Atlas-Guided Regional Representation Adaptation for Brain Foundation Models

Brain foundation models provide a powerful starting point for transferring knowledge across neuroimaging tasks, yet their learned representations are often organized around generic image tokens rather than anatomically defined brain regions. This makes it challenging to directly exploit the regional structure of the brain when adapting pretrained representations to downstream applications. We introduce AnatoQ, an efficient anatomy-aware adaptation framework that converts generic foundation-model features into region-specific representations using anatomical priors. At its center is an Atlas-Guided Q-Former (AGQ), which uses atlas-defined regions as queries to selectively aggregate informative features from pretrained image tokens. Rather than altering the architecture or retraining the foundation model, AnatoQ operates as a lightweight adaptation module, allowing anatomical structure to be incorporated while retaining the knowledge encoded by the original backbone. The resulting regional representations provide a common interface for downstream brain imaging tasks and can be combined with different pretrained backbones. Experiments on a broad range of downstream tasks and multi-session neuroimaging datasets show general gains across foundation models and aggregation methods. Additional analyses demonstrate that the learned representations exhibit stable correspondence with anatomically meaningful regions and support task-dependent interpretation. These results suggest that atlas-guided regional adaptation offers an effective and general approach for injecting anatomical structure into pretrained brain representations.