Author

Élise Bannier

Centre National de la Recherche ScientifiqueORCID 0000-0002-8942-7486Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Neuroscience· 3

Signature Assays, Organisms & Methods

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Approach:experimental assay· 3 Organism:human· 2 Research Focus:benchmarking· 1 Research Focus:database / resource· 1 Assay:electronic health records· 1 Assay:imaging· 1 Assay:multiome· 1

Frequent Co-Authors

Christian BarillotiD· 3 shared Marsel ManoiD· 2 shared Anatole LécuyeriD· 1 shared Claire CuryiD· 1 shared Emmanuel J. BarbeauiD· 1 shared Giulia LioiiD· 1 shared Isabelle CorougeiD· 1 shared Jean‐Christophe FerréiD· 1 shared

Activity by Year

2019-2020 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 23 mutual co-author links

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3 shared papersNeuroscience

Christian BarillotORCID

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Author profile →

3 papers

Recent·Most cited

Building memories on prior knowledge: behavioral and fMRI evidence of impairment in early Alzheimer's Disease

Pierre‐Yves Jonin, Quentin Duché, Élise Bannier, Isabelle Corouge, Jean‐Christophe Ferré, Serge Belliard, Christian Barillot, Emmanuel J. Barbeau

This study investigates whether prior knowledge enhances memory encoding in early Alzheimer's Disease using fMRI, finding that patients do not benefit from pre-experimental knowledge while controls do.

bioRxiv · Jun 17, 2020Neuroscience

Simultaneous MRI-EEG during a motor imagery neurofeedback task: an open access brain imaging dataset for multi-modal data integration.

Giulia Lioi, Claire Cury, Lorraine Perronnet, Marsel Mano, Élise Bannier, Anatole Lécuyer, Christian Barillot

This paper presents a multimodal dataset combining EEG and fMRI during a motor imagery neurofeedback task, enabling research on multi-modal data integration and neurofeedback methodologies.

Scientific Data · Jun 10, 2020Peer-reviewedNeuroscience

Automated Electrodes Detection during simultaneous EEG/fMRI

Mathis Fleury, Christian Barillot, Marsel Mano, Élise Bannier, Pierre Maurel

This paper introduces an automated method to detect EEG electrodes in MR images during simultaneous EEG/fMRI, achieving 94% detection accuracy and 3.1 mm average position error.

Frontiers in ICT · Jan 13, 2019Peer-reviewedNeuroscience