ArticleFrontiers in neuroscience2020
Machine Learning-Based Identification of Suicidal Risk in Patients With Schizophrenia Using Multi-Level Resting-State fMRI Features.
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 14 papers, 1 of them a synthesis that pooled it.
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14 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.
- Machine learning and the prediction of suicide in psychiatric populations: a systematic review.Translational psychiatry · 2024Pooled it
- Article
- [Classification of systemic lupus erythematosus resting-state functional magnetic resonance imaging data based on the end-to-end model].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- Altered Functional Connectivity of Amygdala Subregions with Large-Scale Brain Networks in Schizophrenia: A Resting-State fMRI Study.Tomography (Ann Arbor, Mich.) · 2025Article
- The Future of Artificial Intelligence in Mental Health Nursing Practice: An Integrative Review.International journal of mental health nursing · 2025Review
- Circuitry correlates of negative urgency and suicidality in schizophrenia spectrum disorders: a LASSO regression study.Frontiers in psychiatry · 2025Article
- Divergent pattern of functional connectivity within the dorsal attention network differentiates schizophrenia and bipolar disorder patients.Frontiers in psychiatry · 2024Article
- Neural activity during inhibitory control predicts suicidal ideation with machine learning.NPP - digital psychiatry and neuroscience · 2024Article
- Machine Learning Techniques to Predict Mental Health Diagnoses: A Systematic Literature Review.Clinical practice and epidemiology in mental health : CP & EMH · 2024Review
- Neurocognitive Suicide and Homicide Markers in Patients with Schizophrenia Spectrum Disorders: A Systematic Review.Behavioral sciences (Basel, Switzerland) · 2023Review
- 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
- Suicide risk detection using artificial intelligence: the promise of creating a benchmark dataset for research on the detection of suicide risk.Frontiers in psychiatry · 2023Article
- Neuroimaging biomarkers for detecting schizophrenia: A resting-state functional MRI-based radiomics analysis.Heliyon · 2022Article
- Machine Learning of Schizophrenia Detection with Structural and Functional Neuroimaging.Disease markers · 2021Article
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10 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundSome studies suggest that as much as 40% of all causes of death in a group of patients with schizophrenia can be attributed to suicides and compared with the general population, patients with schizophrenia have an 8.5-fold greater suicide risk (SR). There is a vital need for accurate and reliable methods to predict the SR among patients with schizophrenia based on biological measures. However, it is unknown whether the suicidal risk in schizophrenia can be related to alterations in spontaneous brain activity, or if the resting-state functional magnetic resonance imaging (rsfMRI) measures can be used alongside machine learning (ML) algorithms in order to identify patients with SR.
methodsFifty-nine participants including patients with schizophrenia with and without SR as well as age and gender-matched healthy underwent 13 min resting-state functional magnetic resonance imaging. Both static and dynamic indexes of the amplitude of low-frequency fluctuation (ALFF), the fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity as well as functional connectivity (FC) were calculated and used as an input for five machine learning algorithms: Gradient boosting (GB), LASSO, Logistic Regression (LR), Random Forest and Support Vector Machine.
resultsAll groups revealed different intra-network functional connectivity in ventral DMN and anterior SN. The best performance was reached for the LASSO applied to FC with an accuracy of 70% and AUROC of 0.76 (
conclusionOur findings suggest that SR in schizophrenia can be seen on the level of DMN and SN functional connectivity alterations. ML algorithms were able to significantly differentiate SR patients. Our results could be useful in developing neuromarkers of SR in schizophrenia based on non-invasive rsfMRI.
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