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Substance Abuse and Mental Health Services Adminis

<p>The purpose of the TOR program is to assist in addressing the overdose crisis in Tribal communities by increasing access to FDA-approved medications for the treatment of opioid use disorder (MOUD), and supporting the continuum of prevention, treatment, and recovery support services for opioid use disorder (OUD) and co-occurring substance use disorders, and stimulant misuse and use disorders, including those involving cocaine and methamphetamine.</p>

Substance Abuse and Mental Health Services Adminis

Tribal Opioid Response

Substance Abuse and Mental Health Services Adminis

The purpose of the TOR program is to assist in addressing the overdose crisis in Tribal communities by increasing access to FDA-approved medications for the treatment of opioid use disorder (MOUD), and supporting the continuum of prevention, treatment, and recovery support services for opioid use disorder (OUD) and co-occurring substance use disorders, and stimulant misuse and use disorders, including those involving cocaine and methamphetamine.

Office of Juvenile Justice and Delinquency Prevention

Grants to federally recognized tribes for juvenile justice, delinquency prevention, and mental health programs for tribal youth.

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 worldwide ($200M+/yr). Funds poverty relief, aging, mental health, education, and community strengthening in New York.

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 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: 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.

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.

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/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.

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

Utilizing Invasive Recording and Stimulating Opportunities in Humans to Advance Neural Circuitry Understanding of Mental Health Disorders (R01 Clinical Trial Optional)

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

Utilizing Invasive Recording and Stimulating Opportunities in Humans to Advance Neural Circuitry Understanding of Mental Health Disorders (R21 Clinical Trial Optional)

National Institutes of Health

Reissue of RFA-20-351.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

Reissue of RFA-20-351.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.

Veterans of Foreign Wars Foundation

Grants for community programs serving veterans and their families including emergency assistance and mental health.

NIA - National Institute on Aging

PROJECT SUMMARY Aging is characterized by the gradual loss of physiological integrity, and this process may be accelerated in the presence of obesity, increasing susceptibility to disease, frailty, and death. Although the shared molecular pathways involved have not been fully elucidated, adipose tissue dysfunction is likely a key contributor to multisystem functional decline in aging and obesity. Despite growing evidence that β3 adrenergic receptor {β3AR) mediated activation of brown adipose tissue {BAT) may alter pathophysiological pathways implicated in various aging-related diseases including metabolic, cardiovascular, and neurodegenerative diseases, BAT has been largely ignored in aging research. In this highly innovative study, we propose to conduct a randomized, double-blind, placebo-controlled trial to investigate whether treatment with a β3AR agonist (vibegron) can improve energy metabolism, cardiometabolic risk factors, and physical and cognitive function. Vibegron {Gemtesa) was FDA-approved in 2020 for the treatment of overactive bladder and has greater selectivity, potency, and activity at the β3AR than other agonists studied to date. This presents a timely opportunity to explore pharmacological activation of β3ARs as a way to improve multiple health outcomes relevant in aging and obesity. To test our hypothesis, 40 middle-aged and older adults {45-75 yrs) with obesity will be randomized to vibegron {75 mg/day) or placebo for 12 weeks to compare their effects on various bioenergetic, cardiometabolic, physical function, and cognitive outcomes. Specifically, in Aim 1 we will assess the effects of vibegron vs. placebo on energy expenditure, core body temperature, mitochondrial bioenergetics, and thermogenic protein expression. In Aim 2 we will assess the effects of vibegron vs. placebo on glucose and insulin indices, lipid levels, body composition, and body fat distribution. In Aim 3 we will assess the effects of vibegron vs. placebo on self-report and objective measures of lower extremity function, muscle strength and pOY1er, global cognition, memory, executive function, quality of life, and depression. Notable innovations include blood-based bioenergetic profiling to assess systemic mitochondrial function, isolation and characterization of adipose tissue-derived small extracellular vesicles to assess target engagement, and continuous monitoring of core body temperature to assess circadian thermoregulation. In exploratory analyses we will compare the effects of vibegron vs. placebo on the accumulation of health deficits {i.e., frailty) and the preservation of physical and mental abilities {i.e., intrinsic capacity), two integrated measures of phenotypic aging that will provide estimates of the potential for vibegron to impact multisystem functional decline. This unique study will be the first clinical trial to explore the potential to repurpose vibegron for the treatment of aging-related obesity and associated comorbidities. If successful, the results of this study will be used to inform the design of a larger, longer trial to confirm the efficacy of vibegron as a novel treatment to IOY1er risk for multisystem functional decline in both aging and obesity.