Multi-Modal Brain Network Fusion for Brain Disease Prediction
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Solution Overview
Problem
Conventional brain disease prediction models face challenges due to reliance on professional expertise, high time and labor costs, and limited accuracy and precision, particularly in handling neural fiber structure features and ignoring heterogeneity in multi-modal brain data fusion, leading to poor practicality and low accuracy.
Innovation Solution
A multi-modal brain network computation method involving an association perception dual-channel generation module, disease feature regression module, topological structure discriminator, and time-space joint discriminator, with interactive fusion and adversarial learning to enhance model accuracy and robustness by integrating brain functional magnetic resonance and diffusion tensor imaging data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional manual registration and image correction methods are used by professional physicians, then the brain connection analysis can be performed, but the time cost and labor cost are high
Solution Approach 1:
The system enables automated brain connection analysis through intelligent computing systems that perform registration and image correction autonomously without requiring professional physician intervention, thereby reducing time cost while maintaining analysis accuracy
Solution Approach 2:
Manual operations by physicians are replaced with automated computational algorithms that perform brain image registration and correction, substituting mechanical human labor with computational processes to reduce time consumption
2Ease of operation
If single-model effective connectivity intelligent computation is used, then the computation does not depend on professional physicians, but the neural fiber structure features between brain regions are missing
Solution Approach 1:
The system merges functional connectivity data with structural connectivity data from diffusion tensor imaging to create a comprehensive multi-modal brain network model, integrating both functional and structural information to avoid loss of neural fiber structure features
Solution Approach 2:
The patent combines multiple data modalities (functional MRI and diffusion tensor imaging) to create a composite brain network representation that captures both functional and structural characteristics, analogous to using composite materials to achieve superior properties
3Ease of manufacture
If multi-modal brain data is fused in affine splicing or weighted summation manner, then the computation is simplified, but the heterogeneity of different modal data is ignored
Solution Approach 1:
The system transforms multi-modal brain data into a unified parameter space through learned transformation functions, changing the representation parameters of different modalities to enable their effective integration while preserving their unique characteristics
Solution Approach 2:
The patent introduces an intermediary transformation layer that mediates between different brain imaging modalities, converting them into a common representation space where their complementary information can be effectively integrated without losing modality-specific features
4Device complexity
If conventional brain disease prediction models are used, then the model structure is simple, but the accuracy and precision are low
Solution Approach 1:
The system transitions from simple univariate prediction models to multi-dimensional deep learning architectures that process brain network data across multiple dimensions (functional connectivity, structural connectivity, spatial, and temporal dimensions), thereby improving prediction accuracy through increased model complexity
Data Source
AI summary
The present disclosure relates to a multi-modal brain network computation method associated with structural function, apparatus, device, and medium. The method is applied to train a brain disease prediction model, and the brain disease prediction model includes an association perception dual-channel generation module, a disease feature regression module, a topological structure discriminator, and a time-space joint discriminator. In a model training process, by performing a multi-level interactive fusion learning on a high-order topological feature of brain functional magnetic resonance data and magnetic resonance diffusion tensor imaging data, a multi-modal time series activity signal of each brain region is obtained.