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
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2012.08.041

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 coefficient maps through a discrete wavelet transformation to obtain a sparse representation in the wavelet space. Subsequently, we assign to the wavelet coefficients a prior that is a mixture of a point mass at zero and a Gaussian white noise. In this mixture prior for the wavelet coefficients, the mixture probabilities are related to the pattern of brain activity across different resolutions. To incorporate this information, we assume that the mixture probabilities for wavelet coefficients at the same location and level are common across subjects. Furthermore, we assign for the mixture probabilities a prior that depends on a few hyperparameters. We develop an empirical Bayes methodology to estimate the hyperparameters and, as these hyperparameters are shared by all subjects, we obtain precise estimated values. Then we carry out inference in the wavelet space and obtain smoothed images of the regression coefficients by applying the inverse wavelet transform to the posterior means of the wavelet coefficients. An application to computer simulated synthetic data has shown that, when compared to single-subject analysis, our multi-subject methodology performs better in terms of mean squared error. Finally, we illustrate the utility and flexibility of our multi-subject methodology with an application to an event-related fMRI dataset generated by Postle (2005) through a multi-subject fMRI study of working memory related brain activation. ► Spatially adaptive methodology that borrows information across subjects ► Compared with MCMC-based methodology, our methodology is computationally fast. ► Methodology provides smaller MSE and better ROC curves than single subject analysis. ► Methodology preserves the shape and discontinuities of activation regions. ► Methodology implicitly links the same anatomical regions across subjects.