ArticleTranslational psychiatry2022
Identification of suicidality in patients with major depressive disorder via dynamic functional network connectivity signatures and machine learning.
Article in Translational psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.
- Machine learning and the prediction of suicide in psychiatric populations: a systematic review.Translational psychiatry · 2024Pooled it
- The Effect of AoA-L2 on Functional Network Dynamics in Bilinguals.Brain topography · 2026Article
- Female vulnerability to microvascular transport dysfunction emerges early across the Alzheimer's disease continuum.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Identification of suicidal ideation in major depressive disorder: A machine learning approach with multimodal digital features.PLOS digital health · 2026Article
- Dynamic Functional Connectivity, Major Depression, and Suicidal Ideation in Children.Human brain mapping · 2026Article
- Interpretable deep learning for functional MRI-based auxiliary diagnosis of major depressive disorder with suicidal ideation.Frontiers in computational neuroscience · 2026Article
- Abnormal functional network connectivity mediates the relationship between depressive symptoms and cognitive decline in late-onset depression.Psychological medicine · 2025Article
- Oral ketamine effects on dynamics of functional network connectivity in patients treated for chronic suicidality.European archives of psychiatry and clinical neuroscience · 2025Article
- Functional brain network dynamics of brooding in depression: Insights from real-time fMRI neurofeedback.Journal of affective disorders · 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
- Multimodal Machine Learning Prediction of 12-Month Suicide Attempts in Bipolar Disorder.Bipolar disorders · 2025Article
- Symptom-based depression subtypes: brain dynamic specificity and its association with gene expression profiles.Translational psychiatry · 2025Article
- Transcriptional Patterns of Nodal Entropy Abnormalities in Major Depressive Disorder Patients with and without Suicidal Ideation.Research (Washington, D.C.) · 2025Article
- Abnormal large-scale brain functional network dynamics in social anxiety disorder.CNS neuroscience & therapeutics · 2024Article
- Functional brain network dynamics of brooding in depression: insights from real-time fMRI neurofeedback.medRxiv : the preprint server for health sciences · 2024Article
- Aberrant resting-state co-activation network dynamics in major depressive disorder.Translational psychiatry · 2024Article
- Dynamic functional network connectivity in patients with a mismatch between white matter hyperintensity and cognitive function.Frontiers in aging neuroscience · 2024Article
- Specific static and dynamic functional network connectivity changes in thyroid-associated ophthalmopathy and it predictive values using machine learning.Frontiers in neuroscience · 2024Article
- Neuroimaging studies in major depressive disorder with suicidal ideation or behaviour among Chinese patients: implications for neural mechanisms and imaging signatures.General psychiatry · 2024Review
- Temporal dysregulation of the somatomotor network in agitated depression.Brain communications · 2024Article
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Authors and funding
10 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Major depressive disorder (MDD) is a severe brain disease associated with a significant risk of suicide. Identification of suicidality is sometimes life-saving for MDD patients. We aimed to explore the use of dynamic functional network connectivity (dFNC) for suicidality detection in MDD patients. A total of 173 MDD patients, including 48 without suicide risk (NS), 74 with suicide ideation (SI), and 51 having attempted suicide (SA), participated in the present study. Thirty-eight healthy controls were also recruited for comparison. A sliding window approach was used to derive the dFNC, and the K-means clustering method was used to cluster the windowed dFNC. A linear support vector machine was used for classification, and leave-one-out cross-validation was performed for validation. Other machine learning methods were also used for comparison. MDD patients had widespread hypoconnectivity in both the strongly connected states (states 2 and 5) and the weakly connected state (state 4), while the dysfunctional connectivity within the weakly connected state (state 4) was mainly driven by suicidal attempts. Furthermore, dFNC matrices, especially the weakly connected state, could be used to distinguish MDD from healthy controls (area under curve [AUC] = 82), and even to identify suicidality in MDD patients (AUC = 78 for NS vs. SI, AUC = 88 for NS vs. SA, and AUC = 74 for SA vs. SI), with vision-related and default-related inter-network connectivity serving as important features. Thus, the dFNC abnormalities observed in this study might further improve our understanding of the neural substrates of suicidality in MDD patients.
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