Adaptive experimental condition selection in event-related fMRI

Christine Bakhous 1 Florence Forbes 1 Thomas Vincent 1, 2 Lotfi Chaari 1 Michel Dojat 3 Philippe Ciuciu 2, *
* Corresponding author
1 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : Standard Bayesian analysis of event-related functional Magnetic Resonance Imaging (fMRI) data usually assumes that all delivered stimuli possibly generate a BOLD response everywhere in the brain although activation is likely to be induced by only some of them in specific brain areas. Criteria are not always available to select the relevant conditions or stimulus types (e.g. visual, auditory, etc.) prior to estimation and the unnecessary inclusion of the corresponding events may degrade the results. To face this issue, we propose within a Joint Detection Estimation (JDE) framework, a procedure that automatically selects the conditions according to the brain activity they elicit. It follows an improved activation detection that we illustrate on real data.
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Submitted on : Thursday, June 21, 2012 - 8:00:52 AM
Last modification on : Wednesday, April 11, 2018 - 1:59:30 AM
Long-term archiving on: Saturday, September 22, 2012 - 2:25:08 AM

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Christine Bakhous, Florence Forbes, Thomas Vincent, Lotfi Chaari, Michel Dojat, et al.. Adaptive experimental condition selection in event-related fMRI. ISBI 2012 - IEEE International Symposium on Biomedical Imaging, May 2012, Barcelone, Spain. pp.1755-1758, ⟨10.1109/ISBI.2012.6235920⟩. ⟨cea-00710489⟩

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