ArticleJournal of affective disorders2019
Treatment-naïve first episode depression classification based on high-order brain functional network.
Article in Journal of affective disorders, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Altered dynamic functional connectivity in antagonistic state in first-episode, drug-naïve patients with major depressive disorder.BMC psychiatry · 2024Article
- Functional magnetic resonance imaging of depression: a bibliometrics and meta-analysis.Annals of general psychiatry · 2024Article
- Natural Language Processing for Depression Prediction on Sina Weibo: Method Study and Analysis.JMIR mental health · 2024Article
- Aberrant resting-state co-activation network dynamics in major depressive disorder.Translational psychiatry · 2024Article
- Topological disruption of high-order functional networks in cognitively preserved Parkinson's disease.CNS neuroscience & therapeutics · 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
- Neuroimaging biomarkers for detecting schizophrenia: A resting-state functional MRI-based radiomics analysis.Heliyon · 2022Article
- Functional MRI in major depressive disorder: A review of findings, limitations, and future prospects.Journal of neuroimaging : official journal of the American Society of Neuroimaging · 2022Review
- Classification of type 2 diabetes mellitus with or without cognitive impairment from healthy controls using high-order functional connectivity.Human brain mapping · 2021Article
- Cross-network interaction for diagnosis of major depressive disorder based on resting state functional connectivity.Brain imaging and behavior · 2021Article
- Application of Functional Magnetic Resonance Imaging in the Diagnosis of Parkinson's Disease: A Histogram Analysis.Frontiers in aging neuroscience · 2021Article
- Brain Networks Connectivity in Mild to Moderate Depression: Resting State fMRI Study with Implications to Nonpharmacological Treatment.Neural plasticity · 2021Article
- Abnormal Functional Connectivity of Posterior Cingulate Cortex Correlates With Phonemic Verbal Fluency Deficits in Major Depressive Disorder.Frontiers in neurology · 2021Article
- A toolbox for brain network construction and classification (BrainNetClass).Human brain mapping · 2020Article
- Altered Resting-State Functional Connectivity of Multiple Networks and Disrupted Correlation With Executive Function in Major Depressive Disorder.Frontiers in neurology · 2020Article
- Quantitative Identification of Major Depression Based on Resting-State Dynamic Functional Connectivity: A Machine Learning Approach.Frontiers in neuroscience · 2020Article
- Identification of Abnormal Circuit Dynamics in Major Depressive Disorder via Multiscale Neural Modeling of Resting-State fMRI.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2019Article
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Authors and funding
9 authors.
Funding
Abstract
backgroundRecent functional connectivity (FC) studies have proved the potential value of resting-state functional magnetic resonance imaging (rs-fMRI) in the study of major depressive disorder (MDD); yet, the rs-fMRI-based individualized diagnosis of MDD is still challenging.
methodsWe enrolled 82 treatment-naïve first episode depression (FED) adults and 72 matched normal control (NC). A computer-aided diagnosis framework was utilized to classify the FEDs from the NCs based on the features extracted from not only traditional "low-order" FC networks (LON) based on temporal synchronization of original rs-fMRI signals, but also "high-order" FC networks (HON) that characterize more complex functional interactions via correlation of the dynamic (time-varying) FCs. We contrasted a classifier using HON feature (C
resultsThe C LIMITATIONS: We only used one imaging modality and did not examine data from different subtypes of depression.
conclusionsDepression classification could be significantly improved by using HON features that better capture the higher-level brain functional interactions. The findings suggest the importance of higher-level cerebro-cerebellar interactions in the pathophysiology of MDD.
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