ROI-Based Multimodal Neuroimaging Feature Fusion Method and Its Graph Neural Network Diagnostic Model

Single-modality neuroimaging data often provide limited information and are constrained by technical issues such as signal-to-noise ratio, and resolution limitations, potentially leading to biases and an incomplete understanding of brain complexities. This can hinder the development of diagnostic an...

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Bibliographic Details
Published in:IEEE access Vol. 13; pp. 26915 - 26926
Main Authors: Wang, Xuan, Yang, Xiaopeng, Zhang, Xiaotong, Chen, Yang
Format: Journal Article
Language:English
Published: Piscataway IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Brain
Brain modeling
Classification
Data models
Diagnostic systems
Feature extraction
feature fusion
feature matrix based on ROIs
Graph neural networks
Graphical representations
Grey matter
Medical imaging
Multimodal neuroimaging data
Multimodal sensors
Neuroimaging
Signal to noise ratio
Sociology
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:Single-modality neuroimaging data often provide limited information and are constrained by technical issues such as signal-to-noise ratio, and resolution limitations, potentially leading to biases and an incomplete understanding of brain complexities. This can hinder the development of diagnostic and therapeutic strategies for brain disorders. To address these challenges, this paper presents the Multimodal Graph Neural Network Model based on Feature Fusion (MMP-DGNN), which leverages sMRI and PET data. The model employs an algorithm to extract and accurately describe sample features using an autoencoder. During feature fusion, a shared adjacency matrix based on feature similarity and phenotypic data is constructed for graph representation. A dual-layer graph neural network then classifies the features, with the results fused at the decision layer for final classification. Experimental results show that MMP-DGNN achieves superior classification performance of 98.17%, outperforming other methods in multimodal neuroimaging data classification.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3435433