SEEG4D: a tool for 4D visualization of stereoelectroencephalography data.

Area of Science:

• Neurology
• Medical Imaging
• Computational Neuroscience

Background:

• Epilepsy affects millions globally, necessitating advanced presurgical planning for drug-resistant cases.
• Stereoelectroencephalography (sEEG) offers high spatial resolution for visualizing seizure onset zones.
• Current tools often lack integrated spatiotemporal visualization of electrophysiological data.

Purpose of the Study:

• To present SEEG4D, an automated tool for merging spatial and temporal data into a 4D VR object.
• To enable simultaneous viewing of anatomy and seizure activity for improved seizure localization.
• To enhance presurgical planning for epilepsy surgery.

Main Methods:

• Developed an automated, containerized pipeline for tissue and electrode contact segmentation.
• Aligned segmented contacts with electrophysiological data and animated them based on relative power.
• Generated 4D virtual reality models for surgical team viewing and planning.

Main Results:

• Automated contact segmentation achieved accuracy within 1 mm of trained raters.
• Generated models demonstrate signal propagation along electrodes.
• The tool successfully merges multimodal spatial and temporal data into an integrated VR environment.

Conclusions:

• SEEG4D provides an integrated spatiotemporal view of electrophysiology and anatomy.
• The VR models have the potential to significantly enhance sEEG presurgical planning.
• This automated approach improves efficiency and accuracy in localizing seizure foci.