SGMDD: Subgraph Neural Network-Based Model for Analyzing Functional Connectivity Signatures of Major Depressive Disorder
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-7074-2_28
Reference17 articles.
1. Abdoli, N., et al.: The global prevalence of major depressive disorder (MDD) among the elderly: a systematic review and meta-analysis. Neurosci. Biobehav. Rev. 132, 1067–1073 (2022)
2. 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)
3. Shi, Y., et al.: Multivariate machine learning analyses in identification of major depressive disorder using resting-state functional connectivity: a multicentral study. ACS Chem. Neurosci. 12(15), 2878–2886 (2021)
4. Gallo, S., et al.: Functional connectivity signatures of major depressive disorder: machine learning analysis of two multicenter neuroimaging studies. Mol. Psych. 1–10 (2023)
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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