Consensus between pipelines in structural brain networks.
Consensus between pipelines in structural brain networks.
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DOI：
10.1371/journal.pone.0111262
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Clayden JD
中科院分区：
文献类型：
--
作者：
Parker CS;Deligianni F;Cardoso MJ;Daga P;Modat M;Dayan M;Clark CA;Ourselin S;Clayden JD
结构脑网络可以重建从扩散MRI纤维束成像数据，并有很大的潜力，以进一步我们的健康和疾病的大脑结构的拓扑组织的理解。神经网络的重建是一个复杂的过程，涉及到一系列的处理方法，包括解剖分割、配准、纤维方向估计和全脑纤维束成像。每个阶段的方法选择可能会影响重建网络的解剖准确性和图论属性，这意味着在网络重建管道中应用不同的组合可能会产生截然不同的网络。此外，哪些连接被认为是重要的选择是不清楚的。在这项研究中，我们评估了使用两个独立的最先进的重建管道获得的结构网络之间的相似性。我们的目标是量化网络的相似性，并确定在两个管道中出现的最稳健的核心连接。采用不同的地图集，通过合并包裹到一个共同的和等效的节点规模的管道之间的网络连接的相似性进行了比较。我们发现，在一系列纤维密度阈值的网络之间的高度一致性。此外，我们确定了一个强大的核心，高度连接的区域与网络密度阈值之间的相似性峰值相吻合，并在不同的节点尺度上用地图集复制了这些结果。这些核心连接的二进制网络特性在管道之间是相似的，但在跨节点尺度的图谱中显示出一些差异。本研究展示了将多个结构网络重构管道应用于扩散数据以识别最重要的连接以供进一步研究的实用性。
Structural brain networks may be reconstructed from diffusion MRI tractography data and have great potential to further our understanding of the topological organisation of brain structure in health and disease. Network reconstruction is complex and involves a series of processesing methods including anatomical parcellation, registration, fiber orientation estimation and whole-brain fiber tractography. Methodological choices at each stage can affect the anatomical accuracy and graph theoretical properties of the reconstructed networks, meaning applying different combinations in a network reconstruction pipeline may produce substantially different networks. Furthermore, the choice of which connections are considered important is unclear. In this study, we assessed the similarity between structural networks obtained using two independent state-of-the-art reconstruction pipelines. We aimed to quantify network similarity and identify the core connections emerging most robustly in both pipelines. Similarity of network connections was compared between pipelines employing different atlases by merging parcels to a common and equivalent node scale. We found a high agreement between the networks across a range of fiber density thresholds. In addition, we identified a robust core of highly connected regions coinciding with a peak in similarity across network density thresholds, and replicated these results with atlases at different node scales. The binary network properties of these core connections were similar between pipelines but showed some differences in atlases across node scales. This study demonstrates the utility of applying multiple structural network reconstrution pipelines to diffusion data in order to identify the most important connections for further study.
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影响因子：
3.7
作者：
Hagmann, Patric;Kurant, Maciej;Gigandet, Xavier;Thiran, Patrick;Wedeen, Van J.;Meuli, Reto;Thiran, Jean-Philippe
通讯作者：
Thiran, Jean-Philippe
影响因子：
3.5
作者：
Echtermeyer C;Han CE;Rotarska-Jagiela A;Mohr H;Uhlhaas PJ;Kaiser M
通讯作者：
Kaiser M
DOI：
10.1006/jmrb.1994.1037
发表时间：
1994-03-01
期刊：
JOURNAL OF MAGNETIC RESONANCE SERIES B
影响因子：
--
作者：
BASSER, PJ;MATTIELLO, J;LEBIHAN, D
通讯作者：
LEBIHAN, D
影响因子：
10.9
作者：
Cardoso, M. Jorge;Leung, Kelvin;Ourselin, Sebastien
通讯作者：
Ourselin, Sebastien
影响因子：
5.7
作者：
Cardoso, M. Jorge;Clarkson, Matthew J.;Ridgway, Gerard R.;Modat, Marc;Fox, Nick C.;Ourselin, Sebastien
通讯作者：
Ourselin, Sebastien