ABSTRACT: The assessment of suicidal risk is critical for treatment planning and monitoring of therapeutic progress for suicidal individuals. Current standard-of-care relies on patient self-report and clinician impression, which are not strongly predictive of imminent suicidal risk. This project advances a highly innovative approach to the assessment of suicidal risk, by using machine-learning detection of brain activation patterns that are neural signatures of individual concepts that have been altered in suicidal individuals. The overarching goal is to establish reliable neurocognitive markers of suicidal ideation (SI) and attempt (SA) in individual participants, and to assess these measures’ ability to predict future ideation and attempts. In previous work, this approach was applied to the fMRI-based neurosemantic signature (NSS’s) during the thinking about each of 30 words related to either to suicide, negative concepts, or positive concepts in 17 SI young adults and 17 healthy controls (HCs). A machine learning classifier was able to discriminate between the SI and HCs with 91% accuracy, based on differential brain activation patterns in the L superior medial frontal cortex and anterior cingulate, areas known to be involved in self-referential thinking. Within the ideators, NSS’s also discriminated between those with a history of a SA from those without such a history with 94% accuracy. Moreover, using the classification algorithm derived from this sample, we were able to accurately classify a second sample of suicidal individuals with 87% accuracy. It was also possible to assess the emotions differentially manifested during the thinking about these words, and thus to differentiate SI from HC with 85% accuracy, and SI with and without SA with 88% accuracy. On the basis of these promising pilot findings, we propose to study 300 young adult SI (about half of whom will have made a SA), 100 never-suicidal psychiatric controls, and 100 HCs, use fMRI to assess NSS at intake and 3 months, and assess for suicidal ideation and behavior at intake, 3, 6, and 9 months thereafter. The goals are to determine if: (1) NSS’s are sensitive to changes in level of suicidal ideation when repeated at 3 months; and (2) whether NSS can predict trajectories of suicidal ideation and behavior upon prospective follow-up. We will also examine the relationship between NSS activation of circuits related to self-referential thinking and the death/suicide Implicit Association Test (IAT) that examines the extent to which a person associates suicide-related concepts with self. Finally, as a translational goal, we aim to develop and test a neurally based IAT that examines associations of suicidal concepts with self and with emotions as informed by NSS findings. This study, by shedding light on alterations in suicidal individuals’ neural representation of suicide-relevant concepts could be extremely useful for: (1) identification of those with suicidal ideation who may not self-report their level of risk; (2) monitoring fluctuations in suicidal risk over time; (3) identification of emotional states associated with suicidal ideation; (4) guiding therapy to mitigate these alterations; and (5) the prediction of future suicidal ideation and behavior.

PROJECT NARRATIVE: This project takes a novel approach to the study of suicidal risk by examining the differences in brain activation patterns between suicidal and non-suicidal young adults when thinking about concepts related to suicide, negative, and positive emotions while undergoing neuroimaging. We use modern computing techniques called machine learning to identify brain activation patterns that differentiate suicidal and non-suicidal individuals and that can help to predict future suicidal ideation and behavior and will develop an easy-to-use computer test based on these brain activation studies that we hope will also be predictive of future suicidal ideation and behavior. If this project is as successful as our preliminary work, it will advance clinical practice by improving clinicians’ ability to: (1) detect and monitor suicidal risk, (2) understand alterations in thinking and feelings related to suicide in their patients; and (3) develop personalized treatment strategies for their suicidal patients based on their altered patterns of thinking and feeling that can more precisely and effectively reduce suicidal risk.