Insights from the Algonauts 2025 Winners

TL;DR

The paper discusses the outcomes of the Algonauts 2025 Challenge, where teams developed models to predict human brain activity from long, multimodal movie stimuli, highlighting current progress and future directions in brain encoding research.

Contribution

This paper provides an analysis of the winning approaches in the Algonauts 2025 Challenge, emphasizing advancements in modeling brain responses to complex naturalistic stimuli.

Findings

Top models achieved significant accuracy in predicting brain activity.

Multimodal and long-duration stimuli improve brain response modeling.

Insights into current capabilities and limitations of brain encoding models.

Abstract

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.