Abstract
Foundation models are emerging as a powerful paradigm for fMRI analysis; however, current approaches are hindered by a dual bottleneck in data and computational efficiency. Atlas-based methods aggregate voxel signals into fixed regions of interest (ROIs), which reduces dimensionality but discards fine-grained spatial details and necessitates massive cohorts for effective pre-training. Conversely, atlas-free methods operate directly on voxel-level data to preserve spatial fidelity, but their memory and compute requirements often make end-to-end voxel-level processing of full-length fMRI sequences impractical. In this work, we introduce SLIM-Brain, an atlas-free foundation model that uses a lightweight global encoder to score temporal windows from long fMRI recordings while applying voxel-level computation only to the selected windows. SLIM-Brain adopts a two-stage adaptive design: (i) a lightweight temporal extractor ranks windows by how well they represent the remainder of the recording, and (ii) a 4D hierarchical encoder (Hiera-JEPA) learns fine-grained voxel-level representations only from the top-k representative windows while pruning approximately 70% of non-brain or masked units. The downstream predictor uses the voxel-level descriptor; the global encoder is used only for routing. Extensive experiments across external public benchmarks show that SLIM-Brain achieves strong performance on diverse tasks while requiring only about 4 thousand pre-training sessions and approximately 30% of the GPU memory used by Swin-based voxel-level models.
