绘制绒猴脑网络组图谱

The common marmoset (Callithrix jacchus) is small and fast-breeding non-human primate, with sequenced genome and transgenic models, making it an important experimental animal for neuroscience research. Brain atlas is an indispensable tool in neuroscience. In literature, we found that in marmoset brain atlases, being published or on-going, the cortical parcellation was based on cytoarchitectural information, while only large fibre bundles were identified. An atlas built from cortical connectivity patterns is still missing. In recent years, new knowledge about brain structures and functions has made it possible to build more accurate and complicated model to describe the connectivity patterns of brain networks. Our group proposed the brainnetome, a concept of investigating the principles of brain function and elucidating the underlying mechanisms of neuronal diseases, through studying the brain network connectivity and its dynamics. To apply this new concept to marmoset, it is on urgent need to build a brainnetome atlas. In this project, we will focus on building the atlas using anatomical connectivity derived from diffusion-weighted magnetic resonance imaging. Utilizing a ultra-high field (9.4 Tesla) scanner, we will construct the first digitalized brainnetome atlas of marmoset by applying pattern recognition and brain network related methodologies, mining the multi-scale information of diffusion weighted-imaging. We believe the atlas will facilitate future researches on neuronal circuits using marmoset.

绒猴体型较小、繁殖较快，并有基因测序和转基因品系，使它成为神经科学领域里一种重要的实验动物。脑图谱是脑神经科学研究的重要工具。基于对国内外文献的查阅，我们发现已经发表和正在构建的绒猴脑图谱都是基于细胞构筑的皮层划分，或是基于纤维束跟踪的白质划分，尚无基于连接模式进行脑区划分的脑图谱。而近年来对脑的结构和功能有了新的认知，能够描述脑区连接模式的脑网络是更加复杂和精确的模型。我们团队提出的脑网络组学从脑网络的连接模式及其演变规律阐明脑的工作机理及脑疾病的发生和发展机制。为了运用这一新的组学从脑网络角度研究绒猴，亟需构建绒猴的脑网络组图谱。在本项目中，我们重点研究基于弥散磁共振图像的解剖连接图谱的构建。基于多模态的超高场磁共振数据，利用模式识别和脑网络的研究方法，通过充分挖掘弥散磁共振数据的多尺度信息，构建首个绒猴的数字化脑网络组图谱。这将为后续的神经环路等研究提供基础。

绒猴体型较小、繁殖较快，并有基因测序和转基因品系，使它成为神经科学领域里一种重要的实验动物。脑图谱是脑神经科学研究的重要工具。近几年，先后有数个狨猴脑图谱发布。然而，目前还没有直接在活体磁共振影像上进行脑区划分的狨猴图谱。我们团队提出的脑网络组学从脑网络的连接模式及其演变规律阐明脑的工作机理及脑疾病的发生和发展机制。为了运用这一新的组学从脑网络角度研究绒猴，亟需构建绒猴活体的脑网络组图谱。在本项目中，我们重点研究基于弥散磁共振图像的解剖连接图谱的构建。基于多模态的超高场磁共振数据，利用模式识别和脑网络的研究方法，通过充分挖掘弥散磁共振数据的多尺度信息，基于10只狨猴的活体磁共振图像数据构建绒猴的数字化脑网络组图谱。这将为后续的神经环路等研究提供基础。图谱发布后，用于生成图谱的数据，包括10只动物的结构像、弥散加权像和静息态功能图像将一并公开。