ArticleNetwork neuroscience (Cambridge, Mass.)2023
Optimizing network neuroscience computation of individual differences in human spontaneous brain activity for test-retest reliability.
Article in Network neuroscience (Cambridge, Mass.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Spatial connectivity for local cortical homogeneity.Research square · 2026Article
- Computational mechanisms of neuroimaging biomarkers uncovered by multicenter resting-state fMRI connectivity variation profile.Molecular psychiatry · 2025Article
- Spacetime concordance in the primate cortex.bioRxiv : the preprint server for biology · 2025Article
- Metrics for estimating individual-level changes in functional brain connectivity and their correspondence with topology changes.bioRxiv : the preprint server for biology · 2025Article
- Challenges in measuring individual differences of brain function.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Network topology and cannabis use following two weeks of monitored abstinence: moderation of sex and patterns of use findings.Frontiers in adolescent medicine · 2025Article
- Systematic evaluation of fMRI data-processing pipelines for consistent functional connectomics.Nature communications · 2024Article
- Modeling the neurocognitive dynamics of language across the lifespan.Human brain mapping · 2024Article
- Investigating robust associations between functional connectivity based on graph theory and general intelligence.Scientific reports · 2024Article
- Conscientiousness associated with efficiency of the salience/ventral attention network: Replication in three samples using individualized parcellation.NeuroImage · 2023Article
- Age-related reorganization of functional network architecture in semantic cognition.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Benchmarking functional connectivity by the structure and geometry of the human brain.Network neuroscience (Cambridge, Mass.) · 2022Article
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6 authors.
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Abstract
A rapidly emerging application of network neuroscience in neuroimaging studies has provided useful tools to understand individual differences in intrinsic brain function by mapping spontaneous brain activity, namely intrinsic functional network neuroscience (ifNN). However, the variability of methodologies applied across the ifNN studies-with respect to node definition, edge construction, and graph measurements-makes it difficult to directly compare findings and also challenging for end users to select the optimal strategies for mapping individual differences in brain networks. Here, we aim to provide a benchmark for best ifNN practices by systematically comparing the measurement reliability of individual differences under different ifNN analytical strategies using the test-retest design of the Human Connectome Project. The results uncovered four essential principles to guide ifNN studies: (1) use a whole brain parcellation to define network nodes, including subcortical and cerebellar regions; (2) construct functional networks using spontaneous brain activity in multiple slow bands; and (3) optimize topological economy of networks at individual level; and (4) characterize information flow with specific metrics of integration and segregation. We built an interactive online resource of reliability assessments for future ifNN (https://ibraindata.com/research/ifNN).
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