Deep Recurrent Encoder: an end-to-end network to model magnetoencephalography at scale
Article dans une revue avec comité de lecture
Date
2022-10Journal
Neurons, Behavior, Data analysis, and TheoryRésumé
Understanding how the brain responds to sensory inputs from non-invasive brain recordings like magnetoencephalography (MEG) can be particularly challenging: (i) the high-dimensional dynamics of mass neuronal activity are notoriously difficult to model, (ii) signals can greatly vary across subjects and trials and (iii) the relationship between these brain responses and the stimulus features is non-trivial. These challenges have led the community to develop a variety of preprocessing and analytical (almost exclusively linear) methods, each designed to tackle one of these issues. Instead, we propose to address these challenges through a specific end-to-end deep learning architecture, trained to predict the MEG responses of multiple subjects at once. We successfully test this approach on a large cohort of MEG recordings acquired during a one-hour reading task. Our Deep Recurrent Encoder (DRE) reliably predicts MEG responses to words with a three-fold improvement over classic linear methods. We further describe a simple variable importance analysis to investigate the MEG representations learnt by our model and recover the expected evoked responses to word length and word frequency. Last, we show that, contrary to linear encoders, our model captures modulations of the brain response in relation to baseline fluctuations in the alpha frequency band. The quantitative improvement of the present deep learning approach paves the way to a better characterization of the complex dynamics of brain activity from large MEG datasets.
Fichier(s) constituant cette publication
- Nom:
- DYNFLUID_NBDT_2022_LOISEAU.pdf
- Taille:
- 9.060Mo
- Format:
- Description:
- Deep Recurrent Encoder: an ...
- Fin d'embargo:
- 2023-05-12
Cette publication figure dans le(s) laboratoire(s) suivant(s)
Documents liés
Visualiser des documents liés par titre, auteur, créateur et sujet.
-
Article dans une revue avec comité de lectureCHERUBINI, Stefania; LERICHE, Emmanuel; ROBINET, Jean-Christophe; LOISEAU, Jean-Christophe (Cambridge University Press (CUP), 2014)The linear global instability and resulting transition to turbulence induced by an isolated cylindrical roughness element of height h and diameter d immersed within an incompressible boundary layer flow along a flat plate ...
-
Article dans une revue avec comité de lecturePICELLA, Francesco; LUSSEYRAN, F; CHERUBINI, Stefania; PASTUR, L; ROBINET, Jean-Christophe; LOISEAU, Jean-Christophe (Cambridge University Press (CUP), 2018)The transition to unsteadiness of a three-dimensional open cavity flow is investigated using the joint application of direct numerical simulations and fully three-dimensional linear stability analyses, providing a clear ...
-
Article dans une revue avec comité de lectureThe objective of this work is to investigate numerically the different physical mechanisms of the transition to turbulence of a separated boundary-layer flow over an airfoil at low angle of attack. In this study, the ...
-
Article dans une revue avec comité de lectureTransition from steady state to intermittent chaos in the cubical lid-driven flow is investigated numerically. Fully three-dimensional stability analyses have revealed that the flow experiences an Andronov-Poincaré-Hopf ...
-
Article dans une revue avec comité de lectureBUCCI, Michele Alessandro; PUCKERT, Dominik K.; ANDRIANO, Cesare; CHERUBINI, Stefania; RIST, Ulrich; ROBINET, Jean-Christophe; LOISEAU, Jean-Christophe (Cambridge University Press (CUP), 2017)The onset of unsteadiness in a boundary-layer flow past a cylindrical roughness element is investigated for three flow configurations at subcritical Reynolds numbers, both experimentally and numerically. On the one hand, ...