Différences entre les versions de « Sylvain Faisan »
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=== Functional MRI -- Brain Mapping === | === Functional MRI -- Brain Mapping === | ||
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+ | Activation detection at voxel v is formulated in terms of temporal alignment between sequences of hemodynamic response onsets detected in the fMRI signal at v and in the spatial neighborhood of v, and the input sequence of stimuli or stimulus onsets (see Fig. a). The multiple event sequence alignment problem is solved within the probabilistic framework of hidden Markov multiple event sequence models (HMMESMs). It consists in hypothesizing about a set of valid scenarios that could explain the N observed HRO event sequences. | ||
A valid scenario (see Fig. b), relies on the combination of: || | A valid scenario (see Fig. b), relies on the combination of: || | ||
* an N + 1th event sequence, namely, a candidate sequence of task-induced hemodynamic activation onsets (HAOs) at the origin of the observations. | * an N + 1th event sequence, namely, a candidate sequence of task-induced hemodynamic activation onsets (HAOs) at the origin of the observations. | ||
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If you are interested, you can find more information in the two following articles. In the second article, the neighborhood of v is not considered (N=1). | If you are interested, you can find more information in the two following articles. In the second article, the neighborhood of v is not considered (N=1). | ||
* S. Faisan, L. Thoraval, J.-P. Armspach, F. Heitz, Hidden Markov multiple event sequence models : a paradigm for the spatio-temporal analysis of fMRI data . Medical Image Analysis, pp. 1--20, Vol. 11, Num. 1, [http://dx.doi.org/10.1016/j.media.2006.09.003 doi:10.1016/j.media.2006.09.003], February 2007. | * S. Faisan, L. Thoraval, J.-P. Armspach, F. Heitz, Hidden Markov multiple event sequence models : a paradigm for the spatio-temporal analysis of fMRI data . Medical Image Analysis, pp. 1--20, Vol. 11, Num. 1, [http://dx.doi.org/10.1016/j.media.2006.09.003 doi:10.1016/j.media.2006.09.003], February 2007. | ||
− | * S. Faisan, L. Thoraval, J.-P. Armspach, M.-N. Lutz, F. Heitz, Unsupervised learning and mapping of active brain functional MRI signals based on hidden semi-Markov event sequence models. IEEE Transactions on Medical Imaging, pp. 263-276, Vol. 24, Num. 2, [http://dx.doi.org/10.1109/TMI.2004.841225 doi:10.1109/TMI.2004.841225], February 2005. [[File:problemStatement.jpg|frameless|thumb|upright=2.9]] | + | * S. Faisan, L. Thoraval, J.-P. Armspach, M.-N. Lutz, F. Heitz, Unsupervised learning and mapping of active brain functional MRI signals based on hidden semi-Markov event sequence models. IEEE Transactions on Medical Imaging, pp. 263-276, Vol. 24, Num. 2, [http://dx.doi.org/10.1109/TMI.2004.841225 doi:10.1109/TMI.2004.841225], February 2005. |
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== Publications == | == Publications == |
Version du 20 mars 2013 à 13:13
Maître de conférences
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Description des activités
Polarimetric Image Processing
To do
Decomposition of Spectroscopic signal sequences
To do
Retinal Image Registration
To do
Skull Image Analysis
functional MRI -- Brain Connectivity analysis
To do
Functional MRI -- Brain Mapping
Activation detection at voxel v is formulated in terms of temporal alignment between sequences of hemodynamic response onsets detected in the fMRI signal at v and in the spatial neighborhood of v, and the input sequence of stimuli or stimulus onsets (see Fig. a). The multiple event sequence alignment problem is solved within the probabilistic framework of hidden Markov multiple event sequence models (HMMESMs). It consists in hypothesizing about a set of valid scenarios that could explain the N observed HRO event sequences. A valid scenario (see Fig. b), relies on the combination of: ||
If you are interested, you can find more information in the two following articles. In the second article, the neighborhood of v is not considered (N=1).
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Publications
<anyweb> http://newlsiit.u-strasbg.fr/papr/appli.php?author=faisan&title=&labo=tous&team=toutes&annee1=&annee2=&display=rap+&nationalRank=toutes&project=tous&hide=0&hide=0 </anyweb>