University Of California Berkeley

NSF Awards · FY 2024 · 2024-11

To successfully understand and address complex and important questions in the field of environmental science, many kinds of communities’ knowledge about their local environment need to be engaged. This one-year Partnership Development project involves a collaboration to design an approach that would yield opportunities for K-12 students to learn about environmental science in ways that honor both traditional STEM knowledge and Native ways of knowing among the Pomo community in California. The goals of the project are to advance knowledge of place-based science by considering how Indigenous knowledge and traditional Western science can be productively partnered. The project is led by a partnership of researchers and educators from the Lawrence Hall of Science, Redbud Resource Group, and California Indian Museum and Cultural Center. Partners will develop a common vision and framework that responds to key questions and guides future research and instructional materials development. To reach this vision, partners will engage in a range of activities including: 1) a series of partnership development meetings; 2) asset-based review of the research and development landscape, including literature review and identification of exemplar sites; 3) a pilot design workshop with school and Tribal partners in Pomo territory; 4) Native listening sessions; and 5) convening an advisory board to inform and evaluate the partners’ approaches and progress. Through its work, the project aims to be a model for future partnerships and collaborations among Native communities, STEM curriculum designers, and preK-12 schools. This project is supported by the Discovery Research preK-12 program (DRK-12) which seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models and tools. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects.  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 · 2024-11

Abstract/Project Summary Heavy drinking in young adults (YA) is prevalent, and associated with serious negative consequences including mortality and risk for alcohol use disorders (AUD) 1-7. However, existing interventions have shown modest efficacy 8-14, and innovative interventions are needed. YA interventions have potential for broad impact if they are brief, computerized (especially web-based), and target core neurocognitive mechanisms underlying heavy drinking 14,15. Two such mechanisms are craving and regulation of craving. Defined in DSM-5 as “a strong desire” 16, craving is prospectively associated with and predicts drinking (e.g.,17- 25, including in YAs (e.g., 26-29). Importantly, alcohol-associated cues increase craving 30; such cue-induced craving is also prospectively associated with and predicts drinking(e.g.,31-35), including in YA 32,33,36,37. These data implicate cue-induced craving as a core mechanism underlying drinking 38. Consistently, skills training in regulation of craving is an important feature of many interventions 39-44, including cognitive-behavioral therapy (CBT) 45 and mindfulness-based treatments (MBT) 46,47. Further, regulation of craving directly relates to reductions in craving and drinking, and better treatment outcomes(e.g., 34,41,42,48-52), including in YA 53. These data implicate regulation of craving as a core mechanism underlying change in drinking/abstinence 54. We developed the Regulation of Craving (ROC) task to investigate cognitive, affective, and neural mechanisms associated with craving and its regulation across substances 55-61. In one study, alcohol drinkers were exposed to alcohol images60. On craving trials, they experienced cue-induced craving and exhibited neural activity in regions including ventral striatum and ventromedial prefrontal cortex 62-64. On regulation trials they used a treatment-based strategy to modulate their craving. We found that self-reported craving and craving-related neural activity were significantly reduced during regulation 60. However, across studies we found that the neural mechanisms by which regulation operates depend on the strategy used. Specifically, regulation with CBT strategies (e.g., ‘think of the negative consequences of drinking’) depends on the PFC 56,60,65 while regulation with MBT strategies (e.g., ‘notice and accept craving without judgment’) does not 57,66,67. Based on this, we developed two brief, web-based, mechanism-focused interventions: CBT-based and MBT-based Regulation of Craving Training (ROC-T) 58,68,69, in which participants repeatedly practice regulating craving in the presence of alcohol images. We propose to evaluate the efficacy of ROC-T and its mechanisms by randomizing 177 YA heavy drinkers to 4x45 minute sessions of (1) CBT-ROC-T, (2) MBT- ROC-T, or (3) CONTROL (no strategy). Alcohol use will be measured via Timeline Followback 70 for 10 weeks as well as a wearable transdermal sensor 71,72. Pre- and post-training, we will evaluate cognitive, affective, and neural mechanisms underlying ROC-T using the ROC task and fMRI. The current project has the potential to significantly advance mechanism-targeted interventions for heavy drinking, AUD, and other addictive disorders.

NIH Research Projects · FY 2026 · 2024-11

Project Summary Rural Americans currently face a significant health crisis marked by a heightened prevalence of cardiovascular disease (CVD), the leading cause of mortality in the United States, responsible for one-third of all deaths and claiming a life every 34 seconds. Although this rural health crisis affects all races and ethnicities, studies suggest that racial disparities are more pronounced than those seen in urban settings. Therefore, there is a critical need to comprehend the key drivers behind these health inequities. Limited understanding exists regarding the multi- level determinants of the elevated burden of poor cardiovascular health in rural areas. Neighborhood environments, in particular, may serve as critical drivers of CVD inequities in rural areas. Previous research highlighting the significance of neighborhood environments in driving CVD risk and racial/ethnic disparities in CVD was conducted in predominately urban areas, leaving approximately 20% of the population understudied. Furthermore, neighborhoods serve as more than just places of residence. They represent environments that shape intricate interactions between individuals and historically contingent settings, influencing access to the social determinants of health associated with CVD risk. Investigating the impact of distinct structural processes of disinvestment in rural areas, particularly in the southern United States, and their influence on neighborhood opportunity and vulnerability, with a focus on the racialized nature of these processes potentially leading to CVD health inequities, is crucial. To address these gaps in knowledge, this proposal seeks to examine the neighborhood-level drivers of persistently poor cardiovascular health outcomes and disparities in the Southeastern region of the United States. It focuses on neighborhood measures of social vulnerability and disinvestment, representing the adverse impacts of neighborhoods on natural disasters and the disproportionate disruption of livelihoods. The proposal also addresses neighborhood racial and economic isolation, emphasizing the critical role of residential segregation. Phase 1 of the proposal will examine the geospatial variation in disinvestment, social vulnerability, and racial and socioeconomic isolation across 4,108 census tracts in four states (AL, MS, LA, and KY) and differences by rurality. Phase 2 will leverage data from the Risk Underlying Rural Areas Longitudinal Study (RURAL; U01 HL146382), which will recruit 4,600 individuals aged 25-64 from 10 rural counties in Appalachia and the Mississippi Delta of the US to explore associations between these measures and CVD risk factors and whether associations are modified by race and socioeconomic position. All in all, this F31 fellowship will bring together a strong interdisciplinary mentorship team and a rich academic environment at UC Berkeley School of Public Health to support the applicant in achieving the long-term career goals of becoming an independent health equity scholar working at the intersection of research and public health practice to investigate and address the macrosocial drivers of racial/ethnic health inequities. Successful completion of the study aims will aid in identifying the macrosocial drivers of CVD risk in rural areas.

NSF Awards · FY 2024 · 2024-10

Generative Artificial Intelligence (AI) technologies will have profound impacts on various domains from science to the economy, education, the environment and more. The future of generative AI technology is under intense debate and a central topic is related to the openness and open sourcing of foundation models. Open source models offer immense promise towards societal impacts as compared to closed models across key areas of research, innovation, transparency, equity, and more. In particular, open foundation models promise to ‘democratize’ access to AI by enabling people to examine, reuse and build on top of these powerful systems. On the flip side, bad actors may more efficiently and effectively create and deploy AI building upon open models in ways that are harmful to individuals, communities, and/or societies. While open source models can create greater opportunities to ‘democratize’ AI development, for both open and closed models, key decisions such as data the models learn from, transparency provided, and other considerations are currently undemocratic. They are informed by the values and market priorities of their largely for-profit driven creators and managers. To truly democratize AI, greater efforts are needed to integrate broader perspectives and voices in the design and development of large open source models. The goal of the project is to conduct a workshop to integrate broader perspectives and voices in the design and development of large open source models, including through co-creating a vision for ‘responsible’ open source models and a roadmap to move towards this vision. The workshop will explore the following critical questions: (1) What do ‘responsible’ open source models look like and how might we co-create a vision for ‘responsible’ open source models, including prioritizing AI principles? (2) What tradeoffs exist between identified AI principles for ‘responsible’ open source models and how do we navigate and mitigate these tensions? (3) What future research is needed to advance progress towards the co-created vision for ‘responsible’ open source AI? The workshop will result in a paper on the results of the workshop including the state of open source foundation models, the co-created vision for “responsible” open source foundation models, and research roadmap. It will also result in an accessible online report summarizing the vision and roadmap, and a Slack channel for participants to connect and collaborate. The workshop and outputs can also help inform future ReDDDoT priorities as it relates to research, innovation, and capacity building for responsible (open source) AI models. 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 · 2024-10

This National Science Foundation Innovations in Graduate Education (IGE) award facilitates the development and implementation of a comprehensive graduate curriculum designed to enhance scientific literacy, peer review skills, and communication capabilities among STEM graduate students. Recognizing these skills' critical role in advancing scientific knowledge and career progress, this program seeks to equip graduate students with the tools necessary to critically evaluate scientific literature and contribute meaningfully to the peer review process. This innovative curriculum will foster a community of skilled reviewers and effective communicators, accelerating the dissemination of accurate scientific knowledge and improving the quality of scientific research. The program will immerse graduate students in peer review and publishing processes through hands-on experiences. This includes navigating preprint platforms, evaluating cutting-edge scientific literature with high impact, and participating in peer review processes with the editorial board of a preprint overlay journal. The program aims to equip graduate students to rapidly evaluate preprint articles, providing scientific credibility to unreviewed articles that are of wide interest. The pilot program will consist of two cohorts of graduate students to initially develop a curriculum based on broadly applicable open-access online educational materials, including audio and video recordings, written content, and interactive workshops. This curriculum will be evaluated for its effectiveness in enhancing graduate student outcomes such as scientific literacy, comprehension, confidence in peer review processes, and community involvement. The educational model and online training materials will also be assessed for accessibility and scalability to expand and disseminate the peer review training program and materials. By facilitating broad public access to scientific materials, this program represents a strategic investment in the quality and availability of the results of U.S. scientific research. The Innovations in Graduate Education (IGE) program is focused on research in graduate education. The goals of IGE are to pilot, test and validate innovative approaches to graduate education and to generate the knowledge required to move these approaches into the broader community. 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 · 2024-10

A sketch of a dataset is a compressed representation of it that allows for answering some family of queries. A streaming algorithm is then simply a sublinear memory dynamic data structure, where the sketch can then be viewed as the streaming algorithm’s memory footprint. Sketching has found applications in streaming, large-scale linear algebra, distributed algorithms, and machine learning. Despite sketching having been studied in the theory community for more than 45 years, we still do not have optimal algorithms (i.e., using the asymptotically least amount of memory) for some of the most basic problems in the field. This project aims to make progress on many of these fundamental problems. The PI will also engage in K-12 outreach activities, mentor both undergraduate and graduate students on research related to the project, and co-organize workshops. The project aims to make progress on some of the core fundamental problems in streaming, such as heavy hitters, quantiles, moment estimation, sampling from streams, and more. 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 · 2024-10

This project will support a cohort of three postdoctoral fellows for two years as they study Data Science Education at The University of California, Berkeley and partner educational institutions. The College of Computing, Data Science, and Society houses a variety of curricular and educational initiatives that offer many opportunities to study pedagogy, educational trajectories, and ethics and inclusion in Data Science. Fellows will be recruited from diverse institution types and disciplinary backgrounds to conduct research on Data Science Education through first apprenticing with multiple research and curriculum development projects, and then designing their own independent research project with support from a mentorship team. Fellows will develop expertise in generating high-quality Data Science Education research and curriculum by working with educators across precollege, 2-year, and 4-year contexts; addressing equity and inclusion in Data Science Education spaces; and examining curriculum and instruction focused on teaching computing and data science to diverse audiences. The rise of data-rich computing has had rippling effects across educational sectors. Since computing and data science are highly contextual and have clear social applications and social impacts, many have argued that they can serve as an integrative thread for diversifying Science, Technology, Engineering, and Mathematics broadly. However, a lack of academic preparation, and of curricular and instructional support, can present roadblocks especially for marginalized populations to enter the field. Despite these challenges, most empirical work in Data Science Education has focused on the design and evaluation of courses or major programs of study, rather than these deeper systemic issues related to student preparation, learning and retention. The Berkeley Data Science Education Fellowship will support training and research toward a coherent, interdisciplinary, mixed-methods approach to studying Data Science Education at a system level. The Fellowship will prepare three postdoctoral researchers to conduct high-quality social sciences research in complex contexts, in collaboration with interdisciplinary colleagues including domain experts and educators. In Year 1, Fellows will be provided broad exposure to Data Science curricular and educational initiatives as research apprentices with research projects that employ different methodologies and work with different student audiences. In Year 2, Fellows will commit to a specific project to complete a longer-term research internship, as well as designing and enacting an independent research project. Fellows will also participate in monthly community-building events and colloquia, teach Data Science workshops and/or courses, and receive professional development feedback and advice from a cadre of interdisciplinary mentors. Through these research and teaching experiences, Fellows will gain comfort and competency in research and development of large-scale, introductory technical Data Science courses and workshops, with an eye toward social and cultural relevance. This project is funded by the Science, Technology, Engineering, and Mathematics (STEM) Education Postdoctoral Research Fellowship Program (STEM Ed PRF) with co-funding from the Improving Undergraduate STEM Education Program (IUSE:EDU) Program. The STEM Ed PRF Program aims to enhance the research knowledge, skills, and practices of recent doctorates in STEM, STEM education, education, and related disciplines to advance their preparation to engage in fundamental and applied research that advances knowledge within the field. The NSF IUSE:EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. 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 · 2024-10

Marine diseases have devastating impacts on ocean ecosystems and this work directly informs understanding of disease transmission in the ocean. To understand the cause and patterns of spread of a disease outbreak that began in late summer of 2022 at the Flower Garden Banks National Marine Sanctuary (northwest Gulf of Mexico, GoM), a team of ecologists, ocean connectivity and disease modelers, microbiologists, and coral immunologists (from Rice University, the University of Virgin Islands (UVI), Louisiana State University (LSU), and Woods Hole Oceanographic Institution) is monitoring the health of corals, and biopsy their tissues. This data aid in developing a model that predicts coral disease transmission and its impacts on economically valuable coral reefs in the GoM. This project supports multidisciplinary field and laboratory research experiences of graduate students at multiple minority-serving institutions, and provides undergraduate students with hands-on training in modeling, ecological and molecular analysis techniques. UVI and LSU are in EPSCoR jurisdictions and have diverse student bodies, including numerous under-represented minority (URM) students. The research team collaboratively provides URM students with research experiences in STEM fields. Project findings are being broadly communicated through virtual public programming, to the Disease Advisory Council, and via direct updates to managers of the Flower Garden Bank National Marine Sanctuary. Over the last four decades, diseases decimated ecosystem engineers in marine coastal environments, including coral reefs. Recent results from studies of white plague and stony coral tissue loss disease (SCTLD) show coral species immune traits can influence disease resistance and therefore predict of coral community structure post-outbreak in the Caribbean. In late August of 2022, an unidentified multi-species acute tissue loss disease with signs and species susceptibility characteristics reminiscent of white plague or SCTLD was documented at the Flower Garden Banks (northwest Gulf of Mexico, GoM). This disease is having significant impacts on FGB and could become widespread across the GoM, offering an opportunity to test hypotheses about the influence of coral community composition and pathogen dispersal on disease spread during the early stages of an outbreak; few studies examine this on relatively isolated, deep, coral-dense reefs. The interdisciplinary research team employs photomosaics and colony fate-tracking, layered molecular datasets and microscopy approaches, as well as modeling of disease reservoirs and dispersal to assess the etiology of the disease and contribute to the development of a generalizable framework for disease spread on reefs. By parsing the impacts of reef-scale community composition versus seascape-scale dispersal in disease transmission and persistence, this work helps reveal the potential resistance and resilience of isolated, coral-dense reefs to diseases that decimate these ecosystems across the wider Caribbean. 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 · 2024-10

Currently, healthcare relies on annual blood and urine tests that often miss early signs of developing diseases. This project will develop innovative sensing technologies that will enable more frequent blood and urine testing, as well as testing for newly discovered disease-associated molecules, which will improve the likelihood of detecting diseases early. This project aims to combine the strengths of microelectronics and emerging molecular nanotechnology to develop sensing platforms that are highly sensitive and easy-to-use. If successful, in the future, rather than waiting for annual checkups, our health will be systematically and comprehensively monitored frequently throughout the day. The research is integrated with an educational and outreach program that introduces these technologies to students from K-12 to the graduate level, providing training and education opportunities for future health personnel. The results of this project will lead to future work that could greatly reduce healthcare costs and vastly improved health outcomes. Although continuous glucose monitoring and rapid COVID-19 tests serve as outstanding currently available examples of biomarker sensing, general purpose integration of biomarker sensing into daily life remains challenging. The challenges include (i) the lack of sensors that can offer signals at low detection limits with high specificity while requiring minimal sample preparation prior to sensing, (ii) the absence of appropriate electronic interfaces to convert low target-sensor interactions into detectable signals within a miniaturized platform that can be easily worn or carried, and (iii) significant measurement variation from different samples. This research aims to overcome the challenges by integrating biosensors enabled by DNA nanotechnology with miniaturized yet high-performance complementary metal-oxide-semiconductor (CMOS) electronics to develop new diagnostic platforms that offer enhanced sensitivity, specificity, throughput, and analyte scope. Specifically, the team will: (1) develop molecular engineering techniques to perform in-situ signal amplification for target-binding aptamer biosensors using DNA origami nanotechnology; (2) develop advanced biosensor-interfacing circuits that overcome noise/power limitations and offer near shot-noise-limited sensitivity; (3) create integration methodologies of molecules with electronics that facilitate the site-specific scalable functionalization of biosensors, enabling multiplexed detection across a scalable array with aptamers of different sequences; and (4) implement a wireless wearable device for continuous monitoring of molecules in interstitial fluids, alongside a CMOS/microfluidics system designed for blood biomarker analysis. Additionally, machine learning and data fusion techniques will be incorporated to enhance the accuracy of molecular quantification with minimal calibration when measuring complex fluids from various types and individuals. The integration of these platforms will enable continuous or more frequent sampling of specific biomarkers by users. This longitudinal data pattern paves the way for early disease detection and offers an improved alternative to current healthcare practices. 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 · 2024-10

To behave adaptively, people need to flexibly align their decisions with their immediate and long-term goals. Failures of such goal-directed decision-making can result in an array of maladaptive behaviors, such as financial risk taking, drug abuse, or gambling. While past research has helped identify the drivers of goal-directed decisions, there are still significant gaps in our understanding of how people are able to reorient those decisions as flexibly as they do, as well as when and why they fail to do so. In a series of studies that combine computational modeling with measures of behavior, attentional focus, and brain activity, we are studying the capacities and limits of human decision-makers to flexibly adjust their information search and actions to different goals and task demands. By uncovering the hidden levers and potential failure modes of goal-directed behavior, our work better disentangles sources of real-world decision-making failures, and provide a path towards targeted interventions to better align choices with long-term goals. Our project addresses critical gaps in research on the neural and computational mechanisms that link decision-making and cognitive control. Past work on the computational and neural mechanisms of goal-directed decision-making has been singularly focused on a narrow subset of human goals (how people select the best option from a set). It is therefore unknown how people flexibly reconfigure to their wider array of goals – for instance, selecting under different criteria or accumulating information across options rather than comparing between them – and when and why they fail to do so. Our project develops and tests a computational framework that accommodates the range of flexibility observed in human decision-making, by representing explicit choice goals that define a) how information is translated into evidence and b) how evidence is then integrated to select responses. We are testing our framework in a series of studies that combine behavioral tasks with eye-tracking, EEG and fMRI. This multi-modal approach allows us to uncover the neural circuits and dynamics that enable people to flexibly transform and integrate information about their options to achieve their current goals, and to understand how and why people vary in these capacities. The project further supports outreach activities aimed at training researchers in computational methods for predicting and testing a wide array of decision-making behavior. A companion project is being funded by the Federal Ministry of Education and Research, Germany (BMBF). 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 · 2024-10

Rapid and extreme warming events such as El Niño and marine heatwaves have had ecological and economic impacts on nearshore marine ecosystems. These impacts include reductions in biomass and collapses in commercial fisheries. For many species, population booms and busts are controlled by shifts in reproduction and juvenile dispersal related to warmer temperatures and ocean circulation. However, how population fluctuations are shaped by interacting processes that control adult reproduction and larval survival remains unclear. Marine heatwaves often accompany major disruptions in ocean circulation, which can affect survival and the distribution of species that produce free-floating, planktonic larvae. As a result, species can be impacted directly by temperature effects on organismal reproduction and survival, and indirectly by shifts in ocean circulation that affect larval success. This project is examining how the joint effects of temperature and ocean circulation are controlling populations of purple sea urchins (Strongylocentrotus purpuratus). To address project objectives, the team is developing oceanographic models to predict dispersal of planktonic larvae in combination with controlled experiments on adult reproductive success. This project is advancing the understanding of how ecologically important species respond to ocean temperature and circulation, which are forecast to shift under future climate change scenarios. Broader impacts of the project include training of students and post-docs in STEM and educational outreach. Curriculum development and implementation is occurring in collaboration with existing K-12 outreach programs that focus on underserved communities and under-represented groups. The goal is to empower the next generation of scientists to use integrative approaches to predict ecological consequences of climate change. Purple sea urchins are an ideal species for studying the coupled impacts of warming and ocean circulation on recruitment and survival given a wealth of ecological and organismal data. The species has a mapped genome, can be transported large distances as larvae by ocean currents, and larval abundances in California exhibit orders of magnitude variation with heatwaves and El Niño fluctuations. To quantify the processes that shape spatial and temporal variability in larval supply, researchers are applying a novel combination of biophysical modeling, experiments and statistical modeling of long-term, high-resolution data on larval settlement across the Southern California Bight (SCB). Research module 1 is quantifying spatial and temporal patterns of larval transport using a 3D-biophysical model of the SCB. The model is testing how interactions among historical changes in ocean circulation and temperature, larval life history, and larval behavioral traits affect variation in larval supply in space and time. Research module 2 is focused on how temperature could affect spatial and temporal variation in egg production. Experiments are characterizing reproductive thermal performance curves and quantifying how these vary among populations and organismal history. A novel assay is assessing epigenetic regulation of gene expression associated with performance curves. Finally, Module 3 will integrate mechanistic models from Modules 1 and 2 to statistically assess their ability to explain spatial and temporal trends in a nearly three-decade dataset of larval settlement from six sites in the SCB. This is one of the first studies that integrates models of larval transport, reproductive performance and settlement data to empirically test how physical and biological processes affect local recruitment patterns in complex marine meta-populations. 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 · 2024-10

This project aims to better understand the brain mechanisms that support the production and comprehension of language, using advanced neuroimaging and computational methods. Information about the brain networks mediating language will be combined with insights gained from large language models developed recently in Artificial Intelligence to create powerful and efficient algorithms for decoding intended speech from human brain activity. In sum, this project will advance scientific understanding of the brain basis of language production, and provide a solid foundation for brain-machine interfaces that improve communication abilities for people affected by language disorders. The project leverages advanced neuroimaging protocols and computational tools that reveal the function of brain circuits mediating language use, at high resolution and in individual participants. The first aim of the project is to use functional MRI to record brain activity during extended language production tasks, and to create high-resolution, participant-specific maps that characterize the spatial distribution and temporal profile of language-related brain representations across the full hierarchy of brain networks involved in language production. The second aim is to record brain activity during a matched language comprehension task, and to compare high-resolution maps of language representations during production versus comprehension. The final aim will use insights gained from the first two aims to develop advanced, efficient methods for decoding intended speech. This could revolutionize communication aids for individuals with speech impairments, offering them a new avenue for interaction. This project will advance basic scientific understanding of the brain circuits mediating language production, which can in turn be used to improve diagnostic and rehabilitation methods. 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 · 2024-10

Forest regeneration is crucial for addressing climate change, but challenges such as scarce seed availability and time-intensive seedling cultivation hinder many projects. To reduce costs and increase the effectiveness of seeding, a convergent team comprised of experts from material science, engineering design, bio- and soil mechanics, soft robotics, plant science, and forest ecology will advance novel bio-inspired, biodegradable, self-burying seed carriers for aerial seeding. Autonomous seed carrier burial at optimal depths provides protection from herbivores and insulation against harsh environments, thereby enhancing germination rates and safeguarding seeds during critical early stages of growth. This research will expand forest restoration efforts, offering economic benefits and promoting ecological resilience. To advance forest regeneration practices through interdisciplinary efforts, this project: (1) addresses the challenge of low germination and seedling establishment rates in aerial seeding by developing self-burying seed carriers; (2) explores the rational design of seed carriers to accommodate the unique requirements of different tree species and environments, aiming to create a library of designs applicable across diverse ecosystems; (3) seeks to understand and optimize the self-burying process of seed carriers by establishing a new modeling framework to improve the efficiency and effectiveness of the proposed seeding techniques; and (4) emphasizes the importance of fostering biodiversity in forest ecosystems, resulting in new ecological field test protocols and an evaluation pipeline that is enabled by engineering and guided by forest ecology. 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 · 2024-10

Large-scale systems with societal relevance, such as power generation systems, are increasingly able to leverage new technologies to mitigate their environmental impact, e.g., by harvesting energy from renewable sources. This NSF CPS project aims to investigate methods and computational tools to design a new user-centric paradigm for energy apportionment and distribution and, more broadly, for trustworthy utility services. In this paradigm, distributed networked systems will assist the end users of electricity in scheduling and apportioning their consumption. Further, they will enable local and national utility managers to optimize the use of green energy sources while mitigating the effects of intermittence, promote fairness, equity, and affordability. This project pursues a tractable approach to address the challenges of modeling and designing these large-scale, mixed-autonomy, multi-agent CPSs. The intellectual merits include new scalable methods, algorithms, and tools for the design of distributed decision-making strategies and system architectures that can assist the end users in meeting their goals while guaranteeing compliance with the fairness, reliability, and physical constraints of the design. The broader impacts include enabling the automated design of distributed CPSs that coordinate their decision-making in many applications, from robotic swarms to smart manufacturing and smart cities. The research outcomes will also be used in K-12 and undergraduate STEM outreach efforts. The proposed framework, termed Automated Synthesis for Trustworthy Autonomous Utility Services (ASTrA), addresses the design challenges via a three-pronged approach. It uses population games to model the effect of distributed decision-making infrastructures (DMI) on large populations of strategic agents. DMIs will be realized via dedicated networked hybrid hardware architectures and algorithms we seek to design. ASTrA further introduces a systematic, layered methodology to automate the design, verification, and validation of DMIs from expressive representations of the requirements. Finally, it offers a set of cutting-edge computational tools to facilitate our methodology by enabling efficient reasoning about the interaction between discrete models, e.g., used to describe complex missions or embedded software components, and continuous models used to describe physical processes. The evaluation plan involves experimentation on a real testbed designed for zero-net-energy applications. 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 · 2024-10

Climate change and local-scale anthropogenic stressors are degrading coral reefs across the globe. When conditions become too stressful on reefs, corals can lose beneficial microbial symbionts (e.g., dinoflagellates in the family Symbiodiniaceae) that live in their tissues via a process called “bleaching”. Although Symbiodiniaceae play key roles in the health of coral colonies, we know little about the processes that make symbionts available in the environment to prospective host corals. This research tests the extent to which the feces of coral-eating fish, which contain live Symbiodiniaceae, facilitate symbiont acquisition by corals in their early life stages. It will generate seminal knowledge on how corallivore feces impact coral symbioses and health, and will assess the ecological importance of corallivorous fishes as drivers of coral symbiont assemblages. This research also test the extent to which corallivore feces are a source of food and nutrients that impact coral health; this has particular relevance to the survival and recovery of bleached adult corals. This research can ultimately inform intervention strategies to support reef resilience and mitigate reef degradation. Results from this project will be communicated widely in scientific arenas, in undergraduate education programs, and to the public via multimedia content and outreach. The Houston Independent School District (HISD, Houston, TX) is the nation’s 7th largest public school system. This work will also support economically disadvantaged and first-generation undergraduate students in pursuing STEM majors and careers through multi-year research experiences. Symbioses between foundation species (e.g., corals, sponges, trees) and microbiota (e.g., microeukaryotes, bacteria) underpin the biodiversity, productivity, and stability of ecosystems. Consumers, such as predators and herbivores, shape communities of these foundation species through trophic interactions. For instance, grazers contribute to the maintenance of coral dominance on reefs via consumption of macroalgal competitors. However, the indirect effects of other consumers on foundation species are rarely examined. Few studies have tested how consumers affect microbiota assembly in corals, even though coral symbionts (e.g., dinoflagellates in the family Symbiodiniaceae) play key roles in reef function and persistence. Corallivorous (coral-eating) fishes were recently demonstrated to egest large quantities of live Symbiodiniaceae cells as they swim across reefs. This research is testing the hypothesis that corallivore feces promote coral dominance on reefs by supporting coral acquisition of key symbionts and nutrients. The following research objectives will be accomplished: (1) to quantify the contribution of corallivorous fish feces to coral symbiont acquisition; and (2) to test the extent to which corallivorous fish feces influence coral health and recovery from thermal stress. Reefs are being degraded globally due to climate-change induced bleaching and associated mortality. This project is teasing apart the extent to which nutrients and/or live symbionts associated with corallivore feces contribute to the resilience of bleached corals under ambient and heat stress conditions. The research is tightly integrated with two education objectives: (1) to organize a Research Experience for Teachers (RET) program in which rigorous learning modules that high school teachers can incorporate into their Environmental Systems course offerings are developed and tested; and (2) to provide undergraduate students with a multi-year research experience through a partnership with the Rice Emerging Scholars Program (RESP). 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 · 2024-10

Robotic navigation and control is reaching new levels with the introduction of neural network-based control mechanisms. Trained largely in simulation environments, these learning-based controllers have been used to guide a kitchen robot, enable a robotic goalie to deflect soccer balls, and control a two-legged robot to jump to new heights and self-stabilize on landing. It is now a reality that robots can help with the decades-old problems of learning to do a complex task, and learning how to interact with a complex environment. However, the more complex the task and environment, the harder it is to ensure that the system will operate safely. Automation in the real world requires a characterization of the safety of such systems. One popular way to do this is through the use of safety certificates (e.g., using mathematical approaches that are guaranteed). The project’s novelties are in developing methods that use deep learning to develop safety certificates in realistic dimensions. The project’s impacts are in the safe design of systems which use learning-enabled perception and prediction modules in an autonomous system. For such systems, it is crucial to understand and predict the behavior of the autonomous system with the learning-enabled components. The project is training the next generation of computer scientists and engineers in these tools and methods, and is further developing a new course at Berkeley in integrated perception, learning, and control. This project addresses two key objectives: (I) Learning safety certificates and their control policies and (II) certifying the learned system. The project is developing new techniques to certify the system with learning-enabled components, employing techniques which propagate uncertainty through system modules. While computing safety certificates has required solving a Hamilton-Jacobi equation, or devising a control barrier function, these methods have been restricted to low dimensional problems. The project is using learning-based methods to compute safety certificates and control policies at scale. To certify the learned system, the project is developing methods for out-of-distribution data detection and management, techniques to characterize uncertainty from learning-enabled components and using it to assess the safety of the learning-enabled system, and tools which take data-specific metrics into account, like Lyapunov density models. 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 · 2024-10

In order to achieve long-term goals, people need to exert cognitive control, a set of mental capacities that allows them to focus their thoughts and direct their actions. However, people often fail to engage sufficient and well-directed cognitive control, leading to poorer outcomes at school and in the workplace. This research investigates the neural and computational mechanisms responsible for making these decisions and studies how people weigh the costs and benefits of exerting mental effort on a given task. Different hypotheses are tested about how these decisions are altered when someone is in a stressful environment, which is known to impair cognitive control. This work will achieve better understanding of the sources of variability in achievement of educational, career, and health goals, and why these achievement outcomes may differ for individuals who regularly encounter stressful environments. This project includes community outreach designed to help to educate students about the role that environment plays in shaping motivation, and about the importance of motivation for achieving their goals. While the mechanisms underlying the exertion of mental effort are well known, much less is known about how people perform the cost-benefit analysis that determines whether and how they will invest their mental effort. The investigators’ model of the neural and computational mechanisms underlying the evaluation of mental effort divides this evaluation into computationally explicit components, including (1) how people weigh the potential outcomes of their effort, (2) how people weigh the extent to which their efforts are efficacious for achieving those outcomes, and (3) how people consider the different ways in which they can direct their efforts. By combining this model with a novel set of tasks that target each of its components, as well as brain imaging measures of neural activity and connectivity, this research maps out the neural architecture underlying the evaluation of mental effort. This research also tests how components of this evaluation process are altered by mild experimentally-induced stress in the laboratory, providing a clearer understanding of how environmental stressors may alter one’s perceptions of the value of their efforts, potentially contributing to poorer performance on cognitive tasks. Knowledge gained through this work contributes to resolving the key role of corticostriatal interactions (between the cortex and subcortical striatum) including reward-related and control-related circuits in motivation and cognition. This study also provides new computational and experimental tools for probing the sources of variability in effort allocation across individuals and contexts, helps understand the causes of inequalities in academic and career achievement for people from disadvantaged backgrounds, and provides strategies for overcoming motivational challenges in order to use one’s available cognitive resources to best advantage and hence better meet one’s goals. 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 · 2024-10

Next-generation cyber-physical systems (CPS) will increasingly rely on machine learning algorithms for situational awareness and decision-making, with the promise of enhancing human capabilities. Examples range from autonomous vehicles and robots to computer-controlled factory lines and wearable medical devices. However, learning-enabled systems have shown to be very sensitive to training data and have difficulty in ensuring functional safety and robustness. The undesired outcomes of recent deployments, such as the accidents involving semi-autonomous vehicles, raise questions about the design principles needed to build learning-enabled systems that are safe. This project aims to develop the foundations of a novel methodology for the design and verification of learning-enabled CPS. It will pursue a compositional framework and computational tools that can reason about the uncertainty and approximation introduced by learning components and enable system design via a hierarchical and modular approach. The proposed research can have a highly positive influence on the design and real-world deployment of safe and cost-effective autonomous systems for a variety of applications, including autonomous driving, robotics, and industrial automation. Moreover, it has the potential to offer a unifying framework for reasoning about a number of robust and fault-tolerant design approaches that are currently based mostly on ad hoc solutions. Collaborations with industry partners will be pursued to facilitate transitioning the research findings into practice. An educational plan including new undergraduate and graduate courses and a program for pre-college students will complement the research effort, aiming to educate the next generation of engineers and researchers on the concepts and the multidisciplinary attitude needed to realize "intelligent" systems that are safe, technologically and economically feasible, and seamlessly interacting with people. The project develops a compositional framework for reasoning about the probabilistic behaviors of CPS built out of unreliable components. The framework relies on stochastic models of the interfaces between the components and their environments, termed deep contracts, together with rigorous rules for composing and refining them. Rich, quantitative, logic-based stochastic specification formalisms and data-driven modeling techniques will be leveraged to express and propagate computationally tractable representations of uncertainty at different abstraction levels. The framework will be vertically-integrated and offer mapping mechanisms to bridge heterogeneous models and heterogeneous decomposition architectures in the design hierarchy. It will provide computational tools to efficiently solve verification and synthesis problems with stochastic contracts. Finally, it will offer mechanisms to monitor requirements throughout the entire system life-cycle and provide assurance both at design time and runtime. 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 · 2024-10

This project investigates the spatial and temporal variation of urban heat and urban pollution interactions at the geographic scale of neighborhoods. Urban Heat Islands (UHI) and Urban Pollution Islands (UPI) are a well-documented phenomena where the ambient temperature and pollution are dis-proportionally high as compared to non-urban areas. This poses a critical health and environmental risk to more than half of the world’s population. Until now these two environmental hazards have been studied independently and at the larger geographic scale of cities, therefore little is known about how they interact to affect heat and air quality for inter-urban residents. This multidisciplinary research will develop geographical methods that will transform knowledge about the patterns and drivers of intra-urban Heat Island and Pollution Island interactions. The project’s educational activities will broaden participation of undergraduate women and military veterans in STEM sciences and will strengthen scientific data literacy in K-12 and college classrooms by developing curricular materials on environmental risks. Using a mixed methods approach, this project couples a dense network of fine-scale ground measurements of air temperature and air particulates collected via citizen scientists, with large-scale remotely sensed data of land surface temperature and aerosols. These data will be analyzed to determine when and where urban heat islands and urban pollution islands occur, and the risks that their interaction poses to urban residents. These results will support informed decision-making for urban heat and air pollution mitigation. 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 2024 · 2024-09

Project Summary Significance: Myopia (short-sightedness) results from a failure of emmetropization in which eyes grow too long for their optical power. Myopia’s association with sight-threatening complications and projected near-epidemic levels world-wide (50% by 2050) make it critical that effective treatments for slowing myopia progression be developed. As an animal model for myopia, the mammalian guinea pig model shares many of the structural features of the human eyes, including a human-like fibrous sclera. Our proposal takes advantage of our recently developed contact lens-wearing guinea pig model and related choroidal thickness/vascularity and retinal pigment epithelium (RPE)-RNA Seq data to explore pharmacological avenues for improved myopia control under 3 aims: (1) Topical levodopa-carbidoa combination therapy in guinea pig: This combination of a precursor to dopamine, and an inhibitor of its breakdown will be investigated with respect to its ability to inhibit defocus-induced myopia, with underlying mechanisms for their anti-myopia actions (retina &/or choroid- based) explored at the same time. (2) Topical atropine-levodopa-carbidopa combination versus atropine only therapy in guinea pig; (3) Increased accommodative tone is encoded by RPE & inhibited by atropine. The goals for aims 2 and 3 are similar to those in (1) of understanding mechanisms. Planned studies will use advanced ocular imaging and functional testing, as well as routine ocular biometric measurements to track and compare treatment outcomes. Treatment efficacy will be compared across all 3 treatment arms, in terms of both interocular differences and changes over time. Comparing data from experiments in Aims 1 and 2 will provide additional insight into their sites of action, with efficacy data for the combination vs. single ingredient treatments having important translational value. Functional testing also has potential translational significance, given the recognized role of dopamine in retinal processing, connections with circuits involving ipRGCs and circadian rhythms and hints of altered visual processing in myopic young adults. Molecular biology tools applied to timely sampling of RPE from treated and control eyes of the three treatment groups will be used to obtain additional insight into treatment mechanisms and interactions. Established RPE gene expression signatures along with choroidal thickness and morphological changes will be used to evaluate retinal versus choroidal contributions to recorded therapeutic effects (Aims 1 & 2). Aim 3 will provide additional insight into the mechanism of action of topical atropine.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY/ABSTRACT: The adult zebrafish has an incredible ability to regenerate up to 20% of its heart after ventricular resection. In comparison, adult humans have little to no regenerative ability. In the zebrafish, this process is known to be driven by pre-existing cardiomyocytes in the heart, but the specific sub-populations of cardiomyocytes involved is not known. Cardiac neural crest cells migrate during development and contribute to several structures in the heart, including a subpopulation of these cardiomyocytes. Though these neural crest derived cardiomyocytes (NCCMs) make up less than 15% of all zebrafish cardiomyocytes, they are essential for proper heart development and function. Thus, we hypothesize that NCCMs may also be essential for proper cardiac regeneration. I aim to dissect the unique contribution of NCCMs to zebrafish cardiac regeneration by measuring their participation in and necessity for distinct stages of cardiac regeneration. I will accomplish this by quantifying NCCMs in comparison to other cardiomyocytes during regeneration utilizing a combination of inducible transgenic manipulations, single cell genomics, and in situ techniques. Interestingly, the ratio of NCCMs to MCMs is conserved in the zebrafish, chick, and mouse though their ability to regenerate differs. Thus, it is not just the presence or absence of NCCMs that confer regenerative ability, but a unique feature of zebrafish NCCMs. I aim to determine the unique gene regulatory circuits in NCCMs during regeneration and the necessary components for cardiac regeneration. This approach will uncover the role of NCCMs and developing neural crest gene regulation in zebrafish cardiac regeneration. Further, this data will help generate future hypotheses and subsequent genetic targets for the investigation of the differential ability to regenerate the heart among species. Ultimately, this work will contribute to our understanding of the role of a unique neural crest derivative in regeneration and generate potential routes of regenerative therapies for heart disease in human patients.

NIH Research Projects · FY 2024 · 2024-09

A fundamental question in vision research is how the visual system creates and maintains a clear retinal image of the world. Understanding the oculomotor mechanisms involved in focusing the retinal image (termed accommodation) can yield insights into how clinical measurements of visual clarity translate to daily life, how to improve visual technologies like augmented reality, and how refractive errors such as myopia develop. It is known that accommodation mechanisms are sensitive to longitudinal chromatic aberration (LCA), which is the inability of the lens in the eye to bring all wavelengths of visible light into focus at once. This means that in polychromatic imagery, some wavelengths will always be out of focus. It is commonly assumed that the visual system prioritizes bringing middle wavelengths into focus on the retina, as it is known to do when light entering the eye is white and broadband. But it is unclear whether this holds true for natural, polychromatic light spectra, or what the mechanisms governing accommodation to such spectra are. The research in this Pathway to Independence proposal aims to quantitatively model the mechanisms supporting visual accommodation to complex natural imagery. During the mentored phase, the candidate proposes to probe color-sensitive accommodation mechanisms by measuring and modeling human accommodative responses to visual stimuli generated from a variety of skewed light spectra, particularly spectra with disproportionate energy at long or short wavelengths. During the independent phase, the candidate proposes to build principled statistical models relating accommodative behavior to the joint statistics of spectral power and depth in natural scenes. The candidate will then expand the scope of the research to study how spatial and color variations in natural scenes interact to impact accommodation. He will build models of accommodative mechanisms that account for the impact of these variations. Understanding how these mechanisms operate under natural conditions will improve our scientific understanding of vision and can support improved procedures for assessing and achieving visual clarity.

NIH Research Projects · FY 2025 · 2024-09

Studies of human perception mostly focus on neural responses to short stimuli and specifically on their initial recognition. How the brain supports sustained perception of longer stimuli is poorly understood. The fundamental novelty of our project is in addressing this neglected segment of human perceptual experience. We recently discovered that distributed neural representations of stimulus category persist for the duration of the stimuli in ventral temporal cortex, whereas in fronto-parietal cortex the stimulus category could only be decoded shortly after stimulus onset. These findings motivate our Specific Aims: 1) How does attention and awareness affect persistent representations of visual stimuli? 2) What is the structure and information content of field potentials and single neuron spikes in individual brain regions and how do they interact during sustained perceptual experience? 3) What are the computational principles and mechanisms underlying sustained perception? The project is embedded in the Pis long term goals of understanding the electrophysiological and computational basis of perception, attention and awareness. We will use advanced computational methods and unique intracranial data of local field potentials (LFP) and single units (SU) from human patients, combining novel experimental paradigms, data analysis and decoding approaches, and modeling and analytic frameworks. The project will provide training to graduate and undergraduate students in interdisciplinary, collaborative research on fundamental issues in neural computations and cognition, with cross-site visits to develop new approaches and discuss results.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY Demographic and social forces that influence human behavior fundamentally shape the epidemiology of infectious diseases, but are often left out from mathematical models of disease transmission. Our recent work has added to the growing body of evidence that patterns of how humans come into contact with one another, and the adoption of behaviors to reduce transmission, are highly heterogeneous across individuals and groups in a population; these behaviors are strongly influenced by how societies are structured and the socio- economic, environmental, and political contexts in which epidemics occur. These same forces influence patterns of exposure, susceptibility, and health outcomes. Human behavior also varies temporally and spatially, leading to heterogeneities in transmission across space and time and the possibility of dynamical feedback between the disease environment and the socio-demographic processes that influence human behavior. Driven by the public health imperative to understand and address disparities in infection exposure and risk, I propose to develop new models and methods for understanding how demographic and social processes – from individual heterogeneities to large-scale patterns of population behavior – drive infectious disease risk across scales. Specifically, my research group will develop a generalized framework for jointly modeling human behavioral changes and disease dynamics, with bi-directional feedback between behavior and the disease environment. We will also build and validate models for incorporating data on human mobility and aggregation at the population level, while incorporating other extrinsic drivers of transmission such as climate. We will parameterize these models by leveraging detailed human behavioral data from an ongoing survey platform as well as novel sources of mobile phone and digital trace data, and validate model results by fitting to epidemiological geo-located time series data. Our modeling approach will be flexible and dynamic, allowing us to adapt it for specific pathogens and regions of interest. The project results will provide a better understanding of the complex interactions between human behavior and the fundamental biological processes underlying disease transmission, with the ultimate aim of guiding control efforts, designing equity-focused interventions, and improving our ability to predict and prepare for outbreaks. 1

NIH Research Projects · FY 2025 · 2024-09

Many neuropsychiatric disorders involve compulsive behavior, including obsessive-compulsive behavior, obesity, eating disorders, alcoholism, and addiction. Compulsive behavior appears to arise from impaired top- down control of striatal learning mechanisms, particularly involving circuits between the orbitofrontal cortex (OFC) and the caudate nucleus (CN). Theories of decision-making have emphasized a complex relationship between fast, automatic, habitual processes and slower, deliberative, goal-directed choices. These distinct processes are thought to map on to distinct frontostriatal circuits with automatic processes and goal-directed choices linked to different striatal regions. Theoretical accounts of OFC control have argued that it is responsible for providing state information to CN, ensuring that the valuation of reward outcomes is contextually appropriate. Recent psychophysics results have shown that habitual responses are prepared simultaneous with goal-directed responses, but are inhibited to allow the slower, goal-directed response to occur. This raises the possibility that OFC may have a gating function, similar to that which has been posited for more dorsolateral frontal regions, whereby it inhibits habitual responses to allow goal-directed behaviors. In the current grant, we will test the hypothesis that OFC is responsible for inhibiting habitual responses in the striatum when top-down control and more deliberative decision-making is required. We will use a novel behavioral task that enables us to manipulate the amount of top-down control required for a decision using two conflicting reward contingencies. This is manifest as an increase in the amount of time necessary to make the decision as well as the number of saccades that the animals make to either option. In addition, we have spent the last year developing methods to lower the new primate Neuropixels probes into deep targets within the brain, including OFC and CN, and for managing the large amounts of data that these probes generate allowing us to use population-level decoding to investigate the dynamics of these cognitive processes with high temporal and single-trial resolution. In addition, we will test a second hypothesis that OFC accomplishes striatal inhibition via coherence in the alpha band. We have previously shown, via closed-loop microstimulation, that reward-based learning depends on theta coherence between OFC and the hippocampus. However, recent work has emphasized the role of the alpha band in mediating inhibitory processes, both in the frontal cortex and in posterior sensory cortex. We will use our expertise with closed-loop microstimulation to test whether OFC inhibits CN via alpha coherence during top-down control. Understanding the dynamics of OFC-CN interactions will lay the groundwork for building devices that can meaningfully interact with these circuits.