Node persistence from topological data analysis reveals changes in brain functional connectivity

Area of Science:

• Neuroscience
• Topological Data Analysis
• Computational Biology

Background:

• Large-scale brain functional connectivity analysis reveals neurological disruptions.
• Healthy aging and autism spectrum disorder (ASD) involve altered brain activity and connectivity.

Purpose of the Study:

• Investigate changes in resting-state functional connectivity using persistent homology (PH).
• Analyze these changes across global, mesoscopic, and local brain scales.
• Introduce and validate 'node persistence' for identifying clinically relevant brain regions.

Main Methods:

• Applied persistent homology (PH) to resting-state functional connectivity data.
• Examined brain changes at global (brain-wide), mesoscopic (network), and local (region) scales.
• Developed and utilized node persistence, a PH-based metric for regional analysis.

Main Results:

• Identified significant differences in functional connectivity at local scales in healthy aging and ASD.
• Node persistence effectively detected brain regions with altered connectivity.
• These identified regions align with those targeted for therapeutic interventions.

Conclusions:

• Persistent homology, particularly node persistence, is a valuable tool for analyzing brain connectivity.
• This approach can identify brain regions impacted by healthy aging and ASD.
• Findings suggest clinical relevance for targeted brain stimulation and interventions.