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No description available.

Blast-induced mild traumatic brain injury (BImTBI) is one of the most common causes of traumatic brain injuries. BImTBI mechanisms are not well identified, as most previous blast-related studies were focused on the visible and fatal injuries. BImTBI is a hidden lesion and long-term escalation of related complications is considered a serious health care challenging due to lack of accurate data required for early diagnosis and intervention. The experimental studies presented in this thesis were performed to investigate aspects of blast shock wave mechanisms that might lead to mild traumatic brain injury. A compressed air-driven shock tube was designed and validated using finite element analysis (FEA) and experimental investigation. Two metal diaphragm types (steel and brass) with three thicknesses (0.127, 0.76, and 0.025mm) were utilized in the shock tube calibration experiment, as a new approach to generate shock wave. The consistency of generated shock waves was confirmed using a statistical assessment of the results by evaluating the shock waves parameters. The analysis results showed that the 0.127mm steel diaphragm induces a reliable shock waveform in the range of BImTB investigations. Evaluation of the shock wave impacts on the brain was examined using two sets of experiments. The first set was conducted using a gel brain model while the second set was performed using a physical head occupied with a gel brain model and supported by a neck model. The gel brain model in both the experimental studies was generated using silicone gel (Sylgard-527). The effects of tested models locations and orientations with respect to the shock tube exit were investigated by measuring the generated pressure wave within the brain model and acceleration. The results revealed that the pressure waveform and acceleration outcomes were greatly affected by the tested model orientations and locations in relation to the path of shock wave propagation. / Thesis / Doctor of Philosophy (PhD)

Recently there has been considerable attention directed towards the increased risk for head injuries that athletes face while participating in high impact sports. Furthermore, there is also heightened interest in the asymptomatic sport related sub-concussive blows, commonly experienced during play, that possibly lead to long term neurological deficits. Purpose: The goal of this study was to investigate retired professional athletes of the Canadian Football League with a history of sport-related concussions, using several advanced MRI methods. The ultimate goal being the identification of any potential synergistic effects between a history of sport-related concussions, and exacerbated cognitive decline later on in life. Materials and Methods: Twenty former professional athletes of the Canadian Football League were scanned using a GE Discovery MR750 3T MRI with a 32-channel RF-coil. Axial FSPGR-3D images were used to define rs-BOLD and DTI scans. Seed based network analysis of the DMN was performed on rs-BOLD data. Voxel-wise tensor fitting of DTI data provided the means for estimating several tensor metrics. Results were normalized through comparison with a database of healthy controls. Potential associations between functional connectivity, white matter integrity, and cortical thickness measures were correlated with retired athlete position and years of professional play. Results: We found widespread cortical thinning in retired CFL subjects, alongside significant increases in axial and mean diffusivity in the corona radiata and splenium and genu of the corpus callosum compared to controls. Seed based correlation analysis of the DMN network revealed interrupted connectivity in retired athletes. Athlete age, po- sition, and number of years played appear to be factors in overall core white matter microstructural integrity. Conclusions: When compared to an age and sex matched control population, differences were observed both in functional and structural con- nectivity, suggesting that even years after retiring the brains of these former athletes still exhibit signs of damage. / Thesis / Master of Applied Science (MASc) / Sport-related concussions affect millions of athletes on a yearly basis in the United States alone. Concussions are often accompanied by short-lived neurological impairments, such as confusion, headaches, dizziness, nausea and memory loss. In addition, there is the potential for development of long term mental health and cognitive impairment. The goal of this work was to identify any neurological changes present in retired athletes of the Canadian Football League, through the use of advanced magnetic resonance imaging techniques evaluating thickness of brain structures, changes in brain activity, and alterations in core microstructure of the brain. Analyzing the results of these techniques revealed changes in a number of brain regions within the retired professional athlete population. These results suggest that a career of high impact sports may lead to short term, in addition to long-term neurological consequences.

The present dissertation details a sequence of studies in mild traumatic brain injury, the progression of its effects on the human brain as recorded by event-related electroencephalography, and potential applications of machine learning algorithms in detecting such effects. The work investigated data collected from two populations (in addition to healthy controls): 1) a recently-concussed adolescent group, and 2) a group of retired football athletes who sustained head trauma a number of years prior to testing. Neurophysiological effects of concussion were assessed across both groups with the same experimental design using a multi-deviant auditory oddball paradigm designed to elicit the P300 and other earlier components. Explainable machine learning models were trained to classify concussed individuals from healthy controls. Cross-validation performance accuracies on the recently-concussed (chapter 4) and retired athletes (chapter 3) were 80% and 85%, respectively. Features showed to be most useful in the two studies were different, motivating a study of potential differences between the different injury-stage/age groups (chapter 5). Results showed event-related functional connectivity to modulate differentially between the two groups compared to healthy controls. Leveraging results from the presented work a theoretical model of mild traumatic brain injury progression was proposed to form a framework for synthesizing hypotheses in future research. / Dissertation / Doctor of Philosophy (PhD)

Healthcare analytics has traditionally relied upon linear and logistic regression models to address clinical research questions mostly because they produce highly interpretable results [1, 2]. These results contain valuable statistics such as p-values, coefficients, and odds ratios that provide healthcare professionals with knowledge about the significance of each covariate and exposure for predicting the outcome of interest [1]. Thus, they are often favored over new deep learning models that are generally more accurate but less interpretable and scalable. However, the statistical power of linear and logistic regression is contingent upon satisfying modeling assumptions, which usually requires altering or transforming the data, thereby hindering interpretability. Thus, generalized additive models are useful for overcoming this limitation while still preserving interpretability and accuracy. The major research question in this work involves investigating whether particular sedative agents (fentanyl, propofol, versed, ativan, and precedex) are associated with different discharge dispositions for patients with acute traumatic brain injury (TBI). To address this, we compare the effectiveness of various models (traditional linear regression (LR), generalized additive models (GAMs), and deep learning) in providing guidance for sedative choice. We evaluated the performance of each model using metrics for accuracy, interpretability, scalability, and generalizability. Our results show that the new deep learning models were the most accurate while the traditional LR and GAM models maintained better interpretability and scalability. The GAMs provided enhanced interpretability through pairwise interaction heat maps and generalized well to other domains and class distributions since they do not require satisfying the modeling assumptions used in LR. By evaluating the model results, we found that versed was associated with better discharge dispositions while ativan was associated with worse discharge dispositions. We also identified other significant covariates including age, the Northeast region, the Acute Physiology and Chronic Health Evaluation (APACHE) score, Glasgow Coma Scale (GCS), and ethanol level. The versatility of versed may account for its association with better discharge dispositions while ativan may have negative effects when used to facilitate intubation. Additionally, most of the significant covariates pertain to the clinical state of the patient (APACHE, GCS, etc.) whereas most non-significant covariates were demographic (gender, ethnicity, etc.). Though we found that deep learning slightly improved over LR and generalized additive models after fine-tuning the hyperparameters, the deep learning results were less interpretable and therefore not ideal for making the aforementioned clinical insights. However deep learning may be preferable in cases with greater complexity and more data, particularly in situations where interpretability is not as critical. Further research is necessary to validate our findings, investigate alternative modeling approaches, and examine other outcomes and exposures of interest. / Master of Science / Patients with Traumatic Brain Injury (TBI) often require sedative agents to facilitate intubation and prevent further brain injury by reducing anxiety and decreasing level of consciousness. It is important for clinicians to choose the sedative that is most conducive to optimizing patient outcomes. Hence, the purpose of our research is to provide guidance to aid this decision. Additionally, we compare different modeling approaches to provide insights into their relative strengths and weaknesses. To achieve this goal, we investigated whether the exposure of particular sedatives (fentanyl, propofol, versed, ativan, and precedex) was associated with different hospital discharge locations for patients with TBI. From best to worst, these discharge locations are home, rehabilitation, nursing home, remains hospitalized, and death. Our results show that versed was associated with better discharge locations and ativan was associated with worse discharge locations. The fact that versed is often used for alternative purposes may account for its association with better discharge locations. Further research is necessary to further investigate this and the possible negative effects of using ativan to facilitate intubation. We also found that other variables that influence discharge disposition are age, the Northeast region, and other variables pertaining to the clinical state of the patient (severity of illness metrics, etc.). By comparing the different modeling approaches, we found that the new deep learning methods were difficult to interpret but provided a slight improvement in performance after optimization. Traditional methods such as linear regression allowed us to interpret the model output and make the aforementioned clinical insights. However, generalized additive models (GAMs) are often more practical because they can better accommodate other class distributions and domains.

This study aims to develop models for predicting rare events, specifically elevated intracranial pressure (ICP) in patients with traumatic brain injury (TBI). Using time-series data of ICP, we created and evaluated several machine learning models, including K-Nearest Neighbors, Random Forest, and logistic regression, in order to predict ICP levels exceeding 20 mmHg – acritical threshold for medical intervention. The time-series data was segmented and transformed into a tabular format, with feature engineering applied to extract meaningful statistical characteristics. We framed the problem as a binary classification task, focusing on whether ICP levels exceeded the 20 mmHg threshold. We focused on evaluating the optimal model by comparing the predictive performance of the algorithms. All models demonstrated good performance for predictions up to 30 minutes in advance, after which a significant decline in performance was observed. Within this timeframe, the models achieved Matthews Correlation Coefficient (MCC) scores ranging between 0.876 and 0.980, where the Random Forest models showed the highest performance. In contrast, logistic regression displayed a notable deviation at the 40-minute mark, recording an MCC score of 0.752. The results presented highlight potential to provide reliable, real-time predictions of dangerous ICP levels up to 30 minutes in advance, which is crucial for timely and effective medical interventions.