Published in Arxiv, 2020
An important goal of cognitive brain imaging studies is to model the functional organization of the brain; yet there exists currently no functional brain atlas built from existing data. One of the main road-blocks to the creation of such an atlas is the functional variability that is observed in subjects performing the same task; this variability goes far beyond anatomical variability in brain shape and size. Function-based alignment procedures have recently been proposed in order to improve the correspondence of activation patterns across individuals. However, the corresponding computational solutions are costly and not well-principled. Here, we propose a new framework based on optimal transport theory to create such a template. We leverage entropic smoothing as an efficient means to create brain templates without losing fine-grain structural information; it is implemented in a computationally efficient way. We evaluate our approach on rich multi-subject, multi-contrasts datasets. These experiments demonstrate that the template-based inference procedure improves the transfer of information across individuals with respect to state of the art methods.
Recommended citation: Richard, H., Gresele, L., Hyvärinen, A., Thirion, B., Gramfort, A., & Ablin, P. (2020). Modeling Shared Responses in Neuroimaging Studies through MultiView ICA. arXiv preprint arXiv:2006.06635. https://arxiv.org/pdf/2006.06635.pdf