An empirical Bayes normalization method for connectivity metrics in resting state fMRI

Area of Science:

• Neuroimaging
• Computational Neuroscience
• Biostatistics

Background:

• Resting-state functional magnetic resonance imaging (rs-fMRI) is crucial for studying brain networks.
• Functional connectivity metrics, like Pearson correlation, are sensitive to image acquisition and preprocessing.
• Accurate quantification is vital for reproducible scientific findings.

Purpose of the Study:

• To develop a novel empirical Bayes method for normalizing functional brain connectivity metrics.
• To map connectivity metrics to a zero-to-one scale for improved graph theory analysis.
• To enhance the robustness and reliability of connectivity quantification and biomarker detection.

Main Methods:

• Proposed a novel empirical Bayes method for normalizing functional brain connectivity.
• Applied the normalization to a simulation study to assess performance.
• Illustrated the method using a resting-state fMRI dataset from the Autism Brain Imaging Data Exchange (ABIDE) study.

Main Results:

• Simulation results demonstrate improved robustness and reliability in quantifying brain functional connectivity.
• The normalization method enhances the detection of group differences (biomarkers).
• The method avoids information loss associated with hard thresholding by mapping metrics to a [0, 1] scale.

Conclusions:

• The proposed normalization method offers a more reliable approach to analyzing functional brain connectivity.
• This technique improves the sensitivity for detecting neuroimaging biomarkers in clinical populations.
• The method is well-suited for graph theory-based network analysis in neuroimaging research.