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Detection, Analysis, and Modeling of Time-scale Separated Modulatory Brain Dynamics: Methods and Applications in Neurocritical Care
No full textClinical monitoring of patients with neurological disease generates large volumes of data, including electrophysiology (e.g. EEG), heart rate, blood pressure, and other physiological measures. The use of these data to generate actionable prognostic measures is a long-held goal in clinical neurophysiology. In this regard, the use of engineering theory, including signal processing methods and computational modeling paradigms, is providing new ways of interpreting neurological data and yielding new insights into brain mechanisms and disease pathophysiology. This research focuses on the analysis and modeling of aberrant brain dynamical phenomena that occur over hours-long temporal epochs. Particularly, we consider time-scale separated electrophysiological modulation, in which brain electrical activity contains distinct harmonic components that differ by orders of magnitude in the frequency domain. We propose methods for detecting such modulation, apply these methods to characterize its incidence in multiple clinical populations, and develop a biophysical dynamical systems model to study its underlying physiological mechanisms. Our primary focus is on the spatiotemporal analysis of slow (millihertz range) narrowband modulation in electroencephalogram (EEG) recordings. We propose a method that constructs sparse spectral estimates of power envelope signals obtained from physiological frequency bands of EEG. The former are obtained through a regularized basis pursuit approach, solved using LASSO methods and applied to temporal windows of variable length. This approach successfully identifies modulation in brain electrical activity on much slower (\u3c0.01Hz) time-scales than conventional power spectral analyses. We apply these methods in several large clinical cohorts: neonatal patients with encephalopathy and adult patients with hemorrhagic or ischemic stroke. We establish validity of the method and its ability to derive a predictive biomarker. In the neonatal encephalopathy patients, we observe correlations between our modulation index values and developmental outcomes measures. Finally we generate a biophysically-informed dynamical systems model of the phenomenon. We identify parameter sets which generate aberrant oscillatory regimes in intracranial pressure (ICP) and other physiological process variables. We describe bifurcations in the model dynamics, providing hypotheses and predictions regarding potential mechanisms underlying millihertz electrophysiological modulation. We compare the model outputs and predictions against findings from patients with multimodal (EEG, hemodynamic, ICP) monitoring data
Bias Regulation Strategy Selection in Daily Life: Using Daily Diary Methodology to Study Personal Bias Regulation Strategies
No full textIntergroup biases perpetuate social inequality. Although racial bias is a part of daily life, individuals vary in their goals for bias regulation. Bias regulation can be a useful process to set, strive, and evaluate progress toward bias regulation goals. This dissertation work provides the first investigation into when and how White individuals regulate their biases in daily life, examining the different strategies that are employed across individuals and situations. These strategies include situation selection, situation modification, attentional deployment, cognitive change, and response modulation. In December 2024, 236 age-diverse White adults completed a baseline and seven daily assessments. The baseline captured general bias regulation strategies and various individual difference measures of interest that were relevant to bias regulation, such as bias awareness, social dominance orientation, and internal and external motivations to respond without prejudice. Daily, participants reported on their interracial experiences with Black people, their emotions, bias, and daily bias regulation strategies. The current study advances our understanding of intergroup relations by exploring how a process model of bias regulation strategies functions at the daily level for White individuals. First, it demonstrated that bias regulation can be assessed at the daily level. Second, it showed that, overall, individuals tend to use situation modification less than other regulatory strategies to meet their bias regulation goals. Third, this work showed that how individuals think they would regulate their bias generally varies from how they report regulating their biases daily in various intergroup situations. Importantly, there were no significant differences in any of the bias regulation strategies comparing in-person to virtual contexts. Fourth, bias regulation showed divergent and convergent validity with many relevant individual differences but also importantly demonstrated that this new daily measure of bias regulation strategies is capturing something unique from pre-existing intergroup-related measures. Finally, bias regulation strategy use was consistently associated with positive and negative emotional experiences, demonstrating that bias regulation is also often a complex affective experience for many individuals. These results provide important insights for beginning to understand how bias regulation operates in daily life, laying the groundwork for improving intergroup experiences and relationships
Food Insecurity and Family-based Treatment for Pediatric Obesity
No full textPediatric obesity puts children at risk for long-term, negative health outcomes and disproportionately impacts children from families with low income. Families with low income are also at increased risk for food insecurity. Consistent with the “scarcity mindset” theory of poverty, food insecurity is associated with increased discounting of delayed rewards and heighted present focus. It is also associated with increased food reinforcement, and decreased likelihood of meal planning. Despite the seriousness of this issue, little research has examined the role food insecurity, and its psychological correlates, play in behavioral treatments for pediatric obesity. This study examined food insecurity in 108 parent-child dyads enrolled in a pilot study seeking to adapt family-based treatment for pediatric obesity (FBT) for families with low income. It was found that compared to parents who were food-secure, parents with food insecurity were more likely to report income volatility (17.2% vs. 10.3%) and financial strain (82.8% vs. 43.6%) but were otherwise similar across sociodemographic, psychological, and behavioral characteristics including measures of scarcity mindset and meal planning. No statistically significant effect of food insecurity was observed with respect to parent or child weight outcomes, but a notable directional effect of food insecurity was observed in children such that, on average, children with food insecurity lost less weight (change in units of % overweight) than those who were food-secure (M=0.53 [SD=14.92] vs. M=-6.90 [SD=16.48]). Parents with longer financial planning horizons, and those who meal planned more, lost more weight (change in BMI kg/m2) during treatment (β=-0.05 [SE=0.015] and β =-0.030 [SE=0.014] respectively). No moderating effect of meal planning on the relationship between financial planning horizon and weight change was detected. The current study highlights the importance of measuring and addressing social determinants of health (SDOH) needs in clinical populations and raises important questions about the feeding and eating practices of parent-child dyads with low income enrolled in FBT
Proteogenomic Analysis to Inform Causal Gene Prioritization for Human Disease
No full textGenome-wide association studies (GWAS) have been key in expanding our understanding of the genetic contributions to common diseases. However, these genetic associations frequently fail to clarify causal disease mechanisms, as they often fall in non-coding regions and are difficult to interpret. One solution is to perform a GWAS for the levels of a cellular trait, known as quantitative trait locus (QTL) mapping. Through methods such as colocalization, Mendelian Randomization, and transcriptome/proteome-wide association studies, the QTL variants can be compared to disease GWAS, identifying shared variation between cellular and disease traits. We can then prioritize cellular traits as potential causative, targetable factors in disease risk. As proteins actively contribute to cellular processes, understanding their regulation offers an important path toward disease target identification. Large-scale protein QTL (pQTL) analyses have been limited to plasma, which may not accurately capture certain tissue contexts. Cerebrospinal fluid (CSF) interacts with the central nervous system, potentially making it a better proxy for neurological traits. However, no well-powered pQTL analyses of CSF have been performed to date. Here, I present the largest-to-date proteogenomic analysis of CSF. We performed pQTL mapping of 3,506 individuals using the aptamer-based SOMAscan 7k platform, identifying 2,477 pQTLs that were split evenly between gene-proximal pQTLs (cis-pQTLs) and those located distally (trans-pQTLs). We prioritized three highly pleiotropic pQTL hotspots near OSTN, HLA, and APOE that reveal novel disease mechanisms. We also generated a pQTL atlas using an orthogonal antibody-based protein measurement approach and identified platform differences in pQTL detection that necessitate careful interpretation of pQTL associations. Next, we determined the novelty of our CSF pQTL atlas compared to other biological contexts. Over 70% of our cis-pQTLs were not identified at the RNA level, demonstrating unique regulation underlying protein levels. A comparison to a plasma pQTL atlas confirmed robust CSF-specific regulation. Using in-house plasma pQTLs, we found extensive fluid-stratified pQTLs highlighting the necessity of multi-context analyses. We identified fluid-specific pQTL hotspots that reflect biological regulation, including a CSF-specific region linked to lysosomal protein trafficking. Lastly, we integrated our CSF and plasma pQTL resources with disease GWAS to identify potential protein drug targets. We connected almost 200 proteins in each tissue to neurological traits, only 57 of which were consistently associated in both fluids. Disease overlap was observed between AD and dementia with Lewy Bodies (DLB) only in CSF. We identified fluid-specific dysregulated pathways for three traits that highlight the importance of analyzing multiple contexts for drug target identification. Focusing on AD, we uncovered 38 CSF proteins that were enriched in immune and lysosomal processes, suggesting potential drug-targetable mechanisms. We also demonstrated a robust ability for our proteins to predict disease status. Finally, we assessed the proteomic profile of an AD-causing mutation carrier with resilience to symptom development, finding a signature consistent with heat exposure that may have contributed to their delayed onset. This work represents a substantial expansion of our understanding of proteogenomic regulation. We identified thousands of CSF pQTLs that were mainly specific to CSF and to proteins. We further emphasized the importance of studying non-plasma tissues to discover pQTL regulation by connecting our associations with a range of diseases and traits to identify trait-relevant biology. This resource will be useful for future studies that investigate more diseases, as substantial work is still needed to completely understand the genetic contributions to common disease
Embodying Social Collapse: Diet, Health, and Burial in the East African Urban Center of Mtwapa, Kenya (15th – 17th C)
No full textThis dissertation explores how the inhabitants of Mtwapa, Kenya, experienced the process of collapse in the East African Coast from the 15th to 17th centuries CE through examination of diet, systemic stress and trauma, and burial practices. Relying on the embodiment framework, I integrate stable isotope, osteological, and archaeothanatological data to assess how broader sociopolitical, economic, and environmental challenges were experienced at the individual and population levels. Carbon and nitrogen stable isotope analyses of human bone and dentine samples reveal a diet composed of predominantly C4 plants, terrestrial sources of protein, and limited marine resources. At Mtwapa, no significant differences across age and sex categories were observed, suggesting that macro-ingredients of diet were likely not differentiated across age or sex. Analysis of incremental dentine samples shows breastfeeding and weaning patterns as well as childhood catabolic events that highlight episodes of physiological stress during early life. Osteological evidence shows high frequencies of cranial injuries and lesions like cribra orbitalia, porotic hyperostosis, and linear enamel hypoplasia. The high prevalence of these lesions reflects that Mtwapa’s population likely experienced conflict and biosocial conditions resulting in systemic stress. Archaeothanatological analysis reveals observance of Sunni Islamic burial traditions, while the presence of pottery, faunal remains, and grave inclusions signals the incorporation of local African funerary practices. Collective burials indicate the continuation of burial among family and lineage members that emphasize corporate identities. Alternatively, multiple interments suggest that individuals were buried during moments of a high number of simultaneous deaths. Adherence to Islamic burial traditions within contexts of multiple burials further highlights the importance of Islamic religion as a shared identity facilitating a cohesive community even during periods of collapse. These data provide a bottom-up perspective on societal collapse, emphasizing the ways in which individuals embodied, enacted, and responded to transformation through daily and ritualized practices
Understanding Loss of Beta-cell Identity and Progression of Diabetes in Polygenic Mouse Models of T2DM
Full text linkType 2 diabetes (T2DM) is caused by the complex interaction of multiple genes and environmental factors. T2DM is characterized by hyperglycemia, insulin resistance in peripheral tissues and impaired insulin secretion by the pancreas. While the decline in insulin production and secretion was previously attributed to apoptosis of insulin-producing β-cells, recent studies indicate that β cell apoptosis rates are relatively low in diabetes. In this dissertation, I investigated β-cell failure in diabetes using KK and KKAy mouse models of polygenic T2DM, which spontaneously develop hyperglycemia, glucose intolerance, glucosuria, impaired insulin secretion and insulin resistance. I found that β-cell failure in KK and KKAy mouse models of polygenic T2DM is associated with loss of β-cell identity and function, along with a decrease in KATP channel density in the β-cell plasma membrane. Interestingly, intermittent fasting, a weight loss regimen, protected against loss of β-cell identity and function while also increasing β-cell plasma membrane KATP channel density. Furthermore, sulfonylurea (glibenclamide) therapy in KK mice led to secondary failure, with loss of β-cell identity and function along with increased α-cell turnover. The findings bolster the idea that loss of β-cell identity and function is the underlying cause in T2DM, and, importantly, a reversible process
Oxycodone Self-Administration Modulates Ventral Pallidal Neuron Activity and Plasticity
No full textOpioid use disorder is an urgent public health crisis in the United States. Over 800,000 Americans have died from opioid-related overdoses since 2000. Nearly 30% of people who misuse opioids begin with prescription oral opioids, such as oxycodone, and continue misusing to avoid the immediate aversive state of withdrawal and the persistent dysphoria associated with protracted abstinence from opioids. The persistency of these dysphoric symptoms increases the risk of relapse even after long periods of abstinence. Treatment for opioid use disorder currently consists of pharmacological opioid treatment to reduce withdrawal symptoms and behavioral counseling. However, these strategies have significant real-world limitations such as social stigma, cost, and lack of availability and infrastructure. To develop treatments and preventative strategies for opioid use disorder, a better understanding of the neural mechanisms that are affected by long-term opioid use and abstinence is needed. The ventral pallidum (VP) contributes to relapse behavior after withdrawal, as it is required for motivated drug seeking and is associated with anhedonia, a symptom of withdrawal. Recent studies have shown that distinct populations of VP neurons have opposing roles in reward processing; positively-valenced stimuli is primarily encoded by inhibitory VP neurons, while the recently defined glutamatergic subpopulation (VPGlu) constrains reward seeking and has a critical role in negative stimuli integration and decision making. VPGlu neurons also express opioid receptors and project to the lateral habenula (LHb), a nucleus that indirectly inhibits dopamine release from the ventral tegmental area and exhibits hyperexcitability in drug withdrawal. Thus, VPGlu neurons are uniquely situated to contribute to the processing of aversion and reward seeking in the context of opioid use disorder. Using an oxycodone self-administration protocol, we investigated the effect of opioid consumption and abstinence on VPGlu neurons using ex vivo electrophysiology and in vivo Ca2+ activity recording. We hypothesized that the continual inhibition of VPGlu neurons by opioid self-administration would lead to compensatory actions to maintain baseline levels of activity and neurotransmitter release in these neurons. This would lead VPGlu neurons to be more excitable, active, and release more glutamate onto LHb neurons following self-administration of opioids. In this dissertation, we show how oxycodone self-administration changes the electrophysiological intrinsic excitability and opioid sensitivity of VPGlu neurons and the VPGlu:LHb synapse. This work defines VPGlu neurons before and after oxycodone self-administration to render a comprehensive view of these neurons over an opioid use time course. Based on the results herein, this dissertation furthers the understanding of how opioids affect the aversion-promoting component of the mesolimbic reward system and provides an avenue for future interventional studies
Supporting health equity in obesity prevention for women of reproductive age
No full textWomen of reproductive age (18-44 years) are at an increased risk of developing obesity due to pregnancy, stressors during this stage in the life course (e.g., leaving home, new jobs, parenthood), and historical and ongoing discrimination and marginalization. Preventing obesity for this population needs to be a public health priority. Obesity negatively affects women’s health (e.g., chronic disease, cancer); leads to poor pregnancy outcomes (e.g., gestational diabetes); and increases the risk children will develop obesity through intergenerational transmission of obesity, further entrenching health disparities for future generations. Health equity principles of social justice and human rights help obesity prevention researchers and practitioners to recognize social determinants of health; meaningfully engage with communities; ensure evidence-based programs are designed and implemented for reach, accessibility, and sustainability; facilitate measurement of complex processes (e.g., discrimination); and evaluate where evidence comes from and who it is developed for. Women of reproductive age are an understudied population. More evidence is needed on the determinants of obesity risk, factors influencing engagement with obesity prevention efforts, and how and to what extent health equity is informing obesity prevention research for women of reproductive age. Aim 1 seeks to identify gaps and opportunities for advancing health equity in obesity prevention research for women of reproductive age through a review of health equity-focused empirical obesity prevention studies. Aim 2 uses a health equity framework to identify factors beyond the individual that may be associated with obesity among a sample of ethnically diverse women. Aim 3 describes how social determinants of health influence health behaviors and engagement with an evidence-based healthy weight intervention through semi-structured interviews. These aims provide evidence for supporting health equity principles in obesity prevention research for women of reproductive age and addressing health disparities for this population
Essays in Supply Behavior in Sharing Economy
Full text linkMy dissertation investigates supply behavior within the sharing economy, focusing specifically on the ride-sharing market. Leveraging quantitative methods such as structural modeling, field experiments, and lab experiments, I examine supplier behaviors to help platforms design more effective incentives—including subsidies and strategically framed rewards—to better match supply with demand. In Chapter 1, “Using Field Experiments to Infer Cross-Side Network Effects in the Ride-Sharing Market: How Does Driver Supply Impact Rider Orders, Cancellations, and Customer Lifetime Value?”, I address the central question of how shifts in driver supply affect rider behavior. Collaborating with a leading ride-sharing platform, I implement a natural field experiment that exploits an instrumental variable strategy. Specifically, by exogenously altering driver subsidy schedules, I employ these subsidies as instruments to causally identify cross-side network effects. The findings indicate that increasing the number of active drivers by 1% leads to a 2.01% rise in rider orders and simultaneously reduces cancellation rates by 0.48%. Furthermore, the results show significant implications for the platform’s long-term profitability: a 1% increase in afternoon or night driver availability enhances aggregate customer lifetime value (CLV) by 1.62% and 0.50%, respectively. These insights inform platform operations, guide strategic incentive adjustments, and ultimately enhance the overall rider experience. In Chapter 2, “Subsidizing Drivers in the Ride-Sharing Market: A Full-Heterogeneity Supply Model”, I explore cost-effective strategies to subsidize drivers while accounting for the flexible and autonomous nature of supplier participation in the sharing economy. By developing a structural model that explicitly incorporates driver-level heterogeneity in work costs and income sensitivity, I combine data from a field experiment and observational sources to estimate the parameters governing driver decision-making. Recognizing the computational complexity introduced by high-dimensional heterogeneity, I propose a novel nested iteration method that significantly enhances estimation scalability. Subsequent counterfactual analyses reveal that conventional, non-targeted time-based subsidies prove economically inefficient due to limited income sensitivity among drivers. However, individualized subsidy programs tailored to estimated driver-specific costs can substantially reduce incentive expenditures—by approximately 40-60%. This chapter highlights the critical role of supplier heterogeneity in the design of effective incentives, underscoring significant profit-enhancing opportunities for platforms. Finally, Chapter 3, “The Effect of the Order of Incentive Framing on Performance”, examines the psychological dimensions of incentive structures. Beyond monetary rewards, I explore how the sequence and framing of goals influence supplier performance and motivation. Through both a controlled lab experiment and a real-world field experiment partnered with a ride-sharing platform, I investigate scenarios in which goals are structured to appear progressively less challenging. The lab experiment employs a video game task to test consumer performance under varied goal sequences, while the field experiment analyzes how framing drivers\u27 ride-completion goals impacts their subsequent activity. Results from both experiments consistently show enhanced performance when participants perceive goals as becoming easier over time—even without additional monetary incentives. This chapter demonstrates the practical value of psychological framing, highlighting an effective, low-cost approach to improve supplier motivation. Collectively, these chapters provide comprehensive insights into how ride-sharing and similar sharing-economy platforms can leverage both economic mechanisms and behavioral insights to more effectively engage, motivate, and manage their supplier bases
Essays in Empirical Asset Pricing
Full text linkMy dissertation explores two broad questions. First, what is the role of intangible forces like ideological narratives in belief formation and asset prices? Second, how can we understand the systematic components in asset pricing? In Chapter 1, using cryptocurrencies as a laboratory, I examine the role of ideological narratives in asset prices—an area remains underexplored. Leveraging social media data and large language models to measure ideology dynamics, I find that fluctuations in two ideological narratives—anarchism and decentralization—are priced in the cross-section of cryptocurrency returns. Consistent with the view that factors proxy for state variables, ideology factors contain distinct information about future crypto market returns and user network growth. Positive shocks to ideology salience are associated with a significant positive spread between more ideology-aligned and less aligned cryptocurrencies, indicating a relative increase in demand for more aligned cryptocurrencies when collective attention to ideological narratives heightens. Neither investor sentiment nor attention explains the results of the ideology factors. Moreover, the role of ideological narratives extends beyond cryptocurrencies. Stocks with greater exposure to the anarchism narrative yield abnormally high returns that cannot be explained by common stock factor models. The results highlight how ideological narratives contribute to the emergence and adoption of new assets. In Chapter 2, with Ai He, Dashang Huang, and Guofu Zhou, we provide a reduced-rank approach (RRA) to extract a few factors from a large set of factor proxies, and apply the extracted factors to model the cross section of expected stock returns. Empirically, we find that the RRA five-factor model outperforms the well-known Fama-French five-factor model as well as the corresponding PCA, PLS and LASSO models for pricing portfolios. However, at the stock level, our RRA factor model still has large pricing errors even after adding more factors, suggesting that the representative factor proxies of our study do not have sufficient information for pricing individual stocks. In Chapter 3, with Songrun He, Lingying Lv, and Guofu Zhou, we find that three widely used survey forecasts fail to predict the stock market out-of-sample, raising important questions about the reliability of survey forecasts and the proper interpretation of the extensive literature that depends on them. In contrast, we demonstrate that a naive Bayesian learning model and analysts’ expectations can significantly predict the stock market out-of-sample. This suggests that these alternatives provide more meaningful insights into investors’ attitudes toward risk. As a result, studying these new sources of information may be more impactful and warrants greater attention compared to the reliance on survey forecasts