A unified framework for multimodal structure–function mapping based on eigenmodes

• Published in: Medical image analysis Vol. 66; p. 101799
• Main Authors: Deslauriers-Gauthier, Samuel, Zucchelli, Mauro, Frigo, Matteo, Deriche, Rachid
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.12.2020; Elsevier BV; Elsevier
• Subjects: Brain; Brain mapping; Computational neuroscience; Computer Science; Functional anatomy; Life Sciences; Mapping; Medical Imaging; Neural networks; Neurons and Cognition; Structure-function relationships; Substantia alba
• ISSN: 1361-8415; 1361-8423; 1361-8423
• DOI: 10.1016/j.media.2020.101799

•Structure-function mappings based on eigenmodes are unified in a general framework.•Two new mappings are proposed and their performance is compared to existing mappings.•Recently published results are reproduced on 50 subjects of the HCP.•A glass ceiling on the prediction of the functional connectivity is obtained. [Display omitted] Characterizing the connection between brain structure and brain function is essential for understanding how behaviour emerges from the underlying anatomy. A number of studies have shown that the network structure of the white matter shapes functional connectivity. Therefore, it should be possible to predict, at least partially, functional connectivity given the structural network. Many structure–function mappings have been proposed in the literature, including several direct mappings between the structural and functional connectivity matrices. However, the current literature is fragmented and does not provide a uniform treatment of current methods based on eigendecompositions. In particular, existing methods have never been compared to each other and their relationship explicitly derived in the context of brain structure–function mapping. In this work, we propose a unified computational framework that generalizes recently proposed structure–function mappings based on eigenmodes. Using this unified framework, we highlight the link between existing models and show how they can be obtained by specific choices of the parameters of our framework. By applying our framework to 50 subjects of the Human Connectome Project, we reproduce 6 recently published results, devise two new models and provide a direct comparison between all mappings. Finally, we show that a glass ceiling on the performance of mappings based on eigenmodes seems to be reached and conclude with possible approaches to break this performance limit.