B
Bertrand Thirion
Researcher at Université Paris-Saclay
Publications - 334
Citations - 91237
Bertrand Thirion is an academic researcher from Université Paris-Saclay. The author has contributed to research in topics: Cluster analysis & Cognition. The author has an hindex of 51, co-authored 311 publications receiving 73839 citations. Previous affiliations of Bertrand Thirion include French Institute for Research in Computer Science and Automation & French Institute of Health and Medical Research.
Papers
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Book ChapterDOI
A comparative study of algorithms for intra- and inter-subjects fMRI decoding
TL;DR: The optimality of decoding methods in two different settings, namely intra- and inter-subject kind of decoding, is discussed, and it is shown that using spatial regularization improves reverse inference in the challenging context of inter- subject prediction.
Book ChapterDOI
Group level MEG/EEG source imaging via optimal transport: minimum Wasserstein estimates
TL;DR: A new procedure is proposed that can infer neural patterns similar to functional Magnetic Resonance Imaging (fMRI) maps while taking into account the geometrical structure of the cortex, called Minimum Wasserstein Estimates (MWE).
Posted Content
CanICA: Model-based extraction of reproducible group-level ICA patterns from fMRI time series
TL;DR: In this article, a probabilistic ICA pattern-extraction algorithm, called CanICA (Canonical ICA), is proposed to identify the group-reproducible data subspace before performing ICA.
Proceedings ArticleDOI
Revisiting non-parametric activation detection on fMRI time series
TL;DR: This paper proposes some new ways of detecting activations in fMRI sequences that require a minimum of hypotheses and avoid any a priori modelling of the expected signal, and tries to avoid linear assumptions and models.
Book ChapterDOI
Relating brain functional connectivity to anatomical connections: model selection
Fani Deligianni,Gaël Varoquaux,Bertrand Thirion,Emma C. Robinson,David J. Sharp,A. David Edwards,Daniel Rueckert +6 more
TL;DR: This work proposes a model selection framework based on cross-validation that selects the appropriate sparsity of the connectivity matrices and demonstrates that choosing an ordering for the MAR that lends to sparser models is more appropriate than a random.