Projects

First Author Papers: Physics-Informed Digital Twin Can Predict Cerebral Blood Flow and Cerebral Vascular Regulation Mechanisms in Neurocritical Care Patients CRISP: Correlation-Refined Image Segmentation Process. Glucokinase activity controls subpopulations of β-cells that alternately lead islet Ca2+ oscillations Personalizing the Pressure Reactivity Index for Neurocritical Care Decision Support β-cell intrinsic dynamics rather than gap junction structure dictates subpopulations in the islet functional network Commentary- Going With the Flow: Pericyte-Regulated Islet Blood Flow Influences Glucose Homeostasis ...

Thanks for visiting my site! My research interests involve using computational methods from dynamical systems theory, complexity science, and machine learning to infer new physiological phenomena, advance biomedicine, and provide predictive capabilities that can change human health through better clinical decision-making. Please see my CV for full list of publications and conferences.

Cerebral blood flow is vital for brain function and is acutely controlled through a set of physiological mechanisms known as cerebral vascular regulation (CVR). It remains challenging to directly measure the dynamics and function of individual CVR mechanisms, limiting our ability to understand and optimize brain perfusion, particularly for neurologically injured patients. Digital twins offer an ideal tool for overcoming this gap because they enable estimation, tracking, and forecasting of unmeasured physiological states. Here, we introduce CereBRLSIM (Cerebral Blood Regulation Latent State Inference and Modeling), a digital twin that integrates physiological knowledge and patient data to infer CVR function and predict cerebral dynamics. Using both in vivo experiments and simulated data, CereBRLSIM predicted cerebral hemodynamics with high accuracy and estimated the dynamics of myogenic, endothelial, and metabolic mechanisms underlying CVR. When personalized to neurocritical care patient data, CereBRLSIM differentiated cerebral hemodynamic phenotypes, predicted patient outcomes, and forecasted blood flow with significantly higher accuracy than machine learning models. This work provides a novel, interpretable, and clinically compatible approach for quantifying CVR function and forecasting cerebral blood flow, enabling new opportunities in precision diagnostics and foundational understanding of cerebral hemodynamics, enabling new opportunities in precision diagnostics and foundational understanding of cerebral hemodynamics. ...

Neurocritical care patients may benefit from personalized treatment based on their cerebral autoregulatory function. The pressure reactivity index is an important, prevalent metric used to estimate the state of a patient’s cerebral autoregulation and guide clinical decision-making. However, the pressure reactivity index is highly sensitive to hyperparameter choices and intrapatient variability. In this manuscript, we develop a new personalized pressure reactivity index methodology (pPRx) that increases robustness and reduces the noise of the pressure reactivity index calculation. Using data from traumatic brain injury patients and simulated data, we first show that pressure reactivity index sensitivity to hyperparameters and interpatient variability is large enough to influence clinical interpretation of cerebral autoregulatory function. We identify that patient heart rate is closely related to errors in the pressure reactivity index, which has vital implications for extending the use of PRx to patients with different regular heart rates, such as pediatric populations. We then remove this heart rate specific sensitivity in the pPRx methodology by adjusting for patient heart rate at resolutions of single heartbeats. Implementing the pPRx methodology decreases error, noise, and sensitivity, and allows the pressure reactivity index to be more robust to variability across patient populations. We also leverage our data and analysis to identify ideal averaging windows in the standard method. ...

Highly heterogeneous beta-cells within the islet are electrically coupled to synchronize their oscillations and secrete insulin in a pulsatile fashion. This pulsatility is disrupted in diabetes. Studying synchronization of calcium oscillations, previous work has suggested that highly synchronized cells have disproportionate influence over the islet and are targeted in diabetes. In this paper, we use network theory to disprove the common assumption that synchronization directly indicates structural connectivity and show that other factors must be taken into account when utilizing network theory to study network breakdown. ...

Spatial patterns of immunity across a population are structured both by natural processes, such as evolutionary history and past epidemics, and by artificial processes, such as vaccination. The diversity of immune types in a population is known to influence pathogen evolution and epidemic dynamics: populations with many immune types are often more robust to epidemics, since a pathogen cannot widely circulate until it evolves the ability to evade many of the established immune types. However, little is known about the effect of the spatial distribution of these immune types on the evolution and transmission of an infectious disease. In this project, we use both a mean-field mathematical model and an agent-based model to understand how “clumpiness” in the spatial distribution of immune types influences the evolutionary and epidemiological dynamics of an infectious disease. Clumpier spatial distributions (higher values of p in Figure 1) contain larger patches of identical immune types and enable less contact between individuals with different immune types. In both of our models we consider a two-dimensional grid with each cell assigned to one of several immune types (Figure 1). A wild-type contagion (which is able to infect only one immune type) is randomly seeded at one cell. We model disease spread between neighbors using an SIR framework, and we model pathogen evolution by allowing for rare, random mutations which generate disease variants that are able to evade one or more of the established immune types. So far, we have found that more clumpy immune type distributions generally result in more cumulative infections and longer epidemics. While the current model focuses on the spatial distribution of vaccines, we anticipate that our analysis will be relevant to many systems which obey similar rules, such as agricultural systems, the spread of beliefs, and zoonosis in multi-host ecosystems. ...

Blood flow regulation throughout the islet is an understudied yet highly important topic for blood hormone regulation. Blood flow regulation has been shown to be impaired at both the capillary and arteriole level. We use experimental results from both mouse and human islets to identify the thresholds for which capillary and arteriole regulation become more essential in defining islet blood flow. Read a pertinent commentary that I had the pleasure of authoring ...

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