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猕猴连接组图谱用于 TheVirtualBrain 中的大规模网络模拟。

A macaque connectome for large-scale network simulations in TheVirtualBrain.

机构信息

Rotman Research Institute, Baycrest, Toronto, Ontario, Canada.

Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.

出版信息

Sci Data. 2019 Jul 17;6(1):123. doi: 10.1038/s41597-019-0129-z.

Abstract

Models of large-scale brain networks that are informed by the underlying anatomical connectivity contribute to our understanding of the mapping between the structure of the brain and its dynamical function. Connectome-based modelling is a promising approach to a more comprehensive understanding of brain function across spatial and temporal scales, but it must be constrained by multi-scale empirical data from animal models. Here we describe the construction of a macaque (Macaca mulatta and Macaca fascicularis) connectome for whole-cortex simulations in TheVirtualBrain, an open-source simulation platform. We take advantage of available axonal tract-tracing datasets and enhance the existing connectome data using diffusion-based tractography in macaques. We illustrate the utility of the connectome as an extension of TheVirtualBrain by simulating resting-state BOLD-fMRI data and fitting it to empirical resting-state data.

摘要

受大脑解剖连接性启发的大规模脑网络模型有助于我们理解大脑结构与其动态功能之间的映射。基于连接组的建模是一种很有前途的方法,可以更全面地了解大脑在时空尺度上的功能,但它必须受到来自动物模型的多尺度经验数据的约束。在这里,我们描述了猕猴(Macaca mulatta 和 Macaca fascicularis)连接组的构建,以便在开源模拟平台 TheVirtualBrain 中进行全皮层模拟。我们利用现有的轴突束追踪数据集,并利用猕猴的扩散式束追踪来增强现有的连接组数据。我们通过模拟静息状态的 BOLD-fMRI 数据并将其拟合到经验性静息状态数据来展示连接组作为 TheVirtualBrain 的扩展的实用性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b622/6637142/2d766d58cfee/41597_2019_129_Fig1_HTML.jpg

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