Unsupervised cross-domain functional MRI adaptation for automated major depressive disorder identification

• Published in: Medical image analysis Vol. 84; p. 102707
• Main Authors: Fang, Yuqi, Wang, Mingliang, Potter, Guy G., Liu, Mingxia
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.02.2023
• Subjects: Brain - diagnostic imaging; Depressive Disorder, Major - diagnostic imaging; Functional MRI; Graph convolutional network; Humans; Magnetic Resonance Imaging - methods; Major depressive disorder; Unsupervised domain adaptation; Graph convolutional network; 41A10; 65D05; Unsupervised domain adaptation; 65D17; Functional MRI; 41A05; Major depressive disorder
• ISSN: 1361-8415; 1361-8423; 1361-8423
• DOI: 10.1016/j.media.2022.102707

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. [Display omitted] •An unsupervised fMRI harmonization framework to reduce inter-site data heterogeneity•An attention-guided spatio-temporal graph convolution module for feature extraction•Locating discriminative functional connectivities and brain regions as biomarkers