title: Investigating univariate temporal patterns for intrinsic connectivity networks based on complexity and low-frequency oscillation: a test-retest reliability study.
abstract: Intrinsic connectivity networks (ICNs) are composed of spatial components and time courses. The spatial components of ICNs were discovered with moderate-to-high reliability. So far as we know, few studies focused on the reliability of the temporal patterns for ICNs based their individual time courses. The goals of this study were twofold: to investigate the test-retest reliability of temporal patterns for ICNs, and to analyze these informative univariate metrics. Additionally, a correlation analysis was performed to enhance interpretability. Our study included three datasets: (a) short- and long-term scans, (b) multi-band echo-planar imaging (mEPI), and (c) eyes open or closed. Using dual regression, we obtained the time courses of ICNs for each subject. To produce temporal patterns for ICNs, we applied two categories of univariate metrics: network-wise complexity and network-wise low-frequency oscillation. Furthermore, we validated the test-retest reliability for each metric. The network-wise temporal patterns for most ICNs (especially for default mode network, DMN) exhibited moderate-to-high reliability and reproducibility under different scan conditions. Network-wise complexity for DMN exhibited fair reliability (ICC<0.5) based on eyes-closed sessions. Specially, our results supported that mEPI could be a useful method with high reliability and reproducibility. In addition, these temporal patterns were with physiological meanings, and certain temporal patterns were correlated to the node strength of the corresponding ICN. Overall, network-wise temporal patterns of ICNs were reliable and informative and could be complementary to spatial patterns of ICNs for further study.
authors.affiliation: School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China; Key Laboratory of Child Development and Learning Science (Ministry of Education), Southeast University, Nanjing 210096, China.
authors.forename: X
authors.initials: X
authors.lastname: Wang
authors.forename: Y
authors.initials: Y
authors.lastname: Jiao
authors.forename: T
authors.initials: T
authors.lastname: Tang
authors.forename: H
authors.initials: H
authors.lastname: Wang
authors.forename: Z
authors.initials: Z
authors.lastname: Lu
mesh_terms: D000328:Adult; D001683:Biological Clocks / Q000502:physiology*; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods / Q000592:standards*; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods / Q000592:standards*; D008297:Male; D009415:Nerve Net / Q000502:physiology*; D009434:Neural Pathways / Q000502:physiology; D015203:Reproducibility of Results; D013997:Time Factors; D055815:Young Adult
keywords: ALFF; Amplitude of Low-Frequency Fluctuations; CCC; DE; DMN; EPI; ICA; ICC; ICNs; ICs; MPRAGE; NIRS; RE; Renyi entropy; Repetition Time; SE; TE; TR; Tsallis entropy; WE; complexity estimators; concordance correlation coefficient; default mode network; differential entropy; echo-planar imaging; fALFF; fMRI; fractional ALFF; functional magnetic resonance imaging; independent component analysis; independent components; interclass correlations; intrinsic connectivity networks; low-frequency oscillation; mEPI; magnetization-prepared rapid acquisition with gradient echo; multi-band EPI; near infrared spectroscopy; resting state fMRI; sample entropy; test–retest reliability; wavelet entropy