Connectome-Difference Map for Brain Functional Architecture Tracking
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Solution Overview
Problem
Current medical imaging technologies struggle to effectively visualize and quantify changes in the brain's functional architecture following events such as surgical removal of brain tissue, traumatic injuries, or cognitive decline, as they fail to account for changes in brain shape and topology, leading to inaccurate diagnostic assessments and therapy planning.
Innovation Solution
The development of a system that generates a connectome-difference map by comparing pre-event and post-event brain connectomes, allowing for topologically invariant analysis and visualization of changes, which can inform therapy plans and aid in surgical interventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional medical imaging technologies are used to visualize brain changes, then the imaging process is simple, but the accuracy of detecting functional architecture changes deteriorates due to failure to account for brain shape and topology changes
Solution Approach 1:
The patent segments the brain into discrete functional nodes and connections (edges) to create a connectome representation. This segmentation allows for precise tracking of functional architecture changes by comparing individual nodes and edges between pre-event and post-event states, resolving the contradiction by enabling accurate measurement through structured decomposition of the brain's functional architecture.
Solution Approach 2:
The patent introduces a connectome-difference map as an intermediary representation that bridges traditional imaging and functional architecture analysis. This difference map specifically highlights changes in functional connections while accounting for topological transformations, enabling accurate detection of functional changes without requiring complete redesign of the imaging system.
2Measurement precision
If topologically invariant analysis is implemented to handle brain shape changes, then the accuracy of functional architecture tracking is improved, but the computational complexity increases
Solution Approach 1:
The patent implements dynamic node matching that adapts to topological changes by allowing functional nodes to be matched across different brain configurations. This dynamic approach maintains measurement precision under topological transformations while managing computational complexity through efficient matching algorithms that respond to actual functional changes rather than requiring exhaustive computation.
Solution Approach 2:
The patent changes the parameter space from fixed anatomical coordinates to topologically invariant functional representations. By representing brain architecture in terms of functional nodes and edges that remain consistent across topological transformations, the system achieves accurate tracking while reducing computational burden compared to analyzing raw volumetric data under topological changes.
3Measurement precision
If connectome-difference maps are generated to show functional changes, then diagnostic accuracy is improved, but the time required for analysis increases
Solution Approach 1:
The patent performs preliminary construction of connectomes from pre-event and post-event imaging data, organizing functional nodes and edges before generating the difference map. This preliminary organization enables efficient comparison and accelerates the generation of diagnostic insights, reducing analysis time while maintaining high diagnostic accuracy through structured functional representation.
Solution Approach 2:
The patent extracts only the functionally relevant changes by generating a difference map that highlights specific altered connections rather than analyzing the entire connectome. This extraction of critical information maintains diagnostic accuracy by focusing on meaningful changes while significantly reducing analysis time by eliminating redundant processing of unchanged functional architecture.
Data Source
AI summary
A pre-event connectome of a subject brain is accessed, the pre-event connectome defining i) first functional nodes in the subject brain and ii) first edges that represent connections between the first functional nodes before the subject has undergone an event. A post-event connectome of the subject brain is accessed, the post-event connectome defining i) second functional nodes in the subject brain and ii) second edges that represent connections between the second functional nodes after the subject has undergone the event. A connectome-difference map data is generated that records the difference between the pre-event connectome and the post-event connectome. An action is taken based on the connectome-difference map data.