ArticleBrain and behavior2024
Predicting suicidality in late-life depression by 3D convolutional neural network and cross-sample entropy analysis of resting-state fMRI.
Article in Brain and behavior, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.
- Predictors and risk factors for suicide in late-life depression: a systematic review and meta-analysis.Frontiers in psychiatry · 2025Pooled it
- Machine learning in mental health promotion for older adults: a scoping review.BMC geriatrics · 2026Article
- Advances in the assessment and study of suicide in late-life depression.Frontiers in psychiatry · 2025Review
- Determinants of suicidal ideation in older adults: evidence from a national panel study in South Korea.Frontiers in public health · 2025Article
- Neural Functioning in Late-Life Depression: An Activation Likelihood Estimation Meta-Analysis.Geriatrics (Basel, Switzerland) · 2024Review
- Predicting suicidality in late-life depression by 3D convolutional neural network and cross-sample entropy analysis of resting-state fMRI.Brain and behavior · 2024Article
Corrections and comments
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Authors and funding
10 authors at 6 institutions in 3 countries.
Funding
Abstract
backgroundPredicting suicide is a pressing issue among older adults; however, predicting its risk is difficult. Capitalizing on the recent development of machine learning, considerable progress has been made in predicting complex behavior such as suicide. As depression remained the strongest risk for suicide, we aimed to apply deep learning algorithms to identify suicidality in a group with late-life depression (LLD).
methodsWe enrolled 83 patients with LLD, 35 of which were non-suicidal and 48 were suicidal, including 26 with only suicidal ideation and 22 with past suicide attempts, for resting-state functional magnetic resonance imaging (MRI). Cross-sample entropy (CSE) analysis was conducted to examine the complexity of MRI signals among brain regions. Three-dimensional (3D) convolutional neural networks (CNNs) were used, and the classification accuracy in each brain region was averaged to predict suicidality after sixfold cross-validation.
resultsWe found brain regions with a mean accuracy above 75% to predict suicidality located mostly in default mode, fronto-parietal, and cingulo-opercular resting-state networks. The models with right amygdala and left caudate provided the most reliable accuracy in all cross-validation folds, indicating their neurobiological importance in late-life suicide.
conclusionCombining CSE analysis and the 3D CNN, several brain regions were found to be associated with suicidality.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.