19/25 𝗕𝗼𝗼𝘀𝘁𝗶𝗻𝗴 𝗕𝗿𝗮𝗶𝗻-𝘁𝗼-𝗜𝗺𝗮𝗴𝗲 𝗗𝗲𝗰𝗼𝗱𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗧𝗥𝗜𝗕𝗘 𝘃𝟐 𝗗𝗮𝘁𝗮 𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 This paper addresses the challenge of low-data regimes in brain decoding by augmenting small fMRI datasets with synthetic data generated by TRIBE v2, a large encoding model pretrained on over 1000 hours of fMRI responses. Evaluated on the 7T fMRI Natural Scenes Dataset and 3T fMRI BOLD5000, this approach achieves up to 68% improvement in Top-10 image-retrieval accuracy. Notably, image decoders trained exclusively on synthetic fMRI can perform above chance, suggesting the potential for zero-shot brain-to-image decoding and significantly improving data efficiency. #BrainDecoding #fMRI #SyntheticData #DataAugmentation #NeuroscienceAI #ZeroShotDecoding #TRIBEv2 Paper Link: arxiv.org/abs/2606.06345