EEG and rsfMRI Co-Registration for Epileptic Network Mapping
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Solution Overview
Problem
Current methods for evaluating epilepsy lack the ability to accurately map network-level disturbances resulting from epileptic discharges, leading to ineffective surgical outcomes in patients, as they do not account for individualized brain network architecture and do not utilize tools for automatic network mapping.
Innovation Solution
A system and method combining non-invasive electroencephalography (EEG) source localization and non-concurrent resting state functional magnetic resonance imaging (rsfMRI) to generate a functional brain map, allowing for the identification of epilepsy networks and simulation of surgical resection plans by co-registering EEG and rsfMRI data to create a 3D graph representing brain connectivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional MRI and scalp EEG hardware are used for epilepsy evaluation, then the assessment is non-invasive and widely available, but the ability to map network-level disturbances is insufficient
Solution Approach 1:
The patent combines data from conventional MRI and scalp EEG hardware with graph theory algorithms to create a comprehensive network mapping system. By merging multiple data sources and analytical methods, the system achieves high network mapping precision while utilizing only widely available, non-invasive hardware.
Solution Approach 2:
The patent introduces graph theory as an intermediary analytical framework that processes data from conventional hardware to reveal network-level disturbances. This intermediary layer transforms standard clinical data into detailed network maps without requiring specialized equipment.
2Reliability
If focal resection surgery is performed based on traditional evaluation methods, then the procedure is straightforward and widely applicable, but seizure freedom is achieved in only two-thirds of cases
Solution Approach 1:
The patent segments the epileptic network into identifiable components using graph theory analysis, allowing surgeons to precisely identify which network elements to target. This segmentation approach improves surgical effectiveness by enabling targeted disruption of the epileptic network while preserving healthy tissue.
Solution Approach 2:
The patent performs preliminary network mapping and identification of critical network nodes before surgery. This advance planning allows for more effective surgical targeting, improving the likelihood of seizure freedom while maintaining straightforward surgical procedures.
3Loss of information
If no automatic network mapping tool is used, then the evaluation process is simple and familiar to clinicians, but individual network architecture cannot be assessed
Solution Approach 1:
The patent creates an automated system that performs network mapping and analysis without requiring manual intervention. The graph theory algorithms automatically process clinical data to generate network maps and identify critical elements, preserving detailed network architecture information while reducing clinician workload.
Solution Approach 2:
The patent replaces manual analysis methods with automated graph theory algorithms. This substitution eliminates the need for manual network assessment while capturing comprehensive network architecture information that would be difficult to obtain through conventional manual evaluation.
4Manufacturing precision
If detailed network analysis is not included in preoperative evaluation, then the evaluation process is faster and less resource-intensive, but surgical target precision is reduced
Solution Approach 1:
The patent performs detailed network analysis as a preliminary step before final surgical planning. By completing the computationally intensive graph theory analysis beforehand, the system enables precise surgical target identification without extending the actual surgical planning time, as the network maps are pre-generated.
Data Source
AI summary
System and method for processing, non-concurrently collected, electroencephalogram (EEG) data and resting station functional magnetic resonance imaging (rsfMRI) data, non-invasively, to create a patient-specific three-dimensional (3D) mapping of the patient's functional brain network. The mapping can be used to more precisely identify candidates of resective neurosurgery and to help create a targeted surgical plan for those patients. The methodology automatically maps the patient's unique brain network using non-concurrent EEG and resting state functional MRI (rsfMRI). Generally, the current invention merges non-concurrent EEG data and rsfMRI data to map the patient's epilepsy/seizure network.