New York University

NSF Awards · FY 2024 · 2024-06

As climate changes rapidly, many cities and communities are facing disaster events at unprecedented scales. The lack of prior data poses significant challenges in learning from and building urban simulation models that forecast both short- and long-term socio-economic impacts. More specifically, human behavior data critical for urban planning and disaster preparedness is often not accessible, interoperable, or re-usable. To fill the data gap, this project supports research on foundational computational methods to improve forecasting impacts by leveraging advances in language models, with results intending to enhance disaster preparedness and response strategies. Universality and heterogeneity of post-disaster mobility behavior are examined from over 150 disaster events in the US. This approach contributes to the need for innovation beyond traditional data sources to enhance disaster resilience and climate change adaptation. Research completed in association with this project looks to develop a data governance mechanism - a data commons platform - that offers inclusive access to and transparent re-usage of synthetic human behavior data under various disaster scenarios. This platform facilitates collaboration between policymakers, researchers, and citizen groups as well as convergent activities across scientific disciplines, including urban science, infrastructure engineering, economics, public health, and social sciences. The policy relevance lies in its potential to transform disaster preparedness and response strategies through informed, data-driven decision-making leveraging non-traditional data. Furthermore, the public-facing platform enables stakeholders to test various mitigation approaches, fostering a culture of proactive planning and actions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-06

In an increasingly connected world, the security and privacy of Internet-of-Things (IoT) devices --commonly known as "smart devices"-- in our homes have never been more crucial. This project aims to develop a global smart home testbed to enable empirical research in this field. By utilizing actual smart home devices and networks, this infrastructure will provide a unique environment where researchers can study IoT security and privacy under real-world conditions, as the infrastructure will be supported by actual smart home users who volunteer to share their network data. The planning effort supported through this award focuses on (1) engaging the research community to discuss their research needs, (2) iteratively test-driving various prototypes, and (3) gathering community feedback to improve the prototypes. The objective is to position the infrastructure to satisfy the diverse needs of the research community. By moving research out of the lab and into actual homes, this project captures a wide array of devices and human behaviors that are difficult to observe in controlled environments. It will democratize access to high-quality research infrastructure, enabling institutions that lack the resources to conduct sophisticated studies on their own. This open approach not only enhances our understanding of IoT security and privacy risks but also fosters a community of interdisciplinary researchers equipped to tackle these issues. Ultimately, the insights gained from this testbed could lead to more secure and privacy-respecting smart home technologies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-06

Many countries have introduced policies and programs to advance cervical cancer prevention, but burden remains high. This project aims to (A) collect primary (qualitative and quantitative) and secondary (quantitative) data about cervical cancer prevention at multiple levels; and use these data in (B) multi-level modeling to identify what is most strongly associated with cervical cancer prevention, and (C) agent-based models to explore increasing population-level coverage of recommended cervical cancer prevention tools. Kenya and Malawi were selected for this study due to their high cervical cancer burden, and different approaches to implementing their national cervical cancer prevention and control programs. To our knowledge, this would be the first large-scale study on this topic. The project will leverage research partnerships between NYU and highly experienced research partners.

NIH Research Projects · FY 2026 · 2024-05

ABSTRACT Recent advances in high-resolution volumetric imaging and single-cell RNA sequencing have enabled the characterization of neuronal diversity and the genetic programs that specify identity. Meanwhile, our understanding of the diversity of synapses and their genetic underpinnings remains limited. Decoding the genetic programs responsible for the formation and maintenance of neural architectures can help us understand the functional role of synapses in the brain and offer entry points towards designing genetic targets for the treatment of mental health disorders related to brain connectivity. Based on the evidence of conservation of neural architectures in a wide range of neural systems and strong preliminary results in C. elegans, I hypothesize that synaptic connectivity is genetically encoded. Specifically, I hypothesize that complimentary gene combinations specify pre-synaptic neurons and their post-synaptic neural partners (resembling a "key-and-lock" combination). Single-cell RNA sequencing and single-cell resolution connectivity datasets make this hypothesis testable. I will test this hypothesis in two parallel aims using the computational Network Differential Gene Expression (nDGE) tool I have pioneered. This technique integrates single-cell resolution gene expression data with single-cell resolution connectivity to assign statistical significance to combinatorial genetic patterns enriched in synaptically connected neurons. Across two aims, I will investigate the transcriptional encoding of the structural and functional connectome of C. elegans (Aim 1) and the micro-connectivity of pyramidal cells and interneurons in the CA1 region of the rodent hippocampus (Aim 2). To accomplish these aims, I will build additional computational tools to extract a functional connectome in C. elegans (Aim 1b) and harmonize spatial transcriptomic data with functional calcium imaging data in the rodent hippocampus (Aim 2a). Together, these aims will provide two substantial entry points towards elucidating the genetic programming of neural architectures across multiple animal nervous systems. Additionally, these aims will generate valuable computational tools for the benefit of the molecular and systems neuroscience community as a whole. The multiple animal approach will ensure the robustness and biological validity of the computational models and tools that I will introduce to the neuroscience community. During the K99 phase of this award, occurring within Columbia's vibrant neuroscience community, I will be mentored by Dr. Liam Paninski, Dr. Oliver Hobert, and Dr. Attila Losonczy while consulting with Dr. Larry Abbott, and Dr. Ashok Litwin-Kumar. These professors represent diverse expertise in computational, molecular, and systems-level neuroscience in C. elegans and rodent models. They will guide me to hone my computational skills further and provide needed training in molecular and circuit neurobiology during my transition to becoming an independent computational investigator at the interface of molecular and systems neuroscience.

NIH Research Projects · FY 2025 · 2024-04

Project Summary Unmeasured confounding is a major source of bias in causal inference for drug-related public policy evaluation, and a sensitivity analysis is typically needed to examine how sensitive a related causal conclusion is to unmeasured confounding. Existing sensitivity analysis methods are underdeveloped in drug-related policy evaluation and can severely harm the evidence for (or against) causal claims. For example, in matched observational studies, one of the most widely used causal inference methods in policy research, existing sensitivity analysis methods typically focus on the case when the treatment is binary or there is a single outcome; also, they often ignore possible subgroup-specific effects. However, many drug-related policy measures are non-binary (e.g., ordinal or continuous), such as alcohol or tobacco tax rates, minimum legal purchase ages, alcohol policy scores, tobacco control index, and mobility scores. Also, drug-related policies are often evaluated using several outcomes, either those related to different types of drug use, or those related to different aspects of society such as health, justice, and economics. Finally, due to existing disparities in drug-related outcomes, there is an intense focus on accurately measuring the effects of drug-related policies among subgroups defined by race and socioeconomic status. The broad objective of this project is to develop a universal sensitivity analysis framework for unmeasured confounding in matched observational studies that can work with binary or non-binary treatments, single or multiple outcomes, and overall or subgroup-specific effects. There are three specific aims. Aim 1 will develop a universal sensitivity analysis framework for matched observational studies with general (binary or non-binary) treatments. Aim 2 will further develop the sensitivity analysis for multiple outcomes and subgroup-specific effects. Aim 3 will illustrate the proposed sensitivity analysis by studying the causal influences of mobility policies, such as social distancing policies and transportation policies where the measures (e.g., mobility scores) are continuous in nature, on drug-related outcomes (e.g., drug overdose deaths, tobacco use, and excessive drinking). We will evaluate the effect on the overall population and those among different racial groups. Aim 3 will also develop a publicly available and user-friendly R package to implement our universal sensitivity analysis framework.

NIH Research Projects · FY 2026 · 2024-04

Project Summary The regulation of body temperature, thermoregulation, is a fundamental homeostatic process in warm-blooded organisms. Brown adipose tissue (BAT) plays an important role in the control of body temperature by generating heat in cold environments. BAT is functionally distinct from white adipose tissue (WAT) which is the primary site of energy storage. Once activated by cold, BAT dissipates the chemical energy as heat in a process called adaptive thermogenesis. Activating and expanding the thermogenic adipose tissue are attractive ways to increase energy expenditure and offer promising strategies to combat obesity and cardiometabolic diseases. Notably, studies in humans and rodents show that the biological significance of thermogenic adipose tissue extends far beyond enhancing energy expenditure. Due to their high metabolic activity, thermogenic adipocytes act as a metabolic sink to improve glucose and lipid metabolism and thus exhibit anti-diabetic and lipid-lowering effects. The major challenge in targeting BAT as an anti-obesity therapy is the limited amount of active BAT in most adult humans. Although using chronic cold exposure as a preventive or treatment strategy is not feasible, understanding the mechanisms of cold adaptation presents a unique opportunity to exploit these pathways and develop strategies to increase BAT thermogenesis and improve systemic metabolism. Using single-cell transcriptomic analysis of BAT and analyzing the cell-type-specific transcriptional changes in BAT from mice housed at different temperatures, we have recently revealed that thermogenic adaptation involves the multi- layered and coordinated remodeling of all adipose resident cells to enhance thermogenesis. Through extensive analysis of ligand-receptor communications, we have constructed the intricate network of intercellular crosstalk within the thermogenic adipose niche. Within this framework, we identified the axon guidance ligand Slit3 as an essential regulator of BAT thermogenesis. Our functional studies demonstrated the role of Slit3 in the regulation of angiogenesis and sympathetic innervation, firmly establishing Slit3 as a key player in the control of BAT thermogenesis. However, the mechanisms by which Slit3 signaling influences these processes remain unknown. Building on our recent discovery and exciting preliminary data, we propose a series of innovative strategies to determine the contribution of Slit3 proteolytic processing to its function in BAT (Aim 1), identify the molecular mechanisms of Slit3 signaling in endothelial cells and sympathetic neurons (Aim 2), and address the potential of Slit3 to ameliorate the detrimental effects of diet-induced obesity by promoting angiogenesis and preserving the sympathetic innervation in BAT and WAT (Aim 3). Successful completion of the proposed studies will provide a mechanistic understanding of an entirely new pathway that regulates adipose tissue function and will have an important and sustained impact on the field.

NIH Research Projects · FY 2026 · 2024-04

Social networks (SNs), and the supports derived from them, are established determinants of health and mortality for the general population and are especially important for individuals with serious mental illnesses (SMIs; e.g., schizophrenia, bipolar, depression). Individuals with SMIs die approximately 10-30 years before their peers without SMIs, in part due to a lack of necessary supports and resources. These inequalities are even more profound for Black and Latina/o individuals with SMIs, whose networks are known to be limited by smaller SNs or SNs with fewer resources to support them over time. Most prior research focused on how social-cognitive deficits due to SMIs can erode SNs and lead to social isolation. However, there is growing evidence that non-clinical (e.g., rejection behaviors from community members) and systemic factors (e.g., incarceration, housing) are equally powerful predictors of SNs. It is critical to understand how risk and protective factors affect trajectories of SN size, composition, function, and experiences because these ultimately influence mental health services outcomes (e.g., mental health services engagement). The proposed longitudinal, mixed-method study (N=600) comprehensively investigates the SNs of Black and Latino/a individuals with SMIs. It assesses how their SNs change over an 18-month period due to clinical (i.e., social cognition, self-efficacy, social motivation, and substance use); non-clinical (i.e., rejection behaviors from community members); and systemic (i.e., incarceration and hospitalization) risk and protective factors. We hypothesize that systemic factors will have stronger associations (as compared to clinical or non-clinical factors) with SN elements and consequently, mental health services outcomes both at baseline and over time. The proposed study builds on the PIs’ previously developed, stakeholder-defined theoretical SN framework encompassing SN structure (size, compositional stability); function (social support, social resources); and derived experiences (sense of belonging, social isolation). We use an innovative ego-centric SN mapping methodology to conceptualize and measure SN elements at 3 time points (baseline, 9, and 18 months) and test if these relationships are moderated by and ethnicity over time. In addition to structured assessments and SN maps, the study utilizes in-depth semi-structured interviews with Black and Latino/a individuals with SMIs (N=50; 25 per group) at baseline and 18 months. To be conducted in collaboration with community partners and key stakeholders, these interviews will help further demystify the relationships between different risk and protective factors and SN elements. We will compare and contrast the qualitative findings with the quantitative results to provide critical mechanistic information about intervention targets to enhance SNs and increase mental health service engagement and satisfaction among Black and Latino/a individuals with SMIs. These individuals are highly vulnerable to SN deterioration, have an increased burden of illness, and remain overwhelmingly under-represented in SN research.

NIH Research Projects · FY 2026 · 2024-04

Project Summary The neuromodulator dopamine is implicated in most neuropsychiatric disorders, and also several movement disorders. A wealth of data has implicated dopamine release in the striatum -the input structure of the basal ganglia- in both learning and motor control. Therefore, a major outstanding question for the field is understanding how dopamine can satisfy these dual functions within the same brain area, using the same neural machinery. For the most part, previous studies have either focused on movement or learning in separate experiments. The proposed work has two main goals: (1) to relate dopamine release in the striatum to both learning and motor control on single trials in the same animal, at task events occurring at distinct timepoints; (2) to test the hypothesis that the neuromodulator acetylcholine dynamically gates whether dopamine in the striatum is used for learning or moving on a moment-by-moment basis. This proposal will use a novel behavioral paradigm in rats; critically, the task involves several sequential events on single trials that differentially elicit movements, or convey information about offered rewards, enabling dissociation of motor and reward-related dynamics at distinct timepoints. High-throughput behavioral training will generate dozens of trained subjects for experiments in parallel, accelerating the rate of research progress. We will use optical dopamine sensors to measure release in the striatum at distinct timepoints, testing the hypothesis that dopamine promotes learning versus moving at distinct timepoints (Aim 1). We will also perform electrophysiological recordings in the striatum to identify neural correlates of learning (i.e., neural plasticity) at certain timepoints but not others (Aim 1). We will next use optogenetics to manipulate dopamine release at distinct timepoints, and evaluate the effects on learning versus moving (Aim 2). Finally, previous experiments suggest that another neuromodulator in the striatum, acetylcholine, might influence the effect of dopamine on neurons. We will use optical methods to measure and manipulate acetylcholine at specific trial events and evaluate effects on trial-by-trial learning and movement (Aim 3). These experiments will test the hypothesis that acetylcholine gates whether striatal dopamine is used for learning or moving. This will address a major outstanding question in the field: how can dopamine support multiple distinct functions via the same circuit elements? Neuromodulatory systems including dopamine and acetylcholine are implicated in myriad neuropsychiatric disorders including schizophrenia and depression. A greater understanding of the circuit mechanisms by which they coordinate different aspects of behavior holds promise for revealing novel therapeutic targets for these disorders.

NIH Research Projects · FY 2026 · 2024-04

SUMMARY My lab has pioneered studies defining how cells initiate and maintain a quiescent state in the face of nutrient deprivation and quantifying the role of copy number variation in rapid adaptive evolution. In this proposal, we have outlined two central areas of research that extend this work. The first project is aimed at investigating how protein expression programs and cellular biology is remodeled in quiescent cells. Cell quiescence is the dominant state of all cells, but the most poorly understood. Our prior research has defined the genetic requirements for quiescence and quantified genetic interactions with evolutionarily conserved signaling pathways that are required for quiescence. In our proposed research we will study the dynamics of gene expression remodeling in quiescent cells at the level of protein expression using metabolic labeling and mass spectrometry. We will define the role of key signaling pathways by studying defects in gene expression remodeling in strains mutant for TORC1, PKA, AMPK, and PHO85. To study remodeling of the cellular environment in quiescence we will use genetically encoded multimeric nanoparticles (GEMs) and live cell imaging to quantify changes in cellular crowding in quiescent cells. We will also study mutants in vacuole biogenesis to test the role of this organelle in reorganizing the cellular environment in quiescence. We will define genetic factors that contribute to drug tolerance in quiescent cells and leverage this information to test approaches to increasing antifungal drug effectiveness in quiescent cells in the fungal pathogens Candida albicans and Candida glabrata. Our second project is aimed at understanding the dynamics of copy number variation in evolving populations and their functional consequences. Copy number variants (CNVs) are a prevalent source of genetic variation in humans, in which they underlie both heritable diseases and pathogenic somatic variation. CNVs underlie phenotypic variation in a range of organisms and often confer resistance to therapeutic treatment in pathogenic microbes. Our studies have demonstrated that CNVs are frequent drivers of rapid adaptive evolution during microbial experimental evolution in chemostat cultures. We have developed a combined CNV reporter and lineage tracking system that enables accurate quantification of CNV dynamics in efficient methods for multiplexed mutant analysis. Using a combination of these approaches, we will study the dynamics of CNV-mediated adaptive evolution in fluctuating environments. We will then test the fitness effects of CNVs in conditions in which they have been selected, in which we expect them to confer a fitness benefit, and a diversity of additional conditions, in which we expect them to be neutral or deleterious. By incorporating known features of CNVs, we will build a quantitative model of their fitness costs and benefits. To investigate the functional effects of CNVs we will quantify their gene expression consequences at the level of mRNA abundance and ribosome occupancy and test their effect on gene expression heterogeneity using single cell RNA sequencing.

NIH Research Projects · FY 2026 · 2024-04

Obesity has been a persistent public health issue for decades. People with obesity experience higher risk of chronic diseases, such as diabetes, chronic kidney disease, hypertension, coronary heart disease, and stroke. Approximately 42% of adults in the United States currently have obesity. Yet, we still have an inadequate understanding of the etiology of obesity and the upstream drivers that contribute to its development and persistence. Exogenous stress, operating at both interpersonal and residential levels, has been implicated as a potential obesogenic factor. However, the relationship between multiple forms of exogenous stress and adiposity remains understudied. Moreover, the cellular and molecular processes through which such exposures affect physiological function and contribute to excess weight gain are not well understood. This study aims to define the relationship between multiple forms of exogenous stress and adiposity, as well as downstream markers of inflammation, in order to inform prevention and treatment strategies that can reduce obesity across the population. Using data from three large, population-based cohort studies—the National Longitudinal Study of Adolescent to Adult Health, Midlife in the United States, and Health and Retirement Study—we will first assess the associations between residential- and interpersonal-level stress and adiposity indicators (i.e., body mass index and waist circumference). We will then examine whether these exposures are associated with alterations in leukocyte gene expression profiles related to inflammatory pathways, identify relevant cellular and molecular mechanisms, and test whether gene expression mediates the relationship between exogenous stress and adiposity. Finally, we will evaluate the moderating effect of network engagement on these associations. This project will lay the groundwork for future research on exogenous stress and age-related health outcomes, and inform strategies to promote healthier metabolic functioning across the life course.

NIH Research Projects · FY 2025 · 2024-04

Young adulthood is increasingly marked by the early onset of chronic conditions, such as systemic inflammation, compared with previous generations. A primary contributor to this trend is engagement in harmful cardiometabolic health behaviors (CHB), including physical inactivity, dysregulated sleep, smoking, and alcohol use. Stress is a key driver of these behaviors, yet research has not fully captured how stress shapes CHB in ways that reflect the lived experiences of young adults. To address this gap, we propose an innovative approach that integrates geographically explicit ecological momentary assessment (GEMA) with qualitative mapping to examine the contextual features of stress, protective social factors, and CHB among young adults. Qualitative mapping combines GPS and EMA activity data with in-depth interviews, allowing participants to describe their thoughts, feelings, and experiences within the mapped locations. This method is critically important because exposures to stress vary by geographic space and are appraised differently depending on contextual factors such as purpose of the space (for example home, work, or school), perceived safety, and time spent there. Our overarching goal is to elucidate the complex relationships between environmental and interpersonal experiences of stress and CHB among diverse young adults. Specifically, this study will (1) examine how daily experiences of stress and CHB differ across levels of structural inequality, measured through integrated GPS and census data, (2) assess how protective social factors such as social cohesion and community relationships buffer the negative impact of stress on CHB, and (3) explore context-specific risks and protective factors associated with CHB across racial and ethnic groups of young adults using the GEMA methodology. By capturing stress in real time and situating these experiences within their geographic and social contexts, this study offers a highly innovative and ecologically valid approach. Findings will provide critical insights to inform the development of tailored CHB prevention interventions for diverse young adults, including future Just in Time adaptive interventions designed to reduce health disparities.

NIH Research Projects · FY 2025 · 2024-03

PROJECT SUMMARY Rheumatoid arthritis (RA) is the most common inflammatory joint disease caused by a chronic inflammation in joints, where no effective treatment is currently available. Currently there is no effective model can accurately dissect RA pathogenesis and consistently predict the effect of a therapeutic agent in patients. Better model systems that can accurately recapitulate the immune responses and pathological processes of RA in the human synovial microenvironment with immunomodulatory treatments are critically required. In this work we aim to develop a fully patient-derived in vitro RA disease model termed “Synovium-on-a-Chip” by including full spectrum of synovial cell types, which can serve as a precision medicine platform to (1) interrogate human RA pathobiology and to (2) achieve "clinical trial on chips" for stratifying patients and pre-screening of novel immunotherapy. Simulating the structure and inflammatory microenvironment of the synovium with such a biomimetic vascularized immunocompetent microphysiological system will represent a major advance over existing models, providing much greater biomimicry and enabling next-step studies to allow rational selection of immunomodulatory drugs to personalize treatment of RA patients.

NIH Research Projects · FY 2026 · 2024-03

Disorders of gut-brain interactions (DGBIs), the most common GI problems world-wide, are characterized by recurring, chronic gastrointestinal (GI) symptoms including dysmotility (constipation/diarrhea), GI pain and abnormal gut-brain communication. Treatment options for DGBIs are extremely limited due to a poor understanding of their pathophysiology. What is known about DGBIs is that the majority of affected patients have a co-morbid mood disorder, mainly anxiety (AD) but also depression (DD). Like DGBIs, AD and DD are common and with limited treatment options. Further, studies show an increased risk of DGBI development with a pre-existent mood disorder and vice versa. Identifying links between DBGIs and mood disorders is thus highly likely to elucidate mechanisms that instigate development of novel therapies for both conditions. The transmitter, serotonin (5-HT) modulates symptoms of DGBIs, AD and DD, leading many treatments to be targeted towards 5-HT regulation. In particular, selective serotonin reuptake inhibitors (SSRIs) are highly prescribed. Yet, SSRIs have severe limitations: low remission (<50%) and resolution (<1/3) rates and; adverse effects (e.g., GI pain, dysmotility, and [paradoxical] anxiety). SSRIs function by inhibiting the serotonin reuptake transporter (SERT), thus increasing 5-HT transmission. SERT is located in the CNS, ENS and the GI epithelium and SSRIs are systemically absorbed, leading to SSRI action/SERT antagonism in the CNS, ENS and GI epithelium. Yet, it is not known where SSRIs precisely act to induce their beneficial versus adverse effects. Our data show that: (1) beneficial SSRI effects (anxiolytic, anti-depressive, anti-nociceptive) result from SERT antagonism in the GI epithelium and may involve vagal- and 5-HT3-mediated signaling and; (2) detrimental SSRI effects result from ENS or CNS SERT antagonism (AD, dysmotility, pain). Thus, selective pharmacologic blockade of GI epithelial SERT could be beneficial for treating DGBIs, AD and DD without producing negative effects linked to systemic SSRI exposure. We have created a state-of-the-art SSRI delivery system that maximally disperses and retains SSRI locally on the intestinal epithelium while minimizing systemic absorption. We now propose to: (1) elucidate the roles of vagal signaling in linking gut epithelial 5-HT to beneficial effects on GI pain and mood; (2) examine how ENS SERT ablation affects DGBI symptoms and mood, to determine if avoidance of ENS SERT antagonism is necessary to prevent negative and efficacy-reducing effects of systemic SSRIs; (3) determine whether our novel SSRI delivery platform provides the beneficial mood effects of SSRIs while avoiding deleterious effects on GI pain, GI motility and mood. If successful, the platform is known to be safe so can be efficiently translated to clinical studies.

NIH Research Projects · FY 2025 · 2024-02

PROJECT SUMMARY Social vocalizations and movement-generated sounds often provide pivotal knowledge about an animal’s identity, location, or state, yet most studies of natural behavior fail to integrate acoustic information with simultaneous recordings of high-dimensional neural activity and behavioral dynamics. This proposal will develop novel experimental and computational methods to attribute vocal and non-vocal sounds to individuals in a naturalistic, acoustically complex, multi-animal environment. By integrating this rich acoustic information with simultaneous video and wireless neural recordings, we seek to predict auditory cortical responses to auditory cues, as a function of social context and individual identity within the family. Aim 1 will develop new tools with which to attribute vocal and non-vocal sounds to individual animals in a multi-animal setting (i.e., the “who said what” problem). In Aim 1A, we will collect, curate, and publicly release a range of benchmark datasets containing simultaneous camera and microphone array recordings of multi-animal interactions with ground truth labels of sound sources. We will use these benchmarks to validate new models for sound localization. In Aim 1B, we will develop and release deep learning models that localize sounds with calibrated confidence intervals, using synchronized video measurements to enhance predictions. Aim 2 will use these tools to identify archetypal, acoustically-driven social behaviors. We will establish a new experimental paradigm that permits months-long monitoring of rodent social behavior in a large, naturalistic environment with simultaneous camera and microphone array recordings. Using this data, we will develop novel data analytic approaches that leverage synchronized audio and video data streams to identify social interaction sequences. A key goal is to assess individual differences in social behavior across families. Aim 3 is a proof-of-concept experiment in which we determine how acoustically-driven social behaviors (established in Aim 2) predict auditory cortex responses to both vocal or movement-generated sounds. To accomplish this, we will make continuous wireless electrophysiological recordings from the auditory cortex of adolescent and adult gerbils within their naturalistic family environment. We will build regression models to infer our ability to predict neural responses from auditory/behavioral covariates (encoding models).

NIH Research Projects · FY 2026 · 2024-01

Allostatic load, characterized by dysregulation across multiple physiological systems, has been linked to increased risk of chronic diseases such as diabetes, heart disease, and stroke. However, the prevalence of allostatic load varies across segments of the US population, with some groups experiencing a higher burden, particularly during middle and older adulthood. The reasons for these differences are not fully understood, despite research into contributing factors such as diet and socioeconomic conditions. Psychosocial stress has emerged as a key risk factor for allostatic load. While it is understood to operate across multiple contexts—such as interpersonal interactions and neighborhood environments—the combined influence of multiple forms of psychosocial stress on physiological dysregulation remains understudied, as do the biological mechanisms that link these experiences to elevated allostatic load. This study aims to clarify the relationship between multiple forms of psychosocial stress and allostatic load using data from three large, nationally representative cohort studies, in order to inform more effective strategies to reduce physiological wear and tear over the life course. By assessing the associations between interpersonal and neighborhood-level psychosocial stress and allostatic load, the study will quantify how these stressors contribute to physiological burden. The study will also investigate whether the “Conserved Transcriptional Response to Adversity” (CTRA) gene expression profile mediates the relationship between psychosocial stress and allostatic load, and evaluate whether social integration buffers these associations. Overall, this study has the potential to inform the optimization of clinical and place-based interventions aimed at reducing allostatic load by identifying when, where, and for whom such efforts may be most effective.

NSF Awards · FY 2024 · 2024-01

This award is funded in part under the American Rescue Plan Act of 2021 (Public Law 117-2). The human-built world is filled with interactive objects that have parts that can be manipulated by humans, ranging from cabinets with doors to dressers with drawers. In order for intelligent machines to be able to understand and assist humans in realistic settings, they must be able to understand these objects from vision, and especially in unconstrained realistic settings. This understanding must include understanding the interactions as they occur, as well as recognizing the opportunity for interaction (i.e., that a cabinet could be interacted with even when it is untouched). These abilities are beyond the capabilities of current AI systems since these largely deal with interactive objects in restricted settings such as simulation engines. This project aims to build AI systems that can learn these properties by combining knowledge from large-scale first-person-view video demonstrations of interactions by humans as well as from 3D simulators that do not include interaction. The project has the potential to enhance efforts in many other disciplines, for instance robotics or assistive technology for people, due to the ubiquity and importance of these interactive objects. Integrated with the research is a plan to support and engage the next generation of researchers in computer vision at multiple levels via research opportunities and enhanced course materials. This project aims to achieve this goal via four directions that advance the visual understanding of interactive objects. The first direction aims to build detailed 3D models of articulating objects in unconstrained first person-video. Building on this physical understanding of articulation, the second direction plans to enhance this physical understanding with information about how a human would achieve the interaction and what it might accomplish or reveal about the scene. The third effort aims to enable understanding of articulations before they occur by building associations in 3D across frames of a video, letting a system associate and learn from examples of ongoing interactions. The fourth direction connects this understanding of interactive objects with the goal of producing a 3D understanding of the full scene, by endowing 3D reconstructions of the world with beliefs about objects that may be just out of view or temporarily occluded. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2023-11

Project Abstract Properly navigating our daily lives necessitates the judicious use of our limited cognitive resources to yield a desired outcome. This ability, known as cognitive control, has been demonstrated in humans to depend heavily on brain regions in the prefrontal cortex, including the anterior cingulate cortex, which is involved in effortful decision making and performance monitoring. The hippocampus has also been implicated in cognitive control function, particularly when the task depends on prior experience. Single-unit recordings of hippocampal neurons have revealed a coordinated cognitive control signal that predicts cognitive control behavior. The mechanism of cognitive control, and which brain regions contribute to it remains unclear, but the anterior cingulate is the most likely candidate. Aim 1 will explore prefrontal cognitive control representations by using in-vivo single photon calcium imaging to record populations of neurons within the anterior cingulate cortex (ACC) while rats perform an active place avoidance task. This is a navigation based cognitive control task that will enable us to determine if ACC also expresses a cognitive control signal, like in the signal in hippocampus. Whether or not a similar signal exists does not preclude its involvement in cognitive control. Therefore Aim 2 will selectively inactivate ACC using inhibitory chemogenetics (DREADDs) during the cognitive control task to test for necessity during various behavioral phases. We will also record population neuronal activity from hippocampus CA1 during the manipulations to evaluate the influence of cingulate inactivation on the hippocampal cognitive control signal. Results from this research will reveal how cognitive control is represented in ACC and how this activity influences hippocampal cognitive control, as well as provide a basis for interpreting how dysfunction that affects executive function (such as schizophrenia and frontotemporal dementia) impact the brain. This work furthers Goal 1 of the National Institute of Mental Health – defining the brain mechanisms underlying complex behaviors. Only by addressing how cognitive control is represented and coordinated across multiple diverse brain regions can we begin to construct a wholistic understanding of complex brain functions and mental illness.

NIH Research Projects · FY 2025 · 2023-09

Use of remote data collection methodology in developmental research has increased significantly in the past five years. These kinds of data collection methods are likely to decrease barriers to participation for families and could potentially address common problems in the field related to statistical power and sampling bias, significant issues associated with construct and ecological validity. The central objective of this proposal is to support the rigorous application and validation of remote infant testing methodology of early cognitive development when infants are 4, 8, and 12-month of age. We will recruit 300 families to participate in this longitudinal study. The primary aims of this project are to (i) establish and validate remote physiological and behavioral measurements of infant attention and memory skills, (ii) investigate the impact of caregiver-infant physiological co-regulation on infant outcomes, and (iii) evaluate predictors of infant attention phenotypes and longitudinal associations with socioemotional outcomes and autism risk. This proposal integrates multi-level data to improve measurement of infant cognition within the home and will substantially enrich our understanding of developmental trajectories and mechanisms across varying contexts, leading to increased precision for prevention and intervention efforts. This study is part of the NIH’s Helping to End Addiction Long-term (HEAL) initiative to speed scientific solutions to the national opioid public health crisis. The NIH HEAL Initiative bolsters research across NIH to improve treatment for opioid misuse and addiction.

NIH Research Projects · FY 2026 · 2023-09

Impacts of hurricanes and social buffering on biological aging in a free-ranging animal model Natural disasters are deeply damaging to human health and welfare. Such disasters have the potential to accelerate the aging process, which is the primary risk factor for most diseases. Identifying age-accelerating consequences of natural disasters and mitigating their impacts is therefore critical. However, natural disasters do not affect all individuals equally - there is abundant variation in individual health outcomes. Evidence suggests that social support is a critical buffer against the consequences of adversity, including natural disasters. But precisely how social support gets under the skin to mitigate disaster-linked declines in health and lifespan remains elusive. Gaps in understanding are partly the result of ethical and logistical challenges to the study of humans in disaster zones, including the availability of baseline data, and our ability to quantify aging across more than a few domains (e.g., molecular markers in blood, physical frailty). Humans are also very long-lived, impeding longitudinal study of accelerated aging within individuals, and they tend to emigrate away from environmental catastrophes, biasing subject pools toward certain members of affected populations. These difficulties can be overcome by studying shorter-lived nonhuman primates, which share much of their biology and behavior with humans, exposed to natural disasters. The objective of this proposal is to leverage pilot data generated by a 1-year R56 (R56- AG071023) in our long-term study of aging in the rhesus macaque population of Cayo Santiago island, Puerto Rico, which was heavily impacted by Hurricanes Maria in 2017 and Fiona in 2022. Our objective is to use this natural experimental model to quantify how natural disasters affect biological age in multiple aging domains (molecular, physiological, physical), and to test if social support buffers these effects. We will quantify the effects of natural disasters on biological age and the pace of aging (Aim 1) in three ways: (a) Using data, particularly post-mortem tissues, across individuals, we will test if animals that experienced a hurricane exhibit older biological ages for their chronological age than those who did not; (b) Using longitudinal data in the same living individuals we will test if their pace at which they are aging is accelerated by a hurricane; (c) Comparing across Hurricanes Maria and Fiona, we will quantify the cumulative age effects of natural disasters, predicting individuals that lived through two disasters will appear biologically older for their chronological age, and have a faster pace of aging, than individuals that only lived through one. We will then quantify the extent to which social support buffers against the effects of natural disasters on biological age (Aim 2), using data across aging domains. We predict that individuals with greater social support will exhibit lower biological ages, and a slower pace of aging, in response to a hurricane, and will be buffered from age effects accumulating over multiple disasters. Our study will provide unprecedented insights into fundamental questions about how natural disasters affect the aging process, and how accelerated aging can be buffered by social resources, in the most human-relevant animal model of health, disease, and aging – the rhesus macaque.

NIH Research Projects · FY 2024 · 2023-09

ABSTRACT The ability to probe the temporal profile of the protein secretion behavior of individual immune cells will impact future immunology, cell biology, and even infectious disease diagnosis. Knowledge of the ordering and timing of cytokines (water-soluble proteins essential for intercellular signaling) secreted by activated T cells can additionally provide the means to discriminate subsets of differentiated T cells by function. Here, the temporal information is one of the pieces of the whole puzzle in monitoring the behavior of the immune system. The other critical piece is the cytokine-mediated interplay between different cell types, which involves spatial transport of cytokines between cells. Putting both pieces of the puzzle together allows us to capture the full picture of the cytokine release dynamics and cytokine-mediated interactions of cells, which allows us to fully understand the intercellular signaling processes underlying immunity. However, no study has yet obtained such a picture due to the lack of a technology for real-time sensing of intercellular cytokine-mediated signaling processes at high spatial resolution. This research aims to develop a novel label-free imaging technique to fully understand cellular behaviors during cytokine-mediated activation and communication at a single-cell level. Our approach will employ biosensors consisting of plasmonic nanoantenna structures, each specifically targeting a particular cytokine species. We will integrate these biosensors in a microfluidic system incorporating an array of sample/reagent-flow channels and single-cell trapping microwells. The microfluidic sensor integration will provide the ability to capture, manipulate, and activate single cells for cell-to-cell communications on a single chip and to obtain the spatiotemporal profile of cellular cytokine secretion processes in real time, both in a massively, parallel manner. We will also develop a theoretical algorithm that allows us to extract the quantitative values of the local cytokine concentration distributions from measured image intensities. SA 1: We will create highly ordered, high-density plasmonic nanoantenna biosensor arrays, each functionalized by highly selective aptamers against targeted cytokines. SA 2: We will integrate the aptamer-conjugated plasmonic nanoantenna arrays into a single-cell manipulation microfluidic system and achieve real-time single-cell secretion imaging at high throughput. SA 3: We will develop a two-mode (fluorescence and dark-field) microscopy imaging technique to image spatiotemporal cytokine secretomic profile patterns and cell surface sytokine binding sites. Using this technique, we will study the IL-6-mediated dynamic intercellular communication between individual human hepatoma Hep3b cells and CD 4+ T cells.

NIH Research Projects · FY 2025 · 2023-09

When making a complex decision, we often consider multiple dimensions, such as costs and qualities, that vary among choice options. Evaluating important attributes of a given option is critical for optimal choice behavior, and poor decision-making can result from an inability to properly weigh attributes, as is commonly observed in psychiatric disorders. These deficits are accompanied by alterations in the structure and function of the orbitofrontal cortex (OFC), an area critical for value-based decision-making. However, the underlying neural mechanisms and how they are disrupted remain unclear, and this limits our ability to map decision-making deficits to neural computation. The long-term goal of this proposal is to understand how the brain uses information to make optimal decisions, and our specific objective is to develop a comprehensive model of information processing in OFC during multi-attribute choices. To do this, we will use a multi-modal approach to evaluate different frameworks of decision formation. A neuroeconomics view posits that the values of different attributes are combined to compute an overall, or integrated value, and comparisons are made in the space of these option values. In contrast, other evidence suggests that direct competition between attributes, perhaps mediated by visual attention, is an important part of the decision process. Arbitrating between these models is critical to advancing theoretical frameworks that can link decision-making deficits to disordered neural computations, but a key challenge is that the steps of decision formation occur rapidly and internally, making them difficult to observe or otherwise measure. Here, we address this by combining a novel multi-attribute choice task with large-scale neural recording and population analyses necessary to reveal within-trial dynamics of otherwise covert decision-making processes. In Aim 1, we will assess how OFC codes individual attributes during multi-attribute decisions, and how this relates to classically reported integrated value signals. Next, we will assess how attention to attributes alters OFC coding, value computation, and subsequent decisions (Aim 2). Finally, in Aim 3, we propose a novel computational model of multi-attribute decisions that can determine the extent to which choices are driven by the relative values of attributes versus integrated options. Our model will also reveal latent variables that evolve during decision formation, which we will map on to neural responses. In doing so, we aim to localize specific choice processes to unique neural circuits, and also demonstrate the biological relevance of the model and its conclusions. Together, these studies leverage our combined expertise in non-human primate behavior, computational analysis, and modeling to define the neural underpinnings of multi-attribute choice in OFC. If successful, our results will not only refine the theoretical frameworks that guide decision neuroscience, but will also shed light on neural processes that underlie decision-making deficits characteristic of human psychiatric disorders.

NIH Research Projects · FY 2025 · 2023-09

PROJECT SUMMARY/ABSTRACT The proposed K23 Mentored Patient-Oriented Research Career Development Award will provide the applicant with necessary training and hands-on experience to successfully transition to independence in alcohol research. Dr. Hogan will learn the intricacies of alcohol administration studies and psychophysiological data collection while gaining experience in the study of emerging adults. Emerging adulthood (EA) is a developmental phase characterized by increased risk-taking behaviors, including heavy episodic drinking (HED). Although alcohol misuse is normative at this stage, HED is associated with negative short- and long-term consequences. Conflict in romantic relationships may be a particularly salient social influence in EA drinking behavior, but little is known about the role of romantic partners and relationship functioning in HED in this population. The proposed study addresses this critical gap in the literature. EA couples will engage in two conflict resolution tasks interspersed with two alcohol administration procedures. Because high frequency heart rate variability (HF-HRV) is associated with alcohol use and emotion regulation during stressful experiences, HF-HRV and other physiological data will be collected throughout the laboratory procedures. The candidate has assembled an expert mentorship team who are perfectly positioned to achieve the training goals and research aims within this application. Dr. Julianne Flanagan, primary mentor, is an expert in the study of relationship functioning and AUD and AUD clinical trials among couples. Dr. James Murphy will provide mentorship on alcohol studies with an emerging adult population and Dr. Jennifer Buckman will provide mentorship on psychophysiology and alcohol use among emerging adults. Finally, Dr. Dominic Parrott will consult on the implementation of dyadic alcohol administration procedures and enrollment of diverse couples. The proposed K23 award will provide the protected research time, expert mentorship, and training opportunities to accomplish the following training goals: (1) become proficient in the conduct of alcohol administration research paradigms, (2) expand and refine my psychophysiological research skills, (3) gain a foundation in alcohol prevention literature and current prevention approaches among EA, (4) extend my quantitative skills, and (5) increase my scholarly productivity and grant writing skills. The proposed K23 award will be an invaluable asset in Dr. Hogan’s transition to becoming a productive, independent alcohol researcher with expertise in critical public health priority areas.

NIH Research Projects · FY 2024 · 2023-09

PROJECT SUMMARY: The act of swallowing food and liquid is a basic human function that most people take for granted. Yet, approximately 15% of older adults suffer from impaired swallowing which can lead to malnutrition, frailty and pneumonia. Our previous NIH-funded research has confirmed that age-related decline in the muscles of the pharynx (throat) is associated with negative changes to swallowing mechanics and function, putting older adults at risk for serious health consequences. Given that exercise and nutrition are known powerful stimulators of positive muscular change, we propose to investigate these methods to reverse age-related decline of the pharyngeal swallowing muscles. Specifically, we will test the hypothesis that proactive swallowing exercises (+/- daily protein supplement drinks) will improve the composition, force, and physiology of the pharyngeal muscles. Our study design begins with 12 weeks of no-treatment control, allowing each of our 80-community dwelling older adult participants to serve as their own experimental control. Next, participants will be randomized to complete 12 weeks of pharyngeal exercises (5 days per week) with or without protein drinks. Before and after each phase, our study team will conduct a comprehensive battery of outcome measures to quantify changes to the pharyngeal muscles. Specifically, we will capture pharyngeal muscle composition with magnetic resonance imaging, pharyngeal muscle force with high resolution manometry, and pharyngeal muscle physiology with videofluoroscopy. We expect that proactive swallowing exercises will improve the composition, force, and physiology of the pharyngeal muscles and that these gains will be enhanced in the +protein condition. Finally, we are motivated to identify health-related predictors of treatment success. To that end, we will assess the influence of baseline measures of nutritional status and physical function on treatment success by analyzing biochemical markers extracted from blood samples and by conducting comprehensive analyses of body composition. The global population is rapidly aging and thus many older adults will experience the debilitating impact of impaired swallowing. Therefore, research establishing effective interventions to reverse and/or prevent age-related swallowing muscle decline is timely and important. Building a physiologic reserve in the swallowing muscles can improve the health and quality of life of community-dwelling older adults.

NIH Research Projects · FY 2025 · 2023-09

The Training Program in Computational Neuroscience (TPCN) supports integrated undergraduate and graduate training in computational neuroscience and computational cognitive science at New York University. The program is hosted by the Center for Neural Science and the Cognition and Perception area in the Department of Psychology, reflecting the dual roles of brain and behavior. The TPCN program faculty consist of 39 highly productive PIs, many of whom are primarily or exclusively engaged in computational/theoretical research. Each trainee is appointed for one year. Each TPCN trainee completes coursework in computational neuroscience, computational cognitive science, and/or data science/machine learning. They conduct research throughout the year in the laboratory of one of the program faculty members. Trainees and mentors participate in TPCN-specific professional development activities (Cross-Level Seminars), which include skills tutorials, career preparation, and workshopping each other’s writings and presentations. Trainees attend the Swartz Seminar in Computational Neuroscience and attend and present in a didactic journal club preceding each seminar. Trainees present in the Annual Symposium on Computational Neuroscience. Taken together, these activities form a comprehensive preparation for an academic career in computational neuroscience. Finally, to equip a broader pool of undergraduate students for research in computational neuroscience and computational cognitive science, the program runs an annual Math and Coding Bootcamp for those who have not previously taken college-level math or coding. The TPCN is directed by a Program Director, a Steering Committee consisting of four faculty members, and evaluated in Years 2 and 4 by an external Advisory Board.

NIH Research Projects · FY 2025 · 2023-09

The Training Program in Computational Neuroscience (TPCN) supports integrated undergraduate and graduate training in computational neuroscience and computational cognitive science at New York University. The program is hosted by the Center for Neural Science and the Cognition and Perception area in the Department of Psychology, reflecting the dual roles of brain and behavior. The TPCN program faculty consist of 39 highly productive PIs, many of whom are primarily or exclusively engaged in computational/theoretical research. Each trainee is appointed for one year. Each TPCN trainee completes coursework in computational neuroscience, computational cognitive science, and/or data science/machine learning. They conduct research throughout the year in the laboratory of one of the program faculty members. Trainees and mentors participate in TPCN-specific professional development activities (Cross-Level Seminars), which include skills tutorials, career preparation, and workshopping each other’s writings and presentations. Trainees attend the Swartz Seminar in Computational Neuroscience and attend and present in a didactic journal club preceding each seminar. Trainees present in the Annual Symposium on Computational Neuroscience. Taken together, these activities form a comprehensive preparation for an academic career in computational neuroscience. Finally, to equip a broader pool of undergraduate students for research in computational neuroscience and computational cognitive science, the program runs an annual Math and Coding Bootcamp for those who have not previously taken college-level math or coding. The TPCN is directed by a Program Director, a Steering Committee consisting of four faculty members, and evaluated in Years 2 and 4 by an external Advisory Board.