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A Spatio‐Temporal Model for Functional Magnetic Resonance Imaging Data – with a View to Resting State Networks

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  • EVA B. VEDEL JENSEN
  • THORDIS L. THORARINSDOTTIR

Abstract

. Functional magnetic resonance imaging (fMRI) is a technique for studying the active human brain. During the fMRI experiment, a sequence of MR images is obtained, where the brain is represented as a set of voxels. The data obtained are a realization of a complex spatio‐temporal process with many sources of variation, both biological and technical. We present a spatio‐temporal point process model approach for fMRI data where the temporal and spatial activation are modelled simultaneously. It is possible to analyse other characteristics of the data than just the locations of active brain regions, such as the interaction between the active regions. We discuss both classical statistical inference and Bayesian inference in the model. We analyse simulated data without repeated stimuli both for location of the activated regions and for interactions between the activated regions. An example of analysis of fMRI data, using this approach, is presented.

Suggested Citation

  • Eva B. Vedel Jensen & Thordis L. Thorarinsdottir, 2007. "A Spatio‐Temporal Model for Functional Magnetic Resonance Imaging Data – with a View to Resting State Networks," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(3), pages 587-614, September.
    • Handle: RePEc:bla:scjsta:v:34:y:2007:i:3:p:587-614
    DOI: 10.1111/j.1467-9469.2006.00554.x
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