Select publications related to current research priorities

Predictive Modeling to Facilitate Targeted Intervention Strategies

This line of research applies machine learning methods to develop models that predict suicide, PTSD, and other adverse psychiatric outcomes at critical intervention points. By identifying individuals at high-risk for these outcomes, such models may facilitate development and testing of targeted prevention or early intervention strategies.

Papini et al. (2024) JAMA Psychiatry

suicide, electronic health records, machine learning

Papini et al. (2023) Translational Psychiatry

PTSD,  electronic health records, machine learning

Papini et al. (2023) JAMA Network Open

PTSD, military, machine learning

Papini et al. (2023) JAMA Psychiatry

suicide, electronic health records, machine learning

Development of Interventions that Target Transdiagnostic Mechanisms

This line of research applies a translational approach to understand transdiagnostic mechanisms of anxiety, stress, and addiction, and leverages technology to intervene on these mechanisms. Virtual reality and mobile health technology is used to target mechanisms more precisely with the aim of enhancing treatment effects while increasing the efficiency and accessibility of interventions.

Young, Papini et al. (2024) Neuropsychopharmacology

smoking, craving, Pavlovian extinction, virtual reality, randomized controlled design

Papini et al. (2023) Cognitive & Behavioral Practice

anxiety sensitivity, mHealth, randomized controlled design

Papini et al. (2022) Journal of Behavior Therapy and Experimental Psychiatry

Pavlovian mechanisms, experimental psychopathology

Papini et al. (2017) Journal of Abnormal Psychology

Pavlovian mechanisms, experimental psychopathology

Integrating Genomic Data to Estimate Risk for Adverse Psychiatric Outcomes

This line of research seeks to enhance the clinical potential of polygenic risk scores from large GWAS studies by applying statistical methods that incorporate complex mixtures of exposures and outcomes.

Stein et al. (2024) Journal of Affective Disorders

suicide, military, polygenic risk

Campbell-Sills et al. (2023) Neuropsychopharmacology

suicide, military, polygenic risk

Campbell-Sills, Papini et al. (2023) Psychological Medicine

PTSD, military, polygenic risk

Rigorous Methods for Causal Inference

When it comes to making causal inference, we use rigorous methods including randomized controlled design (including large cluster-randomized multi-site trials) and doubly-robust statistical approaches that leverage machine learning to estimate intervention effects with observational data. We strive to maximize open science practices including pre-registration and data sharing policies that protect participant privacy.

Smits et al. (2024) Psychiatric Clinics

exposure therapy, multisite cluster-randomized design

Zaizar et al. (2023) Psychological Medicine

exposure therapy, brain stimulation, randomized design

Papini et al. (2022) Drug and Alcohol Dependence

brief intervention, alcohol, causal machine learning

Papini et al. (2020) Contemporary Clinical Trials

smoking, craving, Pavlovian extinction, virtual reality, randomized controlled design

Complete list of publications