SLG-NET: Subgraph Neural Network with Local-Global Braingraph Feature Extraction Modules and a Novel Subgraph Generation Algorithm for Automated Identification of Major Depressive Disorder-Reference-Cited by-同舟云学术

SLG-NET: Subgraph Neural Network with Local-Global Braingraph Feature Extraction Modules and a Novel Subgraph Generation Algorithm for Automated Identification of Major Depressive Disorder

Published:2023-11-15 Issue: Volume: Page:31-42
ISSN:0302-9743
Container-title:Neural Information Processing
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Author:

Zhang Yan,Liu Xin,Tang Panrui,Zhang Zuping

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-99-8070-3_3

Reference21 articles.

1. Kishi, T., et al.: Antidepressants for the treatment of adults with major depressive disorder in the maintenance phase: a systematic review and network meta-analysis. Mol. Psychiatry 28(1), 402–409 (2023)

2. Lu, S., Shi, X., Li, M., Jiao, J., Feng, L., Wang, G.: Semi-supervised random forest regression model based on co-training and grouping with information entropy for evaluation of depression symptoms severity. Math. Biosci. Eng. 18(4), 4586–4602 (2021), https://www.aimspress.com/article/doi/10.3934/mbe.2021233

3. Gallo, S., et al.: Functional connectivity signatures of major depressive disorder: machine learning analysis of two multicenter neuroimaging studies. Mol. Psychiatry 1–10 (2023)

4. Cao, L.: Aberrant functional connectivity for diagnosis of major depressive disorder: a discriminant analysis. Psychiatry Clin. Neurosci. 68(2), 110–119 (2014)

5. Noman, F., et al.: Graph autoencoders for embedding learning in brain networks and major depressive disorder identification. arXiv preprint arXiv:2107.12838 (2021)

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