ArticleFrontiers in neuroscience2020
Quantitative Identification of Major Depression Based on Resting-State Dynamic Functional Connectivity: A Machine Learning Approach.
Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- The role of the subcortical structures in subthreshold depression: evidence from static and dynamic functional connectivity.Brain structure & function · 2026Article
- Development of a multimodal magnetic resonance imaging-based machine learning prediction model for flight cadets.Scientific reports · 2026Article
- Machine learning-based identification and brain network characterization of flight cadets compared with air traffic control (ATC) students using static and dynamic functional connectivity.Frontiers in human neuroscience · 2026Article
- Article
- Aberrant brain dynamics in major depressive disorder during working memory task.European archives of psychiatry and clinical neuroscience · 2025Article
- Altered static and dynamic functional network connectivity in individuals with subthreshold depression: a large-scale resting-state fMRI study.European archives of psychiatry and clinical neuroscience · 2025Article
- Aberrant static and dynamic functional network connectivity in patients with noise-induced hearing loss.Quantitative imaging in medicine and surgery · 2025Article
- Modulation of functional network co-activation pattern dynamics following ketamine treatment in major depression.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Multi-atlas ensemble graph neural network model for major depressive disorder detection using functional MRI data.Frontiers in computational neuroscience · 2025Article
- Recognition of flight cadets brain functional magnetic resonance imaging data based on machine learning analysis.PloS one · 2025Article
- Sex-specific resting state brain network dynamics in patients with major depressive disorder.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024Article
- Improved clinical outcome prediction in depression using neurodynamics in an emotional face-matching functional MRI task.Frontiers in psychiatry · 2024Article
- Neuroimaging Study of Brain Functional Differences in Generalized Anxiety Disorder and Depressive Disorder.Brain sciences · 2023Article
- Altered brain dynamic in major depressive disorder: state and trait features.Translational psychiatry · 2023Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Heterogeneous brain dynamic functional connectivity patterns in first-episode drug-naive patients with major depressive disorder.Human brain mapping · 2023Article
- Default Mode and Frontoparietal Network Dynamics: Associations with Familial Risk for Depression and Stress Sensitivity.Journal of mood and anxiety disorders · 2023Article
- Alterations in regional homogeneity and functional connectivity associated with cognitive impairment in patients with hypertension: a resting-state functional magnetic resonance imaging study.Hypertension research : official journal of the Japanese Society of Hypertension · 2023Article
- Robust dynamic brain coactivation states estimated in individuals.Science advances · 2023Article
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10 authors.
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Abstract
introductionDeveloping a machine learning-based approach which could provide quantitative identification of major depressive disorder (MDD) is essential for the diagnosis and intervention of this disorder. However, the performances of traditional algorithms using static functional connectivity (SFC) measures were unsatisfactory. In the present work, we exploit the hidden information embedded in dynamic functional connectivity (DFC) and developed an accurate and objective image-based diagnosis system for MDD.
methodsMRI images were collected from 99 participants including 56 healthy controls and 43 MDD patients. DFC was calculated using a sliding-window algorithm. A non-linear support vector machine (SVM) approach was then used with the DFC matrices as features to distinguish MDD patients from healthy controls. The spatiotemporal characteristics of the most discriminative features were then investigated.
resultsThe area under the curve (AUC) of the SVM classifier with DFC measures reached 0.9913, while this value is only 0.8685 for the algorithm using SFC measures. Spatially, the most discriminative 28 connections distributed in the visual network (VN), somatomotor network (SMN), dorsal attention network (DAN), ventral attention network (VAN), limbic network (LN), frontoparietal network (FPN), and default mode network (DMN), etc. Notably, a large portion of these connections were associated with the FPN, DMN, and VN. Temporally, the most discriminative connections transited from the cortex to deeper regions.
conclusionThe results clearly suggested that DFC is superior to SFC and provide a reliable quantitative identification method for MDD. Our findings may furnish a better understanding of the neural mechanisms of MDD as well as improve accurate diagnosis and early intervention of this disorder.
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