Real-time Brain Region Mapping Using Hidden Markov Models
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Solution Overview
Problem
Current deep brain stimulation (DBS) procedures for treating Parkinson's disease face challenges in accurately mapping the dorsolateral oscillatory region (DLOR) of the subthalamic nucleus (STN) during surgery, which is crucial for optimal macroelectrode placement and minimizing side effects.
Innovation Solution
A real-time method using a Dynamic Bayesian Network, specifically a Hidden Markov Model (HMM), to map the DLOR of the STN by recording neurophysiological activity, calculating power spectral analysis values, and assigning regions with high probability, aiding in precise electrode placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional MRI or CT scanning is used to locate the target region, then the preoperative localization is improved, but the real-time accuracy during surgery cannot be guaranteed
Solution Approach 1:
The patent uses preoperative MRI or CT scanning to establish a preliminary localization of the target region (subthalamic nucleus) and creates a planned insertion trajectory. This preliminary action guides the electrode insertion process, but the actual real-time mapping is performed during surgery using microelectrode recordings and power spectral analysis to verify and refine the target location.
Solution Approach 2:
The patent implements real-time feedback by continuously monitoring neurophysiological signals (local field potentials) and power spectral density during electrode insertion. The system provides immediate feedback about the electrode's position relative to the target region, allowing the neurosurgeon to adjust the insertion trajectory in real-time to achieve precise mapping of the dorsolateral oscillatory region.
2Ease of operation
If manual mapping methods are used during surgery, then the flexibility is improved, but the mapping precision and consistency deteriorate
Solution Approach 1:
The patent employs automated algorithms that independently analyze the microelectrode recordings and power spectral density to identify the dorsolateral oscillatory region boundaries. The system self-determines the optimal trajectory and target region without requiring constant manual intervention, thereby maintaining high precision while reducing the burden on the neurosurgeon.
Solution Approach 2:
The patent replaces manual visual and tactile mapping methods with automated computational analysis. The system uses power spectral density calculations and pattern recognition algorithms to automatically demarcate the DLOR boundaries, substituting the mechanical process of manual tracking with an automated electronic system that provides more consistent and precise results.
3Measurement precision
If the electrode insertion trajectory is adjusted to reach the exact target, then the mapping accuracy is improved, but the surgical time and complexity increase
Solution Approach 1:
The patent calculates and plans the optimal electrode insertion trajectory in advance based on preoperative imaging and the patient's anatomical characteristics. This predetermined trajectory is designed to efficiently reach the target region (subthalamic nucleus) and its dorsolateral oscillatory region, reducing the need for time-consuming intraoperative adjustments while maintaining high placement accuracy.
Solution Approach 2:
The patent maintains continuous monitoring and mapping throughout the electrode insertion process without interruption. The system continuously analyzes power spectral density and neurophysiological signals from the moment the electrode enters the brain until the optimal target is reached, eliminating idle time and ensuring that every moment of insertion contributes to accurate mapping, thereby optimizing the overall surgical time.
4Manufacturing precision
If the macroelectrode is placed precisely in the DLOR, then the DBS effectiveness is improved, but the risk of causing cognitive or limbic side effects increases
Solution Approach 1:
The patent applies local quality by precisely differentiating between the dorsolateral oscillatory region (DLOR) and adjacent nonoscillatory regions of the subthalamic nucleus. The system uses power spectral density analysis to identify the specific local characteristics of the DLOR, enabling the macroelectrode to be placed with high precision in this specific region while avoiding adjacent areas that may cause cognitive or limbic side effects.
Solution Approach 2:
The patent uses real-time neurophysiological feedback during electrode insertion to monitor the electrical characteristics of the tissue being traversed. By analyzing changes in local field potentials and power spectral density, the system provides feedback about the electrode's proximity to the DLOR boundaries, allowing the neurosurgeon to adjust the placement to achieve optimal DBS effectiveness while minimizing the risk of stimulating adjacent cognitive or limbic regions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables accurate demarcation of the DLOR and nonoscillatory regions within the STN, improving the effectiveness of DBS by optimizing macroelectrode placement and reducing cognitive or limbic side effects.
Implementation Method 1
recording neurophysiological activity by the one or more electrodes along the insertion trajectory
Implementation Method 2
calculating power spectral analysis values for the neurophysiological activity recorded along the insertion trajectory
Data Source
AI summary
A real-time method and system to accurately demarcate sub-territories of the subthalamic nucleus area in the brain during surgery, based on microelectrode recordings and a Hidden Markov Model. Root mean square and power spectral density of the microelectrode recordings are used to train and test Hidden Markov Model in identifying the dorsolateral oscillatory region and non-oscillatory sub-territories within the subthalamic nucleus. After the dorsolateral oscillatory region in the subthalamic nucleus is mapped, the microelectrodes are removed, and a macroelectrode is inserted in the mapped dorsolateral oscillatory region for producing deep brain simulation for treatment of Parkinson's disease.