ArticleQuantitative imaging in medicine and surgery2019
Reliability evaluation on weighted graph metrics of fNIRS brain networks.
Article in Quantitative imaging in medicine and surgery, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 31 citations in OpenAlex.
- Interpretable Network-Level Biomarker Discovery for Alzheimer's Stage Assessment Using Resting-State fNIRS Complexity Graphs.Brain sciences · 2026Article
- Correlation of prefrontal-based resting state functional connectivity with the degree of cognitive impairment in Alzheimer's disease: a functional near-infrared spectroscopy study.Behavioral and brain functions : BBF · 2025Article
- Developmental alterations in brain network asymmetry in 3- to 9-month infants with congenital sensorineural hearing loss.Science advances · 2025Article
- Forming Connections: Functional Brain Connectivity is Associated With Executive Functioning Abilities in Early Childhood.Developmental science · 2025Article
- Transcutaneous auricular vagus nerve stimulation improves cortical functional topological properties and intracortical facilitation in patients with Parkinson's disease.NPJ Parkinson's disease · 2025Article
- Comparing different motion correction approaches for resting-state functional connectivity analysis with functional near-infrared spectroscopy data.Neurophotonics · 2024Article
- Altered Functional Connectivity of Temporoparietal Lobe in Obstructive Sleep Apnea: A Resting-State fNIRS Study.Bioengineering (Basel, Switzerland) · 2024Article
- Advances in the etiology and neuroimaging of children with attention deficit hyperactivity disorder.Frontiers in pediatrics · 2024Review
- Test-retest reliability of fNIRS in resting-state cortical activity and brain network assessment in stroke patients.Biomedical optics express · 2023Article
- Impact of thresholding on the consistency and sensitivity of diffusion MRI-based brain networks in patients with cerebral small vessel disease.Brain and behavior · 2022Article
- Effect of Shift Work on Cognitive Function in Chinese Coal Mine Workers: A Resting-State fNIRS Study.International journal of environmental research and public health · 2022Article
- Is There a Difference in Brain Functional Connectivity between Chinese Coal Mine Workers Who Have Engaged in Unsafe Behavior and Those Who Have Not?International journal of environmental research and public health · 2022Article
- Identifying Individuals by fNIRS-Based Brain Functional Network Fingerprints.Frontiers in neuroscience · 2022Article
- Applications of Resting-State fNIRS in the Developing Brain: A Review From the Connectome Perspective.Frontiers in neuroscience · 2020Review
- Disrupted functional brain connectivity networks in children with attention-deficit/hyperactivity disorder: evidence from resting-state functional near-infrared spectroscopy.Neurophotonics · 2020Article
- Study on cognitive impairment evaluation based on photoelectric neural information.Journal of Alzheimer's disease reportsArticle
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundResting-state fNIRS (R-fNIRS) imaging data has proven to be a valuable technique to quantitatively characterize functional architectures of human brain network. However, whether the brain network metrics derived using weighted brain network model is test-retest (TRT) reliable remains largely unknown.
methodsHere, we firstly constructed weighted brain networks on a group of 18 participants, and then applied graph-theory approach to quantify topological parameters of each weighted network. The intraclass correlation coefficient (ICC) was further applied to quantify the TRT reliability of network metrics.
resultsWe found that the reliability of the weighted network metrics is threshold-sensitive, and most of these network metrics showed fair to excellent reliability. Specifically, the global network metrics, e.g., clustering coefficient, path length, local efficiency and global efficiency were of excellent level reliability (ICC >0.75) on both HbO and HbR signals. The nodal network metrics, e.g., degree and efficiency, generally also showed excellent level reliability on both HbO and HbR signals, and the reliability of these two metrics was better than that of nodal betweenness.
conclusionsOverall, these findings demonstrated that most weighted network metrics derived from fNIRS are TRT reliable and can be used for brain network research.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.