Dynamical Network Model for Epileptogenic Zone Localization

The patent replaces the mechanical invasive procedure (craniotomy or burr hole drilling for electrode insertion) with a non-invasive imaging-based system. The system uses MRI, PET, and SPECT imaging combined with computational modeling to identify the epileptogenic zone without physically penetrating the skull, thereby eliminating surgical risks while maintaining localization accuracy.

The patent introduces computational models and imaging data as intermediaries between the non-invasive external measurements and the internal brain structures. By using image processing algorithms and neural network models to analyze imaging data, the system indirectly localizes the epileptogenic zone without direct physical contact with brain tissue.

The patent performs all necessary diagnostic imaging and computational analysis before any surgical intervention. By obtaining MRI, PET, and SPECT scans and processing them through computational models in advance, the epileptogenic zone is identified prior to surgery, eliminating the need for prolonged post-admission monitoring and reducing overall treatment time.

The patent creates a virtual model of the patient's brain using imaging data and computational algorithms. This digital copy allows for simulation and analysis of seizure propagation patterns without requiring physical electrodes in the brain, enabling rapid diagnosis and treatment planning that reduces hospital stay duration.

The patent implements automated feedback loops where computational models continuously analyze imaging data and refine epileptogenic zone identification. The system processes multiple imaging modalities and computational metrics simultaneously, providing objective feedback that eliminates the need for manual review of multiple seizure events and multidisciplinary consensus meetings.

The patent transforms the diagnostic approach by changing from manual parameter assessment to automated computational parameter analysis. By using algorithms to quantify imaging features and calculate epileptogenic probability scores, the system achieves both high precision and improved productivity through objective, reproducible measurements that do not require subjective expert interpretation.

The patent employs dynamic computational models that capture the temporal evolution of seizure activity across brain networks. By modeling the time-dependent propagation of epileptic discharges and analyzing how network properties change during seizure events, the system achieves both automated analysis efficiency and high precision in identifying the epileptogenic zone.

The patent combines multiple imaging modalities (MRI, PET, SPECT) with computational modeling approaches to create a composite diagnostic system. This integration of diverse data sources and analysis methods allows the system to capture both structural and functional network properties, achieving superior measurement precision while maintaining automated productivity.