Toward reliable characterization of functional homogeneity in the human brain: Preprocessing, scan duration, imaging resolution and computational space

By Xi-Nian Zuo, Ting Xu, Lili Jiang, Zhi Yang, Xiao-Yan Cao, Yong He, Yufeng Zang, F. X. Castellanos, Michael P. Milham. NeuroImage, 2012

524 citations recorded by the source corpus.

While researchers have extensively characterized functional connectivity between brain regions, the characterization of functional homogeneity within a region of the brain connectome is in early stages of development. Several functional homogeneity measures were proposed previously, among which regional homogeneity (ReHo) was most widely used as a measure to characterize functional homogeneity of resting state fMRI (R-fMRI) signals within a small region (Zang et al., 2004). Despite a burgeoning literature on ReHo in the field of neuroimaging brain disorders, its test–retest (TRT) reliability remains unestablished. Using two sets of public R-fMRI TRT data, we systematically evaluated the ReHo’s TRT reliability and further investigated the various factors influencing its reliability and found: 1) nuisance (head motion, white matter, and cerebrospinal fluid) correction of R-fMRI time series can significantly improve the TRT reliability of ReHo while additional removal of global brain signal reduces its reliability, 2) spatial smoothing of R-fMRI time series artificially enhances ReHo intensity and influences its reliability, 3) surface-based R-fMRI computation largely improves the TRT reliability of ReHo, 4) a scan duration of 5 min can achieve reliable estimates of ReHo, and 5) fast sampling rates of R-fMRI dramatically increase the reliability of ReHo. Inspired by these findings and seeking a highly reliable approach to exploratory analysis of the human functional connectome, we established an R-fMRI pipeline to conduct ReHo computations in both 3-dimensions (volume) and 2-dimensions (surface).

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/e87cbf45a153160a940db3975d72c1886c419ce9

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Citations in the corpus

ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data (2015, unknown): “Next to a variable loss in tDoF, volume-removal will decrease reliability of fMRImetrics (Birn et al., 2013; Zuo et al., 2013) and destroy the autocorrelation structure of the fMRI data.”
A Comprehensive Assessment of Regional Variation in the Impact of Head Micromovements on Functional Connectomics (2013, unknown): “This is not surprising, as Z-standardization (i.e., within-subject mean centering and variance normalization) was applied in accord with common practice (e.g., Buckner et al., 2009; Zou et al., 2008; Zuo et al., 2010a, 2012, 2013).”
DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging (2016, methodology): “ICC values were derived by linear mixed models (LMMs) as per Zuo et al. (2013).”
Clinical applications of the functional connectome (2013, unknown): “Still, the news is generally good, with acceptable to good test–retest reliability for a wide range of measure describing the functional connectome across intervals as long as one year (Blautzik et al., 2013; Chou et al., 2012; Shehzad et al., 2009; Zuo et al., 2010a, 2010b, 2013).”
Influences on the Test–Retest Reliability of Functional Connectivity MRI and its Relationship with Behavioral Utility (2017, background): “Additionally, differences in acquisition and processing procedures also impact test–retest reliability (Zuo et al. 2013; Aurich et al. 2015; Shirer et al. 2015; Varikuti et al. 2017).”
Standardizing the intrinsic brain: Towards robust measurement of inter-individual variation in 1000 functional connectomes (2013, unknown): “ICC values were derived by linear mixed models (LMMs) as Zuo et al. (2013).”
Local-Global Parcellation of the Human Cerebral Cortex From Intrinsic Functional Connectivity MRI (2017, result): “A final observation was that homogeneity computed in MNI space (Figure 3C) appeared significantly lower than homogeneity computed in surface spaces (Figures 3A and 3B), consistent with observations from previous studies (Zuo et al. 2013; Jiang and Zuo 2016).”