Sylvain Faisan

Maître de conférences

ICube - MIV 300 Bd Sébastien Brant BP 10413 67412 Illkirch CEDEX - France Tel : + 33 (0) 3 68 85 44 89 Fax : + 33 (0) 3 68 85 44 97 Bureau : C211 Courriel : faisan(at)unistra(dot)fr

Description des activités

Polarimetric Image Processing

To do

Decomposition of Spectroscopic signal sequences

To do

Retinal Image Registration

To do

functional MRI -- Brain Connectivity analysis

To do

vignette|test

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: an N + 1th event sequence, namely, a candidate sequence of task-induced hemodynamic activation onsets (HAOs) at the origin of the observations. HAO signatures, that is, associations of HRO events across channels, each association corresponding to the observable counterpart of a single HAO event. By definition, a signature is composed of one HRO event by observation channel, the event being observed (black line) or not (black point) (see Fig. b). Causality constraints between signatures given an HAO sequence. 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, 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, doi:10.1109/TMI.2004.841225, February 2005.||

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>