SEEG-Based Bilateral Seizure Network Analysis for Neurostimulation Treatment
Epilepsy patients with drug-resistant seizures emanating from two or more distinct regions of left and right hemispheres are the primary candidates for neurostimulation treatment. Stereo-electroencephalography (SEEG) is a minimally invasive technique to monitor and evaluate brain activities during s...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10852360/ |
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| Summary: | Epilepsy patients with drug-resistant seizures emanating from two or more distinct regions of left and right hemispheres are the primary candidates for neurostimulation treatment. Stereo-electroencephalography (SEEG) is a minimally invasive technique to monitor and evaluate brain activities during seizures before stimulator implantation. This work proposes a seizure network modeling method using SEEG to analyze the functional connectivity of epileptogenic zone during bilateral seizures. Network nodes are selected subset of SEEG contact points, and network edges are directed signal correlations calculated from directed transfer function. Based on signal directionality, four connectivity values are extracted to measure the intra- and inter-activities that are within or between the left and right hemispheres, respectively. Statistical difference between connectivity values is used to quantify the seizure impact of each hemisphere. A subset of network nodes is selected from impactful side as stimulation target candidates. Experimental results are validated on ten patients having different seizure types with bilateral onset. Each seizure type has specific connectivity patterns that show different importance from each brain side. Selection of neurostimulation targets from primary side are consistent with clinicians’ decision. Relationships are found among connectivity differences, seizure types and stimulation outcomes. Using SEEG signals, we can capture specific connectivity differences associated with bilateral seizure networks. Such differences are related with corresponding neurostimulation targets and stimulating outcomes. The proposed work elucidates the difference of network connectivity for bilateral patients, and assists clinicians to choose the stimulation targets and to predict the potential outcomes. |
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| ISSN: | 1534-4320 1558-0210 |