Evaluating the retest reproducibility of intrinsic connectivity network using multivariate correlation coefficient
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ORIGINAL ARTICLE
Evaluating the retest reproducibility of intrinsic connectivity network using multivariate correlation coefficient Junhui Gong1,2 • Xiaoyan Liu1 • Gang Sun1 • Jiansong Zhou3 Received: 4 May 2019 / Accepted: 24 February 2020 Springer-Verlag London Ltd., part of Springer Nature 2020
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 zeromean 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. Keywords Intrinsic connectivity network Reproducibility Correlation coefficient Sparse representation fMRI
1 Introduction Intrinsic connectivity network (ICN), which was first found in the low-frequency (\ 0.1 Hz) domain of resting-state fMRI dataset, usually consists of multiple spatially separated but functionally connected brain regions [1]. Some subsequent studies verified that ICNs are widely presented in different age groups [2–4] and found that some activated brain networks obtained from tasked-based fMRI datasets are similar to some ICNs in spatial distribution [5–8]. & Xiaoyan Liu [email protected] 1
College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
2
College of Electrical and Information Engineering, Hunan Institute of Engineering, Xiangtan 411104, China
3
Mental Health Institute of the Second Xiangya Hospital, Central South University, Changsha 410011, China
Consequently, Biswal et al. [5] and Smith et al. [6] believed that ICN could express the basic functional architecture of the brain, which suggests that the approach is meaningful for exploring the brain from this perspective of
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