Dataset Information

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

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.

SUBMITTER: Fang Y

PROVIDER: S-EPMC9850278 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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

Fang Yuqi Y Wang Mingliang M Potter Guy G GG Liu Mingxia M

Medical image analysis 20221128

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 addre ...[more]

PMID: 36512941

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Project description:BackgroundIn major depressive disorder (MDD), self-blame-related fMRI measures have shown the potential to be used as prognostic markers for recurrence risk. Like most potential fMRI markers, however, their reliability is unclear. Here, we probed the internal reliability of self-blame-related fMRI measures, as well as the impact of different modelling approaches on reliability metrics and validity.MethodsInternal consistency (i.e. split-half reliability) was calculated for blood oxygen level-dependent (BOLD) responses and psychophysiological interactions (PPI) related to self-blame-related biases in medication-free remitted MDD participants (n = 81) and healthy controls (n = 41). Trial-length was modelled using three durations (0, 2 and 5 s), which was convolved with the haemodynamic response function (HRF) with and without time and dispersion derivatives. Intraclass correlation coefficients (ICCs) were calculated for simple contrasts examining activation to self-blaming emotions and other-blaming emotions and the more complex contrast of the subtraction-based difference between self- and other-blaming emotions within the following a priori ROIs: right superior anterior temporal lobe seed region, anterior subgenual cingulate cortex, posterior subgenual cortex and right striatum / pallidum.ResultsAcross ROIs, we obtained fair reliability (ICC ≥ 0.40) for simple, but poor reliability (ICC < 0.40) for more complex fMRI measures related to self-blame. Despite this low internal consistency of complex measures at the individual level, we observed robust activation at the group-level, reproducing previously published results.ConclusionsWhile simple BOLD contrasts had fair reliability, previously employed PPI models had poor reliability and simple connectivity measures lacked predictive validity. This calls for the development of functional connectivity measures that strike a better balance between reliability and validity for future clinical applications, which require robust measures at the individual rather than group-level.

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Dataset Information

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

Publications

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

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