Test-retest reliability of structural brain networks from diffusion MRI
Test-retest reliability of structural brain networks from diffusion MRI
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DOI：
10.1016/j.neuroimage.2013.09.054
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发表时间：
2014-02-01
期刊：
影响因子：
5.7
通讯作者：
Bastin, Mark E.
中科院分区：
文献类型：
--
作者：
Buchanan, Colin R.;Pernet, Cyril R.;Bastin, Mark E.
由弥散MRI（dMRI）和纤维束成像构建的结构性脑网络已在健康志愿者中得到证实，最近在影响脑连接的各种疾病中得到证实。然而，很少有研究解决了由此产生的网络的可重复性。我们通过改变影响网络构建的几个因素来测量这种网络的重测特性，使用10名健康志愿者，他们在两个不同的场合接受了1.5 T的dMRI协议。每个T-1加权的大脑被分成84个感兴趣区域，并使用dMRI和两种替代的纤维束成像算法，两种替代的播种策略，一个白色物质路径点约束和三个备选网络权重。在每种情况下，都获得了四种常见的图论度量。网络属性进行了评估节点和每个网络的组内相关系数（ICC），并通过比较内和之间的主题difference.Our研究结果表明，重测性能得到改善时：1）播种从白色的问题，而不是灰色的;和2）使用概率纤维束成像与双纤维模型和足够的流线，而不是确定性张量纤维束成像。在网络加权方面，流线密度的测量产生了更好的重测性能比区间平均扩散各向异性，虽然它仍然不清楚，这是一个更准确的表示底层的连接。对于最佳性能配置，总体受试者内差异在3.2%至11.9%之间，ICC在0.62至0.76之间。受试者内平均淋巴结差异在5.2%至24.2%之间，平均ICC在0.46至0.62之间。对于83.3%（70/84）的节点，受试者内差异小于受试者间差异。总体而言，这些研究结果表明，虽然目前的技术产生的网络能够表征真正的主题之间的差异连接，未来的工作必须进行，以提高网络的可靠性。(C)版权所有© 2013 Elsevier Inc.
Structural brain networks constructed from diffusion MRI (dMRI) and tractography have been demonstrated in healthy volunteers and more recently in various disorders affecting brain connectivity. However, few studies have addressed the reproducibility of the resulting networks. We measured the test-retest properties of such networks by varying several factors affecting network construction using ten healthy volunteers who underwent a dMRI protocol at 1.5 Ton two separate occasions.Each T-1-weighted brain was parcellated into 84 regions-of-interest and network connections were identified using dMRI and two alternative tractography algorithms, two alternative seeding strategies, a white matter way-point constraint and three alternative network weightings. In each case, four common graph-theoretic measures were obtained. Network properties were assessed both node-wise and per network in terms of the intraclass correlation coefficient (ICC) and by comparing within- and between-subject differences.Our findings suggest that test-retest performance was improved when: 1) seeding from white matter, rather than grey; and 2) using probabilistic tractography with a two-fibre model and sufficient streamlines, rather than deterministic tensor tractography. In terms of network weighting, a measure of streamline density produced better test-retest performance than tract-averaged diffusion anisotropy, although it remains unclear which is a more accurate representation of the underlying connectivity. For the best performing configuration, the global within-subject differences were between 3.2% and 11.9% with ICCs between 0.62 and 0.76. The mean nodal within-subject differences were between 5.2% and 24.2% with mean ICCs between 0.46 and 0.62. For.83.3% (70/84) of nodes, the within-subject differences were smaller than between-subject differences. Overall, these findings suggest that whilst current techniques produce networks capable of characterising the genuine between-subject differences in connectivity, future work must be undertaken to improve network reliability. (C) 2013 Elsevier Inc All rights reserved.