Identifying the epileptogenic zone in interictal resting-state MEG source-space networks

Area of Science:

• Neuroscience
• Medical Imaging
• Network Theory

Background:

• Epilepsy surgery offers a potential cure but relies on accurate identification of the epileptogenic zone.
• A significant portion of patients continue to experience seizures post-surgery, highlighting the need for improved preoperative evaluation methods.
• Network theory provides a framework for analyzing brain activity, applicable even in the absence of clear epileptiform discharges.

Purpose of the Study:

• To investigate the utility of network theory in identifying the epileptogenic zone using magnetoencephalography (MEG).
• To determine if 'hub' status of brain regions in MEG networks correlates with seizure control after epilepsy surgery.

Main Methods:

• Retrospective analysis of resting-state MEG data from 22 epilepsy patients undergoing surgery.
• Reconstruction of time series for 90 brain regions using beamforming.
• Estimation of functional connectivity using the phase lag index in artifact-free epochs.
• Identification of network hubs based on betweenness centrality.

Main Results:

• Hubs were located within the resection cavity in 57% of seizure-free patients versus 0% of non-seizure-free patients.
• Hub localization in the resection lobe showed 64% sensitivity and 100% specificity for seizure freedom.
• Hubs coincided with the resection hemisphere and temporal/extratemporal location in 79% of seizure-free patients.

Conclusions:

• Noninvasive identification of brain network hubs via MEG is a valuable pre-surgical tool.
• This approach shows potential for localizing the epileptogenic zone, particularly in patients lacking interictal abnormalities.
• Hub analysis may improve the accuracy of pre-operative planning for epilepsy surgery.