用80小时电影预测人脑活动，冠军团队突破了自然场景建模瓶颈。
Insights from the Algonauts 2025 Winners
- 基于多模态长视频构建脑活动编码模型，覆盖1000个脑区
- 在未见电影上实现高精度预测，验证模型泛化能力
- 揭示当前模型对复杂自然输入的响应机制，适合神经科学与AI交叉研究者
Algonauts 2025挑战赛刚刚结束，这是每两年一次的计算神经科学竞赛，旨在通过精心设计的刺激构建能预测人类脑活动的模型。此前的2019、2021、2023年均聚焦静态图像和短视频，而2025年首次采用长时间、多模态电影作为刺激材料。参赛团队需预测4名受试者在观看近80小时自然主义电影时的全脑1000个脑区的fMRI响应。数据来自CNeuroMod项目，包含65小时训练数据（约55小时《老友记》1-6季及四部电影：《谍影重重2》《隐藏的真相》《生命》《华尔街之狼》），其余用于验证：《老友记》第7季用于分布内测试，最终冠军由在六个未见电影上的预测表现最优团队获得。本文回顾了排名第4的MedARC团队的方法，分析其成功之处、对脑编码现状的启示及未来方向。
原文摘要 · Abstract (English)
The Algonauts 2025 Challenge just wrapped up a few weeks ago. It is a biennial challenge in computational neuroscience in which teams attempt to build models that predict human brain activity from carefully curated stimuli. Previous editions (2019, 2021, 2023) focused on still images and short videos; the 2025 edition, which concluded last month (late July), pushed the field further by using long, multimodal movies. Teams were tasked with predicting fMRI responses across 1,000 whole-brain parcels across four participants in the dataset who were scanned while watching nearly 80 hours of naturalistic movie stimuli. These recordings came from the CNeuroMod project and included 65 hours of training data, about 55 hours of Friends (seasons 1-6) plus four feature films (The Bourne Supremacy, Hidden Figures, Life, and The Wolf of Wall Street). The remaining data were used for validation: Season 7 of Friends for in-distribution tests, and the final winners for the Challenge were those who could best predict brain activity for six films in their held-out out-of-distribution (OOD) set. The winners were just announced and the top team reports are now publicly available. As members of the MedARC team which placed 4th in the competition, we reflect on the approaches that worked, what they reveal about the current state of brain encoding, and what might come next.
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