Reward Learning in Late-Life Suicidal Behavior

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ABSTRACT The US is facing rising suicide rates. Yet, we have only a limited understanding of why some people, but not others, progress from contemplating to attempting suicide. In the past funding period, we have shown that depressed older adults whose decision-making is impaired are more likely to progress from suicidal ideation to action. Specifically, using decision experiments, computational modeling, and fMRI, we have found replicable deficits in learning and choice processes paralleled by altered ventromedial and dorsolateral prefrontal abstract learning signals. In this renewal application, we propose to extend these findings by examining how people at risk for suicide make decisions under cognitive and emotional demands that are more representative of the suicidal crisis. In our computational framework these demands include (i) a high information load and (ii) constraints on information processing imposed by time pressure and impending threats. We have developed and validated new experimental and computational methods for studying information-processing bottlenecks during decision-making. Specifically, our reinforcement learning computational model applied to behavioral and neuroimaging data, enables us to examine how people use their limited neurocomputational resources to make good decisions under high information load. Our preliminary studies show that decision-making in this context (i) relies on resource-rational strategies for managing information load, (ii) is subserved by dorsal attention and cingulo-opercular networks, (iii) is likely disrupted in attempted suicide, (iv) a deficit paralleled by abnormal dorsal attention network responses to information load. We thus propose to test the general hypothesis that people at risk for suicide are prone to information-processing bottlenecks arising from alterations in these cortical networks. We will perform decision experiments and cognitive computational models (Aim 1) in a discovery sample and a non-overlapping replication sample (n = 200 each) to ensure that findings are robust to the clinical and cognitive heterogeneity of suicidal behavior. Both samples will include individuals maximally representative of suicide victims, namely older depressed suicide attempters, about half of whom survived near-lethal attempts. Functional neuroimaging experiments manipulating information load will interrogate the neurocomputational dynamics of the dorsal attention network and cingulo-opercular network during decision- making in one sample (n = 200, Aim 2). A careful characterization of psychopathology, personality, cognition, psychotropic exposure and brain damage from suicide attempts will allow us to control for key confounds. The interdisciplinary team has expertise in mechanisms of suicidal behavior (Dombrovski), decision neuroscience (Dombrovski, McGuire, Hallquist), imaging methods (Hallquist), and suicide risk management (Szanto, Dombrovski). This work aligns with a key objective of the NIMH’s prioritized research agenda on suicide: “to identify cognitive dysfunction/neural circuitry profiles … associated with suicide risk” and taps into the reinforcement learning and limited capacity constructs of the RDoC framework.