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

2026-05-24

AI Summary

Existing spherical-based fMRI decoders struggle to balance performance and geometric fidelity due to inefficient spherical tokenization and the neglect of individual anatomical structure. This work addresses these limitations by explicitly modeling cortical anatomical features as inductive priors, introducing a Selective Region-of-Interest Spherical Tokenizer (SRST) and a Structure-Guided Mixture-of-Experts module (SG-MoE) to enable efficient geometric encoding and seamless integration of anatomical information. Evaluated on the Natural Scenes Dataset, the proposed method achieves a new state-of-the-art in surface-based decoding, matching the performance of leading 1D baselines while accelerating training convergence by 30× and enabling rapid adaptation to new subjects with only 20% of the data.

Abstract

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.

Problem

• fMRI decoding
• surface-based modeling
• anatomical priors
• performance-fidelity trade-off
• cortical anatomy

Innovation

• inductive priors
• surface-based fMRI decoding
• spherical tokenization
• mixture of experts
• anatomical features