Personalized Brain Network Model for Epilepsy Surgery Planning
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Solution Overview
Problem
Current surgical options for epilepsy are limited for patients with epileptogenic zones (EZ) located in multiple brain regions or eloquent areas, as they are often unsuitable for conventional surgery due to the risk of neurological complications, and there is a need for minimally invasive methods that effectively reduce seizure propagation while preserving normal brain functions.
Innovation Solution
A method using computerized platforms to model and personalize brain networks, employing modularity analysis and simulations to identify potential target zones outside the epileptogenic zone that, when surgically operated or removed, minimize seizure propagation and maintain normal brain functions, by analyzing structural and functional data from MRI and EEG/SEEG signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional surgery is performed to remove the epileptogenic zone, then seizure generation is eliminated, but neurological complications occur when EZ is located in eloquent areas
Solution Approach 1:
The patent introduces a computational model of the brain network as an intermediary between the epileptogenic zone and surgical intervention. This model simulates seizure propagation pathways and identifies alternative target zones that can interrupt seizure spread without directly removing the EZ in eloquent areas, thereby preventing neurological complications while maintaining seizure control effectiveness
Solution Approach 2:
The patent segments the brain network into functional modules using graph theory analysis. By identifying and targeting specific modular regions or connection hubs rather than the entire EZ, the surgery can disrupt seizure propagation pathways while preserving critical eloquent areas, thus resolving the contradiction between seizure control and neurological function preservation
2Reliability
If resection surgery removes brain regions generating seizures, then seizure-free outcomes are achieved, but the procedure is not feasible when EZ involves multiple brain regions or eloquent areas
Solution Approach 1:
The computational brain network model serves as an intermediary tool that enables surgical planning for complex cases. It simulates various surgical scenarios including disconnection strategies, identifies alternative target zones in non-eloquent areas, and predicts seizure propagation patterns, thereby making surgery feasible for patients with multi-regional or eloquent EZ who would otherwise be ineligible
Solution Approach 2:
The patent employs dynamic simulation of seizure propagation through the brain network model. By modeling the temporal evolution of seizure activity and testing different surgical interventions dynamically, the system can identify effective disconnection strategies for complex EZ configurations, expanding surgical applicability to cases with multiple brain region involvement
3Reliability
If disconnection surgery severs nerve pathways to limit seizure propagation, then seizure spread is reduced, but normal brain functions may be affected
Solution Approach 1:
The patent implements a feedback mechanism where the computational model simulates the effects of proposed disconnection surgeries on both seizure propagation and normal brain functions. The simulation results provide feedback on potential side effects, allowing surgeons to adjust the disconnection strategy to achieve optimal seizure control while minimizing impact on normal functions such as language, memory, and motor skills
Data Source
AI summary
The method of identifying a potentially surgically operable target zone in an epileptic patient's brain includes: providing a computerized platform modelling various zones of a primate brain and connectivity between said zones; providing a model of an epileptogenic zone and a model of the propagation of an epileptic discharge from an epileptic zone to a propagation zone; obtaining a patient's personalized computerized platform; deriving the potential target zones based on modularity analysis; evaluating the target zones' effectiveness by simulating epileptic seizures propagation in the personalized patient's computerized platform; evaluating the target zones' safety by simulating spatiotemporal brain activation patterns in a defined state condition and comparing the simulated spatiotemporal brain activation patterns obtained before removal of the target zone with the spatiotemporal brain activation patterns obtained after removal of the target zone; identifying the target zones which satisfy both effectiveness and safety evaluation criteria as potentially surgically operable target zones.