Brainana provides anatomical and functional (fMRI) processing, image registration, surface reconstruction, fMRIPrep-compatible confound regressors, and HTML QC reports for macaque neuroimaging data in a reproducible workflow (FSL, ANTs, AFNI, FreeSurfer, Nextflow).
Status: Research software – feature-complete for main workflows; bugs and edge cases are still possible.
Brainana runs as a reproducible Docker image. Read the Docs, start with Installation, then Usage notes.
To get a feel for the pipeline, try the demo on the bundled examples/dataset_example/ dataset.
Lightweight volumetric T1w preprocessing for a single subject (no functional MRI, no surfaces). Run interactively in Jupyter or Google Colab—no Docker required.
- Notebook:
examples/BrainanaLite.ipynb— on Colab, use Run all; the first pass may restart the runtime once, then run again. - Docs: Brainana Lite guide — Jupyter / Colab T1w workflow
For the full multi-modality pipeline with surfaces and QC reports, use the installation above for docker.
To visualize brainana's outputs interactively, use the companion Brainana Viewer — a cross-platform viewer.
If you use Brainana, please cite:
Brainana: an end-to-end preprocessing framework for macaque neuroimaging
Please also cite the toolboxes Brainana uses (FSL, ANTs, AFNI, FreeSurfer, FastSurfer, FireANTs, and any macaque templates). Detailed references can be found in the QC report.
Copyright (c) the Brainana Developers. Licensed under the GNU Affero General Public License v3 (AGPL-3.0). See LICENSE for the full text. Some dependencies (e.g. FreeSurfer) have their own licenses; you must comply with those as well.