Multi-Dimensional Disconnection Networks for Brain Lesion-Symptom Mapping
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Solution Overview
Problem
Existing single-lesion symptom mapping analysis technologies suffer from low accuracy due to the indirect relationship between brain lesions and symptoms, often leading to misjudgment of the 'lesion-symptom' relationship.
Innovation Solution
A method and system for extracting a multi-dimensional disconnection network region of symptom mapping using fiber tracking and cross-correlation verification methods to construct structural and functional disconnection networks, integrating brain images of healthy individuals to accurately determine the relationship between lesions and symptoms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single-lesion analysis method is used, then the analysis process is simple, but the mapping accuracy between lesions and symptoms is low
Solution Approach 1:
The patent transitions from single-lesion analysis to multi-dimensional disconnection network analysis by incorporating both structural connectivity (DTI fiber tracking) and functional connectivity (resting-state fMRI) dimensions. This dimensional expansion allows comprehensive mapping of lesion impacts across multiple brain networks simultaneously, resolving the contradiction between analytical simplicity and mapping accuracy.
Solution Approach 2:
The patent combines multiple imaging modalities (DTI for structural connectivity, resting-state fMRI for functional connectivity, and lesion masks) to create a composite disconnection network model. This composite approach integrates different types of brain connectivity data to achieve higher mapping accuracy while maintaining a systematic analysis framework.
2Ease of operation
If single-lesion analysis is used, then the method is straightforward, but it leads to misjudgment of lesion-symptom relationship
Solution Approach 1:
The patent introduces disconnection networks as intermediary structures that mediate between lesions and symptoms. Instead of directly mapping lesions to symptoms, the method uses disconnection networks (structural and functional) as intermediaries to establish indirect but more reliable mappings, reducing misjudgments while maintaining operational feasibility.
Solution Approach 2:
The patent segments the brain connectivity analysis into distinct components: structural disconnection network (SDN) from DTI, functional disconnection network (FDN) from resting-state fMRI, and their integration. This segmentation allows systematic processing of complex relationships while maintaining methodological clarity and reducing errors.
3Device complexity
If traditional lesion location analysis is used, then the analysis is simple, but it cannot capture indirect relationships between symptoms and lesion locations
Solution Approach 1:
The patent creates a multi-functional disconnection network framework that simultaneously captures direct and indirect relationships between lesions and symptoms. The framework serves multiple functions: mapping structural disconnections, mapping functional disconnections, and integrating both to reveal indirect relationships, thereby preventing information loss while managing analytical complexity.
Data Source
AI summary
A method and system for extracting a multi-dimensional disconnection network region of symptom mapping: registering a lesion image to a brain standard space; obtaining diffusion magnetic resonance images and resting-state functional magnetic resonance images of healthy control groups; constructing a structural disconnection weighting network corresponding to lesions using a fiber tracking method according to the lesion image in the brain standard space and the diffusion magnetic resonance images; constructing a functional significant disconnection network corresponding to the lesions using a cross-correlation verification method according to the lesion image in the brain standard space and the resting-state functional magnetic resonance images; and determining the multi-dimensional disconnection network region of the lesions of symptom mapping according to the structural disconnection weighting network and the functional significant disconnection network, where the multi-dimensional disconnection network region of the lesions is configured to locate network mapping of a brain lesion in the brain.