Abstract

Recently, the retest reproducibility of intrinsic connectivity networks (ICNs) has become an increasing concern in the fMRI research community. However, few indexes can be applied to directly quantify the similarity of three or more ICNs for evaluating the retest reproducibility of ICNs. To solve this problem, a multivariable correlation coefficient based on zero-mean normalization and intraclass correlation coefficient (Z-ICC) is proposed. After demonstrating the calculation method and performance analysis on theory, Z-ICC is adopted to evaluate the similarity of three ICNs from three ICN sets, which are inferred from the open retest resting-state fMRI dataset NYU_TRT with dual temporal and spatial sparse representation (DTSSR). The reproducible ICNs and quantization index of retest reproducibility are obtained by the calculated Z-ICC values and the accepted evaluation criterion. The experimental results and visual inspection show that Z-ICC can effectively identify the reproducible ICNs and quantify the retest reproducibility of ICNs. Eighteen (Z-ICC > 0.8) of the inferred twenty ICNs with DTSSR that are found to be reproducible are far more than the seven reproducible ICNs based on temporal concatenation group ICA (TC-GICA). Furthermore, the result of the one-tailed two-sample T test demonstrates that the Z-ICC values of the reproducible ICNs by DTSSR are significantly greater than those of TC-GICA, indicating that more reproducible group-level ICNs with higher retest reproducibility can be achieved with DTSSR.

中文翻译：

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使用多元相关系数评估内在连接网络的重测重现性

摘要

最近，内在连通性网络（ICN）的重测可再现性已成为fMRI研究界日益关注的问题。但是，很少有指标可用于直接量化三个或更多ICN的相似性，以评估ICN的重测可重复性。为了解决这个问题，基于零均值归一化和内相关系数一个多变量相关系数（ž - ICC提出）。在论证了理论上的计算方法和性能分析后，Z - ICC用来评估来自三个ICN集的三个ICN的相似性，这是从开放式重测静息状态fMRI数据集NYU_TRT与时空稀疏表示（DTSSR）推断得出的。通过计算的Z - ICC值和公认的评估标准可获得可重现的ICN和重测重现性的量化指标。实验结果和目视检查表明，Z - ICC可以有效地识别可重现的ICN，并量化ICN的重测重现性。十八（Z - ICC 推断得出的20种具有DTSSR的ICN可以重现的ICN（> 0.8）远远超过基于时间级联ICA（TC-GICA）的7种可重现的ICN。此外，一尾两样本T检验的结果表明，DTSSR可重现的ICN的Z - ICC值显着大于TC-GICA的Z - ICC值，表明重现性更高的组水平ICN具有更高的重试可重现性。通过DTSSR实现。