Bayesian Models for fMRI Data Analysis.
Functional magnetic resonance imaging (fMRI), a noninvasive neuroimaging method that provides an indirect measure of neuronal activity by detecting blood flow changes, has experienced an explosive growth in the past years. Statistical methods play a crucial role in understanding and analyzing fMRI data. Bayesian approaches, in particular, have shown great promise in applications. A remarkable feature of fully Bayesian approaches is that they allow a flexible modeling of spatial and temporal correlations in the data. This paper provides a review of the most relevant models developed in recent years. We divide methods according to the objective of the analysis. We start from spatio-temporal models for fMRI data that detect task-related activation patterns. We then address the very important problem of estimating brain connectivity. We also touch upon methods that focus on making predictions of an individual's brain activity or a clinical or behavioral response. We conclude with a discussion of recent integrative models that aim at combining fMRI data with other imaging modalities, such as EEG/MEG and DTI data, measured on the same subjects. We also briefly discuss the emerging field of imaging genetics.
Department of Statistics, Rice University, Houston, TX 77005, USA.
Linlin
L
Zhang
Department of Biostatistics, UT M.D. Anderson Cancer Center, Houston, TX 77230, USA.
Michele
M
Guindani
Department of Statistics, Rice University, Houston, TX 77005, USA.
Marina
M
Vannucci
Bayesian Statistics; Brain Connectivity; Classification and Prediction; Spatio-Temporal Activation Models; fMRI