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Cited by in F6Publishing
For: Jun E, Na KS, Kang W, Lee J, Suk HI, Ham BJ. Identifying resting-state effective connectivity abnormalities in drug-naïve major depressive disorder diagnosis via graph convolutional networks. Hum Brain Mapp 2020;41:4997-5014. [PMID: 32813309 DOI: 10.1002/hbm.25175] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
Number Citing Articles
1 Li C, Liu M, Xia J, Mei L, Yang Q, Shi F, Zhang H, Shen D. Predicting Brain Amyloid-β PET Grades with Graph Convolutional Networks Based on Functional MRI and Multi-Level Functional Connectivity. J Alzheimers Dis 2022. [PMID: 35213377 DOI: 10.3233/JAD-215497] [Reference Citation Analysis]
2 Li M, Zhang J, Zhai Q, Kang J, Lu S, Yang J. AUTOMATED RECOGNITION OF DEPRESSION FROM FEWER-SHOT LEANING IN RESTING-STATE fMRI WITH ReHo USING DEEP CONVOLUTIONAL NEURAL NETWORK. J Mech Med Biol 2021;21:2140068. [DOI: 10.1142/s0219519421400686] [Reference Citation Analysis]
3 Zhang Z, Li G, Xu Y, Tang X. Application of Artificial Intelligence in the MRI Classification Task of Human Brain Neurological and Psychiatric Diseases: A Scoping Review. Diagnostics (Basel) 2021;11:1402. [PMID: 34441336 DOI: 10.3390/diagnostics11081402] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
4 Jun E, Na KS, Kang W, Lee J, Suk HI, Ham BJ. Identifying resting-state effective connectivity abnormalities in drug-naïve major depressive disorder diagnosis via graph convolutional networks. Hum Brain Mapp 2020;41:4997-5014. [PMID: 32813309 DOI: 10.1002/hbm.25175] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]