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arXiv Computer Vision By Mo Wang, Wenhao Ye, Zihan Ning, Jiayu Zuo, Junfeng Xia, Hongkai Wen, Quanying Liu

FlatClip: Reusing Image Foundation Models for fMRI Representation Learning via Cortical Flatmaps

Read the original on arXiv Computer Vision →

FlatClip is a method that leverages frozen image foundation models to predict fMRI activity by mapping cortical signals onto anatomy‑linked flatmaps and extracting features with a SigLIP2 encoder. Only a lightweight downstream probe is trained, avoiding fMRI‑specific encoder pretraining or fine‑tuning. Experiments on four resting‑state benchmarks show FlatClip surpasses ROI‑level baselines on HCP and ADNI, while its performance varies on PPMI and remains below the best voxel‑level models; on NSD visual‑fMRI decoding, restricting inputs to task‑active cortex improves recognition, and spatial perturbations confirm the importance of cortical arrangement and input‑region selection.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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