An Integrative Approach to Study Structural and Functional Network Connectivity in Epilepsy Using Imaging and Signal...

Area of Science:

• Neuroscience
• Epileptology
• Medical Imaging
• Network Science

Background:

• Understanding the dynamic evolution of epileptic networks during seizures is crucial for epilepsy research.
• Current methods often analyze functional or structural connectivity in isolation, limiting a comprehensive view of seizure dynamics.
• Patient-specific analysis is essential for characterizing the complex nature of epileptic activity.

Purpose of the Study:

• To develop and apply an integrative workflow for analyzing functional and structural brain connectivity during epileptic seizures.
• To characterize the organization and evolution of epileptic networks using a combination of SEEG and DWI data.
• To investigate patient-specific network dynamics and underlying structural influences during seizure events.

Main Methods:

• An integrative workflow was developed to combine stereotactic electroencephalogram (SEEG) and diffusion weighted imaging (DWI) data.
• Structural connectivity was computed using SEEG electrode locations to filter diffusion tensor imaging (DTI) fiber tracts.
• Functional connectivity was derived using a novel workflow tool based on non-linear correlation coefficients, creating directed graph structures.
• Hierarchical clustering was applied to functional connectivity data to identify modular network organization.
• Statistical analysis examined changes in correlation values during seizure onset and ictal phases.

Main Results:

• Hierarchical clustering revealed the formation of connected clusters within the insula, followed by inter-insular merging, with significant variation across seizures.
• Structural connectivity analysis identified strong intra-hemispheric connections, particularly in perisylvian/opercular areas.
• A decrease in correlation values was observed during seizure onset, with varied changes during the ictal phases.
• The combined analysis provided insights into patient-specific dynamic functional networks and their underlying structural connections.

Conclusions:

• The developed integrative workflow effectively characterizes patient-specific epileptic network dynamics by combining functional and structural connectivity measures.
• The findings highlight the complex, evolving nature of epileptic networks during seizures and their relationship with underlying brain structure.
• This approach offers a powerful tool for understanding the neural basis of epilepsy and potentially guiding therapeutic strategies.