Patient Population: Adults

A scalable psychoeducational intervention to improve sexual quality of life in women by addressing reproductive anxiety

This project aims to understand how concerns about pregnancy, whether planning for a child or trying to avoid an unintended one, affect women’s sexual well-being. Despite public health goals to reduce unintended pregnancies and STDs (Higgins et al., 2009), and the importance of sexual health to overall quality of life (Flynn et al., 2016), little research has explored the specific impact of pregnancy-related worries on sexual satisfaction and experience in women who are not currently pregnant (Bond et al., 2023a; Higgins et al., 2009). We know that sexual function issues are prevalent even among women planning pregnancy. We propose to explore pregnancy concerns and sexual quality of life among women (Bond et al., 2023b). We will then provide targeted education (depending on the responses in the survey and can differ for each individual) addressing their specific concerns and measure their sexual quality of life again to see if this information helps improve their sexual experience. The findings will highlight the sexual health needs related to pregnancy concerns and inform future educational programs.

Developing person-specific signatures of momentary risk for alcohol use

Alcohol use disorder remains a major public health concern, with persistent disparities in treatment outcomes. Traditional interventions often fail to account for the heterogeneity of drinking triggers, limiting their effectiveness. This study aims to develop and evaluate an idiographic, mobile-based clinical tool to identify personalized triggers for alcohol use. Idiographic methods allow for individualized assessments of momentary risk factors, providing tailored insights into when a person is most vulnerable to drinking.

The study will allow for customization of a given participant’s data collection process, such that individuals can track what is clinically meaningful to them (e.g. one individual may track feelings of loneliness following divorce, while another may track experiences of racial microaggressions) through diverse data collection techniques including ecological momentary assessment (EMA), audio diaries, and GPS. We will evaluate the feasibility and acceptability of this approach by pilot testing the data collection process, analyzing each participant’s data, and providing personalized feedback on the momentary conditions that influence one’s drinking.

This approach leverages current advances in mobile monitoring and precision idiographic machine learning analysis to pilot a novel clinical tool. If successful, this tool could enhance treatment equity and effectiveness by empowering individuals to recognize their unique drinking triggers.

How fentanyl changes the brain: assessing mood, cognition, and withdrawal using animal models of addiction and brain-wide neural activity markers

Fentanyl overdose is responsible for nearly 75,000 deaths each year in the U.S. and causes severe psychological, physical, financial, and social harm. Despite existing treatments, fentanyl addiction remains difficult to overcome due to the chronic and complex nature of fentanyl addiction which contributes to patterns of chronic use and high relapse rates. This is partly due to fentanyl’s ability to rewire the brain’s reward and executive cognitive system to cause lasting changes in mood and cognition while also triggering intense withdrawal symptoms that drive continued use. To better understand the widespread impact of fentanyl on the brain, this project uses mouse models of addiction to explore the effects of fentanyl on various brain regions and neural populations involved in reward, motivation, mood, and cognition. Using artificial intelligence-guided behavioral and cellular analyses, we then correlate these neural signatures to mouse behaviors during withdrawal and a cognitive working memory task. We will then test whether two promising emerging treatments, semaglutide and ketamine, can improve cognition, withdrawal symptoms, or mood in mice exposed to fentanyl. Through this, we will contribute to our understanding of how fentanyl exerts its negative effects which can inform the development of more effective therapies for its devastating impact.

Substance Use Disorder assessment tools in jails & prisons: a systematic review

Over 65% of people who are incarcerated have a substance use disorder (SUD). Many jails and prisons provide substance use treatment, including behavioral and pharmacotherapy, and SUD identification is the first step. Prisons and jails are a distinct setting for SUD assessment, and tools used for SUD screening in community settings may not perform the same way in carceral settings. This systematic review will identify psychometric evaluation studies in carceral settings of screening and diagnostic tools for SUDs, generally, or specific SUDs (excluding alcohol and nicotine). Additionally, will use and adapt an existing clinical usability scale and develop new metrics to assess acceptability of screening tool use in carceral settings for a person-centered evaluation. This will be guided by further analysis of interviews with prison staff and people with lived experience of incarceration. The additional assessments will capture potential barriers and facilitators to SUD assessment in jails and prisons as they represent resource-limited settings with unique challenges compared to other health systems. The goals for the study are: 1) to help carceral facilities make informed decisions about SUD assessment, 2) to reveal key gaps in the literature, and 3) to inform the development and testing of future tools.

Improving risk-stratification of substance-induced psychosis: incorporating stakeholder perspectives and implementing a standardized assessment of risk factors

First-episode substance-induced psychosis (SIP) presents a clinical challenge in which treatment decisions cannot rely on evidence-based guidelines and long-term outcomes are not well understood. Preliminary findings from our retrospective study of psychiatry inpatients at Harborview Medical Center (HMC) suggest that patients with first-episode SIP had similar rehospitalization rates to those with first-episode psychosis (FEP) but were less likely to receive long-acting injectable antipsychotics (LAIs) even though LAIs may reduce the risk of 30- and 180-day rehospitalization. Our study has also revealed inconsistent assessment of key risk factors for rehospitalization—such as family history of psychosis and patterns of cannabis use—that may be limiting informed decision-making, including appropriate LAI use. This project seeks to improve the risk stratification of first-episode SIP by addressing these gaps. Aim 1 will use qualitative interviews with inpatient attending psychiatrists, psychiatry residents, and patients to explore factors influencing the management of first-episode SIP. Aim 2 will evaluate the acceptability and feasibility of implementing a standardized assessment of cannabis use and family history of psychosis on HMC inpatient psychiatry units. This project will lay the groundwork for future clinical interventions that optimize treatment decisions and improve patient outcomes in psychiatric inpatient settings.

Unraveling the genetics of schizophrenia and bipolar disorder with large-language models

Schizophrenia (SCZ) and bipolar disorder (BPD) are among the most heritable psychiatric conditions, yet their genetic foundations remain poorly understood. Historically, unraveling these disorders’ genetic architectures was limited by inadequate technology. However, breakthroughs in next-generation sequencing have recently produced expansive genomic datasets—including those from the Psychiatric Genomics Consortium (PGC)—for SCZ and BD. Simultaneously, genomic foundation models—advanced large-language models (LLMs) trained on biological data rather than corpuses of text—have emerged as next-generation artificial intelligence platforms that offer unparalleled abilities to predict the functional effects of genetic variants, many of which were previously unclassified. This proposal harnesses these models to analyze publicly available PGC genomic datasets, aiming to annotate both common and rare genetic variants associated with SCZ and BD, pinpoint disease-associated genes, and map the biological pathways they influence. By bridging these recent advances in artificial intelligence with robust genomic data, this proposal seeks to illuminate the genetic underpinnings of SCZ and BPD. The anticipated insights promise to deepen our understanding of heritable psychiatric conditions, laying the groundwork for enhanced diagnostics and novel therapies aimed at biology, rather than nosology

Psychomotor function of Locus Coeruleus-Norepinephrine system during decision-making

Many psychiatric disorders involve an abnormality in movements, termed ‘psychomotor’
dysfunction, that reflects aberrant activity of the brain circuits producing behavior. Nevertheless,
psychomotor mechanisms remain poorly understood. One possible source of psychomotor dysfunction is alterations in neuromodulatory transmitters, such as norepinephrine (NE), which is broadcast throughout the brain from a small brainstem region called locus coeruleus (LC). LC-NE is implicated in psychiatric disorders including depression, PTSD, ADHD, and dementia, with behavioral neuroscience studies demonstrating roles in arousal and decision-making. LC-NE is often studied in psychiatry on the timescale of minutes to hours with a focus on NE drugs, leaving underexamined the precise temporal relationship between LC neural activity and discrete components of motivated behavior. This proposal aims to identify psychomotor functions of LC-NE in decision-making at the level of neural circuit activity in mice. We leverage powerful techniques to record LC neural activity with high spatiotemporal precision while simultaneously deploying advances in AI machine vision technology to quantify the mouse’s movements. By precisely quantifying movement patterns and LC-NE activity, we aim to characterize basic psychomotor functions related to cognition. In turn, our work will build a foundation for noninvasive, mechanistic biomarkers that enhance the diagnosis and management of psychiatric disorders.

Brain, Environment, and Alcohol Research (BEAR) Study

This project examines how brain responses to alcohol cues interact with everyday social contexts to shape drinking in young adult heavy drinkers. We pair multimodal neuroimaging (fMRI, EEG) with a 2-week ecological momentary assessment including transdermal alcohol monitoring and photo-based context capture. We test whether neural incentive salience predicts real-life intoxication, how social features (group size, familiarity, gender mix) influence drinking, and how perceived norms mediate these effects. We further assess whether incentive salience moderates context and norm influences. Findings will refine models of alcohol use disorder etiology and inform prevention and intervention strategies by linking precise brain markers with ecologically valid, context-rich assessments.