Publications

Acute Stress Disorder (ASD) is a psychological response to a terrifying, traumatic, or surprising event and can be a precursor to Post Traumatic Stress Disorder (PTSD) if left untreated. The current method of diagnosing ASD is anecdotal in practice, with clinical evaluation involving patient history and physical examination within days following a traumatic event. Diagnostic tools typically include screenings and questionnaires, but they require significant training and often miss initial physiological symptoms. The addition of Artificial Intelligence (AI) data-driven decision support using electroencephalography (EEG) and heart rate monitoring of biopotential activity may aid in detecting early signs of trauma-related disorders. 

To test this hypothesis, participants from Greenville Memorial Hospital Emergency Department and Level 1 Trauma Center wore an EEG headset prior to their work shift to determine a reference signal (preliminary sample size of ~n=20). Participants then wore the EEG headset during their work shift while the timing of traumatic events and severity was recorded and biopotential data was collected and stored. De-identified data was exported and processed into various waveforms before being fed into an AI model to develop parameters to identify increased stress levels. 

The Acute Stress Center (ASC) developed an application that enables physicians to drag and drop EEG data into the application, select which waveforms are of interest to their research, and view summary statistics of their data and graphs indicating stressful events along their EEG waveform. In addition, ECG data is displayed alongside the EEG data to allow a visual comparison.

The prototype and application were both further enhanced to gather and evaluate heart rate in addition to EEG data. Data was collected outside of a hospital setting with lower stress levels to test the algorithm.  From preliminary testing, the addition of heart rate did not show much significance in predictors of traumatic responses, ranking second to last in feature importance. Furthermore, among both trials, the GammaMid waveform still proved to be the most important waveform in the indication of a traumatic response with a score of 100%. Additionally, correlations between waveforms and heart rate were fairly insignificant with the highest correlation of .26. Similarly to testing with only EEG waveforms, a plateau in accuracy occurs when 6 variables are included. However, in EEG only trials, accuracies of 95% were achieved, while in the heart rate trial a maximum accuracy of 80% could be achieved. Discrepancies in data could be attributed to the fact that data was collected in stressful situations rather than traumatic due to preliminary testing of the prototype before gathering data at Prisma Health. Variance in results may be seen in more high stress situations and will be evaluated in future work.

An electroencephalogram (EEG) is a medical test that measures brain activity and helps diagnose conditions like acute stress disorder (ASD). According to the World Health Organization (WHO), stress is a state of worry or mental tension caused by challenging situations [1]. When someone experiences an especially traumatic, shocking, or terrifying event, their body can respond with ASD, which is a short-term condition that can develop into post-traumatic stress disorder (PTSD). ASD symptoms, such as anxiety, detachment, and irritability, are similar to those of PTSD [2].

Clinicians regularly face high-stress situations, putting them at greater risk of ASD. Over time, this stress can lead to burnout, which the Mayo Clinic describes as a constant state of exhaustion, fatigue, and emotional overwhelm due to ongoing workplace stress [3]. Burnout doesn’t just affect individual clinicians—it leads many to change careers or leave the medical field entirely, worsening the nationwide healthcare worker shortage. Identifying ASD early could help prevent burnout and improve clinician well-being, making real-time stress monitoring a valuable tool.

A portable, wearable EEG with built-in heart rate monitoring was developed to address this. The prototype was improved using computer-aided design (CAD) and 3D printing to incorporate the heart rate sensor into an earpiece, decreasing the weight, cost, and volume of materials by 54.5%, 85.92%, and 55.95%, respectively. These updates make the device more comfortable and discreet, allowing clinicians to wear it throughout their workday without it being intrusive.

Moving forward, we aim to downsize components further, explore biocompatible 3D-printed materials, and enhance overall ergonomics. As EEG and heart rate sensors continue to get smaller, this technology could eventually be integrated into something as subtle as a pair of glasses, offering a seamless way to monitor stress levels and support clinician well-being.

Magnetic Resonance Imaging with tagging (tMRI) has long been utilized for quantifying tissue motion and strain during deformation. However, a phenomenon known as tag fading, a gradual decrease in tag visibility over time, often complicates post-processing. The first contribution of this study is to model tag fading by considering the interplay between T₁ relaxation and the repeated application of radio frequency (RF) pulses during serial imaging sequences. This is a factor that has been overlooked in prior research on tMRI post-processing. Further, we have observed an emerging trend of utilizing raw tagged MRI within a deep learning-based (DL) registration framework for motion estimation. In this work, we evaluate and analyze the impact of commonly used image similarity objectives in training DL registrations on raw tMRI. This is then compared with the Harmonic Phase-based approach, a traditional approach which is claimed to be robust to tag fading. Our findings, derived from both simulated images and an actual phantom scan, reveal the limitations of various similarity losses in raw tMRI and emphasize caution in registration tasks where image intensity changes over time.

Magnetic Resonance Elastography (MRE) is a noninvasive method for quantitatively assessing the viscoelastic properties of tissues, such as the brain. MRE has been successfully used to measure the material properties and diagnose diseases based on the difference in mechanical properties between diseased and normal tissue. However, MRE is still an emerging technology that is not part of routine clinical imaging like structural Magnetic Resonance Imaging (MRI), and the acquisition equipment is not widely available. Thus, it is challenging to collect MRE, but there is an increasing interest in it. In this study, we explore using structural MRI images to synthesize the MRE-derived material properties of the human brain. We use deep networks that employ both MRI and Diffusion Tensor Imaging (DTI) to explore the best input images for MRE image synthesis. This work is the first study to report on the feasibility of MRE synthesis from structural MRI and DTI.

Objective: Chronic subdural hematoma (cSDH) is prevalent globally and its management is evolving to minimize morbidity while  optimizing theater utilization. We present our institution’s experience with subdural evacuation port system (SEPS) as a first-line  treatment approach to cSDHs.

Methods: A retrospective review was performed of patients undergoing bedside SEPS placement in a  single institution. Pre- and post-procedural radiographic and clinical data were collected and analyzed to identify predictive variables of  procedural success for the SEPS approach. For procedure failures, subsequent procedures were analyzed for rates of success.

Results:  268 patients were identified for a total of 326 initial procedures. Pre-procedural variables associated with improved odds of a good  outcome were: unilateral cSDH, prior use of anticoagulation, GCS > 13 at presentation, larger cSDH, and greater degree of midline shift (MLS). 65% success rate was observed for initial SEPS placement and an overall success of 78% after repeat SEPS. Bilateral SDH with  bilateral SEPS placement had 56% success, a significantly lower success rate than unilateral placement (p=0.0147). Patients with  subsequent failures underwent craniotomy. Patients who had a successful SEPS procedure had an average LOS of 13 ± 39 days compared  to 25 ± 65 in incidents of failure (p=0.047). Average follow-up after discharge was 2.8 ± 3.8 months.

Conclusions: Bedside SEPS  placement is a low-risk option for first-line treatment of cSDH and may spare patients from the risks of general anesthesia while  reducing burden on surgical theaters in resource-limited settings. Performing a repeat SEPS procedure is a reasonable surgical option if  the first procedure fails to completely evacuate the cSDH. 

Introduction

Computational head injury models are promising tools for understanding and predicting traumatic brain injuries. However, most available head injury models are “average” models that employ a single set of head geometry (e.g., 50th-percentile U.S. male) without considering variability in these parameters across the human population. A significant variability of head shapes exists in U.S. Army soldiers, evident from the Anthropometric Survey of U.S. Army Personnel (ANSUR II). The objective of this study is to elucidate the effects of head shape on the predicted risk of traumatic brain injury from computational head injury models.

Materials and Methods

Magnetic resonance imaging scans of 25 human subjects are collected. These images are registered to the standard MNI152 brain atlas, and the resulting transformation matrix components (called head shape parameters) are used to quantify head shapes of the subjects. A generative machine learning model is used to generate 25 additional head shape parameter datasets to augment our database. Head injury models are developed for these head shapes, and a rapid injurious head rotation event is simulated to obtain several brain injury predictor variables (BIPVs): Peak cumulative maximum principal strain (CMPS), average CMPS, and the volume fraction of brain exceeding an injurious CMPS threshold. A Gaussian process regression model is trained between head shape parameters and BIPVs, which is then used to study the relative sensitivity of the various BIPVs on individual head shape parameters. We distinguish head shape parameters into 2 types: Scaling components Txx⁠, Tyy⁠, and Tzz that capture the breadth, length, and height of the head, respectively, and shearing components (⁠Txy,Txz,Tyx,Tyz,Tzx⁠, and Tzy⁠) that capture the relative skewness of the head shape.

Results

An overall positive correlation is evident between scaling components and BIPVs. Notably, a very high, positive correlation is seen between the BIPVs and the head volume. As an example, a 57% increase in peak CMPS was noted between the smallest and the largest investigated head volume parameters. The variation in shearing components Txy,Txz,Tyx,Tyz,Tzx⁠, and Tzy on average does not cause notable changes in the BIPVs. From the Gaussian process regression model, all 3 BIPVs showed an increasing trend with each of the 3 scaling components, but the BIPVs are found to be most sensitive to the height dimension of the head. From the Sobol sensitivity analysis, the Tzz scaling parameter contributes nearly 60% to the total variance in peak and average CMPS; Tyy contributes approximately 20%, whereas Txx contributes less than 5%. The remaining contribution is from the 6 shearing components. Unlike peak and average CMPS, the VF-CMPS BIPV is associated with relatively evenly distributed Sobol indices across the 3 scaling parameters. Furthermore, the contribution of shearing components on the total variance in this case is negligible.

Conclusions

Head shape has a considerable influence on the injury predictions of computational head injury models. Available “average” head injury models based on a 50th-percentile U.S. male are likely associated with considerable uncertainty. In general, larger head sizes correspond to greater BIPV magnitudes, which point to potentially a greater injury risk under rapid neck rotation for people with larger heads.

Background

Standardized exercise protocols have been shown to improve overall cardiovascular fitness, but direct effects on left ventricular (LV) function, particularly diastolic function and relation to post-transcriptional molecular pathways (microRNAs (miRs)) are poorly understood. This project tested the central hypothesis that adaptive LV remodeling resulting from a large animal exercise training protocol, would be directly associated with specific miRs responsible for regulating pathways relevant to LV myocardial stiffness and geometry.

Methods and results

Pigs (n = 9; 25 Kg) underwent a 4 week exercise training protocol (10 degrees elevation, 2.5 mph, 10 min, 5 days/week) whereby LV chamber stiffness (K_C) and regional myocardial stiffness (rK_m) were measured by Doppler/speckle tracking echocardiography. Age and weight matched non-exercise pigs (n = 6) served as controls. LV K_C fell by approximately 50% and rK_m by 30% following exercise (both p < 0.05). Using an 84 miR array, 34 (40%) miRs changed with exercise, whereby 8 of the changed miRs (miR-19a, miR-22, miR-30e, miR-99a, miR-142, miR-144, miR-199a, and miR-497) were correlated to the change in K_C (r ≥ 0.5 p < 0.05) and mapped to matrix and calcium handling processes. Additionally, miR-22 and miR-30e decreased with exercise and mapped to a localized inflammatory process, the inflammasome (NLRP-3, whereby a 2-fold decrease in NLRP-3 mRNA occurred with exercise (p < 0.05).

Conclusion

Chronic exercise reduced LV chamber and myocardial stiffness and was correlated to miRs that map to myocardial relaxation processes as well as local inflammatory pathways. These unique findings set the stage for utilization of myocardial miR profiling to identify underlying mechanisms by which exercise causes changes in LV myocardial structure and function.

In general, the present disclosure is directed to an endovascular device. The device may include an outer body comprising a biocompatible polymeric material; and a core comprising a first layer and a second layer, wherein the first layer comprising an anti-contractile agent and the second layer comprising a second agent.

Methods and systems for utilizing myocardial strain imaging in an inverse framework to identify mechanical properties of the heart and to determine structural and functional milestones for the development and progression to heart failure.

Background:

Paclitaxel (PTX) is touted as an essential medicine due to its extensive use as a chemotherapeutic agent for various cancers and an antiproliferative agent for endovascular applications. Emerging studies in cardio-oncology implicate various vascular complications of chemotherapeutic agents.

Methods:

We evaluated the inflammatory response induced by the systemic administration of PTX. The investigation included RNAseq analysis of primary human endothelial cells (ECs) treated with PTX to identify transcriptional changes in pro-inflammatory mediators. Additionally, we used dexamethasone (DEX), a well-known antiinflammatory compound, to assess its effectiveness in counteracting these PTX-induced changes. Further, we studied the effects of PTX on monocyte chemoattractant protein-1 (MCP-1) levels in the media of ECs. The study also extended to in vivo analysis, where a group of mice was injected with PTX and subsequently harvested at different times to assess the immediate and delayed effects of PTX on inflammatory mediators in blood and aortic ECs.

Results:

Our RNAseq analysis revealed that PTX treatment led to significant transcriptional perturbations in pro-inflammatory mediators such as MCP-1 and CD137 within primary human ECs. These changes were effectively abrogated when DEX was administered. In vitro experiments showed a marked increase in MCP-1 levels in EC media following PTX treatment, which returned to baseline upon treatment with DEX. In vivo, we observed a threefold increase in MCP-1 levels in blood and aortic ECs 12 h post-PTX administration. Similar trends were noted for CD137 and other downstream mediators like tissue factor, vascular cell adhesion molecule 1, and E-selectin in aortic ECs.

Conclusion:

Our findings illustrate that PTX exposure induces an upregulation of atherothrombotic mediators, which can be alleviated with concurrent administration of DEX. Considering these observations, further long-term investigations should focus on understanding the systemic implications associated with PTX-based therapies and explore the clinical relevance of DEX in mitigating such risks.