STH-Brain: A Hierarchical Spatiotemporal Brain Time-Series Foundation Model

Resting-state functional magnetic resonance imaging (fMRI) captures spontaneous fluctuations in blood-oxygen-level-dependent signals across the brain. Differences in temporal sampling complicate consistent modeling across scans, while spatial aggregation can obscure the temporal context of individual brain regions. We propose STH-Brain, a brain time-series foundation model combining physical spatiotemporal encoding with hierarchical representation learning. Physical-Time Rotary Encoding maps elapsed seconds to rotary phases, while Spatial-Aware Encoding incorporates atlas coordinates into spatial attention. The hierarchy models temporal context within each region of interest (ROI) and retains regional states alongside network representations. During self-supervised pretraining, dual-level latent prediction supervises both levels, with visibility-based routing selecting same-ROI or network memory for regional prediction according to the available context. Across seven disorder-classification tasks, STH-Brain achieves the best mean performance among the evaluated baselines, improving accuracy by an average of 5.91 percentage points over the strongest baseline for each task. It also achieves the best mean results for all three age-prediction metrics across seven disease groups and outperforms the compared models on the evaluated external-cohort and zero-shot transfer tasks. Interpretability analyses reveal shared and distinct regional patterns across disorders. Code is available at https://anonymous.4open.science/r/STH-Brain/.