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Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

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Local structural-functional coupling with counterfactual explanations for epilepsy prediction.

Jiashuang Huang¹, Shaolong Wei¹, Zhen Gao²

¹School of Artificial Intelligence and Computer Science, Nantong University, Nantong, 226019, China.

|January 4, 2025

Summary

This study introduces a novel local structural-functional brain connections coupling (SC-FC coupling) pattern for predicting brain disorders. This new method offers improved accuracy and insights into localized brain network changes in disorders.

Keywords:

Brain disorders Counterfactual explanations Functional connections SC-FC coupling Structural connections

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Area of Science:

Neuroscience
Medical Imaging
Computational Biology

Background:

Structural-functional brain connections coupling (SC-FC coupling) is crucial for identifying brain disorders.
Existing SC-FC coupling research primarily examines global and regional scales.
The impact of brain disorders on local-scale multi-brain region cooperation remains underexplored.

Purpose of the Study:

To propose and validate a local SC-FC coupling pattern for enhanced brain disorder prediction.
To investigate the relationship between structural and functional brain networks at a subgraph level.
To refine abnormal patterns for counterfactual explanations in brain disorder identification.

Main Methods:

Constructed multimodal brain networks using diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI).
Extracted and selected subgraphs based on frequency to generate local SC-FC coupling patterns.
Employed these patterns for brain disorder identification and abnormal pattern refinement.

Main Results:

The proposed local SC-FC coupling pattern method demonstrated superior accuracy compared to existing approaches.
The study provided novel insights into local SC-FC coupling patterns and their alterations in brain disorders.
The method successfully identified brain disorders and generated counterfactual explanations.

Conclusions:

Local SC-FC coupling patterns offer a promising new avenue for brain disorder prediction.
This approach enhances understanding of brain network dynamics in neurological conditions.
The findings highlight the importance of local-scale network analysis in neuroscience.