Characterization of 'Local' Functional Network Connectivity in 4D Spatial Dynamic fMRI Networks

Area of Science:

• Neuroimaging
• Cognitive Neuroscience
• Brain Network Analysis

Background:

• Functional magnetic resonance imaging (fMRI) is crucial for mapping brain activity via functional network connectivity (FNC).
• Current research often overlooks time-varying dynamics within spatial brain networks, focusing on static or dynamic FNC between predefined nodes.
• Existing voxel-level dynamic network methods do not explore FNC between these dynamic spatial networks.

Purpose of the Study:

• To propose and validate a novel method for assessing FNC within spatially dynamic brain networks using resting-state fMRI (rsfMRI).
• To enable the calculation of network-specific FNC across localized voxel subsets.
• To investigate local FNC dynamics within varying voxel subsets.

Main Methods:

• Development of a novel voxel-based FNC approach for analyzing rsfMRI data.
• Application of the method to the baseline dataset of 100 participants from the Adolescent Brain and Cognitive Development (ABCD) study.
• Calculation of network-specific FNC across localized voxel subsets, including static FNC (sFNC), global voxel FNC (GvFNC), and local voxel FNC (LvFNC).

Main Results:

• The voxel-based FNC approach successfully replicated traditional static FNC findings, showing significant modularity in sFNC and GvFNC matrices.
• The novel method demonstrated the ability to investigate local FNC within different voxel subsets.
• A reduction in anticorrelations was observed within the average local voxel FNC (LvFNC) as the voxel inclusion rate decreased.

Conclusions:

• The proposed method offers a new way to examine FNC within spatially dynamic brain networks.
• This technique allows for detailed analysis of local FNC across varying voxel resolutions.
• Findings suggest dynamic changes in network anticorrelations based on the scale of analysis.