ArticleHuman brain mapping2020
Identifying resting-state effective connectivity abnormalities in drug-naïve major depressive disorder diagnosis via graph convolutional networks.
Article in Human brain mapping, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 44 citations in OpenAlex.
- Disruptions in Cognitive-Affective Circuitry in Major Depression Disorder: Insights From REST-Meta-MDD and Its Implication for Predicting TMS Treatment Efficacy.CNS neuroscience & therapeutics · 2025Article
- Advancing the diagnosis of major depressive disorder: Integrating neuroimaging and machine learning.World journal of psychiatry · 2025Article
- Addressing multi-site functional MRI heterogeneity through dual-expert collaborative learning for brain disease identification.Human brain mapping · 2023Article
- Unsupervised cross-domain functional MRI adaptation for automated major depressive disorder identification.Medical image analysis · 2023Article
- DeepEZ: A Graph Convolutional Network for Automated Epileptogenic Zone Localization From Resting-State fMRI Connectivity.IEEE transactions on bio-medical engineering · 2023Article
- The diagnostic performance of machine learning based on resting-state functional magnetic resonance imaging data for major depressive disorders: a systematic review and meta-analysis.Frontiers in neuroscience · 2023Review
- Ensemble graph neural network model for classification of major depressive disorder using whole-brain functional connectivity.Frontiers in psychiatry · 2023Article
- Brain functional network changes associated with psychological symptoms in emergency psychological responding professionals after the first wave of COVID-19 pandemic.Frontiers in psychiatry · 2023Article
- Multiscale functional connectome abnormality predicts cognitive outcomes in subcortical ischemic vascular disease.Cerebral cortex (New York, N.Y. : 1991) · 2022Article
- Graph Convolutional Networks Reveal Network-Level Functional Dysconnectivity in Schizophrenia.Schizophrenia bulletin · 2022Article
- Using graph convolutional network to characterize individuals with major depressive disorder across multiple imaging sites.EBioMedicine · 2022Article
- BNCPL: Brain-Network-based Convolutional Prototype Learning for Discriminating Depressive Disorders.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2021Article
- Application of Artificial Intelligence in the MRI Classification Task of Human Brain Neurological and Psychiatric Diseases: A Scoping Review.Diagnostics (Basel, Switzerland) · 2021Article
- Identifying resting-state effective connectivity abnormalities in drug-naïve major depressive disorder diagnosis via graph convolutional networks.Human brain mapping · 2020Article
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
Major depressive disorder (MDD) is a leading cause of disability; its symptoms interfere with social, occupational, interpersonal, and academic functioning. However, the diagnosis of MDD is still made by phenomenological approach. The advent of neuroimaging techniques allowed numerous studies to use resting-state functional magnetic resonance imaging (rs-fMRI) and estimate functional connectivity for brain-disease identification. Recently, attempts have been made to investigate effective connectivity (EC) that represents causal relations among regions of interest. In the meantime, to identify meaningful phenotypes for clinical diagnosis, graph-based approaches such as graph convolutional networks (GCNs) have been leveraged recently to explore complex pairwise similarities in imaging/nonimaging features among subjects. In this study, we validate the use of EC for MDD identification by estimating its measures via a group sparse representation along with a structured equation modeling approach in a whole-brain data-driven manner from rs-fMRI. To distinguish drug-naïve MDD patients from healthy controls, we utilize spectral GCNs based on a population graph to successfully integrate EC and nonimaging phenotypic information. Furthermore, we devise a novel sensitivity analysis method to investigate the discriminant connections for MDD identification in our trained GCNs. Our experimental results validated the effectiveness of our method in various scenarios, and we identified altered connectivities associated with the diagnosis of MDD.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.