The ISLab Solution to the Algonauts Challenge 2025: A Multimodal Deep Learning Approach to Brain Response Prediction

TL;DR

This paper introduces a multimodal deep learning approach that predicts brain responses to complex movies by modeling functional brain networks separately, significantly improving accuracy and achieving a top ranking in the Algonauts Challenge 2025.

Contribution

The work presents a novel network-specific modeling strategy that clusters brain networks and trains separate models, enhancing prediction accuracy over previous homogeneous approaches.

Findings

Achieved nearly double the baseline correlation scores in OOD tests.

Significantly improved prediction accuracy across 1,000 cortical regions.

Ranked eighth in the Algonauts Challenge 2025.

Abstract

In this work, we present a network-specific approach for predicting brain responses to complex multimodal movies, leveraging the Yeo 7-network parcellation of the Schaefer atlas. Rather than treating the brain as a homogeneous system, we grouped the seven functional networks into four clusters and trained separate multi-subject, multi-layer perceptron (MLP) models for each. This architecture supports cluster-specific optimization and adaptive memory modeling, allowing each model to adjust temporal dynamics and modality weighting based on the functional role of its target network. Our results demonstrate that this clustered strategy significantly enhances prediction accuracy across the 1,000 cortical regions of the Schaefer atlas. The final model achieved an eighth-place ranking in the Algonauts Project 2025 Challenge, with out-of-distribution (OOD) correlation scores nearly double those of the baseline model used in the selection phase. Code is available at https://github.com/Corsi01/algo2025.