SynBrain: Enhancing Visual-to-fMRI Synthesis via Probabilistic Representation Learning

TL;DR

SynBrain is a probabilistic generative framework that models the transformation from visual stimuli to neural responses, capturing biological variability and improving fMRI synthesis and decoding performance.

Contribution

It introduces a novel probabilistic model with semantic constraints for visual-to-fMRI synthesis, surpassing existing methods and enabling efficient adaptation to new subjects.

Findings

Outperforms state-of-the-art in subject-specific encoding

Enables few-shot adaptation to new subjects

Synthesizes high-quality, interpretable fMRI signals

Abstract

Deciphering how visual stimuli are transformed into cortical responses is a fundamental challenge in computational neuroscience. This visual-to-neural mapping is inherently a one-to-many relationship, as identical visual inputs reliably evoke variable hemodynamic responses across trials, contexts, and subjects. However, existing deterministic methods struggle to simultaneously model this biological variability while capturing the underlying functional consistency that encodes stimulus information. To address these limitations, we propose SynBrain, a generative framework that simulates the transformation from visual semantics to neural responses in a probabilistic and biologically interpretable manner. SynBrain introduces two key components: (i) BrainVAE models neural representations as continuous probability distributions via probabilistic learning while maintaining functional consistency through visual semantic constraints; (ii) A Semantic-to-Neural Mapper acts as a semantic transmission pathway, projecting visual semantics into the neural response manifold to facilitate high-fidelity fMRI synthesis. Experimental results demonstrate that SynBrain surpasses state-of-the-art methods in subject-specific visual-to-fMRI encoding performance. Furthermore, SynBrain adapts efficiently to new subjects with few-shot data and synthesizes high-quality fMRI signals that are effective in improving data-limited fMRI-to-image decoding performance. Beyond that, SynBrain reveals functional consistency across trials and subjects, with synthesized signals capturing interpretable patterns shaped by biological neural variability. Our code is available at https://github.com/MichaelMaiii/SynBrain.