MSARAE: Multiscale adversarial regularized autoencoders for cortical network classification

•A new autoencoder structure for data augmentation and classification of cortical structural connectivity.•A new topological enhancement module that uses the Laplacian matrix to extract topological information on cortical structural connectivity.•The encoder structure in the autoencoder is modified...

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Vydané v:Medical image analysis Ročník 106; s. 103775
Hlavní autori: Zhu, Yihui, Zhou, Yue, Zhang, Xiaotong, Li, Yueying, Yuan, Yonggui, Kong, Youyong
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Netherlands Elsevier B.V 01.12.2025
Predmet:
Algorithms
Alzheimer Disease - diagnostic imaging
Autoencoder
Cerebral cortex
Cerebral Cortex - diagnostic imaging
Connectome - methods
Data augmentation
Deep Learning
Generative adversarial network
Humans
Image Processing, Computer-Assisted - methods
Magnetic Resonance Imaging - methods
Structural connectivity
Variational autoencoder
Variational autoencoder
Data augmentation
Cerebral cortex
Structural connectivity
Generative adversarial network
ISSN:1361-8415, 1361-8423, 1361-8423
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Popis
Shrnutí:•A new autoencoder structure for data augmentation and classification of cortical structural connectivity.•A new topological enhancement module that uses the Laplacian matrix to extract topological information on cortical structural connectivity.•The encoder structure in the autoencoder is modified to better capture cortical structural connectivity at different spatial scales.•An adversarial regularization module is introduced to improve the accuracy of multi-scale graph variational autoencoder generation. Due to privacy regulations and technical limitations, current research on the cerebral cortex frequently faces challenges, including limited data availability. The number of samples significantly influences the performance and generalization ability of deep learning models. In general, these models require sufficient training data to effectively learn underlying distributions and features, enabling strong performance on unseen samples. A limited sample size can lead to overfitting, thereby weakening the model’s generalizability. To address these challenges from a data augmentation perspective, we propose a Multi-Scale Adversarial Regularized Autoencoder (MSARAE) for augmenting and classifying cortical structural connectivity. The approach begins with data preprocessing and the construction of cortical structural connectivity networks. To better capture cortical features, the model leverages Laplacian eigenvectors to enhance topological information. Structural connectivity is then generated using variational autoencoders, with multi-scale graph convolutional layers serving as encoders to capture graph representations at different scales. An adversarial regularization mechanism is introduced to minimize the distribution discrepancy in the latent space. By training a discriminator, the model encourages the encoder to produce latent representations that closely match the distribution of real data, thereby improving its representational capacity. Finally, extensive experiments on the major depression disorder (MDD) dataset, the Human Connectome Project (HCP) dataset, and the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset demonstrated the superiority of the model.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1361-8415
1361-8423
1361-8423
DOI:10.1016/j.media.2025.103775