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Updated: May 9, 2026
Modeling the Functional Network for Spatial Navigation in the Human Brain
Published on: October 13, 2023
pFDR and pFNR estimation for brain networks construction
Sara Sala1, Piero Quatto, Paola Valsasina
1Department of Economics, Management and Statistics, University of Milano-Bicocca, Milan, Italy; Neuroimaging Research Unit and Department of Neurology, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy.
This study introduces a novel method for selecting thresholds in brain network analysis, controlling for false discoveries and non-discoveries in functional connectivity. This approach enhances the reliability of brain network models derived from functional magnetic resonance imaging data.
Area of Science:
- Neuroscience
- Graph Theory
- Network Science
Background:
- Brain functional connectivity studies increasingly utilize graph theory for network analysis.
- Deriving adjacency matrices for brain networks lacks a standardized thresholding method for correlation matrices.
- Existing methods for thresholding can lead to uncontrolled error rates, impacting network interpretation.
Purpose of the Study:
- To propose a robust strategy for selecting correlation matrix thresholds in brain network analysis.
- To control for multiple comparison issues by managing false discovery and false non-discovery rates.
- To provide reliable estimators for positive false nondiscovery rate (pFNR) and balance error types.
Main Methods:
- Developed a thresholding strategy for correlation matrices based on controlling positive false discovery rate (pFDR) and positive false nondiscovery rate (pFNR).
- Introduced point and interval estimators for pFNR.
- Implemented a method for balancing both pFDR and pFNR.
Main Results:
- Demonstrated the proposed thresholding strategy using functional magnetic resonance imaging (fMRI) data.
- Validated the method through extensive Monte Carlo simulations.
- Showcased a practical approach to error rate control in brain network construction.
Conclusions:
- The proposed method offers a principled way to determine thresholds for brain network adjacency matrices.
- Controlling pFDR and pFNR enhances the statistical rigor of functional connectivity analyses.
- This work provides tools for more reliable brain network modeling and interpretation.
