Modeling the Spatial and Temporal Dependence in fMRI Data
Version of Record online: 13 NOV 2009
© 2009, The International Biometric Society
Volume 66, Issue 3, pages 949–957, September 2010
How to Cite
Derado, G., Bowman, F. D. and Kilts, C. D. (2010), Modeling the Spatial and Temporal Dependence in fMRI Data. Biometrics, 66: 949–957. doi: 10.1111/j.1541-0420.2009.01355.x
- Issue online: 13 NOV 2009
- Version of Record online: 13 NOV 2009
- Received February 2009. Revised July 2009. Accepted September 2009.
- Covariance modeling;
- Simultaneous autoregressive model;
- Spatio-temporal model
Summary Functional magnetic resonance imaging (fMRI) data sets are large and characterized by complex dependence structures driven by highly sophisticated neurophysiology and aspects of the experimental designs. Typical analyses investigating task-related changes in measured brain activity use a two-stage procedure in which the first stage involves subject-specific models and the second-stage specifies group (or population) level parameters. Customarily, the first-level accounts for temporal correlations between the serial scans acquired during one scanning session. Despite accounting for these correlations, fMRI studies often include multiple sessions and temporal dependencies may persist between the corresponding estimates of mean neural activity. Further, spatial correlations between brain activity measurements in different locations are often unaccounted for in statistical modeling and estimation. We propose a two-stage, spatio-temporal, autoregressive model that simultaneously accounts for spatial dependencies between voxels within the same anatomical region and for temporal dependencies between a subject's estimates from multiple sessions. We develop an algorithm that leverages the special structure of our covariance model, enabling relatively fast and efficient estimation. Using our proposed method, we analyze fMRI data from a study of inhibitory control in cocaine addicts.