University Of California Santa Barbara

NIH Research Projects · FY 2025 · 2024-07

Recent technological advances in mobile health have catalyzed the rapid growth of large volumes of digital health data due to its potential for scientific impact and health relevance for general populations. This growth imposes unique challenges for machine learning, computation, and information science due to the sheer volumes and high frequencies of longitudinal measurements over time. While machine learning, particularly deep learning, has led to major advances across various data types, its application in modeling time series health data to develop smarter health care systems remains challenging, with relatively low adoption in real-world healthcare settings. Primary reasons for these challenges lie in the irregular, multi-resolution health data collected from diverse subjects who display significant variations in their behaviors and responses to different treatments. Neglecting population diversity contributes significantly to health disparities, as policies and decisions often disregard marginalized minority groups. This proposal provides fundamental and rigorous solutions to address these challenges for complex heterogenous data with a particular focus on mobile health data. More specifically, our research is inspired by social determinants of health (SDoH) that impact health disparities among women in the U.S using two ethnically diverse women’s health datasets, where the data present high heterogeneity and multi-resolution features over time. We aim to flexibly extract the essential information from heterogeneous signals across high-dimensional omni-channels over time and integrate information from multi-resolution variables, to enhance prediction precision and interpretability. We further target online sequential policy optimization that provides non-invasive intervention treatments over time to maximize individuals’ health outcomes according to their heterogeneity. In summary, our research aims to: (1) Develop deep neural models for understanding and predicting individual health over time, while learning shared patterns across multiple individuals to enhance interpretability of the results; (2) Create novel data integration techniques for multi-resolution mobile health data, accommodating irregular time intervals, diverse sampling rates, and measurement variations among subjects; (3) Implement a reinforcement learning platform to find subject-specific optimal policies under populational heterogeneity; (4) Apply the proposed methods to identify biopsychosocial factors affecting women’s health, enabling the development of early prevention and intervention strategies to enhance overall well-being in women.

NIH Research Projects · FY 2024 · 2024-07

Project Summary Ovarian hormones such as estradiol and progesterone are increasingly implicated in the sex-specific etiology of neurological disorders. Conditions like Alzheimer’s disease, for instance, are more than twice as likely to arise in women compared to men, with accumulating evidence pointing to a depletion of circulating hormones across the menopause transition as a critical risk factor. However, the mechanisms underlying steroid hormone modulation of neural circuits remain unclear. The estrous cycle offers an ideal window into the sex-dependent mechanisms underlying neurological disorders, as structural and functional plasticity are tightly coupled with naturally cycling levels of steroid hormones. In this project, we will use 2-photon microscopy, viral gene editing, and computational analysis of neural and behavioral data to assess the effect of the estrous cycle on sensory and spatial representations. Our preliminary data indicate that pyramidal neurons in hippocampus exhibit elevated dendritic spine density and greater place field remapping during high-estradiol stages of the estrous cycle. In the F99 phase of this proposal I will establish the molecular mechanisms underlying hormonal changes in spatial coding by using CRISPR/Cas9 to target specific receptors. In the K00 phase I will elucidate how hippocampal circuits are modulated by sensory perception across the estrous cycle, and how this influences ethological behaviors. To accomplish this, I will first track dendritic spine turnover as a function of estrous cycle stage and identify the specific endocrine receptors responsible for driving estrous-dependent changes in spine turnover (Aim 1.1). Using two-photon microscopy, I will measure dendritic spine turnover in hippocampal region CA1 and use viral gene editing to knock down targeted hormone receptors in a subset of CA1 neurons. Dendritic spine turnover will be evaluated between wild-type and knockdown cells. Second, I will record the functional responses of place cells across the estrous cycle as they remap between environments (Aim 1.2). Targeted receptor knockdowns will be used to determine the mechanistic role of endocrine receptors in shaping the flexibility of spatial representations. In the K00 phase, I will take the skills and insights gained from the F99 phase and use them to investigate the role of the estrous cycle in modulating ethological behavior (Aim 2). I will leverage unsupervised machine learning to evaluate a caching behavior dependent on the hippocampal-olfactory circuit, while recording the activity of olfactory bulb neurons that project to CA1 by way of entorhinal cortex. Chemogenetic silencing of these projections will be used to uncover the mechanism underlying behavioral and circuit-level changes across the estrous cycle. Ultimately, the proposed work will represent a significant advance in our understanding of the modulatory role of steroid hormones in cognition, and facilitate the development of individualized sex-dependent treatments for neurological disorders.

NSF Awards · FY 2024 · 2024-07

Behavioral economics has had enormous success in the last several decades in making progress towards its goal of making economic theory more realistic. Through the use of careful experiments, clever modeling and convincing analysis of data, behavioral economists have developed a powerful, predictive portrait of how humans reason about and respond to risk, uncertainty, time delay and small scale social interactions. But behavioral economics thus far has not yet seriously turned its powerful lens towards the classical focus of economic theory: understanding how individuals respond to and reason about the mass behavior of large groups of people. Yet understanding how individuals think about mass behavior and the impacts on markets, online cohorts, and other large groups has never been more urgent in our increasingly connected world. This project uses the powerful conceptual and empirical tools of contemporary behavioral economics to answer some basic questions about the psychology of mass behavior in order to guide the development of predictive models and guide decisions. Using experiments, the researcher will explore how large groups reason together, how individuals reason about large groups and how people respond to the strategic powerlessness of acting as a member of a large group. The dataset gathered by the investigator and the conceptual insights gleaned from it will allow us to better understand the function of human institutions, the way people form beliefs, and the rationality of large groups of people acting and interacting in concert. Behavioral economics has mostly, so far, focused its attention on understanding how people reason about and form preferences over either (i) exogenous variables or (ii) very small-scale strategic interaction. For instance, the vast majority of empirical and theoretical work in the field focuses on understanding individual-level optimization, statistical inference, risky choice, ambiguous choice, intertemporal choice, social preferences, and behavior in games that involve only a handful of players. This focus has been highly productive, generating both (i) a set of sharp characterizations of the structure of human reasoning and preference formation in these settings and (ii) a set of conceptual and empirical methods for characterizing and modeling individual behavior. This work will extend these methods to include how people reason about and respond to the behavior of large groups of other people (“mass behavior”). The idea is to use the same style of experiments that have been honed for decades for understanding statistical inference, optimization and preference formation in response to exogenous variables, and simply replace these exogenous variables with the endogenous aggregate behaviors of large groups of other people. The research project will focus on understanding (i) how large groups of people reason or compute together in various canonical institutional settings, (ii) how individuals respond to the strategic powerlessness of being a member of a large group (i.e., of being a “price taker”) and (iii) how people reason about the outcomes of mass interaction when forming social opinions and views. The data collected will be relevant to (a) building better models of large-scale institutions like markets and organizations; (b) understanding the behavior of mass groups of people, and (c) designing better decisions for an increasingly integrated and coordinated world. 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 · 2024-07

This award supports research in gravitational physics at the University of California, Santa Barbara. Many of the deepest problems in theoretical physics revolve around combining Einstein’s theory of general relativity with quantum theory. The resulting theory is called “quantum gravity” and is needed to understand the origin of the universe, the nature of space and time on small scales, and what happens inside black holes. The research supported by this award will use the latest techniques and tools to try to answer some of these fundamental problems. An essential part of this award is the training of graduate students and postdoctoral researchers in the knowledge and techniques that are central to understanding and discovery in gravitational physics. Through a range of forums from public lectures, to informing media reporters, the Principal Investigators will disseminate the directions and results of their research to a broad audience. Society at large will benefit by increasing their understanding of science and the world they live in. This award involves three related programs of research. PI Horowitz will explore black holes at very low temperatures in more general situations than has been done before. This will include breaking some spatial symmetry (both with and without a cosmological constant) and including quantum corrections to general relativity. It has recently been shown that these black holes can have much larger tidal forces near their horizon than previously thought. PI Horowitz will investigate new examples and look for possible astrophysical applications. PI Marolf will explore evidence for and implications of the idea that gravitational systems have a finite density of states, meaning that in a finite region of space, they can have only a finite number of states with energy less than any chosen value. While this idea would explain the thermodynamic properties of black holes, it is well known to fail in perturbative treatments of quantum gravity. In particular, one can find black hole geometries with arbitrarily large volumes of space behind the horizon, and which can thus hold arbitrary numbers of independent states. A focus of PI Marolf’s work will be the study of toy models in which the density of states in the full theory is much smaller than that of the naive perturbative theory. Horowitz and Marolf will both study possible definitions of quantum gravity associated with Feynman’s reformulation of quantum mechanics as a sum of all possible histories. For gravitational systems, such histories are entire spacetimes and so are associated with both geometry and topology. If one considers positive definite metrics, the sum over topologies has recently been argued to lead to the above-described finite density of states. But relativity teaches us that the metric of a physical spacetime is not positive definite, and the desired so-called Lorentz-signature metrics are not compatible with general such topologies. This tension will be explored from a variety of viewpoints. The methods employed in all three programs of research will be primarily analytic, though these will be supplemented with numerical calculations on desktop computers. 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

As we navigate the ever-evolving landscape of artificial intelligence, the pursuit of energy-efficient solutions stands as a paramount challenge. The nexus of neuroscience and engineering holds the promise of unlocking groundbreaking advancements in this realm. This NSF one-day workshop aims at creating a collaborative educational and research platform to bridge the gap between these disciplines. The workshop will be held in Santa Barbara, CA in Summer 2024. It will feature two sessions - one dedicated to recent developments in brain-inspired algorithms, and another to innovations in energy efficient hardware. Each session will be comprised of discussion panel and talks from invited experts in the field, and followed by solicited student poster presentations. The award will directly support accommodations for more than 40 participants, including at least 25 students and postdoctoral scholars. A particular focus is on inviting principal investigators and student researchers involved in recently started NSF BRAID projects across the United States. Supported by the NSF, this initiative aligns with national interests by nurturing a diverse scientific community, bolstering educational frameworks, and fostering interdisciplinary collaborations. The technical significance of this workshop is to provide a platform for discussions on identifying the next generation of neuromorphic computing hardware by integrating cutting-edge neuroscience with engineering principles, which aligns with the larger aim of NSF BRAID program. The sessions are divided into two core themes: (a) advanced brain-inspired algorithms (continual, online local learning, spiking neural networks, and compartmental neuron/dendrite models); and (b) the development of energy-efficient hardware (devices, circuits, architectures, and systems). Discussions will focus on the emulation of neural processes and the optimization of power consumption in hardware designs, assessing their integration into existing systems, scalability, and potential impacts on energy use in AI operations. A dedicated student poster session will encourage the exchange of ideas and provide mentorship opportunities, enhancing student engagement with top experts in the field. The workshop will also explore practical applications of these technologies to boost their commercial viability and societal benefits. Concluding the event, a comprehensive report summarizing the current status, challenges, and promising research directions of the BRAID program will be developed. This report will be published in a peer-reviewed, open-access journal and made publicly available, ensuring broad dissemination of the workshop's impactful findings to both the scientific community and the public. 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

Abstract The goal of this R03 is to leverage computational linguistics to identify sensitive, efficient, and scalable measures of social communication that can be used to inform intervention efforts for transition-aged youth on the autism spectrum. Such measures would yield significant benefits for both (a) quantifying post-intervention clinical outcome and (b) precision tailoring for future interventions. By applying sophisticated computational linguistic approaches specifically designed to assess social communication in autistic youth (developed by MPI Parish- Morris and Co-I Cho) to a rich pre-existing dataset from a completed RCT of the START intervention model (extracted from 421 video-recorded conversations from 35 participants collected across 6 timepoints by MPI Vernon), the proposed project will significantly advance the goal of objective, efficient, and scalable clinical outcome measurement for youth with autism. Our collaborative research teams will use computational linguistics to identify objective, quantitative predictors of clinical intervention outcomes in autistic youth and chart individual trajectories of change. We hypothesize that (a) youth assigned to the START intervention group will demonstrate significant gains in linguistic markers of social phenotype in comparison to waitlist controls, (b) across the entire START cohort, modeling individual trajectories of growth in linguistic markers of social phenotype will identify subgroups with unique baseline factors that moderate the slope of change, and (c) linguistic markers that predict social communication success will vary by speaker (male vs. female) and context (same-sex vs opposite-sex conversations). We will also validate our linguistic features as social communication outcome metrics over time. We also anticipate that identified vocal/linguistic features will have high convergent validity with existing measures of social communication outcome and social impression ratings, and high discriminant validity with measures of restricted/repetitive behaviors and interests. This R03 proposal addresses NIDCD Strategic Plan Theme 3 to promote a precision medicine approach to prevention, diagnosis, and treatment of conditions that impact speech and communicative functioning, such as autism. A key output of this project will be validated computational linguistic features that can be used for clinical outcome measurement in autism. These metrics hold promise for both (a) efficiently and objectively quantifying post-intervention clinical outcome in autism and (b) guiding precision tailoring for future intervention implementations. Thus, the proposed project fills an important knowledge gap that could significantly advance the goal of objective, efficient, and scalable clinical outcome measurement and intervention response monitoring for transition-aged autistic youth. This proposal has high

NIH Research Projects · FY 2026 · 2024-05

Summary. Current methods for monitoring kidney function and the effectiveness of renal replacement therapy, which rely on infrequent, blood-draw measurements of plasma creatinine and urea, are simply inadequate. In response, here the team that invented electrochemical aptamer-based (EAB) sensors, the first high-frequency, real-time molecular measurement platform able to work in situ in the body, have combined forces with world- class experts in both aptamer selection and nephrology to advance the measurement of renal function and uremic solute clearance. To realize this vision, we are pursuing three specific aims. First, our aim-1 goal is the development of minimally-invasive, subcutaneous EAB sensors as a clinical tool supporting the measurement of plasma urea and creatinine concentrations, thus adding renal clearance to the list of vital signs that, like pulse and blood oxygenation, can be monitored unobtrusively and in real time. By providing accurate, real-time information regarding the rate of change of plasma creatinine, such an advance would accelerate the diagnosis and treatment of acute kidney injury and enable the immediate assessment of treatment efficacy. By providing real-time information regarding plasma urea, the proposed technology would likewise enable the high-precision personalization of renal replacement therapies, which has the potential to improve outcomes, lower costs, and enhance patient quality of life. In parallel, under aims 2 and 3 we will advance the EAB platform as a research tool to improve our understanding of uremic toxin clearance. Specifically, in aim 2 we will use the platform to characterize the clearance of uremic solutes from the blood, brain, and muscles in animal models of uremia. By improving our understanding of the extent to which the clearance of urea and creatinine from the plasma reflects their clearance from the solid tissues that are the major sites of uremic toxicity, success in these efforts will increase the clinical value of both traditional blood-draw/benchtop tests and our proposed real-time monitoring technology. And in aim 3 we will expand the platform to the measurement of a range of physicochemically and physiologically diverse uremic toxins, with our goal being to identify markers that, either alone or in conjunction with urea and creatinine, provide a more complete, more clinically meaningful descriptions of renal function, uremia, and the efficacy of renal replacement. The successful outcome of this work will be decisive demon- stration of a powerful new clinical tool for monitoring renal function, kidney failure, and renal replacement, and two powerful new research tools aimed at improving understanding of the clearance of the toxins underlying uremia. Together, these advances will significantly enhance the detection, study, monitoring, and treatment all stages of kidney disease.

NIH Research Projects · FY 2025 · 2024-05

Studying eye movements has been central to understanding active vision, attention, and cognition. Computational models have helped advance the field by assessing the visual features and computations guiding eye movements and have helped understand human visual and cognitive dysfunctions. However, even with 20 years of computational models, we are still far from adequately modeling eye movements and decisions in natural tasks with real-world images. Models often miss incorporating how our vision degrades towards the visual periphery, do not incorporate a human’s intention (task), and critically do not have a learned understanding of scenes and objects nor language to guide the fixations. Our goal is to combine developments in powerful vision Transformer models with computational models of human vision to create a Foveated Search Transformer Model (FST) that can understand simple linguistic instructions to execute eye movements that gather information for the task with an understanding of other objects in the scene. Our work will focus on visual search for objects in real-world scenes “never seen” by the model. We hypothesize that the developed FST model will reach human accuracy levels and will capture some of the landmark eye movement behaviors such as manipulations of context (location, size, and semantic relationship of the target object to the surrounding scene). ). We also hypothesize that the model will predict human behavior and fixations better than baseline models such as Saliency, Deep Gaze, and a version of the FST model with disabled contextual understanding. To achieve our goal, we propose two specific aims. SA1. To develop a Foveated Transformer Search (FST) model that learns eye movements that are task-optimizing, understands scene semantics, and captures landmark contextual effects of human search; SA2. To develop a visual-language Foveated Search Transformer (FST-L) model that can interpret language and search for specific targets with descriptive details provided in a sentence. The developed FST models will be compared to human eye movements and search decisions as well as baseline models. If successful, the newly developed model will open many new avenues of research on eye movements with more naturalistic tasks and allow prediction of the functional impact of visual disorders in eye movements and subsequent perceptual decisions. The model will also provide a tool to expand current investigations of search-related neural activity using computational models.

NIH Research Projects · FY 2025 · 2023-09

Project Summary More than one million Americans present with foot drop after stroke and often develop compensatory strategies to avoid scuffing the foot. Current treatment for foot drop addresses the hazard of tripping by applying non- volitional torques to the ankle to prevent the foot from plantarflexing. The two most common such devices are the Ankle Foot Orthosis (AFO) and Functional Electrical Stimulation (FES), which both provide well-documented orthotic effects to help users walk while the device is worn. However, therapeutic effects are limited, and users rarely improve enough to cease needing their device. Accordingly, here we propose an alternative strategy that addresses the hazard of tripping even while allowing full volitional control of the ankle: an inexpensive Variable Friction (VF) shoe. Its outsole is high-friction during the stance phase of gait and low friction during swing; further, it produces a “click” when a scuff occurs. Evidence suggests that such feedback can enhance therapeutic outcomes. Our preliminary data suggests that the VF shoe is effective at increasing gait speed immediately (orthotic effect) and after long term use, even when the shoe is removed (therapeutic effect). Our central hypothesis is that allowing volitional motion of the ankle while mitigating the hazard of tripping coupled with gait-phased auditory biofeedback will result in improved gait for subjects with drop foot. Specifically, we hypothesize that the VF shoe will show significantly greater therapeutic effects than an AFO, yet maintain the desirable orthotic effect of the AFO. We arrange our work in two Aims. Specific Aim 1: Characterize the scuff-force reduction of the VF shoe over the lifetime of use. Critical to understanding the effects of the VF shoe is a characterization of the level of scuff-force reduction. Specific Aim 2: Evaluate the effects of the VF shoe on gait in individuals with chronic stroke and drop foot. During each 12-week phase of an AB-BA clinical trial, participants will walk for at least 30-45 minutes per day for at least 5 days per week at home. We expect that this work will validate our VF shoe as a tool for improving gait after stroke and will introduce a new paradigm for drop foot therapy: fully volitional ankle control while mitigating tripping. The results will lay the foundation for a future pivotal device trial on the VF shoe to assess clinical efficacy. Importantly, the VF shoe is simple, low-cost and easy-to-use, making wide-ranging home-based adoption feasible.

NIH Research Projects · FY 2024 · 2023-09

PROJECT SUMMARY/ABSTRACT The proposed career development application provides research training for Dr. Jiaying Liu to facilitate her transition to independence. The goal of the proposed research is to identify neurobehavioral makers of nicotine use escalation and cigarette smoking initiation among young adult (YA) users of electronic nicotine delivery systems (ENDS). Findings are expected to inform regulatory policy and improve YA responsiveness to public health campaign communications. Given the accumulating evidence that ENDS use conveys 5-fold additional risk for smoking initiation and other tobacco use escalation, recent YA increases in use are alarming, threatening a resurgence of dependence that may reverse decades of tobacco control success. Therefore, it is crucial to identify predictive markers of smoking initiation and tobacco use escalation in this vulnerable population, and to provide actionable evidence that informs regulatory and prevention efforts. Functional magnetic resonance imaging (fMRI) generates information complementary to traditional behavioral risk assessments, with direct observations of the neural substrates that underlie the subjective states known to perpetuate addiction, in order to yield objective and putatively more predictive measures. Assessment of behavioral and brain markers associated with smoking onset and tobacco use escalation is proposed. The goal is to determine whether baseline neurobehavioral markers will predict smoking transition and tobacco use escalation beyond traditional makers at 3, 6, 9 and 12 months. A one-year public service announcement (PSA) intervention with a cross-over design will also be conducted, in which two message exposure orders and one control condition allow testing whether novel anti-ENDS PSAs addressing harms associated with ENDS flavors will more effectively prevent tobacco use escalation compared to the existing regular PSAs. The proposed research is among the first that aims to inform regulation of flavored ENDS marketing, and to provide recommendations for developing effective PSAs for prevention campaigns. These aims directly address multiple priorities of FDA Center for Tobacco Products (Marketing Influences, Communications, Behavior and Addiction), and they are expected to pinpoint effective regulatory gates to address the current ENDS epidemic. Dr. Liu’s long-term goal is to become an independent researcher translating communication neuroscience research to regulatory actions for improvements in health campaigns and interventions utilizing persuasive anti-tobacco messaging. Her near-term goal is to prepare a competitive R01 application to implement randomized controlled trials of PSAs to maximize impact on deterrence of smoking and substance use transition among ENDS users. The proposed training experience is designed to develop competencies in (a) neuroscience, (b) functional neuroimaging, (c) tobacco regulatory science, and (d) grant writing. The proposed research study provides focus for training and preliminary data to support a competitive R01 application.

NIH Research Projects · FY 2025 · 2023-09

Project summary Visual stimuli drive activity across a range of brain areas, including primary visual cortex (V1) and higher visual areas (HVAs). These HVAs vary in their downstream connectivity to support a range of visually guided behaviors. Similarly, these HVAs can vary in how they represent visual stimuli. Our recent results have revealed how a subset of HVAs in the mouse can vary in representations of motion and texture. However, we do not yet have a complete picture of how HVAs vary in their representations of visual stimuli during visually guided behavior. Moreover, it remains unclear how neural circuitry can support HVA-specific representations of components of visual stimuli. In this project, we will provide a definitive account of how components of complex visual stimuli are represented across cortical areas in behaving mice. Moreover, we will use novel measurements of statistical dependencies of neuronal activity across cortical areas to infer principles of population activity coding and circuit organization. This work will take advantage of several technologies our lab has helped to develop: large field- of-view two-photon calcium imaging, open-world naturalistic virtual reality for mice, and advanced analysis tools based on both classic approaches and modern statistical analysis. The results from this work will reveal insights into how HVAs parse complex visual stimuli into representations that can guide adaptive behavior.

NIH Research Projects · FY 2025 · 2023-09

PROJECT SUMMARY When looking for a red ladybug in a bush, our attention may be efficiently guided by the red color among the green leaves. If, instead, finding a green caterpillar is our goal, the same red color would need to be ignored, and instead a moving leaf should guide our search. Searching for an object involves a complex interplay between features of the environment that are unique and capture our attention (e.g., the ladybug) and our current task goals (e.g., look for the movement associated with a green caterpillar). It remains unknown how different brain regions contribute to the guidance of visual attention based on different types of features of the environment, and how activation patterns in these brain regions are impacted by our behavioral goals. Our long-term goal is to understand the principles governing how distributed neural processing systems support flexible visual cognition. Our overall objective, which is the next step in pursuit of our long-term goal, is to ascertain how top- down goals and bottom-up stimulus properties jointly mold activation patterns across feature-selective brain regions. Our central hypothesis is that the core computations supporting attentional selection – bottom-up enhancement of salient locations and top-down enhancement of relevant locations or stimulus dimensions – occur at the level of compartmentalized dimension maps instantiated in feature-selective cortical areas, and these modulations are aggregated into a unified priority map to guide behavior. The rationale for the proposed research is that, once we establish how attentional selection modulates dimension maps in healthy participants, we can build improved diagnostic and therapeutic techniques to understand and treat disorders of attentional control. This hypothesis will be tested across 3 Aims: (1) assay how bottom-up stimulus salience impacts stimulus representations in dimension maps and priority maps; (2) characterize the independent impact of top- down attentional selection on activation profiles within dimension maps, and (3) establish the role of neural dimension maps in guiding attention during visual search tasks. Across all Aims, we will apply model-based neuroimaging analyses to assay stimulus representations in feature-selective retinotopic brain regions measured with fMRI. In Aim 1, participants will perform a fixation task while we present different types of stimuli to measure responses associated with stimulus salience in different features. In Aim 2, participants will selectively attend to one of several feature values defining a stimulus while we manipulate properties of the stimulus display. In Aim 3, participants will perform a demanding visual search task while we manipulate aspects of the task and stimulus display. Overall, this project will generate data to determine the role of dimension maps in guiding visual attention. This project is innovative because it uses model-based neuroimaging analyses to characterize spatial maps carried by feature-selective brain regions during demanding cognitive behavior, and because it tests a key aspect of an important theoretical framework. This project is significant because it will lead to a new understanding of how disparate brain regions collaborate to incorporate stimulus- and task-related factors to guide visual attention.

NIH Research Projects · FY 2025 · 2023-08

Abstract The education of K-12 students and their recruitment and retention into STEM and biomedical careers remain a national problem. SciTrek, a successful outreach program run from the University of California at Santa Barbara, takes a unique approach to STEM education. Reaching ~3000 2nd-8th grade students yearly since 2010, SciTrek’s core philosophy is to immerse students in the practice of STEM, meant as a way of doing and a way of thinking. SciTrek students investigate questions prompted by an observation presented to them by a lead (teacher, SciTrek personnel) and four-six university volunteers/mentors, trained in the SciTrek method. This is the start of a journey of discovery where student groups are not passive spectators, but are given full ownership of the project. They formulate “testable questions,” and decide on an experimental plan, implemented over several days. K-12 grade groups present evidence-based claims based on data analysis, fostering student numeracy. Students present posters, discuss their findings, and provide an argument for their conclusions promoting critical thinking and writing skills. SciTrek’s diverse volunteers contribute to development of a “self as scientist” belief in diverse students. In this proposal, we seek to expand SciTrek, both to new schools, as well as partner institutions (Aims 1 and 3) with the overarching goal of altering the attrition of under-represented minorities (URMs) and girls from STEM. Recent research suggests that beyond a lack of understanding of science and engineering practices, attitudes developed in elementary school negatively impact the pipeline of students who choose STEM and biomedical careers. Understanding how these beliefs (interest, identity, possible/plausible self) arise and may be shaped is particularly relevant for retaining URMs and girls. SciTrek works within the current organizational structures to promote NGSS-aligned science pedagogy. Using SciTrek’s multilevel approach of (1) inquiry- based NGSS-aligned science experimentation focused on Science and Engineering Practices (2) four-six university volunteers/mentors in each classroom, and (3) teacher professional development we aim to: a) increase students’ understanding of what scientists do, and how knowledge is created, b) positively affect students’ attitudes towards and interest in science, as well as science identities, and c) maintain students’ interest in STEM careers.

NIH Research Projects · FY 2026 · 2023-08

Project Summary PLP-dependent enzymes are one of the most versatile biocatalysts and catalyze a diverse range of chemical transformations. They are widespread in nature and play critical roles in metabolism and numerous cellular processes. Studying PLP enzymes is hence important for us to understand biology and develop therapeutics. Because of their exquisite and versatile catalytic activity, PLP-dependent enzymes are also remarkable biocatalysts to build diverse structurally complex and bioactive natural products; and are indispensable biocatalytic tools for asymmetric synthesis of noncanonical amino acids and chiral amine pharmaceuticals. However, despite the vast number of PLP-dependent enzymes characterized to date, our abilities to predict, manipulate, and harness their activities are still largely limited. This research program desires to fill the knowledge gap by integrating discovery, mechanistic investigation, and biocatalytic application to systematically and comprehensively study four types of carbon-carbon (C-C) bond forming and cleaving PLP enzymes, including our recently discovered PLP-dependent Mannich cyclase. These enzymes represent the frontier of PLP enzymology because of their unusual activity, complementary synthetic utility to existing biocatalysts, and unexpected evolutionary relationship with well-characterized PLP enzyme family. All proposed aims are supported by strong preliminary data gathered in our laboratory. Our overarching goal is to understand the chemical and substrate specificity and leverage this understanding to uncover previously unknown functions of PLP-dependent enzymes and explore their non-native catalytic utility. Ultimately, the proposed research will expand our mechanistic understanding on PLP enzymology, shed new light on metabolism, and provide novel biocatalytic tools for amino acid biosynthesis.

NIH Research Projects · FY 2025 · 2023-07

SUMMARY: The stability of neural connections (synapses) and long-term survival of neurons are critically important to human health, as many neurological and neurodegenerative disorders, including dementia, result in the loss of vital synaptic connections in the brain. These dementia disorders currently affect 28 million people worldwide, a number that will increase precipitously as our population continues to age. The proposed research will explore how synapses are maintained when faced with exposure to extreme environmental stressors with the aim of identifying translatable molecular targets to prevent synaptic loss during the normal aging process and the diseased state. To evaluate synaptic stability in the extremes, we will use an invertebrate species, the tardigrade Hypsibius exemplaris, which has the ability to survive near-complete desiccation, and which I recently found can survive extreme hyper-gravity equivalent to 500,000 times the earth’s gravity for an hour. Mechanisms by which the animal survives desiccation are relatively well understood, in that the animal forms a ‘tun,’ an inanimate state of metabolic suspension which is accompanied by gross morphological changes and the loss of nearly 99% of their water content. In contrast, the mechanisms by which these animals survive the extreme forces exerted by hyper-gravity remain wholly unexplored. Following reanimation from desiccation or return to normal gravity, animals rapidly restore coordinated walking and head motions suggesting that their nervous system remains grossly unperturbed by these phenomenal feats of extremotolerance. A critical question is how the nervous system and synaptic function remain stable under these extraordinary environmentally induced stresses. The proposed research will unveil the underpinnings of tardigrade nervous system survival by testing the hypothesis that tardigrades fortify their nervous system through the stabilization of synapses under extreme environmental insults. We will first explore anatomical changes to synapse density and morphology during desiccation and hyper-gravity by direct visualization of synapses and neurons in the nervous system (Aim 1). We will assess the functional maintenance of synapses throughout desiccation and hyper-gravity via a memory-retention paradigm (Aim 2). Finally, we will identify novel targets by monitoring the dynamic changes in the “proteome” that are triggered by extreme hyper-gravity and desiccation, analyze the functional roles of synaptic proteins via the removal of key synaptic regulatory proteins and ultimately apply our identified target molecules to analysis of an in vitro mouse model of neurodegeneration (Aim 3).

NIH Research Projects · FY 2026 · 2023-07

PROJECT SUMMARY Emotion-regulatory deficits are a hallmark of mood and anxiety disorders, which afflict over 20% of adults in the United States. Poor emotion regulation is often characterized by the context-inappropriate expression of emotion, including the unwarranted persistence and influence of negative states outside their temporal context. Therefore, the ability to respond to emotional events in a temporally and contextually sensitive manner is paramount to mental health and wellbeing. Evidence from cognitive control studies indicates that function of the lateral prefrontal cortex (LPFC), including lateral frontal pole and mid-LPFC, is essential for temporally organized cognitive control and behavior sensitive to goals and context. However, mechanistic studies of LPFC function in emotion are lacking, even though there are strong indications of a prominent but little-understood role for LPFC in promoting adaptive emotional functioning, including hypoactivation and reduced LPFC connectivity in mood and anxiety disorders, associations between LPFC lesions and incidence of major depression, and the use of transcranial magnetic stimulation (TMS) to LPFC to treat depression. The central goal of this proposal is therefore to elucidate the organization, representational and causal contributions of distinct LPFC regions for adaptive time and context sensitive emotional responding. This proposal tests the central hypothesis that the human lateral frontal pole (FPl) integrates emotional and temporal information to promote goal-oriented and context-sensitive responses via downstream mid-LPFC function. This hypothesis is informed by documented neuroanatomical projections, insights into the organization of temporal control in LPFC, and recent work unveiling functional specificity in distinct LPFC regions during emotion-dependent cognitive control. Using an innovative combination of multivariate analysis of fMRI data and information-guided TMS, the proposed studies examine the representational and causal roles of distinct LPFC regions for (1) goal-oriented action that requires accurate tracking of temporally extended emotional information (Aim 1) and (2) temporal- context sensitive regulation of affect (Aim 2). Full-factorial representational similarity analysis will permit quantifying emotional valence, temporal, and contextual goal signals—as well as, critically, their interaction. Information-guided TMS, followed by task fMRI acquisition, will establish functional and representational specificity of distinct LPFC regions—FPl and mid-LPFC—with causal inference (Aims 1b-2b). Task-based functional connectivity analysis will uncover the topology of amygdala-LPFC interactions (including intermediary mPFC nodes) associated with emotion-temporal integration and affect regulation (Aim 3). Collectively, these Aims will advance a directional model of how LPFC function and amygdala-LPFC interactions support adaptive time-and-context appropriate responses in the face of emotional challenges.

NIH Research Projects · FY 2026 · 2023-06

Summary. The ability to measure molecules and monatomic ions in the body in real-time and with high-precision would revolutionize many aspects of both biomedical research and clinical practice. It would, for example, provide clinicians with immediately actionable information monitoring regarding electrolyte imbalances, and the plasma levels of drugs of dangerous narrow therapeutic windows. To this end, we are developing Electrochemical Aptamer-Based (EAB) sensors, a demonstrably generalizable platform technology for measuring analyte concentrations in situ in the body. Using this technique, we have already demonstrated the real-time, seconds-resolved measurement of more than a dozen drugs, metabolites and protein biomarkers in the veins, brains, and peripheral tissues of live rats and the subcutaneous space of human subjects for periods of up to 24 h. Building on this, we propose here aptamer selection and aptamer-engineering approaches aimed at improving the sensitivity of these receptors to small changes in the concentration of their target ligands. Our first approach to this end is overcome the often-poor affinity of small-molecule-binding aptamers, thus “tuning” of their affinities to optimally match the concentration range of clinical interest. To achieve this, we are developing unprecedented new selection schemes, including analog-selection, an approach for obtaining initial, if sometimes low-performance, aptamers against difficult targets, and insertion-reselection, which recursively (and dramatically) increases the structural complexity, and thus the performance, of these initial aptamers. Our second aim uses the excess binding energy (i.e., dissociation constants several-fold below the necessary measurement range) afforded by these advanced selection schemes as a basis for introducing allosteric cooperativity, a mechanism that greatly steepens binding curves. In the near term, the expected outcome of the proposed research will be a suite of high-precision, in-vivo EAB sensors against a set of clinically important, narrow-clinical-window drugs, metabolites, and electrolytes. The expected long-term impact of our work however, is much broader, as our success will establish approaches by which the responsiveness of biomolecular receptors to changing ligand concentrations can be rationally improved, a development that will positively impact many receptor-based biotechnologies.

NIH Research Projects · FY 2026 · 2023-03

Many leading questions in neuroscience such as how neurons encode experience, modify behavior, and degenerate, require neural activity to be monitored throughout the brain in living animals. Neuronal activity is tightly linked to an increase in intracellular calcium. Therefore, a cornerstone technology for monitoring neural activity involves the use of genetically encoded fluorescent reporters of intracellular calcium. While fluorescent tools for calcium sensing have proven immensely transformative for neuroscience research, optical approaches do not allow neural activity to be monitored with brain-wide coverage or at any arbitrary depth. To address this challenge, we will develop a new type of genetic sensor for visualizing cumulative calcium signals at a brain- wide scale using magnetic resonance imaging (MRI). To construct these sensors, we will leverage water channels known as aquaporins. We will build on our earlier discovery that aquaporins can be used to generate diffusion-weighted MRI contrast by increasing the rate of water exchange across the cell membrane. Unlike conventional MRI reporters, aquaporin-based contrast does not involve the use of metals, thereby permitting fully autonomous, single-gene imaging with high sensitivity. To accomplish our goals, we propose two inter- connected specific aims. In the first aim, we will develop aquaporin-based reporters of calcium signaling (ARCS) by assembling a synthetic multi-gene cluster for coupling stimulus-evoked rises in intracellular calcium to aquaporin expression. ARCS will permit neural activity to be integrated over defined stimulation epochs in awake, freely behaving animals and subsequently read out by MRI. Following optimization in cell lines, we will validate key performance attributes and safety profiles of ARCS in primary neurons. In the second aim, we will establish in vivo functionality of ARCS by imaging local and brain-wide activation in response to well-established neuromodulation paradigms involving chemogenetic and optogenetic inputs to the ventral tegmental area (VTA). Concurrently, we will benchmark ARCS against multiple complementary readouts of neural activity including blood oxygenation level dependent (BOLD) fMRI, calcium-sensing fluorescence reporters, and c-fos immunohistochemistry. The anticipated outcome of this project is an optimized and well-validated set of genetic tools that will provide neuroscientists with new avenues for unbiased exploration of neural networks involved in coordinating everything from sensory function to behavior generation.

NIH Research Projects · FY 2025 · 2022-09

Towards a Smart Bionic Eye: AI-Powered Artificial Vision for the Treatment of Incurable Blindness How can we return a functional form of sight to people who are living with incurable blindness? Despite recent advances in gene and stem cell therapies are showing great promise, there are no effective treatments for many people blinded by severe degeneration or damage to the retina, the optic nerve, or cortex. In such cases, an electronic visual prosthesis (“bionic eye”) may be the only option. However, the quality of current prosthetic vision is still rudimentary and does not differ much across different device technologies. A major outstanding challenge is translating electrode stimulation into a code that the brain can understand. Rather than aiming to one day restore natural vision with visual prostheses, we might be better off thinking about how to create practical and useful artificial vision. Specifically, a visual prosthesis has the potential to provide visual augmentations through the means of artificial intelligence (AI) based scene understanding, tailored to specific real-world tasks that are known to affect the quality of life of people who are blind (e.g., face recognition, outdoor navigation, self-care). The goal of this proposal is thus to address fundamental questions at the intersection of neuroscience, computer science, and human-computer interaction that will enable the development of a Smart Bionic Eye; that is, a visual neuroprosthesis that functions as an AI-powered visual aid by providing visual augmentations to support specific everyday tasks. To enable such a technology, we first need to 1) understand how visual prostheses interact with the human visual system to shape perception, 2) identify visual augmentation strategies that best support specific real-world tasks, and 3) develop a prototyping system that allows us to validate as well as iteratively improve upon our augmentation strategies with the bionic eye recipient in the loop. This work will further our understanding of how brain stimulation leads to perception, and the insights gained from identifying optimal visual augmentation strategies may be broadly applicable to different visual aids and sensory substitution devices, therefore potentially benefitting both people who are blind and people with low vision. Last but not least, the ability of a visual prosthesis to support everyday tasks might make the difference between abandoned technology and a widely adopted next-generation neuroprosthetic device.

NIH Research Projects · FY 2025 · 2022-09

Title: Investigating the mechanisms of peroxisome homeostasis Abstract The overarching goal of my lab is to understand how cells make and maintain peroxisomes, a ubiquitous membrane-bound organelle that harbors specialized metabolic reactions. Peroxisomes are both versatile and dynamic: cells use them to adapt to their environment, and thus can rapidly remodel their peroxisomes by altering enzyme content, morphology, and number through peroxisome-specific autophagy and de novo biogenesis. Approximately 35 Pex proteins are known to contribute to peroxisome formation and maintenance, yet the mechanisms by which they act are not resolved at a molecular level. Furthermore, we are likely missing many important players, especially in human cells, and this lack of basic mechanistic knowledge hinders our understanding of how peroxisome contribute to human health, both in rare, genetic Peroxisome Biogenesis Disorders (PBDs), and during the aging process. Our approach is to use techniques in protein biochemistry and yeast cell biology to dissect the mechanism of the Pex proteins, particularly focusing on the AAA-ATPase Pex1/Pex6. We aim to identify the full repertoire of Pex1/Pex6’s endogenous substrates and the features that are important for substrate selection. Since mutations in Pex1/Pex6 cause the majority of PBDs, we are further focused on using disease-causing alleles to understand Pex1/Pex6 function in the human cells and the cellular consequences of peroxisome stress induced by these alleles. Finally, we have identified novel regulators of peroxisome homeostasis in human cells, and are now exploring how peroxisome function integrates with the implicated canonical signaling pathways. We anticipate that this research will improve our understanding of how peroxisomes contribute to human health and disease.

NIH Research Projects · FY 2024 · 2022-09

Project Summary: It is becoming increasingly clear that one of the ways that cells interpret and encode information into multiple cell fates is by multiplexing information through dynamic encoding. This is especially true for the MAPK/Erk pathway, that governs many cell processes including cell proliferation, differentiation and migration. For years, how such diverse outcomes were controlled by the same pathway remained elusive, but the advent of single cell studies and optogenetics has elucidated the many ways in which Erk activity can be interpreted into distinct cell fates, and even more recently, the role of dynamics in these complex decisions. The role of developmental Erk dynamics in determining and coordinating human gastrulation, however, has not yet been investigated. We will combine live cell kinase activity reporters and optogenetic control over intracellular signaling pathways to probe the role of ERK dynamics in positioning and coordinating the three germ layers. Additionally, we will uncover whether RASopathy causing mutations influence human gastrulation to pace the way for potential therapeutic intervention. This proposal brings together recent advances in stem cell and molecular engineering. We utilize advances in 2D micropatterning, cellular optogenetic control, live cell kinase activity reporters and CRISPR Cas9 genome editing. Together, these technologies give us unprecedented control over and visualization of microengineered models of human gastrulation, thereby enabling us to investigate the principles of dynamic information transmission. Our platform does not face the same ethical barriers that have limited human embryo research, allowing us to provide otherwise unavailable information about human embryonic development. In this proposal we focus on the role of Erk signaling dynamics in coordinating the three germ layers, as well as uncover impact of RASopathy mutations. In Aim 1 we will image and quantify Erk signaling dynamics using the Erk kinase translocation reporter during the process of gastrulation and determine which features of signaling dynamics are predictive of germ layer fate. Aim 2 will allow us to identify which of these dynamical features are sufficient to determine the cell fate outcome using cellular optogenetics. Finally, in Aim 3 we will uncover whether RASopathy mutations lead to gastrulation defects and investigate whether these are linked to disruption to Erk dynamical signatures using CRISPR Cas9 gene editing and our 2D gastruloid model. Approaching this cell biological question from a systems level perspective, using reproducible precisely controllable tools that are otherwise unavailable without optogenetics and microengineered platforms, has the potential to shine new light on the field.

NIH Research Projects · FY 2024 · 2022-08

Project Summary: Widefield imaging and spatial multiplexing are crucial to advancing the field of neuroscience. Current imagers do not offer the speed and versatility needed for calcium or voltage imaging experiments. In the case of lifetime imaging the functionality is completely lacking in CMOS imagers. The problem is more subtle than it seems because it is not just a matter of brute force speed-up through technology. Speed increases come with large amounts of power dissipation and the need for faster data interfaces. One imaging technique with the potential to solve this issue is the single photon avalanche detector (SPAD). SPADs by virtue of operation result in digital pulse practically eliminating read noise that commonly plagues regular imagers. However, SPAD imagers till date have not seen widespread use due to low pixel density. One of the greatest advantages of the SPAD imager is its ability to perform time correlated measurements, enabling lifetime imaging. Lifetime imagers can yield absolute quantitative measurements not possible with regular imaging modalities. However, the current SPAD imagers take seconds to minutes to compute a lifetime image, much too slow for neural imaging. We propose to overcome these fundamental barriers by innovating at the device, architecture and packaging levels. Our proposed approach utilizes a transistor amplified SPAD design coupled with in pixel analog counting and lifetime estimation. These two innovations make it possible to read the pixel level data at a slower rate, while maintaining a fast frame rate. We additionally propose a new chip level integration approach which packages the imager die and processing die in a silicon package enabling reading from subarrays. This approach enables the imager to maintain the frame rate of the pixel subarray as the pixel density scales. Finally, we demonstrate the advantages of our imager by imaging dendritic activity, both in intensity and lifetime imaging modes, in neural cultures at unprecedented spatiotemporal scales.

NIH Research Projects · FY 2026 · 2022-07

Morrissey Project Summary: (30 lines) The long-term goal of this project is to understand how cells control their sensitivity to external stimuli. Macrophages are a particularly interesting example of this because they constantly encounter background stimuli, like IgG, in their surrounding environment. Macrophage signaling pathways are carefully tuned to rapidly detect IgG-bound pathogens without reacting to healthy cells. Macrophages respond to IgG by activating phagocytosis and/or inflammation. The short-term goal of this project is to define how macrophages set a response threshold for these outputs. We are approaching this problem from two directions. The first direction is examining how receptor clustering or co-clustering affects macrophage sensitivity. Our previous data shows that clustering of activating ligands enhances activation of the Fc Receptor, and increases phagocytosis. Phagocytosis is also controlled by inhibitory ‘Don’t eat me’ signals on viable cells. We are now expanding our studies to examine how clustering of inhibitory ligands, and co-clustering of inhibitory and activating ligands affects phagocytosis. This will suggest how ligand or receptor pattern can tune macrophage sensitivity. Our second direction is examining the intracellular signaling that filters out sub-threshold stimuli. Our preliminary data suggests that a higher IgG signal is required for inflammation than phagocytosis. We will address the following questions: What parameter do macrophages measure to determine IgG signal intensity – signal duration, IgG density, or total number of IgG molecules? What is the molecular breakpoint in the IgG signaling pathway that is only activated by high IgG? How does IgG stimulus intensity affect signaling dynamics in the NFkB and MAPK signaling pathways? Overall, this will suggest how macrophages are able generate two separate responses from a single input based on dose.

NIH Research Projects · FY 2025 · 2022-07

Project summary Bacteria have evolved complex strategies to compete and communicate with one another. One important mechanism of inter-bacterial competition is contact-dependent growth inhibition (CDI). CDI systems are found in a wide variety of Gram-negative bacteria, including many important human pathogens. CDI is mediated by the CdiB/CdiA family of two-partner secretion proteins. CdiB is an Omp85 outer-membrane protein that is required for the export and assembly of the CdiA exoprotein onto the cell surface. CdiA binds to receptors on susceptible bacteria and then delivers its C-terminal toxin domain (CdiA-CT) into the target cell. These systems also encode CdiI immunity proteins, which specifically bind to the CdiA-CT and neutralize toxin activity to protect CDI+ cells from auto-inhibition. CdiA-CT/CdiI sequences are highly variable, with >130 distinct toxin/immunity protein sequence types recognized in bacterial genomes. CdiA-CT toxins are modular and can be exchanged between CdiA proteins to generate functional chimeras. These observations indicate that many different kinds of toxic cargo can be delivered into the cytoplasm of target bacteria. This application seeks to determine the molecular and structural underpinnings that enable this remarkable functional plasticity. We will use a combination of genetic, biochemical and biophysical approaches to gain mechanistic insight into the network of protein-protein interactions that govern CDI. This research will significantly increase our understanding of the ecology and evolution of bacterial pathogens and could inform novel strategies for antimicrobial therapy.

NIH Research Projects · FY 2026 · 2022-07

Project Summary Despite substantial advancements in small-molecule catalysis, general methods to control the stereoselectivity in radical-mediated transformations remain a formidable challenge facing synthetic chemists. On the other hand, enzymes are known for their ability to exert exquisite control over the stereochemical outcome of the reactions they catalyze. However, the catalytic repertoire of enzymes has been largely limited to their native biochemistry. Using an interdisciplinary approach combining ideas and technologies from organic chemistry, organometallic chemistry, biocatalysis, enzyme engineering and computational modeling, we will reprogram naturally occurring metalloenzymes to catalyze unnatural radical reactions with excellent stereoselectivity. Capitalizing on the innate redox property of metallocofactor present in a plethora of natural metalloproteins, we will develop metalloredox radical biocatalytic reactions with significant synthetic utility. First, we will develop new-to-nature metalloredox atom transfer radical addition reactions that proceed with excellent diastereo- and enantiocontrol. Second, we will advance stereoselective radical additions to aromatic compounds in a metalloenzyme-controlled fashion. Furthermore, enantioselective C-H functionalization reactions involving metalloenzymatically formed nitrogen- centered radicals will be developed. All the three aims are supported by strong preliminary data gathered in our laboratory. Together, these novel metalloredox biocatalytic processes constitute a powerful toolbox for the rapid assembly of diverse bioactive small molecules that are highly valuable in biomedical sciences. Furthermore, the development of new-to-nature catalytic functions will dramatically broaden the biochemical landscape of metalloenzymes.