Evaluating the Reliability of Brain Functional Networks
Author Information
Author(s): Wang Jin-Hui, Zuo Xi-Nian, Gohel Suril, Milham Michael P., Biswal Bharat B., He Yong
Primary Institution: State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
Hypothesis
What is the test-retest reliability of topological metrics of functional brain networks derived from resting-state fMRI data?
Conclusion
The study found that the reliability of global network metrics was generally low, while local nodal metrics showed more consistent reliability across different brain regions.
Supporting Evidence
- Global network metrics showed overall low reliability.
- Local nodal metrics exhibited fair to good reliability in specific brain regions.
- Weighted networks were more numerically stable against noise compared to binarized networks.
Takeaway
This study looked at how reliable brain network measurements are when taken at different times, finding that some measurements are more stable than others.
Methodology
The study used a test-retest fMRI dataset to evaluate the reliability of various global and local network metrics derived from resting-state functional MRI data.
Potential Biases
Potential biases may arise from the inclusion of negative correlations in the analysis.
Limitations
The study's findings may be influenced by the specific preprocessing steps used in fMRI data analysis.
Participant Demographics
Mean age of participants was 30.7 years, with 9 males.
Statistical Information
P-Value
p<10−300
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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