Abstract
In this paper, we present a new way to derive low dimensional representations of functional MRI datasets. This is done by introducing a state-space formalism, where the state corresponds to the components whose dynamical structure is of interest. The rank of the selected state space is chosen by statistical comparison with null datasets. We study the validity of our estimation scheme on a synthetic dataset, and show on a real dataset how the interpretation of the complex dynamics of fMRI data is facilitated by the use of low-dimensional, denoised representations. This methods makes a minimal use of priors on the data structure, so that it is very practical for exploratory data analysis.
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Thirion, B., Faugeras, O. (2003). Dynamical Components Analysis of FMRI Data: A Second Order Solution. In: Moreno-Díaz, R., Pichler, F. (eds) Computer Aided Systems Theory - EUROCAST 2003. EUROCAST 2003. Lecture Notes in Computer Science, vol 2809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45210-2_50
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DOI: https://doi.org/10.1007/978-3-540-45210-2_50
Publisher Name: Springer, Berlin, Heidelberg
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