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.
Geographic Area: National
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.
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.
Existential distress among those with chronic pain, PTSD, and co-occurring chronic pain and PTSD
This research project focused on existential distress (death anxiety, existential isolation, true-self incongruence, inauthenticity, low self-concept clarity, and limited future time perspective) among individuals with chronic pain, PTSD, and co-occurring chronic pain and PTSD.
Evaluating the role of virtual whole health in PC-MHI
The COVID-19 pandemic facilitated simultaneous paradigm shifts in healthcare delivery: virtual care (telehealth and videoconferencing) and the need for “Whole Person” healthcare that targets mind, body, and spirit, per recent US Surgeon General1 and National Academy of Medicine2 calls-to-action. The pandemic also highlighted treatment delivery inequities involving rural Veterans. The current proposal will address these trends, assessing virtual VA Whole Health care use in Primary Care-Mental Health Integration (PC-MHI) for rural and non-rural Veterans with chronic pain and co-occurring posttraumatic stress disorder (PTSD).
Evaluation of the VHA Acute Pain Service Expansion Program implementation and impact
The objective of the project is to evaluate the implementation and impact of the Acute Pain Services Expansion Program (APSEP), an expansion of the independent and formal set of services that provide comprehensive inpatient pain management consultative services and is designed to meet perioperative care needs of veterans receiving inpatient pain management.
Using Large Language Models to identify video platform interactions indicating suicide risk
This project will identify interaction patterns with online video platforms that are indicative of suicide risk, focusing on YouTube and TikTok. Leveraging archival data including over 5 million interaction events collected from participants in previous research, we will use combinations of neural language models to identify suicide-related “like”, “search” and “watch” events. We will then assess the temporal relationships between suicide-related interaction events and suicidal ideation, behavior and mental health challenges reported by these participants. Building on these analyses, we will proceed to model patterns of interaction, differentiating between user-initiated (e.g. search) and algorithm-prompted (e.g. recommended content without a preceding search) content to characterize the ways in which intentional and algorithmically-driven behavior drive exposure to suicide-related content. In addition, we will develop a prototype of a privacy-preserving risk monitoring tool, which will detect interactions with concerning content and leverage light-touch intervention strategies to mitigate its impact.
