ArticleBrain sciences2023
Test-Retest Reliability of Resting Brain Small-World Network Properties across Different Data Processing and Modeling Strategies.
Article in Brain sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Topological disruptions of metabolic brain networks in early-stage chronic kidney disease.BMC medical imaging · 2026Article
- Household cannabis cessation and adolescent mental health outcomes in a prospective cohort study.BMC medicine · 2026Article
- Crosstalk between the gut microbiota and brain network topology in poststroke aphasia patients: perspectives from neuroimaging findings.Therapeutic advances in neurological disorders · 2025Article
- Continuous Dictionary of Nodes Model and Bilinear-Diffusion Representation Learning for Brain Disease Analysis.Brain sciences · 2024Article
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10 authors.
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Abstract
Resting-state functional magnetic resonance imaging (fMRI) with graph theoretical modeling has been increasingly applied for assessing whole brain network topological organization, yet its reproducibility remains controversial. In this study, we acquired three repeated resting-state fMRI scans from 16 healthy controls during a strictly controlled in-laboratory study and examined the test-retest reliability of seven global and three nodal brain network metrics using different data processing and modeling strategies. Among the global network metrics, the characteristic path length exhibited the highest reliability, whereas the network small-worldness performed the poorest. Nodal efficiency was the most reliable nodal metric, whereas betweenness centrality showed the lowest reliability. Weighted global network metrics provided better reliability than binary metrics, and reliability from the AAL90 atlas outweighed those from the Power264 parcellation. Although global signal regression had no consistent effects on the reliability of global network metrics, it slightly impaired the reliability of nodal metrics. These findings provide important implications for the future utility of graph theoretical modeling in brain network analyses.
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