Resting-state functional magnetic resonance imaging (rs-fMRI) serves as a powerful tool for studying brain function, yet deriving the optimal representation of brain function remains a challenge. Traditional methods often rely on region of interest (ROI)-based analyses, which simplify complexity and reduce noise but are constrained by fixed ROI partitions and averaged voxel signals, potentially missing valuable information. In our study, we introduce a novel framework for autonomously learning representations for any brain region directly from voxel-level fMRI data. This approach is designed to manage arbitrary ROIs. It incorporates two primary stages: During the pre-training stage, a global-adapt encoder captures whole-brain feature representations from 4D fMRI data, while a mask encoder processes brain region masks to extract geometric features. These features merge with their corresponding fMRI representations to reconstruct the mean BOLD signal of the region, facilitating self-supervised training. By providing a range of brain region masks, our framework enables the learning of representations for a set of arbitrary ROIs, whether derived from established brain atlases or crafted manually. In the fine-tuning stage, the pre-trained model adapts to downstream tasks like gender classification, age prediction, and intelligence prediction. Experiments conducted with the HCP and UK Biobank datasets reveal that our method surpasses competing approaches, delivering highly interpretable and neurofunctionally relevant brain region representations.