Dynamic Model Selection for Brain Connectivity Clustering
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Solution Overview
Problem
Current diffusion tensor imaging (DTI) technologies provide complex and cluttered images of brain neural tracts and fibers, making it difficult for neurosurgeons to identify specific connections and conditions, which can lead to risks during surgery and diagnosis.
Innovation Solution
The method involves selecting a specific model based on subject characteristics, such as age, gender, or ethnicity, using unsupervised machine learning algorithms like k-means clustering to group connectivity matrices and apply trained models from fMRI data to identify potential brain conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If DTI images capture the whole brain with all neural tracts and fibers, then the completeness of brain representation is improved, but the image complexity and clutter increase making it difficult to identify specific connections
Solution Approach 1:
The patent segments the complex whole-brain DTI data by grouping connectivity matrices into distinct clusters based on similarity. This segmentation separates the complete brain information into organized groups, allowing comprehensive representation while reducing visual complexity through structured categorization of neural connections.
Solution Approach 2:
The patent applies local quality by providing different levels of detail for different regions or types of connections. By clustering connectivity matrices, the system highlights specific local patterns and characteristics of neural tracts while maintaining overall completeness, allowing users to focus on specific areas of interest without being overwhelmed by the entire brain's complexity.
2Quantity of substance
If DTI images include all brain fibers and tracts, then the comprehensiveness of brain data is improved, but the difficulty of detecting and measuring specific conditions increases
Solution Approach 1:
The patent performs preliminary action by pre-processing and clustering connectivity matrices before analysis. The system pre-groups similar brain connection patterns into clusters, creating an organized framework that simplifies subsequent detection and measurement of specific brain conditions. This preliminary organization makes it easier to identify abnormalities by comparing against pre-established patterns.
Solution Approach 2:
The patent introduces clustering algorithms and connectivity matrix groupings as intermediaries between the raw comprehensive DTI data and the final condition identification. These intermediaries process and organize the comprehensive data into meaningful clusters, facilitating easier detection and measurement of specific brain conditions without losing the comprehensiveness of the original data.
3Device complexity
If a single model is used for all subjects, then the simplicity of the system is improved, but the accuracy of identifying subject-specific brain conditions decreases
Solution Approach 1:
The patent applies parameter changes by adapting the model selection based on subject characteristics. Instead of using a fixed single model, the system changes model parameters or selects different models based on individual subject data, connectivity patterns, and clustering results. This allows the system to maintain relative simplicity while improving accuracy through personalized model application.
Data Source
AI summary
Methods, systems, and computer programs encoded on computer storage media, for selecting a model out of a number of models based on subject characteristics. One of the methods includes obtaining present subject connectivity matrix data for a present subject, obtaining present subject data describing the present subject where the present subject data is different from the present subject connectivity matrix data, determining a specific model to apply to the present subject connectivity matrix data based at least in part on the present subject data, determining the specific model using a model trained with fMRI data for brains of a plurality of past subjects and past subject data describing the past subjects, applying the specific model to identify a potential present subject brain condition based at least in part on the present subject connectivity matrix data, and taking an action based on identification of a potential present subject brain condition.