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Olivier Bisson

Publications and source records attributed to Olivier Bisson.

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Log-Euclidean Lie Groups

We develop a self-contained theory of log-Euclidean Lie groups: smooth manifolds diffeomorphic to finite-dimensional vector spaces, equipped with the pullback of a constant Euclidean metric. This framework encompasses symmetric positive-definite (SPD) matrices S+(n) and full-rank correlation matrices Cor+(n), and explains why many seemingly different log-Euclidean constructions yield the same Riemannian geometry. We provide explicit Riemannian isometries (and Lie group isomorphisms) linking several log-Euclidean metrics on SPD and correlation matrix manifolds, and we characterize quotient log-Euclidean metrics in a principal-bundle setting. Finally, using the diagonal correction map underlying the off-log parametrization, we construct an explicit log-Euclidean metric on S+(n) for which the standard inclusion i\,: Cor+(n) $\rightarrow$ S+(n) becomes an isometric (indeed, totally geodesic) embedding, yielding closed-form formulas for geodesics and orthogonal decompositions in adapted coordinates. The nested isometric embeddings constructed here also provide a simple solution to the comparison of matrices of different dimensions in the log-Euclidean setting: SPD or correlation matrices may be transported to a common dimension via explicit maps while preserving all intrinsic distances.

math.DG

Log-Euclidean Frameworks for Smooth Brain Connectivity Trajectories

The brain is often studied from a network perspective, where functional activity is assessed using functional Magnetic Resonance Imaging (fMRI) to estimate connectivity between predefined neuronal regions. Functional connectivity can be represented by correlation matrices computed over time, where each matrix captures the Pearson correlation between the mean fMRI signals of different regions within a sliding window. We introduce several Log-Euclidean Riemannian framework for constructing smooth approximations of functional brain connectivity trajectories. Representing dynamic functional connectivity as time series of full-rank correlation matrices, we leverage recent theoretical Log-Euclidean diffeomorphisms to map these trajectories in practice into Euclidean spaces where polynomial regression becomes feasible. Pulling back the regressed curve ensures that each estimated point remains a valid correlation matrix, enabling a smooth, interpretable, and geometrically consistent approximation of the original brain connectivity dynamics. Experiments on fMRI-derived connectivity trajectories demonstrate the geometric consistency and computational efficiency of our approach.

stat.AP