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Conference Paper

Spatio-temporal covariance model for medical images sequences: Application to functional MRI data


Kruggel,  Frithjof J.
MPI of Cognitive Neuroscience (Leipzig, -2003), The Prior Institutes, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Benali, H., Pelegrini-Issac, M., & Kruggel, F. J. (2001). Spatio-temporal covariance model for medical images sequences: Application to functional MRI data. In M. F. Insana (Ed.), Information Processing in Medical Imaging (pp. 197-203). Berlin: Springer. doi:10.1007/3-540-45729-1_20.

Cite as: https://hdl.handle.net/21.11116/0000-0003-4C52-C
Spatial and temporal correlations which affect the signal measured in functional MRI (fMRI) are usually not considered simultaneously (i.e., as non-independent random processes) in statistical methods dedicated to detecting cerebral activation.We propose a new method for modeling the covariance of a stationary spatio-temporal random process and apply this approach to fMRI data analysis. For doing so, we introduce a multivariate regression model which takes simultaneously the spatial and temporal correlations into account. We show that an experimental variogram of the regression error process can be fitted to a valid nonseparable spatio-temporal covariance model. This yields a more robust estimation of the intrinsic spatio-temporal covariance of the error process and allows a better modeling of the properties of the random fluctuations affecting the hemodynamic signal. The practical relevance of our model is illustrated using real event-related fMRI experiments.