Predicting Active Suicidality from Temporal Dynamics of Implicit Life-Death Associations

Background: Interview-based suicide assessments are insufficient for near-term risk detection, but there are no evidence-based, high-performing alternatives. The Death Implicit Association Test (DIAT) has been proposed as a suicide risk assessment tool. However, traditional DIAT scoring methods exhibit limited association with suicidality. Methods: We tested whether temporal structure in reaction times (RTs) from the Brief Death Implicit Association Test (BDIAT) tracks active suicidal ideation (SI), which was ascertained by ecological momentary assessment (EMA). Seventy-seven participants who either did or did not endorse active SI during EMA (SI+ n=22; SI- n=55) completed 18 alternating blocks of the BDIAT (9 Life + Me, 9 Death + Me). We computed the conventional D-score, an aggregate index of self-life versus self-death associations, and separately treated RTs as time-varying signals, examining entrainment to block alternation and habituation during conflict-related trials. We then developed a bilinear logistic regression classifier to extract these temporal RT features and trained it to predict SI status, with performance validated using leave-one-out cross-validation. Results: The conventional D-score did not reliably classify SI+/SI- individuals. In contrast, temporal RT dynamics reveal group differences in block-level entrainment and trial-level habituation. A bilinear logistic regression classifier leveraging these dynamics predicts SI status with 77% balanced accuracy and AUC 0.799. Conclusions: Results demonstrate that temporal dynamics of RT during the BDIAT track active ideation and can be used to distinguish SI status at the individual level, implicating RT temporal dynamics evoked during BDIAT as a candidate behavioral marker of near-term suicide risk.