ArticleBrain imaging and behavior2021
Cross-network interaction for diagnosis of major depressive disorder based on resting state functional connectivity.
Article in Brain imaging and behavior, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Brain connectivity changes underlying depression and fatigue in relapsing-remitting multiple sclerosis: A systematic review.PloS one · 2024Pooled it
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- Shared neurobiological changes in individuals with Crohn's disease and major depressive disorder.Communications medicine · 2025Article
- Random Walk-Based Node Feature Learning for Major Depressive Disorder Identification Through Multi-Site rs-fMRI Data.Human brain mapping · 2025Article
- Classification of Major Depressive Disorder Using Graph Attention Mechanism with Multi-Site rs-fMRI Data.Neuroinformatics · 2025Article
- Higher amplitudes of visual networks are associated with trait- but not state-depression.Psychological medicine · 2024Article
- Unveiling convergent and divergent intrinsic brain network alternations in depressed adolescents engaged in non-suicidal self-injurious behaviors with and without suicide attempts.CNS neuroscience & therapeutics · 2024Article
- Altered static and dynamic spontaneous brain activity in patients with dysthyroid optic neuropathy: a resting-state fMRI study.Frontiers in neuroscience · 2024Article
- Atrophy in the supramarginal gyrus associated with impaired cognitive inhibition in grieving Chinese Shidu parents.European journal of psychotraumatology · 2024Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- The classification of brain network for major depressive disorder patients based on deep graph convolutional neural network.Frontiers in human neuroscience · 2023Article
- Ensemble graph neural network model for classification of major depressive disorder using whole-brain functional connectivity.Frontiers in psychiatry · 2023Article
- Resting-State Functional Connectivity Impairment in Patients with Major Depressive Episode.International journal of environmental research and public health · 2022Article
- Distinct Functional Cortico-Striato-Thalamo-Cerebellar Networks in Genetic Generalized and Focal Epilepsies with Generalized Tonic-Clonic Seizures.Journal of clinical medicine · 2022Article
- Adaptation of Lipid Profiling in Depression Disease and Treatment: A Critical Review.International journal of molecular sciences · 2022Review
- Identifying depression disorder using multi-view high-order brain function network derived from electroencephalography signal.Frontiers in computational neuroscience · 2022Article
- Widespread aberrant functional connectivity throughout the whole brain in obstructive sleep apnea.Frontiers in neuroscience · 2022Article
- Random Network and Non-rich-club Organization Tendency in Children With Non-syndromic Cleft Lip and Palate After Articulation Rehabilitation: A Diffusion Study.Frontiers in neurology · 2022Article
- Rich-Club Analysis of Structural Brain Network Alterations in HIV Positive Patients With Fully Suppressed Plasma Viral Loads.Frontiers in neurology · 2022Article
- Altered Static and Dynamic Interhemispheric Resting-State Functional Connectivity in Patients With Thyroid-Associated Ophthalmopathy.Frontiers in neuroscience · 2021Article
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9 authors.
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Abstract
Previous studies have suggested that resting-state functional connectivity plays a central role in the physiopathology of major depressive disorder (MDD). However, the individualized diagnosis of MDD based on resting-state functional connectivity is still unclear, especially in first episode drug-naive patients with MDD. Resting state functional magnetic resonance imaging was enrolled from 30 first episode drug-naive patients with MDD and age- and gender-matched 31 healthy controls. Whole brain functional connectivity was computed and viewed as classification features. Multivariate pattern analysis (MVPA) was performed to discriminate patients with MDD from controls. The experimental results exhibited a correct classification rate of 82.25% (p < 0.001) with sensitivity of 83.87% and specificity of 80.64%. Almost all of the consensus connections (125/128) were cross-network interaction among default mode network (DMN), salience network (SN), central executive network (CEN), visual cortex network (VN), Cerebellum and Other. Moreover, the supramarginal gyrus exhibited high discriminative power in classification. Our findings suggested cross-network interaction can be used as an effective biomarker for MDD clinical diagnosis, which may reveal the potential pathological mechanism for major depression. The current study further confirmed reliable application of MVPA in discriminating MDD patients from healthy controls.
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