An open resource combining multi-contrast MRI and microscopy in the macaque brain

Area of Science:

• Neuroscience
• Neuroimaging
• Microscopy

Background:

• Linking microscale cellular structures to macroscale brain organization requires multimodal and multi-scale data.
• Current datasets often lack comprehensive integration across different imaging modalities and spatial scales.

Purpose of the Study:

• Introduce the BigMac dataset, a novel resource for multimodal characterization of a single macaque brain.
• Facilitate quantitative analyses by co-registering MRI and microscopy data across different spatial scales and tissue states.

Main Methods:

• Acquisition and curation of a comprehensive dataset including in vivo MRI, postmortem MRI, and multi-contrast microscopy for a single macaque brain.
• Co-registration of MRI and microscopy data to enable direct quantitative multimodal analyses.
• Open dissemination of all data and associated code for global research access.

Main Results:

• The BigMac dataset provides a unique resource spanning four orders of spatial magnitude, from micrometer-resolution microscopy to millimeter-resolution MRI.
• Demonstrated utility of the dataset for improving diffusion MRI connectivity estimates and providing neuroanatomical insights from histology.
• Enabled joint analysis of MRI and microscopy data for reconstruction of a microscopy-inspired connectome.

Conclusions:

• The BigMac dataset is a valuable, openly available resource for advancing the understanding of brain structure and function across multiple scales.
• The integrated multimodal data facilitates novel analyses, bridging the gap between cellular and whole-brain organization.
• Open science principles are upheld through the public release of data and code, fostering collaborative research worldwide.