Research

The OPAD Lab conducts research with three overlapping aims:

(1) To identify and understand personality (e.g., neuroticism), emotional (e.g., emotion regulation), environmental factors (e.g., life stress; trauma; childhood adversities) that influence the development, persistence, and treatment response of internalizing psychopathology.

(2) To develop clinically-usefully tools that optimally predict the onset, severity, course, and treatment response of internalizing psychopathology and associated behavioral outcomes (e.g., suicide).

(3) To improve the assessment, diagnosis, and classification of internalizing psychopathology.

Current Projects 

Neural Markers of Treatment Mechanisms and Prediction of Treatment Outcomes in Social Anxiety (R01 MH128377)

We are actively recruiting research participants for a study of neural markers of social anxiety disorder and prediction of treatment response. This project is a collaboration between BU, MIT (MPI John Gabrieli, Ph.D.), McLean Hospital (MPI Dan Dillon, Ph.D.), and experts in social anxiety disorder (Stefan Hofmann, Ph.D.). Individuals with social anxiety disorder undergo EEG and fMRI assessments before and after completing a cognitive-behavioral therapy program at the Center for Anxiety and Related Disorders. The goal of this project is to understand the neural mechanisms and predictors of treatment response. For more information about the study including how to participate, click here.

Identifying the Longitudinal Outcomes of Suicide Loss in a Population-Based Cohort (R01 MH133670)

The impact of suicide reaches well-beyond individual suicide decedents. In collaboration with researchers at BU School of Public Health (MPI Jaimie Gradus, Ph.D.) and Aarhus University Department of Clinical Epidemiology, the goal of this project is to leverage Danish national registry data to understand the impact of suicide loss (i.e., knowing someone who died by suicide) on mental and physical health. We will develop a cohort of all first-degree relatives and cohabitants exposed to suicide loss between 1994 and 2024, as well as two comparison cohorts (1) exposed to accident loss (i.e., knowing someone who died in an accident), and (2) from the general population. We will identify mental and physical health ICD-coded diagnostic outcomes that are specific to suicide loss, including patterns of comorbidity, and examine how outcomes vary by time since loss, relationship type, and sex. This approach will inform novel and more precise targets for prevention and intervention within the field of suicide postvention.

Current Collaborations

Using Machine Learning to Optimize User Engagement and Clinical Response to Digital Mental Health Interventions (R01 MH127469, PI: Todd Farchione, PhD)

In collaboration with the BU TREND UP lab and Silvercloud, the objective of this study is to develop precision treatment rules for cognitive-behavioral digital interventions for anxiety and depression, optimizing user engagement and treatment response for patients receiving digital care through large healthcare organization.

Identifying Cardiotoxic Manifestations of Posttraumatic Psychopathology: A Population-based Longitudinal Investigation (R01 HL160850, MPIs: Jen Sumner, PhD & Jaimie Gradus, PhD)

In collaboration with researchers at UCLA, BU School of Public Health, and Aarhus University Department of Clinical Epidemiology, the goal of this project is to leverage Danish national registry data to understand the impact of trauma and post-trauma psychopathology on cardiovascular disease outcomes.

Completed Projects 

Predicting Differential Treatment Response (R21 MH119492)

We are using data collected from CARD patients over a 20 year period (and machine learning methods) to develop algorithms that predict differential response to CBT monotherapy versus CBT combined with medication (e.g., SSRI/SNRI). The goal of this project is to develop “optimal treatment rules” that could be used to match patients to the treatment that is most likely to provide benefit.

Emotional Reactions to COVID-19 among Outpatients

Individuals who  participated in the Classification of Depression and Anxiety study prior to COVID-19 were re-contacted to complete a survey assessing symptoms of anxiety and depression and COVID-related stressors. The goal of this project is to determine how personality traits, pre-COVID symptoms of anxiety and depression, and COVID-related stressors are associated with symptoms of anxiety and depression experienced during the pandemic.

Anxiety and Mood Risk Algorithms (K01 MH106710)

This project used publicly available psychiatric epidemiological datasets (e.g., NCS-R, NESARC) and machine learning methods (e.g., super learning) to develop risk algorithms that predict the onset and chronic course of DSM-defined posttraumatic stress disorder, generalized anxiety disorder, major depressive disorder, and bipolar disorder. This project also involved collecting data to develop additional machine learning risk algorithms using other measures and units of analysis. Participants were recruited through Amazon Mechanical Turk (MTurk) to complete detailed surveys assessing current and past anxiety and mood symptoms and associated risk factors (e.g., personality traits, traumatic events, childhood experiences and adversities, recent stress). Several follow-up surveys occurred over a one-year period. In addition to the surveys, participants completed neuro-cognitive tasks (e.g., assessing emotion recognition, decision-making) through TestMyBrain.

Interested in volunteering as a lab research assistant? Contact us!