Fast Whole-Brain, Geometry-Aware Functional Alignment for Cross-Subject Decoding

Pierre-Louis Barbarant; Florent Meyniel; Bertrand Thirion

q-bio.NCJul 12, 2026

Fast Whole-Brain, Geometry-Aware Functional Alignment for Cross-Subject Decoding

Authors: Pierre-Louis Barbarant, Florent Meyniel, Bertrand Thirion

Organizations: Université Paris-Saclay, Inria, CEA, Palaiseau 91120, France · Cognitive Neuroimaging Unit, Institut National de la Santé et de la Recherche Médicale, Commissariat à l’Energie Atomique et aux énergies alternatives, Université Paris-Saclay, NeuroSpin center, 91191 Gif/Yvette, France · Institut de neuromodulation, GHU Paris, psychiatrie et neurosciences, centre hospitalier Sainte-Anne, pôle hospitalo-universitaire 15, Université Paris Cité, Paris, France

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.

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NeurIPS: Neuro-anatomical Inductive Priors for Sphere-based Brain Decoding

Sijin Yu, Zijiao Chen, Zhenyu Yang, Zihao Tan+6

South China University of Technology · Stanford University · King’s College London · Foshan University · Pazhou Lab.

Current fMRI decoders face a performance-fidelity trade-off where efficient ID encoders outperform geometrically faithful surface-based models. We argue this is partly driven by inefficient surface tokenization and the failure to use anatomy as a predictive signal. We present NeurIPS, a framework that improves surface-based decoding by reframing anatomical variation from a nuisance to a powerful inductive prior. NeurIPS unites two innovations: a Selective ROI Spherical Tokenizer (SRST) for efficient geometric encoding, and a Structure-Guided Mixture of Experts (SG-MoE) that explicitly models individual anatomy using cortical features. On the Natural Scenes Dataset, NeurIPS establishes a new state-of-the-art for surface decoders and achieves performance comparable to strong 1D baselines. This is achieved with unprecedented efficiency, as the model converges dramatically faster (10 vs. 600 epochs). This efficiency enables rapid adaptation to new subjects using only 20% of data and ensures robust scalability as the training cohort is expanded. Ablations provide causal evidence that these gains are driven by the model's use of cortical features, not by memorizing subject IDs. By leveraging anatomical priors, NeurIPS provides a principled and scalable path toward robust, generalizable brain decoding.

Fmri Anatomical Priors Human Visual Cortex Implicit Neural Representations

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Abstract

Explore similar work

NeurIPS: Neuro-anatomical Inductive Priors for Sphere-based Brain Decoding

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