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利用神经元示踪和髓鞘染色数据优化猕猴脑 DMRI 连接组学。

Optimization of macaque brain DMRI connectome by neuron tracing and myelin stain data.

机构信息

School of Automation and Brain Decoding Research Center, Northwestern Polytechnical University, Xi'an, Shaanxi, China.

Emory University, Atlanta, GA, United States.

出版信息

Comput Med Imaging Graph. 2018 Nov;69:9-20. doi: 10.1016/j.compmedimag.2018.06.001. Epub 2018 Jun 25.


DOI:10.1016/j.compmedimag.2018.06.001
PMID:30170273
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6176488/
Abstract

Accurate assessment of connectional anatomy of primate brains can be an important avenue to better understand the structural and functional organization of brains. To this end, numerous connectome projects have been initiated to create a comprehensive map of the connectional anatomy over a large spatial expanse. Tractography based on diffusion MRI (dMRI) data has been used as a tool by many connectome projects in that it is widely used to visualize axonal pathways and reveal microstructural features on living brains. However, the measures obtained from dMRI are indirect inference of microstructures. This intrinsic limitation reduces the reliability of dMRI in constructing connectomes for brains. In this work, we proposed a framework to increase the accuracy of constructing a dMRI-based connectome on macaque brains by integrating meso-scale connective information from tract-tracing data and micro-scale axonal orientation information from myelin stain data. Our results suggest that this integrative framework could advance the mapping accuracy of dMRI based connections and axonal pathways, and demonstrate the prospect of the proposed framework in constructing a large-scale connectome on living primate brains.

摘要

准确评估灵长类动物大脑的连接解剖结构可能是更好地理解大脑结构和功能组织的重要途径。为此,许多连接组学项目已经启动,旨在创建一个跨越大空间范围的连接解剖结构的综合图谱。基于扩散磁共振成像 (dMRI) 数据的轨迹追踪已被许多连接组学项目用作工具,因为它广泛用于可视化轴突通路并揭示活体大脑的微观结构特征。然而,从 dMRI 获得的测量值是对微观结构的间接推断。这种内在限制降低了 dMRI 在构建大脑连接组方面的可靠性。在这项工作中,我们提出了一个框架,通过整合示踪数据中的中尺度连接信息和髓鞘染色数据中的微观轴突方向信息,来提高基于 dMRI 的猕猴大脑连接组构建的准确性。我们的结果表明,这种集成框架可以提高基于 dMRI 的连接和轴突通路的映射准确性,并展示了该框架在构建活体灵长类动物大脑的大规模连接组方面的前景。

相似文献

[1]
Optimization of macaque brain DMRI connectome by neuron tracing and myelin stain data.

Comput Med Imaging Graph. 2018-6-25

[2]
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[3]
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[4]
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[5]
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[6]
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[7]
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[8]
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[9]
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[10]
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引用本文的文献

[1]
Optimization and validation of diffusion MRI-based fiber tracking with neural tracer data as a reference.

Sci Rep. 2020-12-18

[2]
Comparison between diffusion MRI tractography and histological tract-tracing of cortico-cortical structural connectivity in the ferret brain.

Netw Neurosci. 2019-9-1

本文引用的文献

[1]
Population based MRI and DTI templates of the adult ferret brain and tools for voxelwise analysis.

Neuroimage. 2017-5-15

[2]
The UNC-Wisconsin Rhesus Macaque Neurodevelopment Database: A Structural MRI and DTI Database of Early Postnatal Development.

Front Neurosci. 2017-2-2

[3]
Using Diffusion Tractography to Predict Cortical Connection Strength and Distance: A Quantitative Comparison with Tracers in the Monkey.

J Neurosci. 2016-6-22

[4]
Optimization of large-scale mouse brain connectome via joint evaluation of DTI and neuron tracing data.

Neuroimage. 2015-7-15

[5]
Spatial embedding of structural similarity in the cerebral cortex.

Proc Natl Acad Sci U S A. 2014-11-18

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Anatomical accuracy of brain connections derived from diffusion MRI tractography is inherently limited.

Proc Natl Acad Sci U S A. 2014-11-18

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Towards quantitative connectivity analysis: reducing tractography biases.

Neuroimage. 2014-5-9

[8]
A mesoscale connectome of the mouse brain.

Nature. 2014-4-2

[9]
Brainnetome and related projects.

Sci China Life Sci. 2014-4

[10]
Deterministic diffusion fiber tracking improved by quantitative anisotropy.

PLoS One. 2013-11-15

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