Adapted K-Core Decomposition and Visualization for Functional Magnetic Resonance Imaging Connectivity Networks

Area of Science:

• Neuroscience
• Computer Science
• Data Visualization

Background:

• Medical imaging, including functional magnetic resonance imaging (fMRI), is crucial for studying the human brain and identifying neurological disorders.
• Functional connectivity network (FCN) analysis is a key method for understanding brain activity and comparing subject groups.
• Current visual analytics for FCNs simplify detailed interpretation but struggle with efficient cohort grouping and aggregation.

Purpose of the Study:

• To develop an improved visual analytics approach for grouping and aggregating functional connectivity network data.
• To incorporate network details into the grouping process, overcoming limitations of existing statistical and visualization methods.
• To present a novel adapted k-core decomposition algorithm and visualization for enhanced FCN analysis.

Main Methods:

• An adapted k-core decomposition algorithm was developed to analyze functional connectivity networks.
• The method calculates connected component information for nodes within FCNs.
• A novel visualization was created to combine k-core decomposition with connected component data for high-level FCN display.

Main Results:

• The proposed method effectively groups and aggregates functional connectivity network data.
• Vital network details are preserved during the grouping and aggregation process.
• A prototype demonstrated the capability to display more high-level FCN details than contemporary methods.

Conclusions:

• The adapted k-core decomposition and visualization offer a powerful tool for analyzing large cohorts of functional connectivity networks.
• This approach facilitates efficient data grouping and aggregation while retaining essential network information.
• The method enhances the ability to perform detailed analysis of brain imaging data for research and clinical applications.