The BigMac Dataset: linking microscopy-derived microstructure with MR signals throughout the macaque brain

Abstract

Understanding brain structure and function often requires combining data across different modalities and scales to link microscale cellular structures to macroscale features of whole brain organisation. In this talk I will introduce the BigMac dataset, an open resource combining in vivo MRI, extensive postmortem MRI and multi-contrast microscopy for multimodal characterisation of a single whole macaque brain. The data spans modalities (MRI and microscopy), tissue states (in vivo and postmortem), and four orders of spatial magnitude, from microscopy images with micrometre or sub-micrometre resolution, to MRI signals on the order of millimetres. Crucially, the MRI and microscopy images are carefully co-registered together to facilitate quantitative multimodal analyses. This talk will detail the acquisition, curation, and first release of the data, that together make BigMac a unique, openly-disseminated resource available to researchers worldwide. Further, I will demonstrate example analyses and opportunities afforded by the data. This will include i) improvement of connectivity estimates from ultra-high angular resolution diffusion MRI, ii) building 3D microscopy volumes to characterise variations in cellular distributions across the brain, and iii) the joint analysis of MRI and microscopy data for reconstruction of the microscopy-inspired connectome.