Precision Phenotyping of Behavioral Risk and Response to Electromagnetic and Psychedelic Therapies

Overview

PRE-EMPT will assemble a study group of 150 civilian and Veteran participants from three populations (low risk, intermediate risk, and high risk for self-harm). The investigators will obtain clinical assessments, MRI, and blood levels for circular RNA (circRNA). The teams will then administer three interventions (neurofeedback, transcranial magnetic stimulation, and psilocybin assisted therapy), and repeat the tests above. A team with expertise in artificial intelligence will then use our data to try to find patterns that identify who is at high risk versus low risk with a high degree of accuracy.

For U.S. Service Members, deployment and combat exposure can result in significant mental strain, with high rates of disability and suicide. Unfortunately our ability to predict and treat those at high risk of intentional self-harm is limited. PRE-EMPT (Precision Phenotyping of Behavioral Risk and Response to Electromagnetic and Psychedelic Therapies) seeks to transform the assessment and treatment of the spectrum of depression, posttraumatic stress, and self-harm. PRE-EMPT will utilize multimodal neuroimaging, blood-based biomarkers, and predictive analytics to devise highly accurate models of behavioral risk, and to characterize response to three neuroplasticity-enhancing interventions. Background: Rates of suicide have increased 37% since the year 2000 despite concerted governmental and institutional programs to address root causes such as stigma and lack of access. Suicide was the number one cause of active-duty fatality from 2014 to 2019, highlighting the vulnerability of Service Members and Veterans. Suicide is a highly multifactorial event and may be conceptualized as a state in which individuals cannot come up with any other option to endure difficult circumstances or intense feelings, representing failures of cognitive control (CC) and emotion regulation (ER). Similarly, the heritability of suicide risk is well known, and transcriptomics, or the study of transcript molecules such as RNA that regulate gene expression, has potential to reveal mechanisms of risk not fully explained by DNA analysis. Better methods of classifying suicide risk according to objective and measurable factors are needed to proactively identify persons at risk and provide interventions tailored to risk. Research Plan: PRE-EMPT will assemble a cohort of 150 civilian and Veteran participants from three populations (low risk, intermediate risk, and high risk for self-harm). The investigators will obtain baseline clinical assessments, structural and functional MRI utilizing tasks pioneered by our team to assess cognitive control (CC) and emotion regulation (ER), and peripheral circular RNA (circRNA) levels to characterize the molecular brain states associated with behavioral risk. In parallel, investigators will mine publicly available databases to identify network nodes and use deep learning techniques on multivariate patterns of brain activation, structural topography, and functional connectivity. The clinical, imaging, and transcriptomic data will be fused and jointly analyzed to increase the accuracy of risk prediction models. PRE-EMPT in three separate arms will then prospectively assess three promising and innovative interventions for their potential to reduce suicidal ideation and alter activity in key neural networks: 1) neurofeedback (NF) using real-time fMRI with simultaneous electroencephalography (EEG), 2) accelerated intermittent theta burst stimulation (aiTBS) with dose optimization through electric field modeling; and 3) psilocybin assisted therapy (PSI), with flexible dosing plan to maximize the depth of psychedelic experience. Assessments will be repeated at post-treatment and at 1, 3, and 6 months after intervention. These therapies were chosen based on our team's prior work in all three interventions demonstrating rapid action and large effects. Each clinical arm will contribute independent insights into mediators of efficacy for the specific interventions and risk groups, while pooling data to identify predictors across the risk spectrum. Specific Aim 1: To construct a neurobehavioral model from structural and functional MRI, clinical, and transcriptomic data that accurately predicts behavioral risk. Specific Aim 2: To test three potential rapid-acting therapies for suicidal ideation and identify mechanistic mediators of response.

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