Even the strongest of individual risk factors are poor predictors of suicide attempts. Many risk factors rely on subjective or self-reported measures and underreporting contributes to poor prediction accuracy. In the midst of the ongoing Veteran suicide crisis, we clearly need new, objective predictors of risk that can be implemented across the VA system. Previous findings indicate that behavioral and neuroimaging correlates of decision- making and cognitive control are promising, objective markers of suicidal risk. Though intriguing, these findings are from research conducted primarily in depressed civilians. It is unknown if these potential markers of suicide risk will generalize to a transdiagnostic sample of Veterans. The primary research objective of this CDA-2 is to evaluate whether structural neuroimaging, and functional neuroimaging during decision-making and cognitive control tasks can be used to identify brain-behavior correlates of suicidal thoughts and behaviors in Veterans. Suicidal thoughts and behaviors are associated with short-sighted, maladaptive, decision-making that is accompanied by differences in striatal-prefrontal reward circuitry. However, studies that have compared decision-making task performance between individuals who have made more organized, lethal attempts at self-harm vs. those making less serious attempts have identified differences in their behavior that may supply more nuanced information about suicidal severity. Interestingly, the decision-making of serious attempters is actually less impulsive. In fact, decision making in serious attempters is less impulsive even relative to healthy controls. This implies that a greater capacity for cognitive control may facilitate, rather than protect against, harm in suicidal individuals. These potential objective markers of suicidal thoughts and behaviors and suicidal risk severity are promising and demand further evaluation. The proposed behavioral and neuroimaging methodologies (diffusion imaging, morphometry analyses, univariate and functional connectivity neuroimaging) included in this proposal have been successfully applied by the candidate, a cognitive neuroscientist, in previous published work. The candidate’s exploratory research objective is to apply machine learning algorithms to a blended dataset of participants’ electronic health data and neurocognitive markers in a preliminary predictive modeling project. This exploratory objective will provide foundational data for future Merit-funded work focused on the development of clinically implementable suicide risk prediction tools. Though the proposed neurocognitive research has the potential to improve the effectiveness of suicide screening and treatment, the candidate acknowledges that all too often, insights from neuroscience fail to improve clinical care because few neuroscientists have the clinical perspective needed to translate mechanistic knowledge into clinical innovatio...