ArticleFrontiers in psychiatry2022
Orbitofrontal Cortex Functional Connectivity-Based Classification for Chronic Insomnia Disorder Patients With Depression Symptoms.
Article in Frontiers in psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 16 citations in OpenAlex.
- Drug-associated insomnia: A pharmacovigilance study based on FDA adverse event reporting system.Medicine · 2026Article
- Aberrant dynamic functional architecture in major depressive disorder: Vertex-Wise large-sample fMRI analyses reveal network-specific alterations and symptom associations.Translational psychiatry · 2026Article
- Alterations in subgenual anterior cingulate cortex functional connectivity underlie depressive symptoms in chronic insomnia disorder.Frontiers in psychiatry · 2026Article
- Diagnosis of adolescent depression with sleep disorder based on network topological attributes and functional connectivity.BMC psychiatry · 2025Article
- Advances in the research of comorbid insomnia and depression: mechanisms, impacts, and interventions.Frontiers in psychiatry · 2025Review
- A surface-based cross-sectional fMRI study on brain function differences between comorbid mild or moderate depression and insomnia.Frontiers in neuroscience · 2025Article
- Discriminating Paradoxical and Psychophysiological Insomnia Based on Structural and Functional Brain Images: A Preliminary Machine Learning Study.Brain sciences · 2023Article
- The role of ascending arousal network in patients with chronic insomnia disorder.Human brain mapping · 2023Article
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Depression is a common comorbid symptom in patients with chronic insomnia disorder (CID). Previous neuroimaging studies found that the orbital frontal cortex (OFC) might be the core brain region linking insomnia and depression. Here, we used a machine learning approach to differentiate CID patients with depressive symptoms from CID patients without depressive symptoms based on OFC functional connectivity. Seventy patients with CID were recruited and subdivided into CID with high depressive symptom (CID-HD) and low depressive symptom (CID-LD) groups. The OFC functional connectivity (FC) network was constructed using the altered structure of the OFC region as a seed. A linear kernel SVM-based machine learning approach was carried out to classify the CID-HD and CID-LD groups based on OFC FC features. The predict model was further verified in a new cohort of CID group (
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