Align, Then Adapt: Few-Shot fMRI Visual Reconstruction For New Subjects

arXiv:2505.01670v3 Announce Type: replace-cross Abstract: Reconstructing visual images from fMRI data is a central but challenging problem in neuroscience. Despite recent progress, current methods fall short when data and computation are limited-precisely the conditions under which this task is most critical. We introduce Adapter Alignment, a staged, architecture-agnostic training procedure for adapting a pre-trained reconstruction model to a new subject with little data. Rather than letting a shared representation space emerge implicitly, lightweight subject-specific adapters are first explicitly aligned to a pre-trained reference subject on a small set of shared calibration images, and the full pipeline is then fine-tuned while preserving this alignment. The resulting representations not only place different subjects in a shared space but also align them semantically. Our results demonstrate significant gains in low-data regimes, improving reconstruction metrics by up to 8%, and pre-diffusion retrieval shows that these gains reflect additional image-specific information in the fMRI embeddings rather than the generative prior. We also propose a coverage-based algorithm to select representative calibration images. Using both techniques together, we surpass baseline models fine-tuned with one hour of data using only 24 minutes of subject-specific data. The procedure is effective across architectures, reference subjects, and datasets, including adapting 3T subjects to a 7T reference.