Raphaël Pierre Liégeois

Cette personne n’est plus à l’EPFL

Publications associées (15)

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Graph Chatbot

Questions and controversies in the study of time-varying functional connectivity in resting fMRI

Raphaël Pierre Liégeois, Jonathan Raël Lurie, Daniel Kessler

The brain is a complex, multiscale dynamical system composed of many interacting regions. Knowledge of the spatiotemporal organization of these interactions is critical for establishing a solid understanding of the brain's functional architecture and the r ...

MIT PRESS2020

Time-resolved analysis of dynamic graphs: an extended Slepian design

Dimitri Nestor Alice Van De Ville, Raphaël Pierre Liégeois, Ibrahim Merad

Graphs are extensively used to represent networked data. In many applications, especially when considering large datasets, it is a desirable feature to focus the analysis onto specific subgraphs of interest. Slepian theory and its extension to graphs allow ...

SPIE-INT SOC OPTICAL ENGINEERING2019

Guided graph spectral embedding: Application to the C. elegans connectome

Dimitri Nestor Alice Van De Ville, Thomas William Arthur Bolton, Maria Giulia Preti, Raphaël Pierre Liégeois, Miljan Petrovic

Graph spectral analysis can yield meaningful embeddings of graphs by providing insight into distributed features not directly accessible in nodal domain. Recent efforts in graph signal processing have proposed new decompositions-for example, based on wavel ...

2019

Resting brain dynamics at different timescales capture distinct aspects of human behavior

Dimitri Nestor Alice Van De Ville, Raphaël Pierre Liégeois

Linking human behavior to resting-state brain function is a central question in systems neuroscience. In particular, the functional timescales at which different types of behavioral factors are encoded remain largely unexplored. The behavioral counterparts ...

Springer2019

Dynamic mode decomposition of resting-state and task fMRI

Dimitri Nestor Alice Van De Ville, Raphaël Pierre Liégeois

Component analysis is a powerful tool to identify dominant patterns of interactions in multivariate datasets. In the context of fMRI data, methods such as principal component analysis or independent component analysis have been used to identify the brain n ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2019

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