论文标题
非人类灵长类神经影像学和神经解剖学项目
The NonHuman Primate Neuroimaging & Neuroanatomy Project
论文作者
论文摘要
多模式神经影像学项目正在使用许多受试者的高质量非侵入性数据来促进我们对人脑结构,功能和连通性的理解。但是,在人类中,使用侵入性示踪剂对连通性的基础真实验证是不可行的。我们的非人类灵长类神经影像学和神经解剖学项目(NHP_NNP)是一项国际努力(5个国家 /地区的6个实验室),可用于:(i)使用协议和方法从HCP中改编的协议和方法来获取和分析高质量的多模式大脑成像数据; (ii)获取针对皮质区域的皮质和皮质下投影的定量侵入性吸引力追踪数据; (iii)用免疫细胞化学染色绘制不同脑细胞类型的分布,以更好地定义大脑面积边界。我们正在获取高分辨率的结构,功能和扩散MRI数据,以及来自100多个单独猕猴和棉花糖的行为度量,以生成对脑结构的非侵入性测量,例如髓磷脂和皮质厚度图,以及基于功能和扩散的基于基于功能和扩散的tractography tractography Connectomes。我们使用经典和下一代的解剖示踪剂来基于标记的皮质和皮层神经元的大脑计数生成定量连通图,从而提供了接地真相的连通性测量。高级统计建模技术解决了个人之间两种数据的一致性,从而可以比较基于示踪剂和非侵入性MRI的连接度量。我们旨在通过结合组织学和成像方法来开发改进的皮质和皮层面积图谱。最后,我们正在收集所有动物中与遗传和社会相关的行为数据,以了解遗传变异如何塑造连接组和行为。
Multi-modal neuroimaging projects are advancing our understanding of human brain architecture, function, connectivity using high-quality non-invasive data from many subjects. However, ground truth validation of connectivity using invasive tracers is not feasible in humans. Our NonHuman Primate Neuroimaging & Neuroanatomy Project (NHP_NNP) is an international effort (6 laboratories in 5 countries) to: (i) acquire and analyze high-quality multi-modal brain imaging data of macaque and marmoset monkeys using protocols and methods adapted from the HCP; (ii) acquire quantitative invasive tract-tracing data for cortical and subcortical projections to cortical areas; and (iii) map the distributions of different brain cell types with immunocytochemical stains to better define brain areal boundaries. We are acquiring high-resolution structural, functional, and diffusion MRI data together with behavioral measures from over 100 individual macaques and marmosets in order to generate non-invasive measures of brain architecture such as myelin and cortical thickness maps, as well as functional and diffusion tractography-based connectomes. We are using classical and next-generation anatomical tracers to generate quantitative connectivity maps based on brain-wide counting of labeled cortical and subcortical neurons, providing ground truth measures of connectivity. Advanced statistical modeling techniques address the consistency of both kinds of data across individuals, allowing comparison of tracer-based and non-invasive MRI-based connectivity measures. We aim to develop improved cortical and subcortical areal atlases by combining histological and imaging methods. Finally, we are collecting genetic and sociality-associated behavioral data in all animals in an effort to understand how genetic variation shapes the connectome and behavior.