Natural Image Autoencoder-Based fMRI Representations for Trait and State Prediction
Foundation models pre-trained on large-scale fMRI datasets have shown strong downstream performance, but at substantial data and computation cost. To investigate how much fMRI-specific pre-training is actually needed for such performance, we introduce FReD, which derives fMRI representations from a frozen Deep Compression AutoEncoder (DCAE) pre-trained exclusively on natural images and pairs them with a task specific readout. For trait prediction, FReD summarizes frame-wise representations by their temporal mean and log-standard deviation and applies linear probing, with late fusion across two normalization schemes. For state prediction, it represents each frame as a single token and mode...
| Authors | Juhyeon Park, Yeonwoo Kim, Peter Yongho Kim et al. |
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| Primary category | cs.CV |
| Published | 28 September 2026 |
| Last updated at source | 29 September 2026 |
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