis of functional MRI data

• Published in: NeuroImage (Orlando, Fla.) Vol. 63; no. 3; pp. 1519 - 1531
• Main Authors: Sanyal, Nilotpal, Ferreira, Marco A.R.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.11.2012; Elsevier Limited
• Subjects: Algorithms; Bayes Theorem; Bayesian analysis; Bayesian inference; Brain - physiology; Brain Mapping - methods; Colleges & universities; Humans; Image Interpretation, Computer-Assisted - methods; Image smoothing; Integrals; Magnetic Resonance Imaging; Mixture prior; Models, Neurological; Models, Theoretical; Multiple subjects; Sparsity; Spatiotemporal analysis; Wavelet modeling; Wavelet transforms; Image smoothing; Wavelet modeling; Bayesian inference; Multiple subjects; Spatiotemporal analysis; Mixture prior

We develop a methodology for Bayesian hierarchical multi-subject multiscale analysis of functional Magnetic Resonance Imaging (fMRI) data. We begin by modeling the brain images temporally with a standard general linear model. After that, we transform the resulting estimated standardized regression c...