Visibility graphs for fMRI data: Multiplex temporal graphs and their modulations across resting-state networks

Area of Science:

• Neuroscience
• Complex Systems
• Data Science

Background:

• Visibility algorithms map time series to graphs, enabling graph theory and network science applications.
• This framework has been extended to multivariate time series for analyzing collective dynamics.

Purpose of the Study:

• To apply visibility graph methods to functional MRI (fMRI) time series.
• To capture local and global dynamics of brain activity intuitively.
• To bridge time series analysis with network theory in network neuroscience.

Main Methods:

• Utilized visibility algorithms on multivariate fMRI time series.
• Analyzed temporal network similarities across resting-state networks.
• Applied methods to a large, open-access fMRI dataset.

Main Results:

• Identified differences in temporal network similarities across resting-state networks.
• Demonstrated the potential of visibility graphs to characterize brain activity patterns.
• Showed that this approach can capture correlated dynamics within brain networks.

Conclusions:

• Visibility graph analysis offers a novel approach to understanding complex dynamics in fMRI data.
• This method provides insights into brain network organization and function.
• The approach shows promise for detecting alterations in brain activity associated with psychiatric disorders.