Brainana: an end-to-end preprocessing framework for macaque neuroimaging

Abstract

Macaque MRI bridges non-invasive systems neuroscience with cellular and circuit-level mechanisms, but preprocessing tools remain difficult to integrate and deploy reproducibly. We present Brainana, an automated, BIDS-compatible preprocessing and visualization framework for macaque neuroimaging. Brainana integrates structural and functional preprocessing, cortical surface reconstruction, quality control, transform tracking, and atlas projection within a containerized package, with cloud access for users without local compute. Macaque-trained deep learning models support brain extraction and tissue segmentation, while image orientation standardization and macaque-specific surface reconstruction optimizations address variability across acquisitions. A viewer automatically organizes derivatives and links volumetric and surface data, enabling users to inspect anatomy, cortical measures, atlas delineations, and activity maps without neuroimaging expertise. Across 23 imaging sites, Brainana processed heterogeneous data from 130 monkeys, yielding anatomical correspondence, reliable native-space surfaces, localized task-evoked activations, and reproducible brain-wide resting-state correlations. Brainana enables reproducible, scalable, and accessible macaque MRI analysis, cross-study comparison, and multimodal integration.