P23: Suicide Prediction in late-life depression by Machine learning and Complexity analysis in resting-state functional MRI data

Abstract

Objective:

Late-life suicide is the most serious consequences of late-life depression (LLD). Nevertheless, suicidal behavior is complex and hard to predict. With the help of MRI scans and machine learning algorithm, we aim to examine the neural signatures of suicidality in patients of LLD.

Methods:

We recruited 83 patients of LLD with a mean age of 68.8 years, where 48 were suicidal (26 with suicidal ideation and 22 with past suicide attempts). Cross-sample entropy (CSE) analysis was employed to analyze the resting-state function MRI data. Three-dimensional CSE volume in 90 region-of-interest of the brain in each participant was input into convolutional neural networks (CNN) to test the classification accuracy of suicidality.

Results:

After six-fold cross-validation, we found several regions in the default mode, fronto-parietal, and cingulo-opercular resting-state networks to have a mean accuracy above 75% to predict suicidality. Moreover, the models with right amygdala and left caudate provided the most reliable accuracy in all cross-validation folds, signifying their unique roles in late-life suicide.

Conclusion:

Our results provide potential targets for intervention or biomarkers in late-life suicide. More research must be conducted to consolidate our results with scalable implementation in clinical setting.