Brain Connectivity Atlas for Personalized Neurosurgery Targeting
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Solution Overview
Problem
Current functional neurosurgery techniques face challenges in accurately and precisely targeting brain structures due to reliance on empirical methods, leading to suboptimal clinical outcomes and adverse effects, especially in personalized treatments like deep brain stimulation where electrode placement and programming require time-consuming adjustments.
Innovation Solution
A system and method utilizing a population-based brain connectivity atlas derived from structural and functional imaging data and clinical outcomes to identify patient-specific neurosurgery target locations, enabling precise targeting and optimal brain stimulation programming by comparing individual patient data to disease-specific connectivity maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If empirical targeting methods based on anatomical landmarks are used, then the surgical procedure is simple to perform, but the targeting precision and clinical outcomes are suboptimal
Solution Approach 1:
The system performs preliminary actions by constructing a personalized brain connectivity atlas before surgery using diffusion tensor imaging and functional MRI data. This pre-surgical preparation creates a customized map of neural pathways and functional networks that guides subsequent targeting, eliminating the need for complex intraoperative adjustments while achieving high precision.
Solution Approach 2:
The patent introduces an intermediary computational system that bridges anatomical imaging and functional outcomes. This system uses tractography algorithms and connectivity analysis to translate structural MRI data into functional pathway maps, serving as a mediator between anatomical landmarks and optimal stimulation targets, thereby improving precision without proportionally increasing surgical complexity.
2Reliability
If personalized connectivity-based targeting is implemented, then the clinical outcomes are optimized, but the time and resources required for pre-surgical planning increase
Solution Approach 1:
The system changes parameters by transitioning from static anatomical coordinates to dynamic functional connectivity metrics. By using diffusion tensor imaging-derived tractography and functional MRI connectivity patterns, the system identifies optimal targets based on actual neural pathway orientation and functional network organization, significantly improving outcome reliability.
Solution Approach 2:
The patent performs preliminary construction of personalized connectivity atlases using automated tractography algorithms that can process diffusion tensor imaging data efficiently. This pre-computation of connectivity patterns and functional network mapping before surgery reduces intraoperative decision-making time and allows for more reliable, pre-planned targeting strategies.
3Ease of operation
If DBS electrode programming is optimized for individual patients, then the therapeutic benefits are maximized, but the number of programming visits and costs increase
Solution Approach 1:
The system performs preliminary identification of optimal electrode contacts and stimulation parameters during the pre-surgical planning phase by analyzing connectivity patterns and functional networks. This pre-programming guidance reduces the number of iterative adjustments needed during follow-up visits, as the initial programming is based on personalized connectivity data rather than trial-and-error approaches.
Solution Approach 2:
The patent incorporates feedback mechanisms by using pre-surgical connectivity analysis to predict optimal stimulation parameters, then comparing actual clinical responses with predictions. This feedback loop allows for refinement of the connectivity-based targeting approach and reduces the need for extensive programming adjustments by providing accurate initial settings.
Data Source
AI summary
A system and method for identifying a patient-specific neurosurgery target location is provided. The system receives brain imaging data for a patient that includes tracts and networks in the patient brain, accesses a quantitative connectome atlas comprising population-based, disease-specific structural and functional connectivity maps comprising a pattern of tracts and networks associated with an optimal target area (OTA) identified from a population of patients, and defines the patient-specific neurosurgery target location based on a comparison between a pattern of the tracts and networks from the brain imaging data for the patient and the pattern of tracts and networks associated with the OTA identified from the population of patients in the quantitative connectome atlas. The quantitative connectome atlas comprises a disease-specific, population-based quantitative connectome atlas that identifies an optimal target location for treatment associated with a maximal clinical improvement for each disease in the population of patients.