Predicting Active Suicidality from Temporal Dynamics of Implicit Life-Death Associations
- 1University of Houston
Abstract
BackgroundInterview-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.
MethodsWe 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.
ResultsThe 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.
ConclusionsResults 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.
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BibTeX
@misc{rajaei2026predicting,
title = {{Predicting Active Suicidality from Temporal Dynamics of Implicit Life-Death Associations}},
author = {P. Rajaei and A. N. McInnes and B. M. Garcia and S. Yadav and B. Brown and M. Freedman and S. M. Balint and M. D. Goodman Keiser and S. Tavasoli and M. V. Bronstein and A. S. Widge and A. Yousefi},
howpublished = {medRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.09.26362642},
url = {https://www.medrxiv.org/content/10.64898/2026.09.09.26362642v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Predicting Active Suicidality from Temporal Dynamics of Implicit Life-Death Associations
AU - P. Rajaei
AU - A. N. McInnes
AU - B. M. Garcia
AU - S. Yadav
AU - B. Brown
AU - M. Freedman
AU - S. M. Balint
AU - M. D. Goodman Keiser
AU - S. Tavasoli
AU - M. V. Bronstein
AU - A. S. Widge
AU - A. Yousefi
PY - 2026
DA - 2026/09/09
PB - medRxiv
DO - 10.64898/2026.09.09.26362642
UR - https://www.medrxiv.org/content/10.64898/2026.09.09.26362642v1
AB - BackgroundInterview-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. MethodsWe 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. ResultsThe 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. ConclusionsResults 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.
ER -