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Paper Detail

Paper:	SS-8.2
Session:	Advanced Methods for Mapping Brain Functions from Functional MRI Datasets
Time:	Thursday, May 18, 14:20 - 14:40
Presentation:	Special Session Lecture
Topic:	Special Sessions: Advanced methods for mapping brain functions from functional MRI datasets
Title:	Hidden markovian modeling and analysis of multiple-event-sequence-based random processes. Application to robust detection of brain functional activation
Authors:	Sylvain Faisan, Laurent Thoraval, Fabrice Heitz, LSIIT / UMR CNRS-ULP 7005, France; Jean-Paul Armspach, UMR CNRS-ULP 7004, France
Abstract:	This paper presents a novel statistical approach for the modeling and analysis of structured random processes observed through multiple event sequences: the hidden Markov multiple event sequence model (HMMESM). This model accounts for several features of these processes: (i) the hidden-observable aspect of the event sequences to be analyzed, (ii) the multiplicity of the observed event sequences, (iii) the non stationary, time-localized character of their events, (iv) the redundancy, complementarity, and strong asynchrony that exist between events across sequences. A first application of this model in functional MRI (fMRI) brain mapping is presented. The developed method shows high robustness to noise and variability of the active fMRI signals.