Brain Parcellation Contribution Visualization
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Solution Overview
Problem
Current medical imaging technologies lack effective methods to visualize and quantify the contributions of brain parcellation pairs to specific medical conditions, symptoms, or behaviors, hindering clinicians' ability to design targeted treatment plans.
Innovation Solution
A method that processes brain data using machine learning to determine contribution values for brain parcellation pairs, utilizing techniques like SHAP values to characterize their impact on medical conditions, and provides visualizations for clinicians to understand the connectivity and contributions of these pairs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If brain data is processed to determine contributions of many brain parcellation pairs (over 1000 pairs), then measurement precision of brain region contributions is improved, but device complexity and computational requirements increase significantly
Solution Approach 1:
The patent segments the complex brain data analysis into manageable components by dividing brain regions into discrete parcellations and evaluating pairs systematically. This segmentation allows the system to handle over 1000 parcellation pairs by processing them in an organized manner rather than as a monolithic complex task.
Solution Approach 2:
The patent changes parameters by introducing contribution values as a new metric to quantify brain parcellation pair contributions. This parameter transformation converts complex neural connectivity data into interpretable contribution scores, enabling precise measurement without proportionally increasing system complexity.
2Loss of information
If contribution values are calculated for over 1000 brain parcellation pairs, then information completeness about brain-condition relations is improved, but loss of time for data processing increases
Solution Approach 1:
The patent applies preliminary action by pre-defining brain parcellations and their pairings before analysis. This preparation allows the system to efficiently calculate contribution values for all pairs without ad-hoc processing, reducing overall computation time while maintaining complete information coverage.
Solution Approach 2:
By transforming raw brain data into contribution value parameters, the system enables faster comparison and interpretation. This parameter transformation allows comprehensive analysis of all parcellation pairs to be completed more efficiently than analyzing raw neural data directly.
3Ease of operation
If detailed visualizations of brain parcellation pair contributions are provided, then ease of operation for clinicians is improved, but device complexity increases
Solution Approach 1:
The patent extracts the essential contribution information from complex brain data and presents it in simplified visual formats. By taking out only the relevant contribution values and relationships, the system provides clinicians with actionable insights without requiring them to navigate the full complexity of the underlying data structure.
Solution Approach 2:
The patent uses visual encoding (implied through contribution value representations) to convey complex information in an easily interpretable format. This visual transformation allows clinicians to quickly grasp brain region contributions without interpreting raw numerical data, improving ease of operation.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining and visualizing contribution values of different brain regions to a medical condition. One of the methods includes receiving brain data for a brain of a patient, processing the brain data to determine a partition of the data into a plurality of brain parcellation pairs, receiving an indication of a medical condition, determining a contribution value for at least some of the plurality of brain parcellation pairs, where the contribution value characterizes a contribution of the brain parcellation pair to the medical condition, and providing the contribution values for display on a user computing device.