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Journal Abstract Search

176 related items for PubMed ID: 37032921

  • 1. Ensemble graph neural network model for classification of major depressive disorder using whole-brain functional connectivity.
    Venkatapathy S, Votinov M, Wagels L, Kim S, Lee M, Habel U, Ra IH, Jo HG.
    Front Psychiatry; 2023; 14():1125339. PubMed ID: 37032921
    [Abstract] [Full Text] [Related]

  • 2. Multi-view graph network learning framework for identification of major depressive disorder.
    Zhang M, Long D, Chen Z, Fang C, Li Y, Huang P, Chen F, Sun H.
    Comput Biol Med; 2023 Nov; 166():107478. PubMed ID: 37776730
    [Abstract] [Full Text] [Related]

  • 3. The classification of brain network for major depressive disorder patients based on deep graph convolutional neural network.
    Zhu M, Quan Y, He X.
    Front Hum Neurosci; 2023 Nov; 17():1094592. PubMed ID: 36778038
    [Abstract] [Full Text] [Related]

  • 4. MAMF-GCN: Multi-scale adaptive multi-channel fusion deep graph convolutional network for predicting mental disorder.
    Pan J, Lin H, Dong Y, Wang Y, Ji Y.
    Comput Biol Med; 2022 Sep; 148():105823. PubMed ID: 35872410
    [Abstract] [Full Text] [Related]

  • 5. Graph Autoencoders for Embedding Learning in Brain Networks and Major Depressive Disorder Identification.
    Noman F, Ting CM, Kang H, Phan RC, Ombao H.
    IEEE J Biomed Health Inform; 2024 Mar; 28(3):1644-1655. PubMed ID: 38194405
    [Abstract] [Full Text] [Related]

  • 6. Classification of recurrent major depressive disorder using a new time series feature extraction method through multisite rs-fMRI data.
    Dai P, Lu D, Shi Y, Zhou Y, Xiong T, Zhou X, Chen Z, Zou B, Tang H, Huang Z, Liao S, REST-meta-MDD Consortium.
    J Affect Disord; 2023 Oct 15; 339():511-519. PubMed ID: 37467800
    [Abstract] [Full Text] [Related]

  • 7. Boosting-GNN: Boosting Algorithm for Graph Networks on Imbalanced Node Classification.
    Shi S, Qiao K, Yang S, Wang L, Chen J, Yan B.
    Front Neurorobot; 2021 Oct 15; 15():775688. PubMed ID: 34899230
    [Abstract] [Full Text] [Related]

  • 8. Using graph convolutional network to characterize individuals with major depressive disorder across multiple imaging sites.
    Qin K, Lei D, Pinaya WHL, Pan N, Li W, Zhu Z, Sweeney JA, Mechelli A, Gong Q.
    EBioMedicine; 2022 Apr 15; 78():103977. PubMed ID: 35367775
    [Abstract] [Full Text] [Related]

  • 9. Graph theory approach for the structural-functional brain connectome of depression.
    Yun JY, Kim YK.
    Prog Neuropsychopharmacol Biol Psychiatry; 2021 Dec 20; 111():110401. PubMed ID: 34265367
    [Abstract] [Full Text] [Related]

  • 10. Identifying resting-state effective connectivity abnormalities in drug-naïve major depressive disorder diagnosis via graph convolutional networks.
    Jun E, Na KS, Kang W, Lee J, Suk HI, Ham BJ.
    Hum Brain Mapp; 2020 Dec 20; 41(17):4997-5014. PubMed ID: 32813309
    [Abstract] [Full Text] [Related]

  • 11. Functional connectivity signatures of major depressive disorder: machine learning analysis of two multicenter neuroimaging studies.
    Gallo S, El-Gazzar A, Zhutovsky P, Thomas RM, Javaheripour N, Li M, Bartova L, Bathula D, Dannlowski U, Davey C, Frodl T, Gotlib I, Grimm S, Grotegerd D, Hahn T, Hamilton PJ, Harrison BJ, Jansen A, Kircher T, Meyer B, Nenadić I, Olbrich S, Paul E, Pezawas L, Sacchet MD, Sämann P, Wagner G, Walter H, Walter M, PsyMRI, van Wingen G.
    Mol Psychiatry; 2023 Jul 20; 28(7):3013-3022. PubMed ID: 36792654
    [Abstract] [Full Text] [Related]

  • 12. GAT-LI: a graph attention network based learning and interpreting method for functional brain network classification.
    Hu J, Cao L, Li T, Dong S, Li P.
    BMC Bioinformatics; 2021 Jul 22; 22(1):379. PubMed ID: 34294047
    [Abstract] [Full Text] [Related]

  • 13. The alterations of brain functional connectivity networks in major depressive disorder detected by machine learning through multisite rs-fMRI data.
    Dai P, Xiong T, Zhou X, Ou Y, Li Y, Kui X, Chen Z, Zou B, Li W, Huang Z, The Rest-Meta-Mdd Consortium.
    Behav Brain Res; 2022 Oct 28; 435():114058. PubMed ID: 35995263
    [Abstract] [Full Text] [Related]

  • 14. Abnormal functional connectivity strength in first-episode, drug-naïve adult patients with major depressive disorder.
    Shi Y, Li J, Feng Z, Xie H, Duan J, Chen F, Yang H.
    Prog Neuropsychopharmacol Biol Psychiatry; 2020 Mar 08; 97():109759. PubMed ID: 31499128
    [Abstract] [Full Text] [Related]

  • 15. Resting-state functional connectivity graph-properties correlate with bipolar disorder-risk in young medication-free depressed subjects: Bipolar-risk Resting State Functional Connectivity in Major Depression.
    Cha J, Spielberg JM, Hu B, Altinay M, Anand A.
    J Affect Disord; 2022 Mar 15; 301():52-59. PubMed ID: 35007643
    [Abstract] [Full Text] [Related]

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  • 17. TE-HI-GCN: An Ensemble of Transfer Hierarchical Graph Convolutional Networks for Disorder Diagnosis.
    Li L, Jiang H, Wen G, Cao P, Xu M, Liu X, Yang J, Zaiane O.
    Neuroinformatics; 2022 Apr 15; 20(2):353-375. PubMed ID: 34761367
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  • 19. Spectral Graph Neural Network-Based Multi-Atlas Brain Network Fusion for Major Depressive Disorder Diagnosis.
    Lee DJ, Shin DH, Son YH, Han JW, Oh JH, Kim DH, Jeong JH, Kam TE.
    IEEE J Biomed Health Inform; 2024 May 15; 28(5):2967-2978. PubMed ID: 38363664
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