MesoSCOUT: A novel tool for revealing mesoscale organization in the white-matter connectome

Area of Science:

• Neuroimaging and Computational Topology
• Brain Connectomics and Schizophrenia Research

Background:

• Schizophrenia (SCH) is a complex psychiatric disorder.
• Understanding alterations in brain connectivity is crucial for diagnosis and treatment.
• Current methods may not fully capture mesoscale connectome differences.

Purpose of the Study:

• To apply persistent homology (PH) via clique topology to analyze white-matter connectome differences.
• To identify and compare topological motifs between healthy controls (HC) and SCH subjects.
• To explore the generalizability of findings across different neuroimaging datasets (COBRE and HCP).

Main Methods:

• Utilized computational algebraic topology, specifically persistent homology (PH) with clique topology.
• Extracted and compared topological motifs from structural connectomes of HC and SCH groups.
• Validated findings using null models and examined cross-dataset structural overlap.

Main Results:

• Significant differences in topological motifs were found between HC and SCH groups.
• Identified shared mesoscale structures across two independent datasets (COBRE and HCP).
• Demonstrated the potential for cross-scanner comparability in connectome analysis.

Conclusions:

• PH via clique topology reveals novel mesoscale connectome differences in schizophrenia.
• This approach enables connectomic fingerprinting for potential neuroimaging-based diagnosis.
• Findings pave the way for developing targeted treatments for psychiatric and neurological conditions.