Suicide risk estimation in bipolar disorder using N200 and P300 event-related potentials and machine learning: A pilot study

Abstract

Background

Individuals with bipolar disorder (BD) face an elevated suicide risk. While machine learning (ML) has been used to estimate suicide risk in BD, early predictors like demographics, past attempts, and self-reports are limited by their inability to provide individualized risk estimation, overemphasis on past attempters, and susceptibility to personal biases, underscoring the need for effective, objective markers. Event-related potentials (ERPs), widely studied in suicide research, remain unexplored in ML applications for BD. This pilot study applies ML to N200 and P300 ERP components from a response inhibition paradigm to estimate suicide risk in BD.

Methods

We collected N200 and P300 peak amplitude and latency data from 57 Type I BD individuals (22 attempters and 35 non-attempters). Our two-stage ML approach employed adaptive Lasso logistic regression for feature selection, followed by deep neural network (DNN) modeling for classification. For post-hoc analysis, we used explainable AI to interpret ERP feature importance in top-performing DNN predictions.

Results

Key features were exclusively identified from latency data. Notably, N200 latency DNN models effectively distinguished attempters from non-attempters, achieving AUCs of 78.2–89.3 %. Explainable AI pinpointed a right visual hemifield Go stimuli-induced ERP from the left-parietal site as the most predictive.

Conclusion

Our ERP-ML approach showed promising preliminary results, with N200 latency identified as a potential suicide marker in BD. Larger samples are required to validate these results. While findings are sample-specific, the methodological approach may have broader applicability and could inform future research to refine clinical strategies for detecting high-risk BD individuals.