Citation

Lin C, Huang CM, Chang W, Chang YX, Liu HL, Ng SH, Lin HL, Lee TMC, Lee SH, Wu SC. Brain Behav. 2024; 14(1): e3348.

Copyright

(Copyright © 2024, John Wiley and Sons)

DOI

10.1002/brb3.3348

PMID

38376042

PMCID

PMC10790060

Abstract

BACKGROUND: Predicting 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).

METHODS: We 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.

RESULTS: We 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.

CONCLUSION: Combining CSE analysis and the 3D CNN, several brain regions were found to be associated with suicidality.

Language: en

Keywords

*Suicidal Ideation; *Suicide; Aged; convolutional neural network; cross-sample entropy; Depression/diagnostic imaging; Entropy; Humans; machine learning; Magnetic Resonance Imaging; Neural Networks, Computer; older adult; resting-state fMRI; suicide; suicide attempt; Suicide, Attempted