Informing the study of suicidal thoughts and behaviors in distressed young adults: The use of a machine learning approach to identify neuroimaging, psychiatric, behavioral, and demographic correlates

Abstract

Young adults are at high risk for suicide, yet there is limited ability to predict suicidal thoughts and behaviors. Machine learning approaches are better able to examine a large number of variables simultaneously to identify combinations of factors associated with suicidal thoughts and behaviors. The current study used LASSO regression to investigate extent to which a number of demographic, psychiatric, behavioral, and functional neuroimaging variables are associated with suicidal thoughts and behaviors during young adulthood. 78 treatment seeking young adults (ages 18-25) completed demographic, psychiatric, behavioral, and suicidality measures. Participants also completed an implicit emotion regulation functional neuroimaging paradigm. Report of recent suicidal thoughts and behaviors served as the dependent variable. Five variables were identified by the LASSO regression: Two were demographic variables (age and level of education), two were psychiatric variables (depression and general psychiatric distress), and one was a neuroimaging variable (left amygdala activity during sad faces). Amygdala function was significantly associated with suicidal thoughts and behaviors above and beyond the other factors. Findings inform the study of suicidal thoughts and behaviors among treatment seeking young adults, and also highlight the importance of investigating neurobiological markers.

Keywords: Amygdala; Implicit emotion regulation; Machine learning; Suicide prediction.