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

Area of Science:

• Neuroscience
• Medical Imaging
• Computational Biology

Background:

• Macaque MRI research connects systems neuroscience with cellular mechanisms.
• Current preprocessing tools for macaque neuroimaging are fragmented and difficult to integrate.
• Lack of standardized, reproducible preprocessing hinders macaque MRI data analysis and cross-study comparisons.

Purpose of the Study:

• To introduce Brainana, an automated and BIDS-compatible preprocessing framework for macaque neuroimaging.
• To address the fragmentation and reproducibility challenges in macaque MRI data processing.
• To facilitate scalable and accessible neuroimaging analysis for non-human primate research.

Main Methods:

• Brainana integrates structural and functional MRI preprocessing, cortical surface reconstruction, quality control, and atlas projection.
• It utilizes macaque-trained deep learning models for brain extraction and tissue segmentation.
• The framework is containerized for easy deployment and offers cloud access for users.
• Conformation methods standardize variable acquisitions, and surface reconstruction is optimized for macaque anatomy.

Main Results:

• Brainana successfully processed data from 23 imaging sites across heterogeneous scanners and protocols.
• It achieved accurate anatomical correspondence in 130 monkeys and generated reliable native-space cortical surfaces.
• The framework enabled localized task-evoked activations and reproducible resting-state functional connectivity.
• Processed data demonstrated consistency across variable resolutions and species.

Conclusions:

• Brainana provides a reproducible, scalable, and accessible solution for macaque MRI preprocessing.
• It supports cross-study comparisons and multimodal integration across different spatial scales.
• The framework empowers researchers to advance non-human primate systems neuroscience.
• Brainana standardizes macaque neuroimaging, enabling more robust and integrated research findings.