SOURCE-LINKED INTELLIGENCE
A Scaling Study for fMRI Foundation Models
Scaling laws have guided large-model development in computer vision and natural language processing, but the relationships among data, model size, and compute remain unclear for functional magnetic resonance imaging (fMRI) foundation models. Here, we conduct a controlled empirical study using pretraining data from more than 200 source datasets and over 10,000 GPU-hours of experiments. Holding the pretraining framework and downstream protocol fixed, we vary pretraining data size, model size, and training duration. Downstream performance generally improves with compute, yet models using similar
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- arXiv · AI, language, vision and robotics · 2026-09-23T02:01:27.000Z
First collected: 2026-09-24T01:22:21.678Z. This is not the publication date.