{"ID":2824941,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.21881","arxiv_id":"2512.21881","title":"SLIM-Brain: A Data- and Training-Efficient Foundation Model for fMRI Data Analysis","abstract":"Foundation models are emerging as a powerful paradigm for fMRI analysis, but current approaches face a dual bottleneck of data- and training-efficiency. Atlas-based methods aggregate voxel signals into fixed regions of interest, reducing data dimensionality but discarding fine-grained spatial details, and requiring extremely large cohorts to train effectively as general-purpose foundation models. Atlas-free methods, on the other hand, operate directly on voxel-level information - preserving spatial fidelity but are prohibitively memory- and compute-intensive, making large-scale pre-training infeasible. We introduce SLIM-Brain (Sample-efficient, Low-memory fMRI Foundation Model for Human Brain), a new atlas-free foundation model that simultaneously improves both data- and training-efficiency. SLIM-Brain adopts a two-stage adaptive design: (i) a lightweight temporal extractor captures global context across full sequences and ranks data windows by saliency, and (ii) a 4D hierarchical encoder (Hiera-JEPA) learns fine-grained voxel-level representations only from the top-$k$ selected windows, while deleting about 70% masked patches. Extensive experiments across seven public benchmarks show that SLIM-Brain establishes new state-of-the-art performance on diverse tasks, while requiring only 4 thousand pre-training sessions and approximately 30% of GPU memory comparing to traditional voxel-level methods.","short_abstract":"Foundation models are emerging as a powerful paradigm for fMRI analysis, but current approaches face a dual bottleneck of data- and training-efficiency. Atlas-based methods aggregate voxel signals into fixed regions of interest, reducing data dimensionality but discarding fine-grained spatial details, and requiring ext...","url_abs":"https://arxiv.org/abs/2512.21881","url_pdf":"https://arxiv.org/pdf/2512.21881v3","authors":"[\"Mo Wang\",\"Junfeng Xia\",\"Wenhao Ye\",\"Enyu Liu\",\"Kaining Peng\",\"Jianfeng Feng\",\"Quanying Liu\",\"Hongkai Wen\"]","published":"2025-12-26T06:10:31Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"q-bio.NC\"]","methods":"[]","has_code":false}