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Regularized partial correlation provides reliable functional connectivity estimates while correcting for widespread

Kirsten L Peterson1,2, Ruben Sanchez-Romero1, Ravi D Mill1

  • 1Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102.

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|September 2, 2025
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Summary
This summary is machine-generated.

Regularized methods significantly improve functional connectivity (FC) reliability in brain imaging. Graphical lasso offers accurate, robust FC estimates, outperforming standard methods for brain network analysis.

Keywords:
Diffusion MRIIndividual differencesNetwork neuroscienceRegularizationStructural connectivityfMRI

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Brain Network Analysis

Background:

  • Functional connectivity (FC) analysis using resting-state fMRI is crucial for understanding brain communication.
  • Standard pairwise correlation methods for FC can be confounded by indirect connections.
  • Unregularized partial correlation methods, while reducing confounding, suffer from low reliability.

Purpose of the Study:

  • To investigate whether adding regularization to partial correlation methods can improve the reliability and accuracy of functional connectivity (FC) estimates.
  • To compare the performance of regularized methods (graphical lasso, graphical ridge, principal component regression) against unregularized partial and pairwise correlation.

Main Methods:

  • Applied unregularized (pairwise correlation, partial correlation) and regularized (graphical lasso, graphical ridge, principal component regression) methods to resting-state fMRI data and simulated datasets.
  • Assessed reliability using between-session similarity and intraclass correlation.
  • Validated accuracy against structural connectivity and ground truth networks.

Main Results:

  • Regularization substantially improved FC reliability across all tested methods.
  • Regularized methods, particularly graphical lasso, yielded more accurate individual FC estimates compared to unregularized approaches.
  • Graphical lasso demonstrated robustness to noise, data quantity, and motion artifacts, common in fMRI.
  • Resting-state graphical lasso FC successfully predicted task activations and behavioral differences.

Conclusions:

  • Regularized methods, especially graphical lasso, offer a more reliable and accurate approach to estimating functional connectivity than standard pairwise correlation.
  • Graphical lasso overcomes the reliability limitations of unregularized partial correlation, providing valid estimates of unconfounded brain connectivity.
  • These findings support the use of regularized methods for advanced brain network analysis in neuroscience research.