Brain Parcellation Data Grouping by Function
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems for brain parcellation data analysis lack the ability to effectively group and visualize brain regions associated with specific functions or characteristics, making it cumbersome for medical professionals to understand brain functionality and make informed clinical decisions.
Innovation Solution
A method and system for grouping brain parcellation data based on user-defined categories and levels, allowing for interactive visualization of grouped parcellations within a graphical user interface, enabling users to customize and analyze brain data in relation to cognitive functions, diseases, or symptoms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If brain parcellation data is analyzed using existing systems, then structural and functional data can be visualized, but the ability to group and analyze regions by specific functions or characteristics is limited
Solution Approach 1:
The patent segments brain parcellation data into multiple grouping levels (e.g., broad functional categories, sub-functions, specific regions) allowing users to navigate and analyze data at different granularities. This segmentation enables flexible grouping by function while maintaining manageable complexity through hierarchical organization.
Solution Approach 2:
The system provides dynamic interaction capabilities where users can selectively filter, sort, and regroup parcellations based on different functions or characteristics. The interface adapts to user needs by allowing real-time modification of grouping parameters, transforming a static analysis system into a dynamic one that responds to specific analytical requirements.
2Measurement precision
If detailed brain parcellation data is provided, then comprehensive analysis is possible, but the difficulty of detecting and measuring specific functional associations increases
Solution Approach 1:
The patent introduces functional labels and metadata as intermediary elements that bridge anatomical parcellation data and functional characteristics. These intermediaries (function names, tags, descriptions) make the relationships between brain regions and functions explicit and easy to detect, eliminating the need for complex automated detection algorithms.
Solution Approach 2:
The system provides feedback mechanisms through interactive visualizations and search results that immediately display functional associations when users query for specific functions or characteristics. This feedback loop allows users to easily identify functional relationships by directly viewing the results of their queries rather than searching through raw data.
3Ease of operation
If a user-friendly interface is implemented for visualizing brain data, then ease of operation improves, but the complexity of the underlying data processing system increases
Solution Approach 1:
The patent extracts complex data processing and grouping operations from the user interface layer and implements them as pre-configured system functions. The interface simply presents pre-processed grouped data rather than requiring users to manually process raw parcellation data, separating user interaction simplicity from underlying processing complexity.
Solution Approach 2:
The system performs preliminary grouping and organization of parcellation data by function and characteristics before presentation to the user. Pre-computed groupings and indices are prepared in advance, allowing the interface to display organized data immediately without requiring complex real-time processing during user interaction.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for grouping brain parcellation data. One of the methods includes receiving brain parcellation data for a subject; receiving an indication from a user of a brain function category; forwarding data for display, the data comprising a set of functions within the brain function category; receiving a selection from the user of a brain function from the set of functions within the brain function category; determining a subset of the brain parcellation data for parcellations that have an overlap with the selected brain function where the overlap exceeds a threshold; and taking an action based on the determined subset of the brain parcellation data for parcellations that have an overlap with the selected brain function where the overlap exceeds a threshold.