Abstract
We address the issue of jointly detecting brain activity and estimating underlying brain hemodynamics from functional MRI data. We adopt the so-called Joint Detection Estimation (JDE) framework that takes spatial dependencies between voxels into account. We recast the JDE into a missing data framework and derive a Variational Expectation-Maximization (VEM) algorithm for its inference. It follows a new algorithm that has interesting advantages over the previously used intensive simulation methods (Markov Chain Monte Carlo, MCMC): tests on artificial data show that the VEM-JDE is more robust to model mis-specification while additional tests on real data confirm that it achieves similar performance in much less computation time.
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Keywords
- Markov Chain Monte Carlo
- Hemodynamic Response Function
- fMRI Time Series
- Canonical Hemodynamic Response Function
- Markov Chain Monte Carlo Scheme
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Chaari, L., Forbes, F., Vincent, T., Dojat, M., Ciuciu, P. (2011). Variational Solution to the Joint Detection Estimation of Brain Activity in fMRI. In: Fichtinger, G., Martel, A., Peters, T. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011. MICCAI 2011. Lecture Notes in Computer Science, vol 6892. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23629-7_32
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DOI: https://doi.org/10.1007/978-3-642-23629-7_32
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