Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Division of Clinical Neurology, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Nat Commun. 2023 Jul 19;14(1):4320. doi: 10.1038/s41467-023-39916-1.
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.
理解大脑结构和功能通常需要结合不同模态和尺度的数据,将微观细胞结构与大脑整体组织的宏观特征联系起来。在这里,我们介绍了 BigMac 数据集,这是一个资源,结合了体内 MRI、广泛的死后 MRI 和多对比显微镜,用于对单个全猕猴大脑进行多模态特征描述。该数据跨越了模态(MRI 和显微镜)、组织状态(体内和死后)以及四个空间数量级,从具有微米或亚微米分辨率的显微镜图像到毫米级的 MRI 信号。至关重要的是,MRI 和显微镜图像经过仔细的配准,以方便定量多模态分析。在这里,我们详细介绍了数据的采集、管理和首次发布,这些数据共同构成了一个独特的、公开传播的资源,供全球研究人员使用。此外,我们还展示了数据带来的示例分析和机会,包括从超高角分辨率扩散 MRI 中提高连接估计的能力、偏光成像提供的神经解剖学见解以及 MRI 和显微镜数据的联合分析,以重建受显微镜启发的连接组。所有数据和代码都公开可用。