A Multimodal Seq2Seq Transformer for Predicting Brain Responses to Naturalistic Stimuli

Qianyi He, Yuan Chang Leong, Monica Rosenberg

Keywords: neural encoding, sequence-to-sequence transformer, naturalistic movies, fMRI

TL;DR: We propose a multimodal sequence-to-sequence Transformer that autoregressively predicts brain responses to movies using perceptual and narrative features, achieving strong in- and out-of-distribution fMRI prediction.

Presentation Preference: Yes

Abstract: The Algonauts 2025 Challenge called on the community to develop encoding models that predict whole-brain fMRI responses to naturalistic multimodal movies. In this submission, we propose a sequence-to-sequence Transformer that autoregressively predicts fMRI activity from visual, auditory, and language inputs. Stimulus features were extracted using pretrained models including VideoMAE, HuBERT, Qwen, and BridgeTower. The decoder integrates information from prior brain states and current stimuli via dual cross-attention mechanisms that attend to both perceptual information extracted from the stimulus as well as narrative information provided by high-level summaries of the content. One core innovation of our approach is the use of sequences of multimodal context to predict sequences of brain activity, enabling the model to capture long-range temporal structure in both stimuli and neural responses. Another is the combination of a shared encoder with partial subject-specific decoder, which leverages common representational structure across subjects while accounting for individual variability. Our model achieves strong performance on both in-distribution and out-of-distribution data, demonstrating the effectiveness of temporally-aware, multimodal sequence modeling for brain activity prediction. The code is available at https://github.com/Angelneer926/Algonauts_challenge.

Submission Number: 26