Browsing by Author "Beck, Kevin D."

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    Improving the prospective prediction of a near-term suicide attempt in veterans at risk for suicide, using a go/no-go task
    (Cambridge University Press, 2023) Myers, Catherine E.; Dave, Chintan V.; Callahan, Michael; Chesin, Megan S.; Keilp, John G.; Beck, Kevin D.; Brenner, Lisa A.; Goodman, Marianne S.; Hazlett, Erin A.; Niculescu, Alexander B.; St. Hill, Lauren; Kline, Anna; Stanley, Barbara H.; Interian, Alejandro; Psychiatry, School of Medicine
    Background: Neurocognitive testing may advance the goal of predicting near-term suicide risk. The current study examined whether performance on a Go/No-go (GNG) task, and computational modeling to extract latent cognitive variables, could enhance prediction of suicide attempts within next 90 days, among individuals at high-risk for suicide. Method: 136 Veterans at high-risk for suicide previously completed a computer-based GNG task requiring rapid responding (Go) to target stimuli, while withholding responses (No-go) to infrequent foil stimuli; behavioral variables included false alarms to foils (failure to inhibit) and missed responses to targets. We conducted a secondary analysis of these data, with outcomes defined as actual suicide attempt (ASA), other suicide-related event (OtherSE) such as interrupted/aborted attempt or preparatory behavior, or neither (noSE), within 90-days after GNG testing, to examine whether GNG variables could improve ASA prediction over standard clinical variables. A computational model (linear ballistic accumulator, LBA) was also applied, to elucidate cognitive mechanisms underlying group differences. Results: On GNG, increased miss rate selectively predicted ASA, while increased false alarm rate predicted OtherSE (without ASA) within the 90-day follow-up window. In LBA modeling, ASA (but not OtherSE) was associated with decreases in decisional efficiency to targets, suggesting differences in the evidence accumulation process were specifically associated with upcoming ASA. Conclusions: These findings suggest that GNG may improve prediction of near-term suicide risk, with distinct behavioral patterns in those who will attempt suicide within the next 90 days. Computational modeling suggests qualitative differences in cognition in individuals at near-term risk of suicide attempt.