Reliable intrinsic connectivity networks: test-retest evaluation using ICA and dual regression approach.
Reliable intrinsic connectivity networks: test-retest evaluation using ICA and dual regression approach.
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DOI：
10.1016/j.neuroimage.2009.10.080
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发表时间：
2010-02-01
期刊：
影响因子：
5.7
通讯作者：
Milham MP
中科院分区：
文献类型：
--
作者：
Zuo XN;Kelly C;Adelstein JS;Klein DF;Castellanos FX;Milham MP
静息状态fMRI数据的功能连接分析正迅速成为高效和强大的工具，用于在体内绘制大脑功能网络，称为内在连接网络（ICNs）。尽管新兴的文献，研究人员继续与定义计算高效和可靠的方法来识别和表征ICNs的挑战作斗争。独立成分分析（ICA）已成为在健康和临床人群中探索ICNs的有力工具。特别是，时间连接组ICA （TC-GICA）与反向重建步骤相结合，为每个组级组件生成参与者级别的静息状态功能连接（RSFC）图。本研究系统地评估了TC-GICA衍生的RSFC测量在短期（< 45分钟）和长期（5 - 16个月）的重测信度。此外，为了研究通过单次ICA检测TC-GICA揭示的成分的程度，我们研究了TC-GICA结果的可重复性。首先，我们发现结合TC-GICA和双重回归得出的icn具有中等至高的短期和长期重测信度。这一发现的例外仅限于生理和成像相关的人工制品。其次，我们的再现性分析揭示了模板匹配程序在个体扫描水平上准确检测基于TC-GICA的组件的显着局限性。第三，我们发现TC-GICA成分的信度和再现性等级高度一致。综上所述，TC-GICA结合双回归是一种有效可靠的静息状态fMRI数据探索性分析方法。
Functional connectivity analyses of resting-state fMRI data are rapidly emerging as highly efficient and powerful tools for in vivo mapping of functional networks in the brain, referred to as intrinsic connectivity networks (ICNs). Despite a burgeoning literature, researchers continue to struggle with the challenge of defining computationally efficient and reliable approaches for identifying and characterizing ICNs. Independent component analysis (ICA) has emerged as a powerful tool for exploring ICNs in both healthy and clinical populations. In particular, temporal concatenation group ICA (TC-GICA) coupled with a back-reconstruction step produces participant-level resting state functional connectivity (RSFC) maps for each group-level component. The present work systematically evaluated the test-retest reliability of TC-GICA derived RSFC measures over the short-term (< 45 minutes) and long-term (5 − 16 months). Additionally, to investigate the degree to which the components revealed by TC-GICA are detectable via single-session ICA, we investigated the reproducibility of TC-GICA findings. First, we found moderate-to-high short- and long-term test-retest reliability for ICNs derived by combining TC-GICA and dual regression. Exceptions to this finding were limited to physiological- and imaging-related artifacts. Second, our reproducibility analyses revealed notable limitations for template matching procedures to accurately detect TC-GICA based components at the individual scan level. Third, we found that TC-GICA component's reliability and reproducibility ranks are highly consistent. In summary, TC-GICA combined with dual regression is an effective and reliable approach to exploratory analyses of resting state fMRI data.
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DOI：
10.1073/pnas.0504136102
发表时间：
2005-07-05
影响因子：
11.1
作者：
Fox, MD;Snyder, AZ;Raichle, ME
通讯作者：
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DOI：
10.1176/appi.ajp.2009.08121894
发表时间：
2009-08
期刊：
The American journal of psychiatry
影响因子：
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作者：
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通讯作者：
Milham MP
DOI：
10.1016/j.cub.2009.04.028
发表时间：
2009-06-23
期刊：
Current biology : CB
影响因子：
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作者：
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通讯作者：
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DOI：
10.1098/rstb.2005.1634
发表时间：
2005-05-29
影响因子：
6.3
作者：
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通讯作者：
Smith, SM
DOI：
10.1073/pnas.0901831106
发表时间：
2009-06-23
影响因子：
11.1
作者：
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通讯作者：
Koetter, Rolf