Test-retest reliabilities of resting-state FMRI measurements in human brain functional connectomics: a systems neuroscience perspective.

By Xi-Nian Zuo, Xiu-Xia Xing. Neuroscience and Biobehavioral Reviews, 2014

652 citations recorded by the source corpus.

Resting-state functional magnetic resonance imaging (RFMRI) enables researchers to monitor fluctuations in the spontaneous brain activities of thousands of regions in the human brain simultaneously, representing a popular tool for macro-scale functional connectomics to characterize normal brain function, mind-brain associations, and the various disorders. However, the test-retest reliability of RFMRI remains largely unknown. We review previously published papers on the test-retest reliability of voxel-wise metrics and conduct a meta-summary reliability analysis of seven common brain networks. This analysis revealed that the heteromodal associative (default, control, and attention) networks were mostly reliable across the seven networks. Regarding examined metrics, independent component analysis with dual regression, local functional homogeneity and functional homotopic connectivity were the three mostly reliable RFMRI metrics. These observations can guide the use of reliable metrics and further improvement of test-retest reliability for other metics in functional connectomics. We discuss the main issues with low reliability related to sub-optimal design and the choice of data processing options. Future research should use large-sample test-retest data to rectify both the within-subject and between-subject variability of RFMRI measurements and accelerate the application of functional connectomics.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/db1cf110f7f29ed37a3ecb46ed907ff815f7c60c

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In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Development of large-scale functional networks from birth to adulthood: a guide to neuroimaging literature (2017, background): “Multiple studies have found evidence for limited test-retest reliability using a multitude of conventional rs-fcMRI network characteristics and analytic approaches (Braun et al., 2012; Wang et al., 2011; Wisner et al., 2013; Zuo et al., 2010; Zuo and Xing, 2014).”
Evaluation of ICA-AROMA and alternative strategies for motion artifact removal in resting state fMRI (2015, background): “However, there is still considerable improvement possible in the reliability of these metrics (Wisner et al., 2013; Zuo and Xing, 2014).”
DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging (2016, methodology): “Before using R-fMRI measures to test inter-individual differences, TRT reliability should be assessed (Shehzad et al., 2009; Zuo et al., 2010a; Zuo and Xing, 2014).”
Quantitative Assessment of Structural Image Quality (2017, neutral): “Third, it is unknown at present how the test-retest reliability of automated measures of data quality (such as the Euler number) compare to manual ratings (Zuo and Xing, 2014).”