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TX Veterans Commission

Grants for veteran employment services, mental health programs, and housing assistance.

UJA-Federation of New York

One of the largest local philanthropies in the world. Funds poverty relief, aging services, mental health, education, and community strengthening in New York.

NIMH - National Institute of Mental Health

Background: Depression is a serious mental disorder, with treatment selection largely relying on trial and error, often prolonging patients' suffering. The increased availability of electronic health records (EHRs) and advancements in AI offer new opportunities to address this clinical challenge. However, current EHR-based approaches have shortcomings: a. they underutilize information in unstructured data that could be important for outcome prediction and confounding adjustments; b. they lack accuracy in cohort definition and treatment response assessments; c. they omit genomic information, which is known to affect treatment response; and d. they are not aware of uncertainties arising from the fitness of assumptions required to produce reliable predictions, potentially providing misleading estimates. In addition, genetic tests currently available are limited to select genetic variations, failing to utilize information from the full genome. Research: We propose to address these limitations by crafting advanced AI models for predicting differential antidepressant treatment responses, leveraging the latest developments in natural language processing (NLP), predictive modeling, causal inference, and the inclusion of both EHR and genomic data. Aim 1 will involve developing a large language model-based, human-in-the-loop active learning framework to identify an incident-user cohort started on antidepressants for depression, assess treatment responses, and extract key depression-related information from clinical notes. Aim 2 will develop uncertainty-aware, EHR-based prediction models for differential antidepressant responses, accounting for cases where a patient-antidepressant pairing falls outside the training data and for residual confounding. Aim 3 will combine EHR and three classes of genomic predictors for response prediction: genome-wide and pathway-specific polygenic risk scores, and variations associated with cytochrome P450 enzymes. This effort will enhance our understanding of integrating EHR and genomic data to predict personalized treatment responses, paving the way for future comprehensive systems. Candidate's Career Development, Goals, and Environment: The research objectives and the candidate's career development will be facilitated by the abundant resources at Massachusetts General Hospital and Harvard Medical School, as well as formal training and mentorship in (G1) advanced clinical NLP, (G2) integration and analysis of large-scale EHR and genomic data, (G3) ‘causal machine learning’ and its uncertainty assessments, and (G4) grantsmanship, leadership, effective collaborations, and research management. The mentorship team comprises Mentor Dr. Jordan Smoller, a leader in precision psychiatry and clinical predictive analytics; Co-Mentor Dr. Tianxi Cai, an authority in bioinformatics and healthcare predictive modeling; and Consultants Dr. Timothy Miller, an expert in NLP and AI, Dr. Issa Dahabreh, a specialist in causal inference, and Dr. Tian Ge, a renowned statistician and geneticist. This award will equip the candidate with the advanced skillset to become an independent researcher in precision psychiatry.

NIMH - National Institute of Mental Health

PROJECT SUMMARY The UNDERSTAND project aims to develop and evaluate an innovative mobile health system designed to support young adults dealing with comorbid Major Depressive Disorder (MDD), Social Anxiety Disorder (SAD), Generalized Anxiety Disorder (GAD), and other anxiety-related conditions. These mental health conditions are highly prevalent, frequently co-occur, and lead to significant functional impairment in social, academic, and daily life settings. Traditional therapeutic approaches, such as Cognitive Behavioral Therapy (CBT) and Dialectical Behavior Therapy (DBT), have demonstrated efficacy in clinical settings but often fail to provide real-time, context-sensitive support during the daily interactions and triggers that exacerbate these symptoms. The overarching goal of UNDERSTAND is to address this critical gap by leveraging mobile and wearable sensors, machine learning models, and Large Language Models (LLMs) to deliver Just-In-Time Adaptive Interventions (JITAI) based on DBT principles. Grounded in transdiagnostic theory, the system targets shared mechanisms such as emotional dysregulation, cognitive distortions, and interpersonal challenges across MDD, SAD, GAD, and related disorders. It will unobtrusively monitor physiological and socio-behavioral indicators such as heart rate variability, speech patterns, and body posture, which are correlated with symptoms across these emotional disorders. In moments of heightened emotional distress, the system will deliver tailored interventions in real-time, helping individuals manage their anxiety, depressive symptoms, and other triggers during challenging situations. The project has three specific aims: (1) to develop and validate predictive models using sensor data to detect emotional distress in real-time across a range of anxiety and depressive triggers, (2) to design and implement personalized, LLM-driven interventions that provide DBT-based guidance through mobile and wearable devices, and (3) to evaluate the feasibility and user experience of the system through iterative co-design with young adults and DBT-trained clinical psychologists. If successful, this project represents a paradigm shift in mental health care by moving from clinic-based, reactive treatments to proactive, real-time interventions that can be seamlessly integrated into daily life. The system has the potential to improve mental health outcomes, reduce the long-term burden of untreated comorbid emotional disorders, and advance the field of mobile health technology by providing scalable, technology-driven solutions for managing complex mental health conditions in naturalistic environments.

NIAAA - National Institute on Alcohol Abuse and Alcoholism

PROJECT ABSTRACT For most people, alcohol use generally peaks before age 25 years and then decreases. For some, however, alcohol use increases throughout adulthood, leading to alcohol use disorder (AUD) and many adverse mental and physical health consequences. Understanding who develops persistent AUD can inform intervention models for AUD and its consequences. Antisocial behavior (ASB) and other substance use disorders (SUDs) are often comorbid with AUD and are associated with more AUD persistence and worse psychosocial functioning among those with AUD. Three key study design features make the proposed project well- positioned to advance the science of AUD and co-occurring ASB and other SUDs. First, studies of AUD, other SUDs, and ASB are often limited by the low prevalence of these behaviors. We propose a new assessment of a highly affected longitudinal sample of 559 participants now in mid-adulthood who have high rates of AUD (67%), ASB (50% arrested), and other SUDs (cannabis = 44%, amphetamine = 16%, cocaine = 16%, opioid = 8%). Second, longitudinal data are critical to understanding when alcohol use is a cause, as opposed to a consequence, of poorer functioning (e.g., psychiatric symptoms, including ASB and other SUDs). The data collected in the proposed project would comprise the fourth assessment wave of these participants (spanning adolescence and mid-adulthood), allowing us to assess the mental and physical health effects of persistent AUD and whether other SUDs and ASB exacerbate such effects. Third, alternative explanations must be considered to understand AUD’s effects fully. We propose a sibling-comparison design that controls familial confounds while testing AUD's potential causes and consequences. Specifically, this project will combine data from the proposed participants with data from their siblings, who have been assessed in parallel with our proposed participants at three prior waves and are currently being assessed for a fourth wave in another project. Leveraging these study design characteristics, we propose to examine two common theoretical models that can help understand the nature and timing of risk/protective factors for AUD and co-occurring ASB and other SUDs. The first is that ASB and other SUDs cause or worsen AUD, or vice versa (Aim 1). The second is that AUD, ASB, and other SUDs are explained by pre-existing risk/protective factors, such as early-life risk factors (e.g., stressful environment, education), personality (e.g., impulsivity), and neurocognitive functioning (Aim 2). A final strength of this project is the inclusion of individuals who are underrepresented in alcohol research, including 36% women and 31% previously incarcerated. These sample characteristics will greatly increase the generalizability of findings from the proposed work and support our ability to examine sex differences in the causes and consequences of persistent AUD (Aim 3).

NIMH - National Institute of Mental Health

PROJECT SUMMARY The Developmental Origins of Health and Disease framework has illuminated that maternal factors during pregnancy, such as exposure to elevated stress, increase children’s risk of mental health problems, via prenatal and postnatal mechanisms. Thus, there is a critical public health imperative to conduct research in this area to understand and ultimately prevent the development of psychopathology. Fetal exposure to elevated maternal inflammation during pregnancy (PMI) increases risk for child psychopathology via placental mechanisms, however, evidence from animal and human models suggests that heightened PMI may also disrupt maternal parenting behaviors. Via a phenomenon called “sickness behaviors,” high levels of inflammation can cause social withdrawal, depression-like feelings, and problems understanding social situations. Although social withdrawal and depressive tendencies may be potentially adaptive, energy- conserving responses that facilitate fighting an infection, affective and social difficulties may impair parents’ ability to recognize and respond optimally to their baby’s signals. Critically, the direct and indirect associations among PMI, parenting, and child mental health have not yet been tested in humans. In this proposal, I will fill critical training gaps in prenatal immune biology, advanced longitudinal statistical modeling, and multidisciplinary intervention research to test 3 Aims. First, I will leverage my primary mentor’s deeply-phenotyped, sociodemographically diverse longitudinal pregnancy cohort of mother-child pairs (n = 1303) to test the novel hypothesis that parenting partially accounts for positive associations between PMI and childhood mental health problems (Aims 1 and 2). Mentored training and findings will inform a pilot intervention study in which I partner with a well-established clinical research program to bridge Aims 1-2 findings with applied solutions (Aim 3). This program delivers an evidence-based intervention targeting traumatic stress exposure (Perinatal Child-Parent Psychotherapy) to pregnant Latina women. In this study, I will collect repeated measures of PMI as well as observations of parenting and infant behavior (n = 20). Preliminary findings from associations among intervention-related changes in PMI, parenting, and infant behavior will validate the mechanism tested in Aims 1 and 2 and lay the groundwork for a follow-on R-34 intervention study testing effects of prenatal psychological intervention on PMI, parenting, and child mental health. Investigating associations between PMI, parenting, and child mental health will elucidate the etiology and maintenance of child psychopathology, as well as mechanisms for targeting prenatal prevention and postnatal intervention. Addressing these potential determinants and solutions are public health and NIMH priorities. Mentored training from this K23 proposal will support my transition to an independent interdisciplinary clinical science career investigating and preventing the intergenerational transmission of stress and psychopathology.

NIMH - National Institute of Mental Health

PROJECT SUMMARY Electroconvulsive therapy (ECT) is a highly effective treatment for severe treatment-refractory depression and other conditions, in which carefully titrated electrical stimulation elicits brief, generalized seizures to change the brain to improve symptoms. An abundance of longitudinal MRI studies report robust and replicable brain plasticity after ECT, including increased hippocampal gray matter. However, it remains unclear how or why seizures are therapeutic in this context. Epilepsy research has demonstrated that seizure activity progresses through different brain regions and networks. Initial detection of seizure activity often occurs in a specific brain region (e.g., in medial temporal lobe), which can propagate locally, and in some cases spread via highly coordinated thalamo- cortical activity during generalization. Endogenous processes terminate the seizure, involving regions like anterior thalamus, basal ganglia, and cerebellum. A similar process appears to occur in ECT targeting temporal lobes, where electrical current initiates seizure activity in seizure-genic regions of medial temporal lobe (MTL), progressing to generalized seizure activity. Some seizure-network nodes have been implicated in antidepressant response to ECT, including parts of the hippocampus and thalamus. However, many seizure-network nodes are understudied in both ECT and epilepsy research in humans, because they are not included in standard MRI atlases (e.g., piriform cortex, substantia nigra, cerebellar nuclei) or due to limited spatial resolution in other neuroimaging techniques (e.g., coarse spatial resolution in molecular imaging, difficulty resolving deep structures in scalp EEG, limited number and position of pre-surgical recording electrodes in intracranial EEG). Thus, a precise, comprehensive understanding of seizure-network connectivity both in therapeutic seizure in ECT and pathological seizure in epilepsy remains elusive. The proposed studies will leverage pre-existing multi-modal MRI datasets to provide fundamental, mechanistic knowledge of entire seizure-network function before and after therapeutic and pathological seizure. The overall goal is to understand how seizure-network nodes interact in typical states and after therapeutic and pathological seizure, and to use that mechanistic knowledge to improve the administration of ECT, by using pre-treatment brain state to predict susceptibility and response to seizure therapy and by manipulating stimulus dose to influence the putative site of seizure initiation. Beyond improving the administration of ECT, the proposed studies have the potential to inform new neuromodulation strategies for depression, epilepsy, and other disorders, and to further knowledge of brain network function.

NIMH - National Institute of Mental Health

PROJECT SUMMARY Psychedelics have shown strong potential to improve multiple difficult-to-treat mental health conditions. Defining and measuring post-acute psychedelic-related adverse effects (AEs) is a necessary step toward enhancing the safety of psychedelic interventions, developing strategies to reduce likelihood of persisting AEs, and for developing treatments for persisting AEs when they occur. Persisting AEs of psychedelics, recognized in scientific literature, are poorly captured by existing assessments. This K23 will validate a measure of post- psychedelic AEs, characterize AE profiles liked with persisting impairment, and develop consensus-based strategies for prevention and treatment of post-acute psychedelic AEs. Candidate: Dr. Palitsky is a clinical psychologist with a strong background investigating determinants of differential response to interventions, acquired through doctoral training and a postdoctoral fellowship. An Assistant Professor at Emory University School of Medicine, his career aim is to lead a R01-funded program of research that enhances the safety of multicomponent psychiatric interventions including, but not limited to, psychedelics. Training: Dr. Palitsky’s career goals require additional training in psychedelic-assisted therapy development, clinical trials, biostatistics including structural equation modeling and psychometrics, and translational impact and scientific leadership to inform standards and policy. Accordingly, this K23 leverages multidisciplinary training in Emory’s departments of Psychiatry, Health Sciences, and Ethics, alongside individualized curricula using extramural resources tailored to his needs. Training includes formal coursework, individualized training with mentors, and structured career development support. Mentoring: An exceptional team of investigators committed to mentoring Dr. Palitsky include: Primary mentor Dr. Barbara Rothbaum, PhD, who has expertise in psychotherapy development and psychedelic-assisted therapies, and co-mentors with expertise in clinical trials and psychopharmacology (Dr. Boadie Dunlop, MD), biostatistics (Dr. Job Chen, PhD), and translational impact and scientific leadership (Dr. Charles Raison, MD). A significant contributor, Dr. Todd Korthuis, MD, will give expert oversight on Delphi methodology and recruitment for population health research. Research: Leveraging existing studies and established partnerships with psychedelics safety research stakeholders, this project will validate a preliminary measure post-acute psychedelic AEs initially developed by the PI (N = 940); characterize persisting AE profiles using a longitudinal study (N = 400, 6 waves in 1 year); and produce consensus on primary and secondary prevention strategies using a Delphi study (N = 25). Products include 7 planned manuscripts, a living protocol for psychedelic AE measurement, and an R34 and R01 submitted by the applicant. Environment: The candidate receives strong institutional support from the Department of Psychiatry and Behavioral Sciences. The training environment includes multiple Emory collaborative health institutes and Emory’s Mood and Anxiety Disorders Clinic, the site for the applicant’s planned R34 and R01.

NIMH - National Institute of Mental Health

Project Summary: Schizophrenia Spectrum Disorders (SSD) are severe, chronic psychiatric conditions that impair cognitive functioning and sensorimotor coordination, affecting approximately 3.5% of the population. While existing treatments primarily address positive symptoms, negative symptoms, and cognitive impairments remain largely unresponsive to current interventions. Cognitive deficits, often more debilitating than positive symptoms, serve as significant predictors of long-term disability and diminished quality of life. Consequently, there is an urgent need for new approaches targeting cognitive dysfunction in SSD. Recent studies underscore the cerebellum’s involvement in cognitive functions such as attention and memory, which are disrupted in SSD. Traditionally linked to motor control, the cerebellum also plays a crucial role in cognitive processing, emotional regulation, and social behavior. However, its precise contribution to SSD remains poorly understood. This study seeks to bridge this gap by examining the cerebellum’s role in SSD through multimodal neuroimaging techniques, including resting-state functional MRI (rsfMRI), diffusion MRI (dMRI), and multi-echo gradient (mGRE) imaging. The research will compare young adults with early-stage SSD to healthy controls, investigating how cerebellar abnormalities contribute to cognitive deficits and identifying potential biomarkers for early intervention. By focusing on early-stage SSD, this study aims to identify biomarkers associated with cerebellar dysfunction in individuals with schizophrenia, laying the groundwork for future diagnostic tools and targeted treatments. The fellowship will provide advanced training in neuroimaging, data analysis, and clinical applications under the mentorship of Dr. Mariana Lazar, equipping the researcher with the expertise needed to become an independent investigator in the field. The potential impact of this research is substantial, offering novel insights into the cerebellum’s role in early-stage SSD and paving the way for innovative treatments to enhance cognitive outcomes and overall quality of life for individuals affected by the disorder.

NIMH - National Institute of Mental Health

Project Summary Uncertainty-related cognitive dysfunctions are central to anxiety disorders, behavioral addictions, attention- deficit hyperactivity disorder, and schizophrenia, yet remain poorly understood. Anxious individuals, for example, are highly intolerant of uncertain situations, while those with gambling addictions often seek uncertainty, leading to loss-chasing behaviors. These diverse clinical presentations may stem from a fundamental computational challenge: the brain must simultaneously distinguish between two types of uncertainty—moment-to-moment stochasticity of observations and environmental volatility (how quickly underlying causes change)—that require opposite learning strategies. Previous work has focused on one factor or the other, but in reality, both volatility and stochasticity are unknown and potentially changing. Importantly, while they both increase experienced noise, they require opposite behavioral responses, making their dissociation both critical and computationally difficult and prone to systematic errors. Our recent work provides a computational framework for how the brain solves this challenge and how this process breaks down in psychiatric illness. In a large-scale neuroimaging program spanning three aims, we combine behavior, computational modeling, simultaneous fMRI-pupillometry, and causal arousal manipulation to elucidate neural mechanisms processing uncertainty while systematically manipulating both volatility and stochasticity across different outcome types. We will test specific hypotheses about the neurocomputational mechanisms of uncertainty processing (Aim 1), determine whether they are causally linked to arousal mediated by the locus coeruleus–norepinephrine system (Aim 2), and examine how these processes differ across outcome types (Aim 3), providing a mechanistic foundation for understanding uncertainty-related symptoms across psychiatric disorders.

NICHD - Eunice Kennedy Shriver National Institute of Child Health and Human Development

PROJECT SUMMARY The psychosocial functioning of youth in our society is currently the cause for widespread concern. By middle childhood, ~20% of children have behavior problems. To address growing concerns about the psychosocial functioning of our youth and consequences for society, the scientific community is interested in the earliest developmental origins of these problems. Stress during gestation and early childhood is a critical determinant of poor bio-psychosocial competence. However, the pronounced heterogeneity of outcomes among early life stress-exposed children makes it difficult to determine when, for whom, and how children are affected. Gaining such knowledge is the long-term goal of our research. This project leverages our previous R01- funded project (NICHD grants # R01HD085990, R01HD100469) that is following a cohort of 374 mother-child dyads oversampled for life stress, with data collection starting at gestation week 15 until age 6. We conducted a granular assessment of pregnancy stress (measured weekly by maternal report), as well as a frequent (every 3 month) assessment of early childhood stress with the goal of understanding critical periods when stress derails later childhood biopsychosocial functioning. The overall objective of this project is to examine when, for whom, and how prenatal and early childhood stress shape biopsychosocial development in middle childhood. Middle childhood is a developmental period in which much of the self-regulation developed in early childhood is consolidated and is highly predictive of functioning in adolescence and adulthood. Specifically, in Aim 1 we will determine when stress during the prenatal and early childhood periods and the interaction of this timing influences 5 salient domains of biopsychosocial competence in middle childhood (internalizing/ externalizing behaviors, stress reactivity, social/academic competence). In Aim 2, we will identify for whom by examining subgroup differences (i.e., sex and supportive contexts) in effects of the timing stress on middle childhood outcomes. Finally, in Aim 3, we will examine which domains of early childhood psychobiological regulation mediate the effects of early life stress. We will use advanced confirmatory data analytic methods to address these questions, as well as machine learning to follow-up our hypothesis testing by leveraging all data to identify which effects among when, for whom, and how are most salient. This project is innovative in its multimethod approach (e.g. observation, biomarkers of stress, lab tasks), its granular assessment of prenatal and early childhood stress, and the novel statistical approaches used to determine which epochs of stress are most relevant for biopsychosocial competence. This highly significant research will be the first longitudinal, prospective, multi-method study of how differential timing of early life stress influences the development of biopsychosocial competence in middle childhood. Thus, this study is critical in developing data-informed, targeted interventions for early-life stress to address the alarming mental health crisis among our youth.

NIAAA - National Institute on Alcohol Abuse and Alcoholism

Abstract The bed nucleus of the stria terminalis (BNST) plays a pivotal role in processing emotional information and is linked to key brain regions relevant to mental health and alcohol use disorders (AUD). Despite decades of foundational studies emphasizing BNST's significance in anxiety, stress, and addiction, these studies have primarily been cross-sectional, often relying on small datasets comprising only a few dozen individuals. Both negative affect (anxiety and depression) and problematic drinking typically emerge during adolescence, highlighting the importance of investigating the BNST during this stage of development. Understanding the development of BNST during adolescence has been hampered by significant time required to manually trace the BNST. To address this barrier, we will develop automatic BNST segmentation algorithm to identify BNST and acquire BNST measures in the NCANDA dataset, facilitating investigation into BNST development in adolescents and the impact of BNST changes on the emergence of anxiety and alcohol consumption. Our central hypothesis is that developmental increases in BNST volume and function will be associated with increased alcohol use and heightened negative affect during adolescence. In Aim 1, we will develop the first automatic BNST segmentation model via deep learning. Our work will address two key challenges in BNST segmentation through deep learning: the scarcity of annotated samples and the inconsistency in segmented BNSTs from diverse sources. In Aim 1-A, we plan to generate synthetic brain-segmented BNST pairs using a generative adversarial network and transfer learning for data augmentation. This method aims to closely replicate the distribution of real data, facilitating the creation of a more comprehensive training dataset for subsequent deep learning-based segmentation. In Aim 1-B, we will construct an automatic BNST segmentation model with label fusion through deep learning. The label fusion net in this model will integrate individually segmented BNSTs obtained through deep learning with multiple BNST atlases, enhancing the overall accuracy of segmentation. In Aim 2, we will investigate whether the increase of BNST volume and function can predict increases in alcohol drinking or negative affect during adolescence. Aim 2-A will examine whether the increase BNST volume and function at initial visit can predict increases in alcohol use or negative affect. Aim 2-B will measure BNST volume and function developmental trajectories and determine whether they are positively correlated with increases in alcohol use or negative affect. The study's impact lies in filling critical knowledge gaps regarding BNST development in adolescents and the impacts BNST development on alcohol use and negative affect. Insights gained could inform targeted interventions to mitigate alcohol-related risks during adolescence.

Indian Health Service

To support urban Indians in four program areas - immunization, health promotion & disease prevention, mental health, alcohol and substance abuse.

NIMH - National Institute of Mental Health

PROJECT SUMMARY Chronic exposure to stress and its hormone cortisol is associated with psychiatric disorders such as depression, anxiety, and bipolar disorder. Studies have suggested that these disorders develop, in part, due to the ability of stress and cortisol to cause epigenetic changes that in turn lead to persistent changes in the function of genes that are critical for brain function and neurodevelopment. These stress-associated epigenetic changes are important because they represent environmental risk factors that work in conjunction with genetics to precipitate psychiatric symptoms. Unfortunately, there are very few effective medications that can reverse or attenuate the epigenetic changes caused by environmental stressors. Recently, we have developed a novel tool that can potentially alleviate the effects of stress on gene dysregulation. We found that a simple fragment of modified, single-stranded (ss) DNA probe designed against specific locations in the genome can cause epigenetic changes to occur. We tested this tool on a candidate stress response gene that we have previously shown to undergo cortisol- and stress-induced epigenetic changes. Application of our ssDNA probe against a crucial regulatory region of this gene in neuronal cells caused the reversal of much of the epigenetic change brought about by cortisol exposure. In addition, we also engineered lipid-based extracellular vesicles (EVs) capable of packaging and delivering the ssDNA probe to the brain. We found that expressing brain-derived proteins on the surface of these EVs can increase their targeting efficiency to the brain. Combined, these approaches constitute a potentially powerful translational tool for treating psychiatric disorders. To apply this technology in an animal model, we propose the following two aims. In Aim 1, we will test whether epigenetic changes in the brain of stressed mice can be reversed by injecting EVs carrying the ssDNA probe. We will use EVs that we have previously demonstrated to show increased uptake efficiency in the brain. EV-injected animals will be assessed for reversal of epigenetic changes as well as other molecular changes in gene expression, protein levels, and protein function. In Aim 2, we will further engineer EVs by performing a survey of cell surface proteins of different brain regions. Brain regions such as the hippocampus, amygdala, hypothalamus, and cortex are all impacted by stress and highly relevant to psychiatric disorders. By expressing surface proteins that are specific to each brain region, we anticipate steering the uptake of EVs to any brain region of interest. Stressed animals will be tested as in Aim 1 but this time incorporating the engineered EVs. Since the ssDNA probes and EVs are easy to manufacture, a successful completion of the aims can lead to the development of a powerful therapeutic tool that can be customized to any gene and brain region of interest to reverse stress-induced epigenetic changes in the brain.

NIH

Background: How can we apply the community health worker (CHW) model to help both care partners and Veterans with serious illness in rural areas? Little is known about this approach. We will test a VA-supported intervention successfully piloted in the Durham VA and surrounding rural communities in 2021. VA’s Office of Rural Health, Caregiver Support Program and National Social Work Office are aware and support this work. Significance: Clinically, this work will help improve care for rural Veterans with serious illness by supporting care partners in their caregiving role in the community thus bolstering the care of Veterans receiving primary support from care partners in rural areas. A strength of our intervention is that it adapts and extends a successful model of individualized support commonly used outside of the VA. This approach maximizes the potential for sustainability, broad dissemination, and care delivery impact across the VA. This work will be generalizable. Strategically, this SDR proposal responds to the National Academies report recommending all health systems, including VA, develop processes to routinely identify, assess, and support needs of care partners. Our project meets rural health access, long-term care/aging, engagement science, and caregiving HSR priorities for investigator-initiated research focused on rural populations. Additionally, our proposed efforts fit squarely with the VA’s Rural Health State of the Art conclusion that we must expand VA partnerships in the community and help Veterans and their families understand their options for care and support in the community and at the VA. Innovation & Impact: This project is innovative because of its focus on social and practical needs of care partners, advances the science of community engagement in VA care and support, and situates a care partner- focused community health worker model squarely in the VA system for the first time. The entire project is guided by a Community Advisory Board (CAB) composed of social service, serious illness care, and rural care experts plus Veterans and care partners with lived experience. Specific Aims: Aim 1. Determine CHW effectiveness in reducing care partner burden, increasing Veterans' well-being, and increasing care partner-Veteran satisfaction with VA care in the intervention group compared with the usual care (CSP) group: We will apply our feasible CHW intervention to a larger sample, randomized control trial. (Hl) Care partners randomized to the intervention group will have lower mean Zarit-12 scores at 6 months compared to the control group. (H2) Care partners and Veterans randomized to the intervention group will have higher mean 1-item CAHPS Global Satisfaction scores at 6 months compared to the control group. (H3) Veterans randomized to the intervention group will have higher mean Warwick Edinburgh Mental Well- Being scores at 6 months compared to the control group. Aim 2: Following intervention, explore Veterans' and care partners' experience of care and support using subgroup semi-structured interviews in the intervention group. We then facilitate CAB Delphi Method sessions (including study Veterans, CHWs, and care partners) exploring Aims 1/2 data using equity-focused intervention mapping for wider implementation. Aim 3: Conduct budget impact analysis from the VA perspective to evaluate cost-drivers and assess feasibility to inform adaptation and implementation of the intervention within Durham VA Health Care System. Methodology: Two-arm randomized control trial using validated measures. We follow this using qualitative exploration with participants plus a Delphi method exploring implementation with the community advisory board and participants. We end with a unique business impact analysis of the intervention. Next Steps/Implementation: We are supported/advised by VA’s Office of Rural Health and Caregiver Support Program in Durham, NC with additional advisement from National Social Work Office, Chaplaincy, Palliative Care, county Veteran Services and Area Agencies on Aging (see LOS). If successful, this intervention can be added to the options available from CSP to support rural care partners and their seriously ill Veterans.

NIDA - National Institute on Drug Abuse

PROJECT SUMMARY/ABSTRACT Despite often being used legally, cannabis use is associated with several harmful outcomes (e.g., mental health problems). Moreover, cannabis use is disproportionately higher among sexual minorities (SM) relative to their heterosexual counterparts. SM who use cannabis are also more likely to develop cannabis use disorder (CUD) compared to heterosexuals. Our understanding of mechanisms that explain disparities in SM cannabis use outcomes is nascent. In line with NIDA’s funding priorities to identify mechanisms underlying differences in SM drug use outcomes, this K99/R00 application aims to evaluate mediators of associations between minority stressors, cannabis use, and use-related negative outcomes among young adult SM. The minority stress psychological mediation framework posits that minority stressors result in substance use outcomes through impaired coping, interpersonal, and cognitive processes. This model has support for SM alcohol use; however, only eight studies have tested putative mechanisms explaining how minority stressors relate to cannabis use. Among these studies, many are cross-sectional and most solely test coping motives mediation. Longitudinal research examining how minority stressors and these mechanisms relate to use and negative outcomes (e.g., CUD) is necessary to address these research gaps and advance understanding of SM health. Moreover, identifying how daily experiences relate to use will provide targets for prevention and intervention development, particularly when certain experiences (e.g., stigma) may be unavoidable. This K99/R00 study leverages ecological momentary assessment (EMA) to capture real-time measures in daily life of stressors, cannabis use, and related outcomes. Foundational work during the K99 phase will collect data to inform EMA item development (Aim 1), then conduct an EMA feasibility and acceptability study (Aim 2). Dr. Parnes’ training goals include (1) refine skills to independently design, develop, execute, and analyze EMA research, (2) build expertise in conducting substance use research with SM populations, (3) continue training in advanced statistical modeling of EMA data, and (4) promote a successful transition from postdoctoral fellow to independent faculty researcher. Dr. Parnes’ mentorship team, Drs. Miranda, Mereish, and Treloar Padovano, are experts in EMA, sexual minority research, and advanced statistical analysis. These training goals build on Dr. Parnes’ F32 (DA054718), which provided foundational training in identifying mechanisms of behavior change in adolescent cannabis treatment. Using the protocol finalized through the K00, the R00 study will conduct a 30-day EMA study among SM who use cannabis to test putative mechanisms relating daily minority stressors to use-related negative outcomes (Aim 3). Findings from the proposed study will inform whether and how SM minority stressors relate to harmful use-related outcomes. Evaluating mechanisms can also be used to identify intervention targets to complement or replace cannabis use, thus reducing liability for CUD or other negative outcomes, and reducing known health disparities in this population.

NIMH - National Institute of Mental Health

Grant Summary Neuromodulatory systems—comprising noradrenergic, serotonergic, cholinergic, and dopaminergic neurons—play a critical role in regulating mood, cognition, motor control, and physiological processes. These systems are central to understanding how the brain encodes reward, action selection, and memory, and they remain key therapeutic targets for neuropsychiatric and neurodegenerative disorders. Recent advances in high-throughput single-cell and spatial genomics have revealed a remarkable diversity among neuromodulatory cell types, with dozens to hundreds of distinct subpopulations. Despite this, tools capable of targeting these subpopulations with precision remain limited, hindering progress in both basic and translational neuroscience. This project seeks to address this gap by developing a comprehensive pipeline to nominate, validate, and disseminate cell-type-specific enhancers for neuromodulatory systems. Building upon prior successes in enhancer discovery for cortical interneurons and pyramidal neurons, we will employ advanced spatial multiomic profiling, computational prediction, and high-throughput AAV-based enhancer testing to generate a robust set of validated enhancers. Specifically, we will: 1) Nominate candidate enhancers by integrating publicly available data and performing spatial multiomic profiling on sorted cholinergic, dopaminergic, serotonergic, and noradrenergic cell types. 2) Quantitatively validate enhancer activity using cutting-edge techniques, including smFISH, Slide-Tag molecular profiling, and functional assays such as optogenetic and chemogenetic manipulation, neuronal activity monitoring, and CRISPR-based gene editing. 3) Disseminate validated tools through collaboration with the Allen Institute, Addgene, and other platforms, ensuring wide accessibility and standardization across the neuroscience community. The proposed research will produce at least 60 highly specific and validated enhancers targeting distinct neuromodulatory subpopulations, each characterized for their activity, specificity, and functional applications. These tools will be invaluable for studying neuromodulatory circuits in health and disease and for advancing therapeutic interventions targeting these systems. By providing the neuroscience community with these transformative tools, this project will facilitate unprecedented insights into the molecular and cellular underpinnings of brain function and dysfunction, addressing pressing challenges in the fields of psychiatry and neurology. The successful completion of this work will not only enable basic scientific discoveries but also lay the groundwork for the development of precision therapies for neuropsychiatric and neurodegenerative diseases.

NIMH - National Institute of Mental Health

PROJECT SUMMARY Major Depressive Disorder (MDD) and Generalized Anxiety Disorder (GAD) affect approximately 300 million people annually worldwide, with profound public health and economic consequences—lifetime prevalence in the U.S. approaches 1 in 3, healthcare costs exceed $40 billion per year, and existing treatments provide only modest relief. These disorders are characterized by persistent and recurrent negative emotional states, particularly during significant, personally meaningful events. However, most research in human affective neuroscience has relied on artificial affective stimuli that fail to evoke the intensity and relevance needed to capture real-world emotional dynamics, and moreover, prior studies have struggled to disentangle anticipatory processes (e.g., the emotional buildup before an event) from reactive processes (e.g., the emotional response to the event), especially within naturalistic conditions. This limitation has hindered progress in understanding the distinct yet interrelated mechanisms underlying emotion dysregulation in GAD and MDD. This study, building upon R21MH125311 (Heller, PI), addresses these critical gaps by leveraging a highly goal- relevant, emotionally impactful real-world event: undergraduate students receiving grades on challenging Chemistry ‘weed-out’ exams. Using fMRI to scan 144 participants (72 with GAD/MDD, 72 controls) across five sessions (four exam-related, one baseline), we will precisely delineate anticipatory and reactive neural processes over time. Advanced Hidden Markov Models will be applied to identify and differentiate negative affective brain states during anticipation, reaction, and recovery, providing insight into how these states emerge and persist. Preliminary findings suggest that hippocampal activity patterns may drive the recurrence of these states, offering novel clues to the neural circuit dynamics underlying emotion dysregulation in GAD and MDD. By using real-world, goal-relevant stimuli and cutting-edge computational tools, this will project uniquely disentangle anticipatory and reactive processes, capturing the full neural dynamics of naturalistic emotion. These insights into emotional brain states will inform the development of novel, brain-based interventions, directly targeting the mechanisms of emotional dysregulation and offering a path forward for improving mental health treatments.

NIMH - National Institute of Mental Health

PROJECT SUMMARY Disordered eating is a critical public health issue in the United States, due to the alarmingly high prevalence, and myriad of negative physical and psychosocial consequences. Adolescence is a critical developmental period for both the prevention and treatment of disordered eating. Many physical and social changes that occur in adolescence increase disordered eating risk, and early treatment intervention is imperative for treatment prognosis. However, social and environmental differences lead to high prevalence and worse clinical outcomes for disordered eating among rural adolescents. Rural adolescents face rates of disordered eating that are approximately double compared to nationally representative samples. Alarmingly high rates of disordered eating among rural adolescents are likely because they face unique social and structural influences that both increase the likelihood of developing disordered eating but also leads to lower likelihood of treatment. Factors that have been shown to be associated with increased risk of disordered eating in other populations are elevated in rural adolescents but also may be more iatrogenic in rural communities. For example, food insecurity is more common in rural populations, but harm may be further compounded by lack of access to healthful foods in rural communities. Additionally, rural adolescents may be more likely to delay or never receive care because of reduced treatment access and social stigma around mental health that is particularly pervasive in rural communities. It is also likely that factors such as social connectedness and body functionality appreciation may be uniquely protective for disordered eating in the rural context. However, there has never been a study designed to examine disordered eating risk factors, protective factors, or treatment obstacles specifically within rural adolescent populations. The proposed study would fill a critical gap in identifying the most salient risk factors, protective factors, and treatment obstacles as well as population-identified solutions within the context of rural adolescents. The study findings can be used to develop culturally relevant prevention and treatment interventions to reduce disordered eating rates among rural adolescents. We will use a concurrent triangulation mixed-methods approach including a cross-sectional survey (N=1,000) among rural high school students that will be analyzed using random forest algorithms, a machine learning technique, and semi-structured interviews (N=40) among rural high school students experiencing disordered eating, who both have and have not received treatment, to achieve the following specific aims: AIM 1: Determine the most salient risk/protective factors that predict disordered eating among rural emerging adults. AIM 2: Identify the most salient obstacles to disordered eating treatment that exist in rural communities and identify population- identified solutions to treatment obstacles.

NIMH - National Institute of Mental Health

PROJECT SUMMARY/ABSTRACT Despite advances in collecting large-scale behavioral datasets, our ability to gain insights into an individual’s learning and decision-making processes remains limited. This is particularly true for characterizing individual dif- ferences in task performance, or how behavior in psychological tasks relates to psychiatric symptoms. Progress towards this ambitious goal depends on computational models that formalize the relationships between behavioral observations, the underlying latent cognitive processes, and individual differences in behavior. Unfortunately, ex- isting modeling approaches are either too simple to handle the highly variable nature of behavior, or too complex to yield interpretable insights into the cognitive processes of interest. An approach combining flexibility and inter- pretability could transform our understanding of healthy decision-making and psychiatric conditions. This proposal addresses this critical need by developing a novel computational framework to model an individual’s learning and decision-making processes in a flexible and interpretable manner. The proposal focuses on reward learning due to its critical role in healthy and dysfunctional decision-making, as well as its prevalence in psychology. Critically, our approach captures behavioral idiosyncrasies in individual subjects, instead of focusing on group averages. To achieve this specificity without undue sacrifices in interpretability, our framework relies on two techniques: very small recurrent neural networks (RNNs) trained to imitate an individual’s behavior, and dynamical systems theory to interpret how the RNN converts observations into decisions. Our prior research shows these tiny RNNs predict individual choices more accurately than classical models while revealing complex, previously unobserved learning strategies. Preliminary analyses suggest this approach discovers relationships in strategy use across tasks and identifies distinct patterns of decision-making based on clinical diagnosis. The proposed work has two primary aims. First, we will validate the stability of individual differences across multiple decision-making tasks by relating subject-specific strategies across tasks. Second, we will relate cognitive processes to psychiatric symp- toms by examining how strategies vary with symptom severity. We will also predict psychiatric symptoms based on individual differences in strategies derived from the fitted RNN models. Both analyses will use a large dataset (N = 815) currently under acquisition in the research lab of co-investigator Dr. Catherine A. Hartley, which in- cludes data from three decision-making tasks and an array of psychiatric symptom assessments. Our approach is a novel integration of data-driven and theory-driven approaches for computational psychiatry, offering a frame- work that can benefit from large datasets while still providing theoretical insights. This ability to generate cognitive theories from data alone could accelerate the study of individual cognitive differences, and particularly benefit the study of mental health. Ultimately, this could lead to more precise diagnostic tools and targeted interventions for psychiatric conditions by providing deeper insights into the cognitive mechanisms underlying decision-making.

NIMH - National Institute of Mental Health

ABSTRACT HIV remains a major public health challenge in Kenya, with untreated depression complicating HIV care and impacting treatment adherence and viral suppression. Despite Kenya's success in meeting international HIV control targets to date, diagnosis and treatment of depression among people living with HIV (PLH) remains insufficiently addressed. Given the well-established adverse impacts of depression on HIV care outcomes, improving depression care is critically important to maintaining Kenya’s laudable progress in addressing their HIV epidemic. The 2022 Kenyan HIV Prevention and Treatment guidelines recommend at least annual use of the Patient Health Questionnaire-9 (PHQ-9) for depression screening in HIV care settings, but only 52% of PLH in care had been screened in the last year according to national administrative data. Our long-term goal is to improve rates of equitable screening, referral, and treatment for depression among PLH in Kenya, thereby improving HIV care outcomes. The objective of this R36 application is to leverage implementation science approaches to evaluate the current utilization of PHQ-9 screening in HIV clinics in Kenya. This project aims to evaluate the utilization of PHQ-9 screening in Kenyan HIV clinics using the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework. This study will pursue three specific aims: (1) Estimate the prevalence of depression among PLH in Kenya and assess variations across key populations, clinic types, and geographic regions to inform targeted interventions. (2) Analyze the reach, effectiveness, and maintenance of PHQ-9 screening in government-funded HIV centers using electronic health record data, including evaluation of equity in screening based on demographic characteristics. (3) Conduct a qualitative evaluation of PHQ-9 implementation in three purposively selected HIV clinics in Kisumu, Kenya, exploring barriers and facilitators through in-depth interviews with purposively selected care providers. For aims 1 and 2, we will conduct quantitative analyses of nationally representative HIV program data. For aim 3, we will conduct qualitative, in-depth interviews with healthcare providers and staff at three selected HIV clinics in Kisumu, Kenya. This proposed research is highly significant because it aims to improve mental health integration into HIV care, enhance screening practices, and guide policy and program improvements, thereby advancing both mental health and HIV care outcomes in Kenya and potentially other African settings.

NIMH - National Institute of Mental Health

Project Summary Current methods for generating brain organoids are limited in their ability to mimic the spatial and structural complexity of the brain. Most organoid models lack reproducibility in cell-type patterning and tissue architecture, and they fail to capture the full cellular diversity seen in the brain. Brain organoids are typically generated by protocols that isotropically applying exogenous morphogens in the media, but they often produce oversimplified, poorly organized architectures and lack multi-regional interactions. A major limitation of these protocols is the lack of spatial control over morphogen signaling, which is a key driver of morphogenesis during natural development. Thus, there remains a critical need for scalable strategies that enable fine spatial and temporal control of morphogen signals to drive coordinated multi-axis and multi-regional patterning in brain organoids. We recently developed platform of engineered synthetic organizer cells – spatially self-assembling cells that produce morphogens from spatially defined positions. Here, we propose to use synthetic organizers to induce multi-axis patterning in 3D brain organoids. Our long-term goal of this exploratory project is to build platforms that produce reproducible, spatially organized brain tissues with greater complexity and functional relevance. Our specific aims are: Aim1: Construct synthetic organizers to control brain organoid axis formation. Engineer synthetic organizer cells (from cell lines) that produce morphogens involved in brain A–P and D–V differentiation (WNT3A, DKK1, FGF, CER, NOGGIN, BMP4 and SHH), providing a core toolkit. Aim2: Create synthetic gradients to shape A–P axis of brain organoids. Using tunable organizers at opposing poles that produce A–P morphogens (e.g. DKK1-Wnt), we can produce embryoids that cover selective ranges of the A–P axis body plan, including embryoids that primarily encompass the brain. We will optimize these organizer-driven brain organoids and use IHC, RNA scope and scRNA-seq to analyze the structures for neural subtypes and formation of key subregions (hind, mid and forebrain, as well as cortical layers). Aim3: Integrative multi-axis patterning in brain organoids. We will combine multiple synthetic organizers, arranged orthogonally to each other, to establish intersecting A–P and D–V axes. We will systematically and independently tune the following morphogen production organizers: WNT3A (posterior), DKK1/CER1/FGF8 (anterior), BMP4 (dorsal) and SHH (ventral). We will assess whether the resulting brain domains exhibit spatially organized A–P and D–V subregions and connections between subregions. Our central hypothesis is that synthetic organizers (programmable cellular morphogen sources) can be used to precisely and reproducible drive symmetry breaking and yield more complex and functional neural tissues. This work may provide a path toward more structured, reproducible, and physiologically relevant brain organoids, with applications in developmental biology, neuroscience, disease modeling, and neural engineering

National Institutes of Health

The purpose of this Notice of Funding Opportunity (NOFO) is to encourage applications to pursue invasive neural recording studies focused on mental health-relevant questions. Invasive neural recordings provide an unparalleled window into the human brain to explore the neural circuitry and neural dynamics underlying complex moods, emotions, cognitive functions, and behaviors with high spatial and temporal resolution. Additionally, the ability to stimulate, via the same electrodes, allows for direct causal tests by modulating network dynamics. This funding opportunity aims to target a gap in the scientific knowledge of neural circuit function related to mental health disorders. Researchers should target specific questions suited to invasive recording modalities that have high translational potential. Development of new technologies and therapies are outside the scope of this NOFO.

National Institutes of Health

The purpose of this Notice of Funding Opportunity (NOFO) is to encourage applications to pursue invasive neural recording studies focused on mental health-relevant questions. Invasive neural recordings provide an unparalleled window into the human brain to explore the neural circuitry and neural dynamics underlying complex moods, emotions, cognitive functions, and behaviors with high spatial and temporal resolution. Additionally, the ability to stimulate, via the same electrodes, allows for direct causal tests by modulating network dynamics. This funding opportunity aims to target a gap in the scientific knowledge of neural circuit function related to mental health disorders. Researchers should target specific questions suited to invasive recording modalities that have high translational potential. Development of new technologies and therapies are outside the scope of this NOFO.