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• W4310261818 endingPage "102707" @default.
• W4310261818 startingPage "102707" @default.
• W4310261818 abstract "Resting-state functional magnetic resonance imaging (rs-fMRI) data have been widely used for automated diagnosis of brain disorders such as major depressive disorder (MDD) to assist in timely intervention. Multi-site fMRI data have been increasingly employed to augment sample size and improve statistical power for investigating MDD. However, previous studies usually suffer from significant inter-site heterogeneity caused for instance by differences in scanners and/or scanning protocols. To address this issue, we develop a novel discrepancy-based unsupervised cross-domain fMRI adaptation framework (called UFA-Net) for automated MDD identification. The proposed UFA-Net is designed to model spatio-temporal fMRI patterns of labeled source and unlabeled target samples via an attention-guided graph convolution module, and also leverage a maximum mean discrepancy constrained module for unsupervised cross-site feature alignment between two domains. To the best of our knowledge, this is one of the first attempts to explore unsupervised rs-fMRI adaptation for cross-site MDD identification. Extensive evaluation on 681 subjects from two imaging sites shows that the proposed method outperforms several state-of-the-art methods. Our method helps localize disease-associated functional connectivity abnormalities and is therefore well interpretable and can facilitate fMRI-based analysis of MDD in clinical practice." @default.
• W4310261818 created "2022-11-30" @default.
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• W4310261818 date "2023-02-01" @default.
• W4310261818 modified "2023-10-13" @default.
• W4310261818 title "Unsupervised cross-domain functional MRI adaptation for automated major depressive disorder identification" @default.
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• W4310261818 doi "https://doi.org/10.1016/j.media.2022.102707" @default.
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• W4310261818 hasPublicationYear "2023" @default.
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