Siibra: a software tool suite for realizing a Multilevel Human Brain Atlas from complex data resources

Area of Science:

• Neuroscience
• Computational Biology
• Bioinformatics

Background:

• Understanding the human brain's complexity requires integrating multi-modal and multi-scale data within an anatomical context.
• Accessing and coherently exploring large-scale brain data remains a challenge for visual exploration, reproducible analysis, and application development.

Purpose of the Study:

• To present siibra, a tool suite designed to connect diverse cloud-based data resources to reference atlases and coordinate spaces.
• To facilitate interoperable and actionable access to brain data for various use cases.

Main Methods:

• siibra provides access through a web viewer, Python library, and HTTP API.
• Implementation of a Multilevel Human Brain Atlas linking macro-anatomical concepts with microstructural measurements.
• Integration with the EBRAINS research infrastructure.

Main Results:

• siibra enables coherent access to diverse brain data from cloud resources.
• A Multilevel Human Brain Atlas was implemented, connecting macro- and micro-scale brain region information.
• The atlas is integrated with EBRAINS, supporting MRI-based and microscopic templates.

Conclusions:

• siibra offers a solution for accessing and integrating complex, multi-scale, and multi-modal brain data.
• The developed Multilevel Human Brain Atlas enhances understanding of brain region variability and composition.
• Open accessibility of siibra software and content promotes reproducible research and application development in neuroscience.