A Riemannian Revisiting of Structure–Function Mapping Based on Eigenmodes

• Published in: Frontiers in neuroimaging Vol. 1; p. 850266
• Main Authors: Deslauriers-Gauthier, Samuel, Zucchelli, Mauro, Laghrissi, Hiba, Deriche, Rachid
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media 25.05.2022; Frontiers Media S.A
• Subjects: Cognitive science; Computer Science; Medical Imaging; Neuroimaging; Neuroscience; structural connectivity; Riemannian distance; brain structure-function mapping; functional connectivity; eigenvalue decomposition
• ISSN: 2813-1193; 2813-1193
• DOI: 10.3389/fnimg.2022.850266

Understanding the link between brain structure and function may not only improve our knowledge of brain organization, but also lead to better quantification of pathology. To quantify this link, recent studies have attempted to predict the brain's functional connectivity from its structural connectivity. However, functional connectivity matrices live in the Riemannian manifold of the symmetric positive definite space and a specific attention must be paid to operate on this appropriate space. In this work we investigated the implications of using a distance based on an affine invariant Riemannian metric in the context of structure–function mapping. Specifically, we revisit previously proposed structure–function mappings based on eigendecomposition and test them on 100 healthy subjects from the Human Connectome Project using this adapted notion of distance. First, we show that using this Riemannian distance significantly alters the notion of similarity between subjects from a functional point of view. We also show that using this distance improves the correlation between the structural and functional similarity of different subjects. Finally, by using a distance appropriate to this manifold, we demonstrate the importance of mapping function from structure under the Riemannian manifold and show in particular that it is possible to outperform the group average and the so–called glass ceiling on the performance of mappings based on eigenmodes.