Mathematical and computational modeling of suicidal thoughts and behaviors

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Project Summary/Abstract Suicide is a devastating public health concern. More than 40,000 people die by suicide in the US each year, making it the 10th leading cause of death and responsible for >$30 billion in lost productivity and medical costs. Unfortunately, whereas scientific advances have led to significant declines in other leading causes of death (e.g., pneumonia, tuberculosis) over the past century, the current suicide rate is nearly identical to the early 1900s. In order to improve prediction and prevention of suicide, a better mechanistic understanding of risk and protective processes underlying suicidal thoughts and behaviors is needed. Computational psychiatry holds such promise for advancing suicide research, particularly through building and testing formal theories. Although many influential suicide theories have existed for decades, these have all been instantiated verbally, which renders them underspecified by nature (due to the inherent imprecision of language). On the other hand, using tools from computational psychiatry, formal theories are instantiated in mathematical equations and computer code. This requires more specificity and precision of the exact strength, form, and time scale of theorized effects. Indeed, formal theories have led to significant advances and breakthroughs in other scientific fields concerned with the understanding and prediction of complex systems (e.g., ecosystems, climate). Thus, the proposed project aims to address this major gap in suicide research by using mathematical and computational modeling to build, evaluate, and test a formal theory of suicide. The candidate and mentorship team, including leading experts in suicide and computational modeling of complex dynamic systems, have developed a preliminary theory of suicidal thoughts and behaviors encompassing cognitive, affective, behavioral, and social factors. Aim 1 of the project is to formalize each of these associations using differential equations (a family of mathematical models that specify the relationship between functions and their derivatives and are extremely useful for modeling change in complex systems over time). Aim 2 is to transform these mathematical equations into computer code to simulate artificial data, allowing for direct observation of the behavior implied by the theory. This step will allow for an evaluation of whether the theory is able to produce fundamental, known phenomena about suicidal thoughts and behaviors. Finally, Aim 3 will leverage data from an ongoing NIMH-funded intensive longitudinal study of suicidal thoughts and behaviors (N = 300) to evaluate the theory-based simulated artificial data against empirical data collected in real-time. The proposed study’s greatest potential impacts are to develop and evaluate the first formal theory of suicidal thoughts and behaviors using mathematical and computational modeling, as well as to promote a program of research uniting the two major NIMH priorities of suicide prevention and computational psychiatry. Together, this can advance the mechanistic understanding of suicidal thoughts and behaviors to improve suicide prediction, as well as provide actionable information about novel treatment and intervention targets for suicide prevention.