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 roadblocks 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. We proposed a new framework based on optimal transport theory to create such a template. We leverage entropic smoothing as a mean to create brain templates efficiently without losing fine-grain structural information; it is implemented in a computationally efficient way. We provide fmralign an open-source package to use this framework.
Publication:
- Bazeille, T. Richard, H., Janati, H., Thirion, B. Local Optimal Transport for Functional Brain Template Estimation. In Proceedings of Information Processing in Medical Imaging (2019) <hal-02278663>
