Balan Puiu F, Zhu Qi, Li Xiaolian, Niu Meiqi, Rapan Lucija, Funck Thomas, Wang Haiyan, Bakker Rembrandt, Palomero-Gallagher Nicola, Vanduffel Wim
Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, KU Leuven Medical School, Leuven, Belgium.
Cognitive Neuroimaging Unit, INSERM, CEA, Université Paris-Saclay, NeuroSpin Center, Gif/Yvette, France.
Imaging Neurosci (Camb). 2024 Feb 2;2. doi: 10.1162/imag_a_00077. eCollection 2024.
Due to their fundamental relevance, the number of anatomical macaque brain templates is constantly growing. Novel templates aim to alleviate limitations of previously published atlases and offer the foundation to integrate multiscale multimodal data. Typical limitations of existing templates include their reliance on one subject, their unimodality (usually only T1 or histological images), or lack of anatomical details. The MEBRAINS template overcomes these limitations by using a combination of T1 and T2 images, from the same 10 animals (), which are averaged by the multi-brain toolbox for diffeomorphic registration and segmentation. The resulting volumetric T1 and T2 templates are supplemented with high-quality white and gray matter surfaces built with FreeSurfer. Human-curated segmentations of pial surface, the white/gray matter interface, and major subcortical nuclei were used to analyze the relative quality of the MEBRAINS template. Additionally, 9 computed tomography (CT) scans of the same monkeys were registered to the T1 modality and co-registered to the template. Through its main features (multi-subject, multimodal, volume-and-surface, traditional, and deep learning-based segmentations), MEBRAINS aims to improve integration of multimodal multi-scale macaque data and is quantitatively equal to, or better than, currently widely used macaque templates. We provide a detailed description of the algorithms/methods used to create the template aiming to furnish future researchers with a map-like perspective which should facilitate identification of an optimal pipeline for the task they have at hand. Finally, recently published 3D maps of the macaque inferior parietal lobe, (pre)motor and prefrontal cortex were warped to the MEBRAINS surface template, thus populating it with a parcellation scheme based on cyto- and receptor architectonic analyses. The template is integrated in the EBRAINS and Scalable Brain Atlas web-based infrastructures, each of which comes with its own suite of spatial registration tools.
由于其根本的相关性，解剖学猕猴脑模板的数量在不断增加。新型模板旨在减轻先前发布的图谱的局限性，并为整合多尺度多模态数据提供基础。现有模板的典型局限性包括依赖单一受试者、单模态（通常仅T1或组织学图像）或缺乏解剖细节。MEBRAINS模板通过使用来自相同10只动物的T1和T2图像的组合克服了这些局限性，这些图像由多脑工具箱进行平均，用于微分同胚配准和分割。由此产生的体积T1和T2模板辅以使用FreeSurfer构建的高质量白质和灰质表面。使用人工编辑的软脑膜表面、白/灰质界面和主要皮质下核的分割来分析MEBRAINS模板的相对质量。此外，对同一只猴子的9次计算机断层扫描（CT）进行配准到T1模态，并共同配准到模板。通过其主要特征（多受试者、多模态、体积和表面、传统和基于深度学习的分割），MEBRAINS旨在改善多模态多尺度猕猴数据的整合，并且在定量上等于或优于目前广泛使用的猕猴模板。我们详细描述了用于创建模板的算法/方法，旨在为未来的研究人员提供类似地图的视角，这应该有助于他们为手头的任务确定最佳流程。最后，最近发布的猕猴下顶叶、（前）运动和前额叶皮质的3D地图被扭曲到MEBRAINS表面模板，从而基于细胞和受体结构分析用一种分割方案填充它。该模板集成在基于网络的EBRAINS和可扩展脑图谱基础设施中，每个基础设施都有自己的一套空间配准工具。
