Static and dynamic fMRI-derived functional connectomes represent largely similar information.

Area of Science:

• Neuroimaging
• Cognitive Neuroscience
• Data Analysis

Background:

• Functional connectivity (FC) analysis of blood oxygen-level-dependent (BOLD) fMRI data is crucial for understanding brain function.
• Methods vary in temporal sensitivity (static vs. dynamic) and region scope (bivariate vs. multivariate).
• Dynamic FC is hypothesized to capture more brain variability than static FC.

Purpose of the Study:

• To systematically compare static and dynamic FC estimation methods.
• To evaluate bivariate versus multivariate approaches in FC analysis.
• To assess the impact of different FC methods on brain-behavior associations.

Main Methods:

• Compared five FC methods: Pearson's correlation (static, bivariate), lagged correlation (dynamic, bivariate), partial correlation (static, multivariate), and multivariate AR models (dynamic, multivariate).
• Assessed similarities between FC matrices and node centrality measures.
• Compared brain-behavior association patterns derived from different FC methods.

Main Results:

• FC estimates showed no significant differences based on temporal order sensitivity (static vs. dynamic).
• Multivariate and bivariate methods yielded distinct FC estimates.
• Dynamic and static FC estimates were highly correlated, particularly at the group level.
• Brain-behavior association patterns were similar across dynamic and static FC methods.

Conclusions:

• Dynamic FC estimates largely reflect the same information as static FC estimates.
• The added value of dynamic FC over static FC for capturing brain-behavior relationships may be limited.
• Multivariate methods offer a different perspective on FC compared to bivariate methods.