Brain Target Positioning via Acute-Chronic Lesion Network Correlation
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Solution Overview
Problem
Existing methods for therapeutic target positioning in brain regions, based on whole-brain functional connectivity, often ignore functional abnormalities caused by lesions and rely on single-time point analysis, leading to inaccurate target identification.
Innovation Solution
A method and system that utilize diffusion-weighted imaging and resting-state fMRI data from multiple time points to construct acute and chronic phase cognitive-lesion mapping functional networks, followed by spatial correlation calculations to determine therapeutic targets, improving accuracy by accounting for changes in lesion mapping networks over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If whole-brain functional connectivity method is used for therapeutic target positioning, then the analysis can be performed based on functional connectivity, but it ignores functional abnormalities caused by lesions and leads to inaccurate target identification
Solution Approach 1:
The patent segments the brain into lesion regions and non-lesion regions, and further divides the functional connectivity analysis into acute phase and chronic phase networks. By segmenting the functional connectivity analysis into distinct phases and regions, the method can accurately detect functional abnormalities caused by lesions while maintaining overall target positioning accuracy.
Solution Approach 2:
The patent applies local quality by constructing lesion mapping functional networks that specifically analyze functional connectivity within and around lesion regions, rather than using uniform whole-brain connectivity analysis. This allows the method to detect local functional abnormalities caused by lesions while maintaining global target positioning capability.
2Measurement precision
If single time point analysis is used for therapeutic target positioning, then the analysis process is simple, but it leads to inaccurate therapeutic target positioning
Solution Approach 1:
The patent applies dynamics by constructing functional networks at multiple time points (acute phase and chronic phase) and comparing their changes over time. This dynamic approach allows the method to capture temporal evolution of functional connectivity, improving target positioning accuracy while managing complexity through systematic comparison of phased networks.
Solution Approach 2:
The patent performs preliminary construction of acute phase and chronic phase functional networks before final target positioning. By pre-constructing these phased networks and comparing them, the method systematically evaluates functional changes over time, improving positioning accuracy through structured multi-time point analysis.
3Measurement precision
If multi-time point functional network construction is performed, then therapeutic target positioning accuracy is improved, but the computational complexity and processing time increase
Solution Approach 1:
The patent segments the functional network construction into distinct acute phase and chronic phase networks, each processed independently before comparison. This segmentation allows parallel computation and reduces overall processing time by dividing the complex multi-time point analysis into manageable phases that can be processed simultaneously.
Solution Approach 2:
The patent merges the acute phase and chronic phase functional networks into a unified comparison framework that identifies key improvement networks. By combining results from both phases through systematic comparison, the method achieves high positioning accuracy while optimizing processing efficiency through integrated analysis.
Data Source
AI summary
A method and system for positioning a target in a brain region are provided. The method includes: obtaining datasets of N persons at a first time point and a second time point after stroke; constructing a first lesion mapping functional network based on each resting-state functional magnetic resonance imaging image in a first stroke dataset; constructing an acute phase cognitive-lesion mapping functional network; constructing a chronic phase cognitive-lesion mapping functional network; comparing the acute phase cognitive-lesion mapping functional network with the chronic phase cognitive-lesion mapping functional network to obtain a key improvement network; calculating a whole-brain functional connectivity network with each voxel as a seed point, and performing spatial correlation calculation on the whole-brain functional connectivity network and the key improvement network to obtain a spatial correlation network; and determining a therapeutic target of the functional image to be positioned.