title: Symmetrical event-related EEG/fMRI information fusion in a variational Bayesian framework.
abstract: In this work, we propose a symmetrical multimodal EEG/fMRI information fusion approach dedicated to the identification of event-related bioelectric and hemodynamic responses. Unlike existing, asymmetrical EEG/fMRI data fusion algorithms, we build a joint EEG/fMRI generative model that explicitly accounts for local coupling/uncoupling of bioelectric and hemodynamic activities, which are supposed to share a common substrate. Under a dedicated assumption of spatio-temporal separability, the spatial profile of the common EEG/fMRI sources is introduced as an unknown hierarchical prior on both markers of cerebral activity. Thereby, a devoted Variational Bayesian (VB) learning scheme is derived to infer common EEG/fMRI sources from a joint EEG/fMRI dataset. This yields an estimate of the common spatial profile, which is built as a trade-off between information extracted from EEG and fMRI datasets. Furthermore, the spatial structure of the EEG/fMRI coupling/uncoupling is learned exclusively from the data. The proposed data generative model and devoted VBEM learning scheme thus provide an un-supervised well-balanced approach for the fusion of EEG/fMRI information. We first demonstrate our approach on synthetic data. Results show that, in contrast to classical EEG/fMRI fusion approach, the method proved efficient and robust regardless of the EEG/fMRI discordance level. We apply the method on EEG/fMRI recordings from a patient with epilepsy, in order to identify brain areas involved during the generation of epileptic spikes. The results are validated using intracranial EEG measurements.
authors.affiliation: Wellcome Department of Imaging Neuroscience, London, UK; INSERM U678, Paris F-75013, France. j.daunizeau@fil.ion.ucl.ac.uk
authors.forename: Jean
authors.initials: J
authors.lastname: Daunizeau
authors.forename: Christophe
authors.initials: C
authors.lastname: Grova
authors.forename: Guillaume
authors.initials: G
authors.lastname: Marrelec
authors.forename: Jérémie
authors.initials: J
authors.lastname: Mattout
authors.forename: Saad
authors.initials: S
authors.lastname: Jbabdi
authors.forename: Mélanie
authors.initials: M
authors.lastname: Pélégrini-Issac
authors.forename: Jean-Marc
authors.initials: JM
authors.lastname: Lina
authors.forename: Habib
authors.initials: H
authors.lastname: Benali
mesh_terms: D000328:Adult; D016477:Artifacts; D001499:Bayes Theorem; D001931:Brain Mapping; D002540:Cerebral Cortex / Q000098:blood supply / Q000503:physiopathology*; D003198:Computer Simulation; D004292:Dominance, Cerebral / Q000502:physiology; D004569:Electroencephalography; D004828:Epilepsies, Partial / Q000175:diagnosis / Q000503:physiopathology*; D005071:Evoked Potentials / Q000502:physiology*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional; D008279:Magnetic Resonance Imaging; D009778:Occipital Lobe / Q000503:physiopathology