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

2026-07-12

AI Summary

This work addresses the limited generalization performance in cross-subject brain functional decoding caused by inter-individual variability in neural responses. To overcome this challenge, the authors propose SpectralOT, a novel method that, for the first time, integrates spectral features of the Laplace–Beltrami operator into functional data and leverages optimal transport theory to construct a geometry-aware whole-brain alignment framework. By explicitly incorporating cortical geometric structure during functional alignment, the approach enhances computational efficiency while preserving anatomical consistency. Experimental results demonstrate that SpectralOT significantly improves the generalization capability of cross-subject decoding models, offering a new paradigm for high-precision brain functional analysis.

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.

Problem

• functional alignment
• cross-subject decoding
• inter-individual variability
• fMRI
• cortical geometry

Innovation

• functional alignment
• SpectralOT
• Laplace-Beltrami eigenmodes
• cross-subject decoding
• geometry-aware