Identifying neural fingerprints of suicidality

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Death by suicide has been steadily increasing in the last 20 years, and the social isolation and financial stress associated with the current pandemic may unfortunately provide the perfect conditions for a dramatic increase in suicidality. This risk is elevated among veterans, particularly those with traumatic brain injury and psychiatric diagnoses, and the current public health crisis has alarming implications for mental health. Current suicide prevention practices are largely informed by the evaluation of suicidal thoughts and behaviors (STBs), and other clinical characteristics. One significant limitation in suicide risk and prevention is the exclusive reliance on self-report, which is severely limited in its effectiveness to predict future suicide attempts and deaths, and does not identify individuals who do not disclose thoughts or acts of self-harm. To test an alternative to current modes of prevention, we propose that complementary neuroimaging- based biomarkers of suicide risk can improve the identification of at-risk individuals. DESIGN AND METHODS. Our lab is a leader in the application of cognitive neuroscience tools toward precision psychiatry. We accomplish this by acquiring functional MRI, known to be consistently reproducible within an individual but subject to great variability across individuals, making it unique to the person, as well as their neuropsychiatric and neurocognitive profile. In this proposal, we will use these scans to parse out brain connections across large-scale networks including emotional and inhibitory control circuitry that are implicated in STBs. Then, by applying machine learning techniques, we will isolate the pattern of brain activity that identifies suicidal individuals. Further, we will validate these neural markers of STBs by collecting new fMRI data from veterans at risk for suicide while they perform the Suicide Implicit Association Test (S-IAT), an objective behavioral measure known to predict future suicide attempt. Finally, we will determine if these neural markers of STBs are also associated with impaired daily and social functioning, a contributor to STBs. This will be one of the first studies to leverage these methods towards the goal of identifying individuals at risk for suicide. The proposed study will accomplish these aims using both existing neuroimaging and clinical data from the Translational Research Center for TBI and Stress Disorders, as well as ongoing data collection in which 60 additional veterans will complete the S-IAT with concurrent fMRI. OBJECTIVES. Aim 1: Develop a neuroimaging-based model to detect individuals with current suicidal ideation and/or a history of suicide attempt(s). Hypothesis 1. Model will distinguish suicidal individuals from those who are not suicidal but who have comparable mental health conditions, based on functional connectivity between brain regions associated with emotional regulation and inhibitory control. Aim 2: Determine if the cross-sectional model from Aim 1 can predict which individuals will attempt suicide in the next 1-2 years. Hypothesis 2. Model will identify at least 50% of individuals who will attempt suicide from those with comparable mental health conditions who will not attempt suicide. Aim 3: Determine if the expression of the STB neural markers is associated with reduced functional outcomes. Hypothesis 3. Neural markers of STBs will be associated with reduced functional outcomes. Aim 4: Determine fMRI activation-based markers of STBs using the S-IAT. Hypothesis 4. Veterans with STBs will have higher associations between “me” and “death” alongside greater activation in brain regions associated with emotional regulation and inhibitory control. IMPACT. Successful identification of a neural signature of suicidality would remove reliance upon self- disclosure of suicidal thoughts and have dramatic clinical impact upon the precise identification and prevention of suicidality. Future studies will then focus on validation of these biomarkers in independent, prospective samples, as well as circuit-specific brain stimulation interventions targeting these biomarkers.