Mclean Hospital

NIH Research Projects · FY 2026 · 2026-06

Project Summary/Abstract Recovery from opioid use disorder occurs against a constantly and rapidly evolving backdrop of new treatments, changes in policy and public health initiatives, and emerging drug threats. These ongoing changes result in major knowledge gaps with respect to long-term treatment outcomes and recovery processes, which can cause treatment and public health efforts to fall behind. For example, major longitudinal studies of opioid use disorder occurred prior to the recent expansion of treatment options (e.g., availability of low-barrier buprenorphine; FDA approval of extended-release injectable buprenorphine) and the saturation of the drug supply with fentanyl and other potent synthetic opioids. The objective of the proposed study is to characterize the longitudinal course of opioid use disorder through the remote follow-up of a cohort of 550 adults with opioid use disorder recruited from sites across the United States. This study is innovative in its use of monthly, remote patient-reported outcomes as well as annual interviewer-administered outcomes, providing high-quality, temporally precise data that is a significant advancement over prior longitudinal studies that have relied on retrospective recall over long time frames (e.g., 1 year or more). In our prior work, participants completed more than 80% of all possible monthly assessments, supporting the feasibility of this approach. Furthermore, our study will consider not only substance use outcomes, but also domains of recovery that can provide novel insights into long-term treatment outcomes, recovery trajectories and factors influencing risk for poor functioning and sustained opioid use. Critically, this will allow for the consideration of improvements that may occur in the absence of full abstinence from opioids (i.e., non-abstinent outcomes). We will use highly rigorous and well-validated methods for longitudinal data analysis, including the management of missing data. Our study aims include: (1) Extend and expand a follow-up study of adults with opioid use disorder to characterize the long-term course of recovery, including changes in substance use and functioning; (2) Identify predictors of opioid use and recovery over time; and (3) Identify recovery profiles and candidates for non-abstinent outcomes. If successful, this project would provide answers to high-priority questions about opioid use disorder recovery and course in the current treatment and drug supply landscape, and (2) a rich, longitudinal dataset that can be used for analysis of newly emerging and time-sensitive questions about opioid use disorder to ultimate improve outcomes for people with opioid use disorder, their families and their communities.

NIH Research Projects · FY 2026 · 2026-06

1 Project Summary/Abstract 2 3 Despite evidence that buprenorphine maintenance is an effective treatment for many individuals with opioid 4 use disorder (OUD), nearly half of patients receiving buprenorphine for OUD either return to illicit opioid use or 5 prematurely discontinue treatment. However, we do not know who is unlikely to respond to buprenorphine 6 treatment or why. Filling this knowledge gap is a vital step to designing interventions that best meet the diverse 7 needs of patients receiving treatment for OUD. Individual clinical trials have provided some information about 8 patient characteristics associated with poor buprenorphine treatment response (e.g., previous use of heroin), 9 however, these individual studies are limited in two keyways. First, individual clinical trials are not powered to 10 test the association of a wide range of possibly valuable patient characteristics and treatment outcome, leading 11 to inconsistent sets of variables being tested and reported across clinical trials. Secondly, previous literature 12 has focused nearly exclusively on identifying patient characteristics that are predictive of treatment outcome at 13 the sample level and have not yet evaluated our ability to make treatment predictions at the individual level. 14 The ability to make predictions at the individual level provides an exciting opportunity to address the 15 heterogeneity of patients with OUD by informing individually adaptive interventions that are responsive to the 16 patient's specific needs and risk factors. The current study aims to harmonize data from five clinical trials on 17 buprenorphine maintenance (N=1,439) to answer these questions in an efficient manner by leveraging 18 previously conducted clinical trials. This approach will allow us to examine a wide range of possibly valuable 19 baseline patient characteristics including demographic factors, clinical history (e.g., co-occurring psychiatric 20 problems), and understudied environmental/contextual factors. Results from the current study will provide 21 invaluable information that can be used to elucidate important mechanisms that underly buprenorphine 22 treatment response at the sample level, while also evaluating our ability to identify individual patients who are 23 unlikely to be successful in treatment prior to treatment initiation. Treatment outcome will be defined in two 24 ways (1) the number of days of illicit opioid use in the last month of treatment and (2) the number of weeks 25 receiving buprenorphine doses (i.e., treatment retention). This project will support the applicant's goal of 26 illuminating important patient characteristics and mechanisms that drive treatment response or non-response. 27 To accomplish this goal, the applicant will receive training in psychosocial determinants of response to 28 medication for OUD, psychopharmacological interventions for SUD, clinical trial design and implementation, 29 and the use of advanced longitudinal statistical techniques.

NIH Research Projects · FY 2026 · 2026-05

Strides have been made in addressing the causes of opiate/opioid use but we remain in crisis mode as the rate of fentanyl- and prescription analgesic-related overdose (OD) deaths remains high. The alarming number of OD deaths despite many efforts to curb use renders this a serious public health problem. The concern is that while many treatment-seeking individuals can successfully become abstinent, relapse remains the number one challenge. Individuals are at greater risk for an overdose when they relapse and so a major gap in the field is that we are unable to predict when a slip or relapse is more likely to occur (in real time) to save lives. The past few years have witnessed an explosion in the popularity of wearable biosensors that inform individuals of many physiologic and behavioral metrics with the goal of improving overall health, sleep and fitness. Consumer Sleep Trackers are the most common and use different approaches that have promise in health and fitness applications. Despite recent advances in other areas of medicine and even in some Substance Use Disorders, there is a paucity of clinically available digital biomarker-based tools designed to target individuals who use opiates and/or opioids. Many tools are in the research and development pipeline and have potential but while wearable biosensors have been used to document opioid-related health issues (e.g., acute administration, withdrawal, and toxic overdose), none to date have been directed at predicting relapse to opiate/opioid use. The present R21 Development Project will use Machine Learning (ML) approaches to combine data from two different experiments and two different populations to develop algorithms that will integrate the unique physiological, behavioral and subjective mood state that exist when individuals begin to crave opiate/opioids, which often heralds the onset of relapse. This project will be the first to establish the foundation for this approach as it is impossible to conduct clinically relevant outpatient trials that call for relapse. GPS location services will play a critical role in developing the model, but that will need to be tested in a subsequent project. Instead, we will conduct one study in a natural setting “studio” apartment environment, which is where many individuals use opiates and opioids. Aim #1 will focus on using commercial biosensors to quantify the physiological, behavioral and subjective "fingerprint" profile of opiate/opioid cue-related craving in individuals who are diagnosed with Opiate Use Disorder. Aim #2 will be conducted in healthy individuals who will be given test doses of a variety of psychoactive drugs to provide additional data for the ML model by adjusting for possible confounds due to polydrug use even when individuals have abstained from opiate/opioid use. By combining the data from both aims into ML models, we will create a database of the key features of relapse to opiate/opioid use and use this to develop rules that will detect the early signs of relapse. Should the data prove to be robust, the next steps will be to deploy the devices and use similar analytical methods with individuals on an outpatient basis.

NIH Research Projects · FY 2026 · 2026-03

ABSTRACT Support persons of loved ones with a substance use disorder are key components of recovery capital, especially at critical points in care such as transitioning from inpatient and residential substance use programs into the community. Having positive support systems to provide emotional and instrumental support to individuals living with addiction is associated with better recovery outcomes. However, historically, substance use interventions involve support persons (e.g., family, friends) in an ancillary manner rather than being the target of intervention, and successful implementation of individualized services for support persons is limited. Yet support persons experience significant negative impacts on their personal well-being (e.g., higher rates of depression, anxiety, lower quality of life) that strain relationships with their loved ones and weaken recovery capital (e.g., poor communication, lack of empathy, and stigma of substance use disorders). Addressing support person well-being is critical to address the exponential impact of substance use especially for those who are co-experiencing the transition from the most acute points of substance use care (inpatient and residential) to the community. Without bolstering services for support persons and their loved ones experiencing addiction, they are often left not knowing what to do, increasing vulnerability to ongoing stress, which is a predictor of substance use craving and relapse. Additionally, the support person’s ability to continue to help their loved one navigate substance use recovery is compromised. Thus, meeting the needs of support persons requires evidence-based interventions that are easy to implement, scale, and sustain that do not require clinical expertise to promote recovery across the social support system, including historically difficult to engage support persons (e.g., fathers, siblings, and adult children). To extend our preliminary data on support person interventions to promote addiction recovery, the present R61/R33 research project will test a highly accessible peer-to-peer delivered telehealth support person strategy, Invitation to Change (ITC), targeting support persons of loved ones with a substance use disorder stepping down from inpatient/residential substance use care to 1) conduct a pilot study of ITC, 2) conduct a randomized controlled trial of ITC + Treatment as Usual (TAU) compared with TAU to assess changes in support person well-being, social recovery capital, and the loved one’s substance use, and 3) identify implementation planning facilitators and barriers from clinicians/administrators/payers and understudied support persons (fathers, siblings, and adult children). Although ITC has been helpful to support persons in group settings, it is unknown whether 1-on-1 ITC is efficacious for support persons of loved ones experiencing addiction at the high-risk transition from inpatient and residential programs to outpatient community care. This R61/R33 will provide critical information to develop subsequent hybrid effectiveness-implementation trials to rapidly and sustainably implement peer-led ITC in routine care to enhance recovery outcomes.

NIH Research Projects · FY 2026 · 2026-03

Project Summary/Abstract The overarching goal of this Career Development Award (K23) is to provide Dr. Robyn Ellis with targeted training and research experiences critical to becoming an independent investigator conducting mechanistic clinical trials of substance use and posttraumatic stress disorder (SUD-PTSD) treatment. This application outlines an integrated training and research plan to be carried out at McLean Hospital and Harvard Medical School that will provide Dr. Ellis with the skills needed to achieve her career goals. Under the mentorship of experts in SUD, PTSD, and endocrinology, Dr. Ellis’ training will focus on: (1) clinical trials with SUD-PTSD populations (Drs. McHugh and Hien), (2) advanced statistical analysis for mechanistic clinical trials (Dr. Fitzmaurice), (3) assessment of autonomic activity (Dr. Seligowski), and (4) the influence of sex on SUD and PTSD, including methods to assess sex hormones (Drs. McHugh, Seligowski, and Miller). Dr. Ellis will apply the skills acquired during the training period to conduct a pilot randomized controlled trial (RCT) of a brief treatment for PTSD, Written Exposure Therapy (WET), within residential SUD care among women with SUD- PTSD. Research shows that individuals with SUD-PTSD demonstrate worse clinical outcomes than those with each disorder alone, and women are disproportionately impacted by SUD-PTSD. Although, there are established mechanisms of PTSD treatment, it is unknown if treatment functions similarly among those with SUD-PTSD and there is initial evidence that co-occurring SUD may negatively influence fear learning. Further, low fluctuating estradiol has been shown to impair fear extinction, a putative mechanism of PTSD treatment, however the influence of estradiol on PTSD treatment mechanisms has yet to be tested. To address these gaps, Dr. Ellis will conduct a pilot RCT of WET in residential SUD care compared to neutral writing. Aim 1 will test the efficacy of WET for PTSD and substance use outcomes. Aim 2 will quantify the effects of WET on fear extinction, indexed using trauma-cued autonomic activity (heart rate, skin conductance, heart-rate variability), and trauma-reactive craving. Aim 3 will quantify the influence of endogenous estradiol on changes in treatment mechanisms. The central hypothesis is that women who complete WET will demonstrate greater improvement in PTSD and substance use outcomes, greater fear extinction, and greater reduction in trauma-reactive craving compared to those who complete neutral writing. We also hypothesize that women with higher versus lower estradiol pre-treatment will demonstrate greater fear extinction. This contribution will provide pilot data for an R01 proposal for a large-scale mechanistic trial of WET to test sex differences in mechanisms and outcomes, and ultimately lead to improved long-term SUD-PTSD outcomes through tailored treatment that addresses the unique needs of women with SUD-PTSD.

NIH Research Projects · FY 2026 · 2026-02

Project Summary Suicide is currently the second leading cause of death amongst adolescents. Interpersonal dysfunction is a significant risk factor for suicidal thoughts and behaviors (STBs), but it is primarily assessed through self-report methods that are inherently problematic due to subjective retrospective recall bias. By depending on self-report of interpersonal functioning, we overlook crucial information about how at-risk youth are interacting with others. To address these gaps, this K23 proposes mentorship in online social networking (OSN) as an objective, ecologically valid assessment of interpersonal behavior (i.e., texting, engagement on social media) in adolescents with STBs. The research aims of the study are to (1) establish active online interpersonal behavior mechanisms in adolescents with STBs, (2) determine passive online interpersonal behavior mechanisms in adolescents with STBs, and (3) identify mediators (e.g., psychopathology, biological sex) relevant to the relationship between OSN and STBs. Multimodal data (OSN, self/parent/clinician report) will be collected from N=84 13–17-year-olds, with the full range of STBs. One month of active (i.e., texting/posting frequency) and passive (i.e., ratio of time spent on apps to texting, inter-day app/platform switching) OSN data will be collected from adolescents’ smartphones and analyzed using mentored statistical approaches to multimodal data including structural equation modeling (SEM). The candidate proposes training in (1) use of OSN as a real- world assessment of interpersonal functioning behaviors in adolescents, (2) gaining expertise in the relationship between STBs and OSN in adolescents, and (3) gaining skills with relevant multimodal data analysis (e.g., SEM) to understand what psychosocial factors may mediate OSN behavioral mechanisms and suicide risk. A team of multi-disciplinary mentors bring expertise in adolescent developmental psychopathology, computer science, interpersonal functioning, and translational research. Combined with the relevant and diverse resources available at McLean Hospital and Harvard Medical School will ensure this candidate receives the necessary training and support to successfully complete the project and launch the candidate’s career in adolescent suicide prevention. Data will directly inform future R01s leveraging this information to prevent suicide risk in adolescents, including (1) probing diagnostic/assessment specificity, (2) using experimental therapeutics to evaluate change in detected interpersonal behavior mechanisms following established evidence-based treatments for STBs, and (3) the development of mechanism-informed just-in-time adaptive social media/mobile health interventions. Completion of the proposed research and training goals will uniquely position the candidate to become a leader in the highly relevant field of adolescent social media and suicide prevention.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY Aggression, a fundamental behavior essential for survival and species propagation, is ubiquitous across the animal kingdom. However, the neural underpinnings of innate aggression, particularly in terms of sexual dimorphism, remain inadequately elucidated. It is noteworthy that dysregulated aggression often surfaces as a symptom in various neurological and psychiatric disorders, with a pronounced prevalence in disorders more common among women, thereby underscoring a crucial gap in our scientific understanding. In response to this challenge, our research is strategically focused on the female Drosophila melanogaster to decipher the sexually dimorphic neural substrates governing aggression. Leveraging our prior breakthroughs in identifying neurons responsible for female-specific aggression, we have significantly enhanced the understanding of the mechanisms encoding sex-specific primal behaviors. In the next five years, we plan to employ the fly connectome for an extensive mapping of neural circuits, aiming to elucidate how stress influences aggression via evolutionarily conserved neuropeptides and to thoroughly investigate the developmental processes of these neural circuits. Moreover, our research will delve into how these circuits are disrupted in disease states associated with aggression dysfunction. Central to our efforts is the investigation of the modulatory effects of stress and peptides on female aggression and the assessment of the consequences of genetic modifications on key functional genes. Our systematic approach to dissecting the circuitry underpinning female aggression and identifying genes related to aberrant behavior is expected to yield profound insights into the genesis of sex differences in neural mechanisms that orchestrate social behaviors. This research program is poised to substantially advance our understanding of sex differences in brain functions related to the regulation of aggression across diverse species.

NIH Research Projects · FY 2025 · 2025-08

Although effective treatments have been developed for substance use disorders (SUDs), many people who receive treatment relapse or dropout of treatment prematurely. For many SUDs, treatment options are limited, particularly those for which there are currently no FDA-approved medications. Accordingly, there is an urgent need for new treatments for SUDs. New treatment development for SUDs has been hampered by the reliance on sustained abstinence as the sole acceptable outcome for FDA approval as well as the absence of well- validated outcome measures that assess symptoms beyond substance use alone. Critically, SUDs are not defined solely by substance use, but also by the negative consequences associated with substance use. These consequences are among the most important outcomes to patients and their families, and often motivate the decision to seek treatment. A measure of consequences would serve as a valuable endpoint for SUD trials and could represent a common metric for trials of different SUDs (e.g., opioid, stimulant) and for trials of treatment for polysubstance use. Furthermore, attempts to determine the impact of non-abstinent reductions in use (e.g., the benefits of reduction in days or quantity of use) require valid measures of functioning that are sensitive to change; a measure of consequences could fill this gap. The objective of RFA- DA-25-028 is to “support the development of Clinical Outcome Assessments for substance use disorders.” Consistent with this objective, our overarching aim is to develop and validate a novel clinical outcome assessment of substance-related consequences for use in SUD treatment trials. Study aims include to: identify item domains and refine a conceptual model of substance use consequences; develop a novel patient-reported outcome measure of substance use consequences; quantify the psychometric properties of the scale; and, determine the measure’s sensitivity to change during SUD treatment and its concordance with changes in substance use. To achieve our study aims, we will follow gold standard procedures for measure development and validation. The specific aims are aligned with benchmarks needed to ultimately seek approval from the FDA as a clinical outcome assessment that can be used to evaluate treatment efficacy. Our investigator team brings together experts in SUD treatment, clinical trials, and psychometrics to engage in a robust and rigorous scale development and validation process. Successful completion of these aims will contribute to our goal to establish a new clinical endpoint for SUD trials, with the goal of broadening the pipeline for promising new treatments to reduce the public health burden of SUDs.

NIH Research Projects · FY 2026 · 2025-07

PROJECT SUMMARY/ABSTRACT FDA-cleared repetitive transcranial magnetic stimulation (rTMS) protocols are known to increase cortical excit- ability in treatment-resistant depression, but for safety and tolerability reasons, many patients receive TMS pro- tocols designed to “quiet” overactive brain networks. Despite their common clinical and research use, we know very little of their mechanism of action, and consequently, improvements in such protocols have been limited. Inhibition induced by continuous theta burst stimulation (cTBS), perhaps the best studied inhibitory rTMS proto- col, is most likely accomplished through either reducing excitation via n-methyl-d-aspartate receptor (NMDAR)-dependent long-term depression (LTD), and/or through enhancing inhibition via gamma-aminobu- tyric acid receptors (GABARs). A knowledge of this mechanism enables us to improve clinical effectiveness, as we have done previously, with mechanism-guided pharmacologic augmentation of an “excitatory” TMS proto- col. Thus, the critical barrier to unlocking the potential of cTBS is that its synaptic mechanism remains un- known. We will test NMDAR-dependent LTD (Aim 1) and GABAR-mediated inhibition (Aim 2) in the dorsolateral pre- frontal cortex (dlPFC) of 70 depressed subjects in a 4-arm double-blind randomized crossover study. Arms in- clude sham cTBS, placebo drug, and active drug and cTBS arms. Pharmacology modulates receptors while cTBS is delivered and cortical excitability is measured with electroencephalography (EEG)-based TMS-evoked potentials (TEPs). Functional integration of synaptic changes measured by the dlPFC-dependent n-back neu- robehavioral task will be a secondary measure. This approach also overcomes the limitations of studying the healthy motor cortex which has un- known translatability to plasticity in the dlPFC and depressed brain. It also overcomes the rate-limiting step to optimizing therapeutic TMS-inhibition to quiet focal brain activity. Results will highlight the path forward to mechanism-guided pharmacologic augmentation for depression and other disorders such as epi- lepsy, addiction, anxiety, posttraumatic stress disorder, which require quieting of brain networks.

NIH Research Projects · FY 2026 · 2025-04

Project Summary/Abstract. Stress and a parental history of Major Depressive Disorder (MDD) are among the most potent risk factors for future MDD development. Evidence suggests that the offspring of parents with MDD are especially vulnerable to the development of maladaptive responses to stress. Early adolescence is a critical window for studying MDD risk, as this developmental stage is just prior to the peak period of initial MDD onsets and a time of enhanced stress sensitivity, especially among females. Despite the importance of stress in MDD onset, the neural mechanisms underlying stress responses in female adolescents at high familial risk for MDD remain unclear. Furthermore, little is known about how stress-induced brain network changes may be linked to physiological and behavioral responses to stress and be predictive of future MDD onset. In this R01 resubmission, we aim to test a model in which having a parental history of MDD increases the risk for dysfunctional dynamic default mode network (DMN) – Central Executive Network (CEN) stress responses, two networks consistently linked to MDD pathophysiology and stress responsivity, prior to the onset of depression. Specifically, we expect that adolescents with versus without a parental MDD history will spend more time and persist longer in a DMN-CEN co-activated pattern in response to stress. We further hypothesize that this maladaptive DMN-CEN pattern will predict maladaptive psychophysiological and behavioral responses seen in the adolescents’ natural environment. Together, we expect that this stress sensitive profile of dysfunctional dynamic DMN-CEN, psychophysiological, and behavioral responses will predict future depression onset. We focus on DMN-CEN dynamics given accumulating evidence showing that, relative to static network properties, brain network dynamic properties may be more robustly associated with MDD pathophysiology as well as the behavioral and psychophysiological processes impacted by stress and MDD. In the PI’s own published work, adults with MDD as well as healthy adolescents with a maternal history of MDD spent more time and persisted longer in a DMN-CEN co-activated pattern relative to healthy controls and adolescents without a parental MDD history, respectively. Moreover, longer DMN-CEN persistence prospectively predicted greater perceived stress among the high-risk adolescents. This suggests that dynamic DMN-CEN properties may be a critical premorbid MDD vulnerability marker that could aid in the identification of adolescents vulnerable to future MDD onset. Building on an extensive set of preliminary data, we will investigate changes in DMN-CEN dynamic properties from before and after an acute psychosocial stressor, along with changes in heart rate (HR), heart rate variability (HRV), skin conductance (SC), and cortisol output in a sample of 148 13–15-year-old unaffected females with (n = 74) and without (n = 74) a parental history of MDD. Wearable and smartphone technology will be used to track daily stress vulnerability markers (HR, HRV, SC, sleep disturbances, perceived stress, stress-reactive rumination) every 6 months during an 18-month follow-up.

NIH Research Projects · FY 2025 · 2025-04

Project Summary/Abstract MDMA is a prototypical entactogen, a drug class defined by its prosocial and prohedonic effects in humans and laboratory animals. Although commonly known for its illicit use as the club drug Ecstasy, recent studies have documented its significant promise in the management of post-traumatic stress disorder (PTSD) and comorbid depression in treatment-resistant patients. These compelling findings led the FDA to grant MDMA the status of a breakthrough therapy in 2017 and, consequently, it currently is in Phase 3 clinical trials. It is important to note, however, that MDMA is also associated with several undesirable effects, including neurotoxicity and abuse liability. MDMA has a complex pharmacology which might lend itself to a neuropharmacological dissection of its beneficial and unwanted effects via rigorous examination of its stereoselective constituents and active metabolites. The major goal of this project will be to employ our recently developed platform that combines reverse-translated touchscreen assays of reward learning and concurrent electrophysiological recording in rats to identify the neurochemical drivers of MDMA’s prohedonic therapeutic efficacy. Indeed, anhedonia, the loss of pleasure derived from previously rewarding activities, is a behavioral phenotype implicated in several neuropsychiatric conditions, including PTSD and depression. Despite this transdiagnostic prevalence, and notwithstanding MDMA’s clinical trial successes, there are currently no approved mediations to abate anhedonic phenotypes. To advance our understanding and inform future medications development, the proposed studies will be conducted by associating electrophysiological biomarkers and task metrics following treatment with MDMA which, we hypothesize, will enhance reward learning and EEG delta wave alterations. Next, we will evaluate key neurochemical drivers of MDMA’s prohedonic outcomes via study of its enantiomers, primary metabolite constituents, and comparators that vary in relative dopaminergic or serotonergic activity. Racemic, S(+)-, R(-)-MDMA and -MDA, and compounds that vary in relative potency for dopamine/serotonin release will be studied to identify ratios of such activity and prohedonic efficacy. Then, we will Identify circuit engagement in which the kappa-opioid receptor (KOR) system and endogenous KOR ligand, dynorphin, regulates the behavioral and electrophysiological processes of enhanced (prohedonic) reward learning, given previous work highlighting its role in modulating symptoms of depression. Finally, in vivo microdialysis studies will be used to quantify dopaminergic and serotonergic efflux following MDMA and key drug comparators, both alone and following KOR circuit engagement to inform neurochemical mechanism. Ultimately, we expect the identification of the neurobiological and neurochemical drivers of MDMA’s therapeutic efficacy will inform subsequent development of candidate medications that improve upon its therapeutic profile for psychiatric conditions in which anhedonia is prevalent.

NIH Research Projects · FY 2026 · 2025-04

Project Summary/Abstract MDMA is a prototypical entactogen, a drug class defined by its prosocial and prohedonic effects in humans and laboratory animals. Although commonly known for its illicit use as the club drug Ecstasy, recent studies have documented its significant promise in the management of post-traumatic stress disorder (PTSD) and comorbid depression in treatment-resistant patients. These compelling findings led the FDA to grant MDMA the status of a breakthrough therapy in 2017 and, consequently, it currently is in Phase 3 clinical trials. It is important to note, however, that MDMA is also associated with several undesirable effects, including neurotoxicity and abuse liability. MDMA has a complex pharmacology which might lend itself to a neuropharmacological dissection of its beneficial and unwanted effects via rigorous examination of its stereoselective constituents and active metabolites. The major goal of this project will be to employ our recently developed platform that combines reverse-translated touchscreen assays of reward learning and concurrent electrophysiological recording in rats to identify the neurochemical drivers of MDMA’s prohedonic therapeutic efficacy. Indeed, anhedonia, the loss of pleasure derived from previously rewarding activities, is a behavioral phenotype implicated in several neuropsychiatric conditions, including PTSD and depression. Despite this transdiagnostic prevalence, and notwithstanding MDMA’s clinical trial successes, there are currently no approved mediations to abate anhedonic phenotypes. To advance our understanding and inform future medications development, the proposed studies will be conducted by associating electrophysiological biomarkers and task metrics following treatment with MDMA which, we hypothesize, will enhance reward learning and EEG delta wave alterations. Next, we will evaluate key neurochemical drivers of MDMA’s prohedonic outcomes via study of its enantiomers, primary metabolite constituents, and comparators that vary in relative dopaminergic or serotonergic activity. Racemic, S(+)-, R(-)-MDMA and -MDA, and compounds that vary in relative potency for dopamine/serotonin release will be studied to identify ratios of such activity and prohedonic efficacy. Then, we will Identify circuit engagement in which the kappa-opioid receptor (KOR) system and endogenous KOR ligand, dynorphin, regulates the behavioral and electrophysiological processes of enhanced (prohedonic) reward learning, given previous work highlighting its role in modulating symptoms of depression. Finally, in vivo microdialysis studies will be used to quantify dopaminergic and serotonergic efflux following MDMA and key drug comparators, both alone and following KOR circuit engagement to inform neurochemical mechanism. Ultimately, we expect the identification of the neurobiological and neurochemical drivers of MDMA’s therapeutic efficacy will inform subsequent development of candidate medications that improve upon its therapeutic profile for psychiatric conditions in which anhedonia is prevalent.

NIH Research Projects · FY 2026 · 2025-02

Project Summary/Abstract Negative affective (NA) states (e.g., high sadness, anger, and anxiety) increase substantially during adolescence, which may heighten risk for the onset of affective disorders, in particular major depressive disorder (MDD), which surges during the adolescent years. Over the past decade, affective disturbances and MDD have been rising in adolescents, and the COVID-19 pandemic has only exacerbated this alarming trend. As a result, the Surgeon General and national pediatric organizations (American Academy of Pediatrics, American Academy of Child and Adolescent Psychiatry, and Children’s Hospital Association) recently declared a national state of emergency for youth mental health. Accordingly, there is an acute need to develop personalized data-driven approaches to predict and ultimately interrupt states of markedly high NA as they occur in the daily lives of teens. In addition to the immediate benefits of alleviating acute states of affective distress, reducing the frequency and duration of episodes of high NA may serve to reduce the risk of depression onset in youth. Relevant in this context, the majority (88%) of U.S. teens own a smartphone, which can continuously and unobtrusively measure behaviors predictive of affective disturbance, including activity levels, location, phone use, sleep, and proxies of social interaction. In addition, smartphone data may also predict risk of MDD onset. The ability to prospectively predict MDD prior to its onset would have important clinical implications for the early identification of – and targeted deployment of interventions for – at-risk youth, which is strongly aligned with the NIMH Strategic Plan. To address these gaps, adolescents ages 12-16 (the age range corresponding to the largest developmental increase in depression) will complete repeated ecological momentary assessment (EMA) surveys of NA (i.e., assessing different negative emotional states) over 30 days. During this period, smartphone sensors and a wrist-worn actigraphy band will collect data on activity levels, location, phone/screen use, calls/texts and estimates of relevant sleep variables (e.g., sleep onset, offset, and duration). The project has two aims. First, a personalized machine learning approach recently developed by the study team will test the accuracy of predicting states of high NA in the daily lives of teens from these passively derived features (Aim 1). The ability to accurately predict states of high NA at the individual level could ultimately inform the development of highly scalable and personalized smartphone-delivered interventions matched to the current affective state (e.g., high sadness vs. anger) of a given teen. Second, during a follow-up phase, participants will be contacted every 6 months to assess changes in symptoms, along with bursts of passive sensor data collection and EMA. Machine learning analyses will test whether passive data, in combination with affect dynamics, predict subject-specific risk of future depression onset with sufficiently high sensitivity and specificity to be clinically useful (Aim 2). To the extent that a data-driven approach could be developed to predict individual risk of future depression onset, it could ultimately inform the development and delivery of individualized, targeted, and timely prevention efforts.

NIH Research Projects · FY 2026 · 2025-01

Project Summary/Abstract The overarching goal of this K24 Award proposal is to enable Dr. R. Kathyn McHugh (PI/applicant) to expand her mentorship of the next generation of investigators in patient-oriented research (POR) on substance use disorders. Dr. McHugh is a clinician-scientist whose work focuses on the nature and treatment of substance use disorders, with a focus on behavioral and affective risk and maintenance factors and behavioral therapy development and testing. The proposed K24 award consists of integrated training and research plans that will provide Dr. McHugh with both (1) increased time to dedicate to mentorship of current and new mentees, and (2) expansion of her own program of POR. This expansion would both broaden the impact of the work of Dr. McHugh’s lab and facilitate enhanced mentorship of junior investigators. Target areas for mentorship will include expanding currently funded projects to provide additional opportunities for investigator training and extending mentorship to new investigators and mentorship programs. Target areas for expansion of POR include (1) extension of existing POR in substance use disorder treatment and epidemiology through secondary data analyses, submission of competing renewals for 2 current R01s, submission of a K12 Institutional Training Award, and grant submissions with new collaborators; and, (2) development of new content expertise in medication treatment research in SUDs across the translational research continuum in collaboration with experts in medication for SUDs at the preclinical (Dr. William Carlezon), human behavioral (Dr. Scott Lukas) and clinical trial level (Dr. Roger Weiss). Mentees would be included in all elements of this work as collaborators, co-authors, and co-investigators. McLean Hospital and Harvard Medical School provide an ideal environment for the attainment of these objectives, including clinical units supportive of POR, seminars and courses in designating training areas, and a highly active Clinical and Translational Science Center, with a wide array of resources for career development and training. This K24 Award would provide Dr. McHugh with the protected time to expand her mentorship in POR and to add new skills in medication trials for substance use disorders designed to complement her prior expertise and to provide an impactful extension of both her own POR and that of her mentees. These expansions of mentorship capacity and breadth of POR will advance the ultimate goal of reducing the public health burden of substance use disorders.

NIH Research Projects · FY 2026 · 2024-11

Project Summary Schizophrenia (SZ) is a severe mental disorder characterized by hallucinations, delusions, disorganized thought, impaired emotional and motivational processes, and cognitive dysfunction. Antipsychotic medications help diminish positive symptoms but do not alleviate negative symptoms or cognitive impairments. Abnormalities in energy metabolism in SZ have attracted growing interest. Mitochondrial and bioenergetic alterations may have a role in the pathophysiology of this illness, either directly or through impacting the neurotransmitter systems. Significantly altered glutamate, creatine kinase reaction flux, pH and redox ratio in the frontal cortex and breakdown of their associations with blood oxygen level dependent (BOLD) signal has been documented in psychosis using 1H/31P MRS and resting-state fMRI. The overall picture is consistent with impairment in high-efficiency oxidative phosphorylation and a compensatory shift towards glycolysis. Lactate is an important intermediate of metabolic activity under glycolysis. A few pioneering studies using 7 T 1H MRS observed substantial lactate increase in chronic SZ compared to first episode (FE) SZ and healthy controls (HC). However, the resting-state lactate level cannot fully address the cognitive impairments observed in SZ, especially in the early stages of the disease. Functional magnetic resonance spectroscopy (fMRS), which acquires multiple spectra over time during stimulation, provides a more direct measure of behaviorally relevant neural activity. It may help us better understand the underlying bioenergetic and neurotransmission abnormalities present in SZ under cognitive stress. Lactate quantification has been subject to overlapping with macromolecule signals and reduced quantification reliability. We recently developed a sequence we term HOPE (Half-intensity with macrOmolecule-suPprEssion) to suppress macromolecule signal while preserving short TE and signal-to-noise ratio. Using HOPE, lactate can be reliably measured with significantly reduced quantification variations on 3 T. In the current study, we propose to measure brain lactate dynamics in FE and chronic SZ in response to the Sternberg working memory task. This will be the first fMRS study to monitor lactate dynamically in the brains of SZ patients during a cognitive task. The HOPE sequence will be used to acquire fMRS on a clinical 3 T scanner. We hypothesize that a hyperactivation of lactate would be observed in FE SZ because the switch to glycolysis happens more than in healthy brain during activation, while the dynamic change from the elevated lactate baseline in chronic SZ may be limited by a potential ceiling of bioenergetic compensation with glycolysis. We will also quantify glutamate dynamics with the same scan and measure BOLD changes using fMRI. We will assess how they are linked to lactate dynamics, and this may reveal a change in the interplay of biological processes from earlier to later phases of illness. We will also evaluate how the fMRS/fMRI measures may potentially contribute to the impaired cognitive functions and negative symptoms.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY The onset of a first episode of psychosis (FEP) in late adolescence or early adulthood often leads to lifelong disability. Timing and precision of treatment are of the essence during this critical developmental period. Unfortunately, FEP patients who do not respond to a conventional first-line antipsychotic (FL-AP) are often delayed in transitioning to clozapine (CLZ) - or never switch at all - despite the clear superiority of CLZ to FL- APs in treatment resistant individuals. However, CLZ treatment involves risks of severe side effects, including agranulocytosis and weight gain. Currently, clinicians and patients currently have no objective, clinically validated tools to guide this complex decision making in FEP. Our collaborative group has recently published work showing that a functional brain scan can help predict which FEP patients might not respond to FL-APs, such as aripiprazole and risperidone. Further, we have shown that a simple genetics test can help predict who is less likely to gain significant weight, and, similarly, who is less likely to develop agranulocytosis. We propose to conduct a multi-center, harmonized, randomized clinical trial with the goal of testing whether the use of biomarkers can lead to better outcomes for FEP patients. The goal of the proposed study is to develop a clozapine decision support tool based on these biomarkers. First, we will characterize 410 people with an FEP using three specific biomarkers: a resting state fMRI scan from which we will derive the striatial connectivity index (SCI) and two genetics tests (one for weight gain and the other for agranulocytosis). Those patients who are predicted to not respond to FL-APs, and who also have low risk of weight gain and agranulocytosis (approximate n=180), will be randomized in a triple-blind controlled study to either clozapine or an FL-AP (either aripiprazole or risperidone) for 12 weeks of treatment. Our main outcomes relate to clinical response, including positive symptoms, suicidal thinking, and days of hospitalization. We will also perform an MRI at study end to determine whether functional patterns in the brain distinguish CLZ responders from non-responders (target engagement). Critically, we are partnering with people with lived experience of psychosis and family members to help guide us during this trial, and to inform the study design and outcomes; information and choice are amongst the strongest elements of a successful therapeutic relationship. Overall, our study will evaluate the efficacy of whether using three biomarkers at the beginning of a first psychotic episode can lead to better patient outcomes for patients at risk for poor response, by rapidly introducing CLZ rather than waiting for multiple failures of FL-APs. Our key deliverable would be a clozapine decision support tool, consisting of the three biomarkers combined with our CLZ dosing strategy for FEP. Such a tool would be a necessary step in the development of precision psychiatry; if this efficacy trial is successful, a future study would then utilize implementation science to optimize strategies for dissemination of the decision support tool.

NIH Research Projects · FY 2026 · 2024-08

PROJECT SUMMARY/ABSTRACT. A lack of outcome-focused quality measures is holding back mental health (MH) progress. This gap means that regulatory bodies and third-party payers do not have a “common denominator” that they can use to compare the impact of MH symptoms and treatment options across all MH and physical health (PH) conditions. For example, we cannot effectively compare the overall impact of treatment for depression to treatment for other MH conditions such as schizophrenia, or to PH conditions such as diabetes. Quality measurement also underpins cost-effectiveness research, and as a result we cannot accurately allocate resources needed for a nationwide MH strategy. Furthermore, clinicians and researchers cannot recommend treatments based on overall impact, but rather they are restricted to narrowly focused symptom and outcome measurements. Without addressing problems in outcome-focused quality measures, patients will continue to face a disjointed MH care system where sufficient resources are not apportioned to their needs, their clinicians cannot select treatments in a way that will maximize their overall functioning, and research to improve their care cannot consistently demonstrate comparative effectiveness. Quality of life (QOL) measures provide a promising approach to serve as a common denominator for outcome- focused quality measurement across conditions. However, current nomothetic approaches are not specific to MH symptoms, which creates measurement insensitivity and substantially reduces measurement accuracy. There are also many idiographic QOL measures that are tailored to specific disorders, but they are not directly comparable across MH or PH conditions. New QOL measurement approaches are needed that are both nomothetically comparable across disease conditions and ideographically tailored to MH phenomenology. New developments in unsupervised machine learning (ML) are well suited to address these limitations in QOL measurement. Specifically, we will use recent advances in mixture modeling to create a new personalized QOL measurement approach that simultaneously produces both nomothetic and idiographic results. The proposed project is significant and impactful because it eliminates a critical bottleneck to efforts by policy makers, researchers, and clinicians. Results from this work will allow all of these stakeholders to better discern differential impact among MH conditions and interventions. As a result, they will be able to better serve patients who experience MH difficulties. This project is also scientifically and methodologically innovative. It creatively uses new developments in unsupervised ML to implement a new measurement process while minimizing disruption to current practices. Overall, the proposed project will provide a new standard for outcome-focused measurement of MH care.

NIH Research Projects · FY 2025 · 2024-07

Project Summary/Abstract Intrusive memories of a traumatic experience are prevalent among individuals exposed to trauma and are leading predictors of the onset, maintenance, and severity of transdiagnostic post-traumatic mental health difficulties. Clinical accounts of these intrusive memories highlight their vivid, mostly visual, sensory details that emerge involuntarily to intrude on conscious thought and elicit a spontaneous reliving of the trauma in the “here and now”. Despite these sensory-rich features, extant biological models of trauma-related intrusive memories (TR- IMs) have largely overlooked the sensory cortex in favor of a prefrontal cortex-hippocampus circuit. The present proposal aims to fill this gap through a multilevel characterization of intrinsic sensory cortical disinhibition and its impacts on large-scale neural networks and the day-to-day phenomenological experience of TR-IMs. Combining magnetic resonance spectroscopy (MRS), electroencephalography (EEG), and functional magnetic resonance imaging (fMRI) in trauma-exposed adults (N = 75), this project will link lower levels of inhibitory neural activity (alpha oscillations) and an imbalance in excitatory/inhibitory neurotransmitter systems (glutamate/GABA) to perturbations in the intrinsic functional organization of resting-state neural network dynamics. Ecological momentary assessments (EMAs) of TR-IMs in this sample will further link such sensory cortical disinhibition to the vivid, sensory-perceptual properties of TR-IMs, offering a novel candidate mechanism for therapeutic interventions in sensory cortical disinhibition. An additional sample of non-trauma exposed adults (N = 25) with intrusive memories of non-traumatic negative life events will undergo the same neuroimaging and EMA protocol to further delineate the specificity of this mechanism to trauma exposure. Taken together, this project will offer the first multilevel characterization of the sensory system in survivors of trauma, spanning molecules, circuits, and ecological self-reports, and pave the way for future investigations utilizing neuromodulation to mechanistically test the proposed sensory cortical disinhibition model of trauma intrusions. Drawing on the expertise from a complementary team of mentors (Drs. Isabelle Rosso, Fei Du, and Diego Pizzagalli) and collaborators (Drs. Christian Webb, Joshua Brown, Lauren Bylsma, and Fabio Ferrarelli), the applicant will receive in-depth, hands-on training in 1) neurochemistry methods and analysis, 2) ecological momentary assessments, 3) multimodal data integration and conceptual model development, and 4) neuromodulation techniques for mechanistic and therapeutic research. The acquired data and proposed training plan will launch the applicant into an independent research career at McLean Hospital, integrating multimodal neuroimaging, ambulatory clinical assessments, and advanced neuromodulation techniques to develop novel, mechanism- based interventions for transdiagnostic post-traumatic sequelae.

NIH Research Projects · FY 2025 · 2024-06

Project Summary Schizophrenia (SZ) is a severe mental disorder characterized by hallucinations, delusions, disorganized thought, impaired emotional and motivational processes, and cognitive dysfunction. Antipsychotic medications help diminish positive symptoms but do not alleviate negative symptoms or cognitive impairments. Our understanding of the potential role of lactate in energy metabolism during brain activation has changed radically over the past three decades, shifting from waste product to supplemental fuel and signaling molecule. The lactate produced within astrocytes can also be shuttled through extracellular space into neurons as a substrate for mitochondrial oxidative metabolism. Emerging evidence suggests lactate generated by glycolysis in glial cells constitutively supports synaptic transmission and plays a key role in memory consolidation and high attentional load in cognitive tasks. It has been hypothesized that defects in bioenergetic interplay of astrocytes and neurons likely contribute to negative symptoms and impaired cognition in SZ. Unlike bipolar disorder (BD), few brain lactate studies are reported in SZ. This is beginning to change with a recent series of studies performed on 7 T. With the strong overlapping macromolecule signal and its low concentration, lactate measurement is subject to low reliability with existing techniques either on 3 T or 7 T. Lactate quantification is challenging and many scans in studies need to be excluded even with relatively loose quantification criteria (e.g. Cramer Rao Lower Bound or CRLB < 30%). We recently developed an MRS sequence which we term HOPE (Half-intensity with macrOmolecule-suPprEssion). HOPE achieves substantial improvement of lactate measurements with CRLB ~ 13% with a 10 min scan time, using a modified SPECIAL sequence with macromolecule suppression. Other metabolites such as glutamate (Glu) and glutathione (GSH) can also be measured with minimal macromolecule contamination using this approach. We propose to measure lactate in dorsolateral prefrontal cortex (DLPFC), centrum semiovale (CSO) and ventricles. With their different compositions of GM (enriched for neuronal cell bodies), WM (with more glial cells and myelin) and CSF (extracellular space), we aim to provide novel insights concerning bioenergetics in the schizophrenia (SZ) brain. These comprehensive lactate measurements will be performed in both first episode (FE) and chronic SZ for the first time, together with a series of functional and behavioral assessments. We will correlate lactate levels from DLPFC, white matter tracts and CSF with clinical assessments, especially negative subscale of PANSS (negative symptoms), Multnomah Community Ability Scale (community function) and Brief Assessment of Cognition in Schizophrenia (cognition). This design will also provide preliminary evidence on the dynamic progression of lactate abnormalities in SZ which can be interpreted in the context of other biological processes which unfold from early to later phases of illness.

NIH Research Projects · FY 2025 · 2024-05

PROJECT SUMMARY/ABSTRACT Post-traumatic stress disorder (PTSD) is a severe mental disorder that develops in some victims of trauma but not all. Notably, the mechanisms underlying the development and pathophysiology of PTSD remain unknown, and reliable diagnostic biomarkers, as well as targeted therapeutic interventions, are still lacking. Our research team has employed genome-wide association studies (GWAS) to successfully identify PTSD risk loci as part of the Psychiatric Genomic Consortium for PTSD. We have also used and developed methods to link PTSD risk loci to molecular mechanisms for experimental follow-up. This proposal seeks to fine map the latest, robust PTSD risk loci by linking multi-omic and cell type- specific molecular mechanisms in the amygdala, hippocampus and prefrontal cortex, and specific PTSD-asso- ciated phenotypes. In Aim 1, we will identify causal variants and genes in the PTSD risk loci and conduct RNA expression, DNA methylation and protein expression analyses to identify region-specific and across-region gene networks associated with PTSD that involve causal variants and genes. In Aim 2, we will identify causal variants and genes in the PTSD risk loci at the brain cell type-specific level and gene molecular alterations across cell types associated with PTSD via single-nucleus (sn) RNA-seq- and Multiome-seq. We validate prioritized variants using in vitro with SNP-sepcific CRISPR-editing of induced pluripotent stem cells (iPSC) derived neural cultures exposed to glucocorticoids. In Aim 3, we will perform a phenomic characterization of the prioritized PTSD vari- ants, genes and networks resulting from Aims 1 and 2, explore PTSD subtypes based on implicated phenotypes, and evaluate associated polygenic risk scores to characterize the clinical significance of the fine-mapping ap- proaches. This study will greatly improve our understanding of the brain multi-omic and multi-cell type mechanisms, as well as phenomically characterize the causal risk variants, genes, and networks, and will point toward novel targets for intervention in PTSD.

NIH Research Projects · FY 2025 · 2024-04

PROJECT SUMMARY/ABSTRACT Psychiatric disorders pose intricate challenges to individuals’ well-being, with their underlying molecular causes and pathophysiological mechanisms remaining elusive. Genome-wide association studies (GWAS) have provided valuable insights into the genetic associations of psychiatric disorders. However, translating these associations into specific causal driver variants and genes to unravel the underlying molecular mechanisms remains a significant challenge. Recent advancements in genetic-based imputation have enabled the inference of genetically- regulated components of trascriptomics from genome-wide genotype data. Our research team has successfully utilized brain-specific transcriptomic imputation approaches across psychiatric disorders to identify novel genes and pathways associated with risk. Here, we aim to further enhance our understanding of psychiatric disorders by performing transcriptome-wide association studies (TWAS) on circular RNAs (circRNA) transcriptomics. CircRNAs are formed through back splicing and exhibit functional importance. Their unique circular structure and enhanced stability make them intriguing candidates for studying gene expression regulation in physiological and pathological conditions. However, their involvement in psychiatric disorders is understudied and their integration with GWAS studies remains unexplored. In Aim 1, we will construct genetically regulated circRNA expression models using RNAseq ribo-depleted libraries of postmortem brain tissues, encompassing diverse ancestries and multiple brain regions. In Aim 2, we will integrate GWAS data with circRNA expression profiles using TWAS to identify disease-relevant genes and pathways influenced by circRNA dysregulation. This integrative approach will enhance our understanding of the molecular mechanisms underlying psychiatric disorders and provide valuable insights into the functional implications of circRNA dysregulation. Ultimately, this research has the potential to transform our understanding of the molecular basis of psychiatric disorders and pave the way for precision medicine strategies in psychiatric care.

NIH Research Projects · FY 2026 · 2024-03

Project Summary/Abstract Exposure therapy is the most effective treatment available for obsessive compulsive disorder (OCD), yet up to 50% of patients do not recover. Progress has stalled because we do not fully understand how or why exposure exerts its effects. Studies that test mechanistic theories of exposure (based on habituation or inhibitory learning models) have yielded mixed findings for both self-report and physiological mechanisms, with a near exclusive focus on group-level effects in tightly controlled settings. While exposure likely works differently for different people, research on which mechanisms are most important for which individuals has been absent. As a result, clinicians are left to navigate mixed findings from different theoretical models, leading to variability in how they conduct exposure and greater treatment failures among patients suffering with OCD. The primary objective of this proposal is to determine which target mechanisms are most critical to engage in real-world exposure sessions to produce treatment response. Adult participants (N = 400) with OCD receiving exposure therapy from two sites (McLean Hospital, San Diego State University) across the continuum of care (outpatient, partial hospital, residential) will complete baseline clinical and demographic measures as well as weekly symptom reports. We will measure exposure mechanisms across three levels of analysis (self-report, observer-rated behavior, physiology) during each exposure session. Mechanisms assessed will include a broad range of variables based on both habituation and inhibitory learning models of exposure. For self-report and observer-rated mechanisms, we will use the Exposure Feedback Form (EFF), created and piloted by the study team. For physiological mechanisms, we will measure skin conductance response, heart rate, and heart rate variability via a wearable device. Leveraging machine learning analyses, we will develop a predictive model of patient outcomes (OCD symptoms) that incorporates both mechanistic data and baseline demographic and clinical characteristics. At the group level, we will test the hypotheses that incorporation of mechanistic variables will improve predictive performance over baseline variables alone, and that greater habituation and inhibitory learning will predict superior clinical outcomes. At the individual level, we will test the hypotheses that specific baseline clinical and/or demographic characteristics will predict stronger habituation-outcome relationships or stronger inhibitory learning-outcome relationships. This will be the first study to predict mechanism-outcome relationships for exposure therapy based on individual characteristics. Consistent with NIMH’s Strategic Objective 3, this proposal aims to identify therapeutic targets for personalized interventions by utilizing innovative machine learning approaches with scalable methods. Future studies will translate insights gained from this project to test implementation of personalized medicine approaches to exposure therapy, with the ultimate goal of improving care for the many patients who do not remit following exposure for OCD.

NIH Research Projects · FY 2026 · 2024-03

Summary The therapeutic window for treatment of schizophrenia (SZ) with antipsychotics is limited. Excessive dosage of antipsychotics may lead to dopamine supersensitivity psychosis (DSP), a condition when dopamine D2high receptors (the active form of the D2 receptor) are upregulated in the mesolimbic system, in turn causing a need to escalate antipsychotics dose to alleviate the symptoms. To avoid DSP, it is necessary to closely monitor the changes in D2high. This can be accomplished using a specific D2high receptor agonist, visualized through positron emission tomography (PET). We have synthesized MCL-524, a highly selective D2 agonist which is characterized by sub-nanomolar binding affinity to D2high, minimal affinity to other D2-like receptors or to other dopamine or non-dopamine receptor families, good metabolic stability, and good permeation through the blood brain barrier. Preliminary data in rats indicate a good ADME profile, with high binding in the D2-rich striatum, and low binding in other brain regions and peripheral organs. This ligand must be rigorously and reproducibly characterized before it can be tested in humans. In aim 1, we will complete preclinical experiments to ensure safety and reliability of MCL-524, including broad off-target binding, ADME evaluation, and a 14-day toxicology study in rats. In aim 2, we will evaluate the test-retest variability and biodistribution of [18F]MCL-524 in rhesus monkeys to increase rigor and reproducibility for clinical studies. In aim 3, we will test in vivo selectivity of [18F]MCL-524 using specific antagonist for D2 (benperidol) and D3 (SB277011A) in PET imaging studies: first we will use micro-PET to assess binding and specificity of [18F]MCL-524 in the presence and absence of benperidol or SB277011A in control and amphetamine sensitized rats, an established model of up-regulated D2high receptors, in vivo. In a second approach, we will test [18F]MCL-524 in analogous blocking studies in non-human primates (macaque monkeys) which are physiologically closer to humans than rodents, using PET. In aim 4, we will perform studies to support an investigational New Drug (IND) application, namely GMP synthesis and formulation in the CMC (chemistry, manufacturing, and control) program to ensure a reproducible and validated method of radiolabeling and dosage administration. Antipsychotics are the primary means of illness management in SZ, and a better understanding of the etiology of DSP will allow us to avoid it in patients, improving their overall outcome. The overarching goal is to develop MCL-524 as a PET radiotracer and a predictive biomarker of dopaminergic treatment responses in SZ, to minimize the incidence of DSP. Ultimately, this tool will improve patient`s lives and reduce the socioeconomic burden associated with schizophrenia.

NIH Research Projects · FY 2026 · 2024-02

Project Summary Schizophrenia (SZ) is a devastating psychiatric disorder that brings heavy burden to health care systems. A large literature indicates that individuals with SZ show markers of early aging, such as faster decline in cognitive functioning, increased risk for dementia, and significantly shorter life expectancies. Interestingly, the increased rate of dementia in SZ cannot be fully accounted for by the established dementia risk factors or the pathology of neurodegenerative disorders, suggesting additional pathological causes of cognitive impairment and increased mortality in older SZ patients. Additionally, many SZ patients develop medical conditions such as diabetes, metabolic syndrome, and cardiovascular disease, conditions often exacerbated by side effects of antipsychotic medications. However, it is unclear whether these phenomena reflect premature aging early in life, accelerated pace of aging later, toxic effects of chronic psychosis with antipsychotic therapy, or a mixture of all three. Recent advances in the field of early psychosis including our own work suggest that an “immuno-oxidative” pathway, including NMDA receptor hypofunction/glutamatergic dysfunction, bioenergetic impairment/redox dysregulation, and neuroinflammation, form a “central hub” of brain pathology in SZ. These biochemical abnormalities result in neuronal dysfunction that eventually might disrupt the long distance and large-scale neuronal communication, thereby leading to cognitive dysfunction. To study these phenomena, we have developed novel neuroimaging approaches to measure the redox ratio (NAD+/NADH) in vivo, as well as other markers of mitochondrial function, including creatine kinase (CK)/ATPase activity and the antioxidant glutathione (GSH)—a molecule essential for cellular repair that has functional ties to NAD. Research has shed some light on these mechanisms in early phases of SZ and transition to chronic illness. However, little progress has been made in understanding their role across the lifespan in SZ, despite the fact that these abnormalities are closely linked with the diverse early aging phenomena seen in SZ. In this application, we propose to extend our previous research on the early stage of SZ to older populations to examine the trajectory of biochemical abnormalities in SZ. Our overarching hypothesis is that metabolic and redox abnormalities continue to unfold over the lifespan and are associated with additional sequelae for impaired brain function in SZ. To capture the full aging trajectory, we propose an accelerated longitudinal design where we recruit patients with SZ and age/sex-matched healthy controls (HC) between the ages of 40-70. We will collect a broad neurochemical profile as described above, but also structural, functional, and vascular neuroimaging, as well as comprehensive cognitive and clinical assessments. This rich dataset will enable us to explore dynamic mental illness trajectories. It may provide targets for future interventions to slow down abnormal aging, reduce risk of dementia, and delay mortality among people with SZ.

NIH Research Projects · FY 2026 · 2023-11

Abstract The clinical high-risk (CHR) or “prodromal” framework has provided a platform for tracking the onset of psychosis and identifying the neurodevelopmental mechanisms of risk for this disorder. It is now well established, however, that affective symptoms in CHR samples are essentially universal, persist longitudinally, and contribute to disability regardless of psychosis transition. Given the substantial neurobiological proximity of psychotic and affective disorders, the extent to which current biomarkers in CHR reflect risk for psychosis, common affective illness, or both remains largely unknown. Broadening the scope of detection to the early affective stages has potential to strengthen pathogenic models of psychosis by highlighting shared and unique vulnerability mechanisms while informing treatments that are relevant to a wider range of individuals. This study will identify shared and differential impairments of stress and reward processing among youth with psychosis-risk syndromes or early-stage depression (ESD). In a task-based fMRI design, youth with CHR, ESD, or no psychopathology will complete a probabilistic reinforcement learning paradigm before and after an acute stress manipulation. Using an established computational model we will generate estimates of theoretically relevant learning parameters, compare them across groups, and relate them to neural and hormonal data. We hypothesize that the striatum represents a shared locus of reward-based dysfunction across the early and high-risk stages of psychosis and depression, but risk-specific differences may lie within the prefrontal cortex. The results of this study will represent a significant step forward in our transdiagnostic understanding of emerging psychopathology, with implications for both generalizable and personalized intervention. By the end of the project period I will have established expertise in task-based fMRI research, independence in computational modeling of behavior, and expert knowledge of the interplay between stress, reward, and neurodevelopment across mood and psychotic disorders. Together this will provide a unique combination of clinical and methodological expertise and prepare me for my long-term goal of improving preventative intervention through longitudinal research.