{"ID":6536301,"CreatedAt":"2026-07-14T01:21:01.169441415Z","UpdatedAt":"2026-07-15T03:28:55.185153975Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.10931","arxiv_id":"2607.10931","title":"Fast Whole-Brain, Geometry-Aware Functional Alignment for Cross-Subject Decoding","abstract":"Decoding brain activity is useful for characterizing brain processes and understanding the functional architecture underlying cognition. However, the inter-individual variability in brain response patterns limits the development of decoders that generalize across individuals. A solution to this challenge is functional alignment: aligning functional data across individuals before training population-level decoders. The core issue is to strike the balance between aligning functional features and preserving the anatomical structure, while maintaining computational efficiency. We introduce a new functional alignment method for fMRI, SpectralOT, that embeds cortical geometry into Laplace-Beltrami eigenmodes along functional data to regularize the alignment.","short_abstract":"Decoding brain activity is useful for characterizing brain processes and understanding the functional architecture underlying cognition. However, the inter-individual variability in brain response patterns limits the development of decoders that generalize across individuals. A solution to this challenge is functional...","url_abs":"https://arxiv.org/abs/2607.10931","url_pdf":"https://arxiv.org/pdf/2607.10931v1","authors":"[\"Pierre-Louis Barbarant\",\"Florent Meyniel\",\"Bertrand Thirion\"]","published":"2026-07-12T21:37:33Z","proceeding":"q-bio.NC","tasks":"[\"q-bio.NC\",\"cs.LG\",\"stat.ML\"]","methods":"[]","has_code":false}