Citation

Sun F, Song W, Wen X, Li H, Ouyang L, Wei S. Acta Psychol. Sin. 2022; 54(9): 1031-1047.

Copyright

(Copyright © 2022, Chinese Psychological Society)

DOI

10.3724/SP.J.1041.2022.01031

PMID

unavailable

Abstract

Depressed students are at high-risk for suicide. Psychological pain, especially pain avoidance, was a more robust predictor for suicide ideation than depression at the behavioral level. Due to suicide as a complex classification model, machine learning algorisms applied to integrate behavioral data and neural characteristic can advance suicide prediction, and the accuracy of multimodality features is superior than clinical interview. The present study aimed to integrate data-driven machine learning algorisms and the three-dimensional psychological pain model to figure out the optimal features in the prediction of suicide ideation. Seventy-seven college students were recruited by advertisement and divided into three groups: depressed group with high levels of suicide ideation (HSI, n = 25), depressed group with low levels of suicide ideation (LSI, n = 20), and healthy controls (HC, n = 32). All participants completed the three-dimensional psychological pain scale (TDPPS), Beck depression inventory-I (BDI), Beck suicide ideation inventory (BSI), and the self-referential affective incentive delay task (SAID). The value of support vector based on machine-recursive feature elimination (RFE-SVM) algorithm applied to combine the scale scores, resting state and punitive-related EEG components for feature ranking in a nonlinear way.

RESULTS showed that: (1) Scores of pain avoidance in the HSI was higher than the LSI group. (2) The multimodal psychological pain-based model for suicide ideation classification (Accuracy = 85.66%, Precision = 0.82, Recall = 0.73, AUC = 0.92) was sufficient and superior than the EEG single-modal model. Importantly, the pain avoidance and BDI scores ranked the top two features in the classification model of suicide ideation, whereas painful feeling and pain arousal subscale scores ranked the top two features in the classification model of depression. The EEG optimal features of overlap in the pain avoidance and suicide ideation classification models were the LPP and target-P3 under self-referential punitive conditions. (3) The powers of delta and beta band were negatively correlated with the BSI-W and pain avoidance subscale scores. The FRN amplitude under other-and self-referential punitive conditions were negatively corelated with the pain avoidance subscale scores. In the HSI group, power of delta elicited by positive feedback under self-referential conditions was significantly lower than those under other-referential conditions. In the HSI group, the amplitude of LPP in other-referential punitive conditions was higher than those under reward and neutral conditions, whereas in the LSI group, the amplitude of LPP under self-referential punitive conditions was higher than that under neutral conditions. As a pilot study, the current study provided a support for the prominent role of pain avoidance and its related neuroelectrophysiological correlates in the prediction of suicide. The clinical significance of this results will be discussed. © 2022.

Language: zh

Keywords

EEG; suicide ideation; machine learning; three-dimensional psychological pain