A descriptive model of resting-state networks using Markov chains

Area of Science:

• Neuroscience
• Computational Neuroscience
• Network Science

Background:

• Resting-state functional connectivity (RSFC) research often relies on pairwise linear correlations, leaving the complex functional network structure incompletely understood.
• Investigating the intricate relationships within resting-state networks (RSNs) is crucial for advancing our comprehension of brain function.

Purpose of the Study:

• To explore resting-state networks (RSNs) using steady-state distributions for a novel perspective on multi-node functional connectivity.
• To evaluate the consistency of different RSNs by comparing their steady-state distributions.

Main Methods:

• Inferred Markov chain models from resting-state fMRI data.
• Calculated the Hellinger distance between steady-state distributions of these models to assess network consistency.
• Compared the consistency of the default mode network with randomly selected nodes from various RSNs.

Main Results:

• Generated steady-state distributions revealed higher consistency for the default mode network across subjects.
• The default mode network demonstrated greater stability compared to random node selections from other RSNs.
• This approach offers a new metric for quantifying network consistency in RSFC studies.

Conclusions:

• Steady-state distributions provide a robust method for analyzing functional network structure in resting-state brain activity.
• The default mode network exhibits inherent consistency, suggesting its distinct organizational properties.
• This study enhances the understanding of RSFC by moving beyond simple correlations to explore network dynamics.