fMRI Brain Network Embedding for Alzheimer’s and MCI Classification
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Solution Overview
Problem
Current methods for diagnosing Alzheimer's disease and mild cognitive impairment using neuroimaging technologies face challenges in accurately classifying brain network patterns due to the non-Euclidean characteristics of brain networks, making it difficult to apply machine learning techniques effectively.
Innovation Solution
The method employs graph theory to construct brain networks from fMRI data, using node2vec graph embedding to convert graph features into feature vectors, and applies classification techniques like LSVM and RELM with feature selection methods such as LASSO to improve diagnostic accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If graph theory is used to model brain networks, then the ability to represent complex brain connectivity patterns is improved, but the difficulty of applying machine learning techniques increases due to non-Euclidean characteristics
Solution Approach 1:
The patent introduces graph embedding as an intermediary technique that transforms non-Euclidean graph data into Euclidean vector representations. This mediator enables standard machine learning algorithms to process brain network data by converting the complex graph structure into a format compatible with conventional ML techniques, thus resolving the incompatibility between graph theory models and machine learning methods
2Measurement precision
If traditional classification techniques are applied to brain network data, then the diagnostic capability is limited, but the computational complexity increases when using advanced methods like graph embedding
Solution Approach 1:
The patent segments the complex diagnostic process into distinct stages: graph construction from fMRI data, graph embedding transformation, feature selection, and classification. This segmentation allows each component to be optimized independently, managing computational complexity while maintaining diagnostic accuracy through a structured multi-step approach
3Measurement precision
If more features are extracted from fMRI data to improve classification accuracy, then the diagnostic precision is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent extracts and selects only the most relevant features from the comprehensive set of graph embedding features using feature selection techniques. This extraction process removes redundant and less informative features, retaining only those that contribute most to classification accuracy, thus reducing processing time and computational resource requirements while maintaining diagnostic precision
Data Source
AI summary
The present invention relates to a method of providing diagnostic information for Alzheimer's disease using a brain network.