Psychobiology of Suicidal Behavior in Borderline Personality Disorder

This competing continuation proposal extends and deepens a productive program of longitudinal research on suicidal behavior in a cohort of 303 people with borderline personality disorder (BPD) with a prospectively ascertained high risk for serious suicide attempts. Our studies in 2017-22 focused on the pathway from interpersonal experiences to suicidal behavior, integrating three timescales: naturalistic prediction of suicidal behavior over years; prediction of suicidal ideation over days; and experimental interrogation of decision processes over minutes. Taken together, our findings show that the emergence of suicidal ideation from interpersonal conflict is catalyzed by internalizing psychopathology, whereas the transition to suicidal behavior is facilitated by externalizing psychopathology and neurobehavioral alterations in decision-making. Building on this work, we propose to (1) examine interpersonal traits and specific facets that cause decompensation in BPD and facilitate transitions in the suicidal process on a timescale of years, (2) improve individualized prediction of emotion dysregulation and suicidal thoughts on a timescale of hours to days, and (3) advance a neurocomputational account of the failed search for solutions in a crisis on a timescale of minutes. For continuity, we retain the historic focus on BPD as a prototypical personality disorder, yet we also emphasize dissociable contributions of multiple underlying internalizing and externalizing dimensions to the suicidal process. Our team has complementary expertise in suicide research (Dombrovski, PI), borderline personality and experience sampling (co-investigators Hallquist and Wright, and consultant Pilkonis), decision neuroscience (Dombrovski and Hallquist), and quantitative methods including machine learning (Hallquist and Wright, with consultant Jacobson joining now). We began leading the study in 2017, boosting recruitment, and quadrupling the number of publications compared to 2012-17. Proposed innovations include a focus on clinically salient facets of interpersonal traits, integration of intensive and extended EMA with passive sensing, an investigation of dynamic decision-making under high cognitive load supported by an original computational model, and a recently developed and validated multi-level approach to fMRI analysis. Clinically, understanding the suicidogenic effects of interpersonal trait facets and elaboration of personalized models of suicide risk will advance suicide prediction and development of just-in-time interventions. Expected results will advance the field of suicide research by unifying conceptual models of the suicidal process with hierarchical dimensional models of psychopathology, identifying general vs. person-specific suicidogenic processes, and elucidating decision-making under cognitive demands representative of the suicidal crisis. In line with the NIMH’s prioritized research agenda on suicide, this work will address mechanisms of suicide risk, while investigating RDoC’s reinforcement learning and limited capacity constructs and thus bridging RDoC with hierarchical dimensional models of psychopathology.