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. Here we introduce the BigMac dataset, a 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. Here we detail the acquisition, curation, and first release of the data, that together make BigMac a unique, openly-disseminated resource available to researchers worldwide. Further, we demonstrate example analyses and opportunities afforded by the data, including improvement of connectivity estimates from ultra-high angular resolution diffusion MRI, neuroanatomical insight provided by polarised light imaging and myelin-stained histology, and the joint analysis of MRI and microscopy data for reconstruction of the microscopy-inspired connectome. All data and code are made openly available.
An open resource combining multi-contrast MRI and microscopy in the macaque brain.
结合多对比度磁共振成像和显微镜技术对猕猴大脑进行研究的开放资源
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作者:Howard Amy F D, Huszar Istvan N, Smart Adele, Cottaar Michiel, Daubney Greg, Hanayik Taylor, Khrapitchev Alexandre A, Mars Rogier B, Mollink Jeroen, Scott Connor, Sibson Nicola R, Sallet Jerome, Jbabdi Saad, Miller Karla L
| 期刊: | Nature Communications | 影响因子: | 15.700 |
| 时间: | 2023 | 起止号: | 2023 Jul 19; 14(1):4320 |
| doi: | 10.1038/s41467-023-39916-1 | ||
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