Bayesian models for functional magnetic resonance imaging data analysis
Author
Zhang, Linlin; Guindani, Michele; Vannucci, Marina
Date
2015Abstract
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 article 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 spatiotemporal 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 electroencephalography/magnetoencephalography (EEG/MEG) and diffusion tensor imaging (DTI) data, measured on the same subjects. We also briefly discuss the emerging field of imaging genetics.
Citation
Published Version
Keyword
Bayesian Statistics; Brain Connectivity; Classification and Prediction; Spatio-Temporal Activation Models; fMRI
Type
Journal article
Publisher
Citable link to this page
https://hdl.handle.net/1911/94844Rights
This is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by Wiley.Metadata
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