分组脑区训练独立模型，提升复杂电影刺激下的脑响应预测精度。
The ISLab Solution to the Algonauts Challenge 2025: A Multimodal Deep Learning Approach to Brain Response Prediction
- 按功能网络聚类分组，为每组训练独立多层感知机模型。
- 在1000个皮层区域上预测准确率显著提升，跨分布测试得分翻倍。
- 适合关注脑区功能特异性与多模态数据建模的研究者。
本文提出一种针对复杂多模态电影刺激的脑响应预测方法，基于Schaefer图谱的Yeo 7网络划分。不将大脑视为均质系统，而是将七个功能网络聚类为四组，为每组训练独立的多被试、多层感知机（MLP）模型。该架构支持特定集群优化与自适应记忆建模，使各模型可根据目标网络的功能角色动态调整时序动态与模态权重。结果表明，该分组策略在Schaefer图谱的1,000个皮层区域上显著提升预测精度。最终模型在Algonauts Project 2025挑战赛中获得第八名，其分布外（OOD）相关性得分接近初选阶段基线模型的两倍。代码已开源：https://github.com/Corsi01/algo2025。
原文摘要 · Abstract (English)
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.
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