Randy P. Auerbach - US grants

Project Summary Major depressive disorder (MDD) is a leading cause of disability worldwide, and it is characterized by vast heterogeneity. As approximately 35-50% of adults will experience recurrent episodes, recent research has sought to parse this heterogeneity by focusing on differences among individuals experiencing single episode (sMDD) versus recurrent (recMDD) MDD. Recurrent MDD is associated with increased rates of comorbidity, greater psychosocial dysfunction, and worse treatment response; consequently, identifying mechanisms implicated in recMDD may provide novel clinical targets for early identification and treatment. The National Institute of Mental Health's Research Domain Criteria (RDoC) provides an innovative framework to investigate promising mechanisms that may underlie recMDD. Constructs within the RDoC domains of the Positive Valence System (PVS, e.g., blunted initial reward responsiveness) may be particularly important for the maintenance and recurrence of MDD. However, the majority of this research has relied on monetary as opposed to social reward (e.g., social acceptance) paradigms despite seminal research directly linking social processes to recMDD. The aims of the present study therefore focus on RDoC constructs that intersect PVS and Social Processes (e.g., affiliation) domains. Specifically, the first aim of the study is to utilize an ecologically valid peer feedback task (i.e., peer acceptance vs. rejection) to elicit event-related potentials and associated neural processes that are believed to confer risk for recurrent episodes of depression. For the second aim, an innovative NIH funded, ecological momentary assessment smartphone app (Beiwe) that measures both `active' (e.g., explicit probes of in vivo affect and stress) and `passive' (e.g., frequency of incoming/outgoing text messages and phone calls) data will be used to test whether a blunted response to social reward among recMDD adults predicts psychosocial dysfunction. For the third aim, given that interpersonal stressors are potent predictors of depression recurrence, we will test whether blunted response to social reward in recMDD individuals prospectively predicts interpersonal stressors over a 6-month follow-up period. Ultimately, testing whether social reward processing deficits prospectively predict interpersonal stress and depressive symptoms may elucidate the pathway to depression recurrence. One limitation of past research on mechanisms of recMDD is that it has largely focused on currently symptomatic individuals. These studies therefore cannot ascertain whether deficits are a function of current symptomatology or whether they also would persist into remission. Thus, the proposed study will test the three aims in remitted recMDD individuals, remitted sMDD individuals, and healthy adults. As a whole, the proposed project has the potential to improve our understanding of neurophysiological markers and social processes that contribute to depression recurrence, which may lead to key developments for early identification and preventative intervention approaches for individuals at risk for depression recurrence.

Project Summary Suicide is the second leading cause of death among adolescents. In addition to deaths, 16% of adolescents report seriously considering suicide each year, and 8% make one or more attempts. Despite these alarming statistics, little is known about factors that confer imminent risk for suicide. Thus, developing effective methods to improve short-term prediction of suicidal thoughts and behaviors (STBs) is critical. Currently, our most robust predictors of STBs are demographic or clinical indicators that have relatively weak predictive value. However, there is an emerging literature on short-term prediction of suicide risk that has identified a number of promising candidates, including rapid escalation of: (a) emotional distress, (b) social dysfunction (i.e., bullying, rejection), and (c) sleep disturbance. Yet, prior studies are limited in two critical ways. First, they rely almost entirely on self-report. Second, most studies have not focused on assessment of these risk factors using intensive longitudinal assessment techniques that are able to capture the dynamics of changes in risk states. These are fundamental limitations. While suicidal ideation may precede an attempt by years, socio-emotional changes preceding a suicide attempt often occurs within the time span of minutes to hours. This study will capitalize on recent developments in real-time monitoring methods that harness adolescents' naturalistic use of smartphone technology. Specifically, we now have the capacity to use: (a) smartphone technology to conduct intensive longitudinal assessments monitoring putative risk factors with minimal participant burden and (b) modern computational techniques to develop predictive algorithms for STBs. The project will include high-risk adolescents (n = 200) aged 13-18 years recruited from outpatient and inpatient clinics: (a) recent suicide attempters with current ideation (n = 70), (b) current suicide ideators with no attempt history (n = 70), and (c) a psychiatric control group with no STB history (n = 60). Effortless Assessment of Risk States (EARS) will be used to continuously measure variables relevant to key risk domains?emotional distress, social dysfunction, and sleep disturbance?through passive monitoring of participants' smartphone use. First, we will test between-group differences in risk factors during an initial 2-week period, and determine the extent to which risk factors derived from mobile phones improves discrimination over self-reported indicators. Second, we will use statistical techniques to test whether the risk factors improve short-term prediction of STBs (e.g., suicide attempts, hospitalization) during the 6-month follow-up period above and beyond clinical assessments. Third, computational machine learning techniques?based on a priori and learned features?will develop predictive models that utilize the full range of intensive longitudinal data collected by the active and passive monitoring methods to predict group membership and STB outcomes. Ultimately, by leveraging smartphone technology, we aim to improve the short-term STB prediction and provide clinicians and patients with reliable, scalable and actionable tools that will reduce the needless loss of life.