Intraclass correlation: Improved modeling approaches and applications for neuroimaging.
Intraclass correlation: Improved modeling approaches and applications for neuroimaging.
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DOI：
10.1002/hbm.23909
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发表时间：
2018-03
影响因子：
4.8
通讯作者：
Cox RW
中科院分区：
文献类型：
--
作者：
Chen G;Taylor PA;Haller SP;Kircanski K;Stoddard J;Pine DS;Leibenluft E;Brotman MA;Cox RW
组内相关性（ICC）是一种可靠性指标，用于衡量相似性，例如，当在相似或甚至相同的良好控制条件下测量实体时，在MRI应用中包括运行/会话，双胞胎，父母/孩子，扫描仪，站点等。在这里，我们提供了一个全面的概述ICC分析在其以前的使用在神经影像学，我们表明，标准的方差分析框架往往是有限的，刚性和不灵活的建模能力。这些固有的局限性激发了一些改进。具体而言，我们从方差分析平台下的传统ICC模型开始，并沿着沿着两个维度对其进行扩展：第一，修复在退化情况下出现负值时ICC估计的失败，第二，将效应估计的精度信息纳入ICC模型。这些努力导致四种建模策略：线性混合效应（LME），正则化混合效应（RME），多级混合效应（MME）和正则化多级混合效应（RMME）。与ANOVA相比，这四个模型中的每一个都直接提供固定效应的估计值及其统计显著性，以及ICC估计值。这些新的建模方法还可以适应缺失数据以及混杂变量的固定效应。更重要的是，我们表明，MME和RMME的方法提供了更准确的特征和方差分量之间的分解，导致更强大的ICC计算。基于这些理论考虑和模型性能与真实的实验数据集的比较，我们提供了以下通用建议。首先，当精确信息（即，更准确地分配数据方差的权重）可用于效应估计;当精度信息不可用时，通过LME或RME进行ICC估计是首选。其次，尽管绝对一致性版本ICC（2，1）目前在该领域更受欢迎，但一致性版本ICC（3，1）是全脑ICC分析的实用和信息选择，当考虑所有潜在的固定效应时，可以实现平衡的折衷。第三，在ICC分析的明确的，有意义的，有用的结果报告的方法进行了讨论。所有模型、ICC公式和相关统计测试方法都已在开源程序3dICC中实现，该程序作为AFNI套件的一部分公开提供。尽管我们在这里的工作集中在整个大脑水平，建模策略和建议可以等效地应用于其他情况，如体素，区域和网络水平。
Intraclass correlation (ICC) is a reliability metric that gauges similarity when, for example, entities are measured under similar, or even the same, well-controlled conditions, which in MRI applications include runs/sessions, twins, parent/child, scanners, sites, etc. The popular definitions and interpretations of ICC are usually framed statistically under the conventional ANOVA platform. Here, we provide a comprehensive overview of ICC analysis in its prior usage in neuroimaging, and we show that the standard ANOVA framework is often limited, rigid, and inflexible in modeling capabilities. These intrinsic limitations motivate several improvements. Specifically, we start with the conventional ICC model under the ANOVA platform, and extend it along two dimensions: first, fixing the failure in ICC estimation when negative values occur under degenerative circumstance, and second, incorporating precision information of effect estimates into the ICC model. These endeavors lead to four modeling strategies: linear mixed-effects (LME), regularized mixed-effects (RME), multilevel mixed-effects (MME), and regularized multilevel mixed-effects (RMME). Compared to ANOVA, each of these four models directly provides estimates for fixed effects as well as their statistical significances, in addition to the ICC estimate. These new modeling approaches can also accommodate missing data as well as fixed effects for confounding variables. More importantly, we show that the MME and RMME approaches offer more accurate characterization and decomposition among the variance components, leading to more robust ICC computation. Based on these theoretical considerations and model performance comparisons with a real experimental dataset, we offer the following general-purpose recommendations. First, ICC estimation through MME or RMME is preferable when precision information (i.e., weights that more accurately allocate the variances in the data) is available for the effect estimate; when precision information is unavailable, ICC estimation through LME or the RME is the preferred option. Second, even though the absolute agreement version, ICC(2,1), is presently more popular in the field, the consistency version, ICC(3,1), is a practical and informative choice for whole-brain ICC analysis that achieves a well-balanced compromise when all potential fixed effects are accounted for. Third, approaches for clear, meaningful, and useful result reporting in ICC analysis are discussed. All models, ICC formulations, and related statistical testing methods have been implemented in an open source program 3dICC, which is publicly available as part of the AFNI suite. Even though our work here focuses on the whole brain level, the modeling strategy and recommendations can be equivalently applied to other situations such as voxel, region, and network levels.
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影响因子：
5.7
作者：
Griffanti L;Rolinski M;Szewczyk-Krolikowski K;Menke RA;Filippini N;Zamboni G;Jenkinson M;Hu MTM;Mackay CE
通讯作者：
Mackay CE
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影响因子：
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作者：
Chen B;Xu T;Zhou C;Wang L;Yang N;Wang Z;Dong HM;Yang Z;Zang YF;Zuo XN;Weng XC
通讯作者：
Weng XC
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9.8
作者：
Lin Q;Dai Z;Xia M;Han Z;Huang R;Gong G;Liu C;Bi Y;He Y
通讯作者：
He Y
影响因子：
3
作者：
Chung, Yeojin;Rabe-Hesketh, Sophia;Liu, Jingchen
通讯作者：
Liu, Jingchen
影响因子：
5.7
作者：
Guo, Christine C.;Kurth, Florian;Zhou, Juan;Mayer, Emeran A.;Eickhoff, Simon B.;Kramer, Joel H.;Seeley, William W.
通讯作者：
Seeley, William W.