M.A. / Violence being a prominent and invasive factor in South Africa has left many people feeling powerless, hopeless and incapable of dealing and coping with the effects that exposure to trauma has produced. This idea appears even more disturbing if one considers that military personnel will inevitably be exposed to some form of trauma in their employment history. As a result of this traumatic exposure, many people develop post traumatic stress disorder or symptoms thereof. The literature ind icates that certain variables may increase vulnerability for the development of this disorder. The purpose of this research was to evaluate whether or not there is a relationship between Post Traumatic Stress (PTS) symptoms and axis II personality traits using Millon’s Clinical Multiaxial Inventory (MCMI - III) as a measure. The results of which will have major implications for our understanding of PTS, as well as aid in the deployment of military personnel. The sample comprised 5853 military personnel who completed the MCMI III as part of a yearly project to determine their mental health status. Inferential and descriptive statistical analyses were used on the data. It was found, in accordance with previous literature findings, that narcissistic, antisocial and borderline personality styles are the best predictors of PTS. In addition, the study found that there is a significant relationship between PTS and various personality styles, namely depressive, schizotypal, borderline, passive -aggressive, compulsive, antisocial and narcissistic personality styles. It is recommended that if the MCMI-III is used to scan military personnel prior to combat, those with high scores on borderline, narcissistic and antisocial personality scales, should be subjected to a more in-depth evaluation.

The ability of the brain to keep a stable and adequate cerebral blood flow (CBF) independently of fluctuations in systemic blood pressure is referred to as cerebral pressure autoregulation (CPA). When the brain is injured by trauma or hemorrhage, this ability may be impaired, leaving the brain vulnerable to events of high or low blood pressure. The aims of this thesis were to study CPA in patients with severe traumatic brain injury (TBI) or subarachnoid hemorrhage (SAH), the relation between CPA and other physiological parameters, and the influence of CPA on outcome. Four retrospective studies are included in the thesis. All patients were treated at the neurointensive care unit, Uppsala University hospital. In paper I, 58 TBI patients were studied. In patients with impaired CPA, cerebral perfusion pressure between 50-60 mm Hg was associated with favorable outcome while CPP > 70 and >80 mm Hg was associated with unfavorable outcome. In patients with intact CPA there was no association between CPP and outcome. In paper II, 107 TBI patients were studied. High CPP was associated with unfavorable outcome in patients with focal injuries. In patients with diffuse injury and impaired CPA, CPP > 70 mm Hg was associated with favorable outcome. In paper III, 47 SAH patients were studied. CBF was measured bedside with Xenon-enhance CT (Xe-CT). Patients with impaired CPA had lower CBF, both in the early (day 0-3) and late (day 4-14) acute phase of the disease. In paper IV, 64 SAH patients were studied. Optimal CPP (CPPopt) was calculated automatically as the level of CPP where CPA works best for the patient, i.e., where PRx is lowest. Patients with actual CPP below their calculated optimum had higher amounts of low-flow regions (CBF < 10 ml/100g/min). The findings in this thesis emphasize the importance of taking CPA into account in the management of TBI and SAH patients, and suggest that treatment should be individualized depending on status of autoregulation. PRx and CPPopt may be used bedside to guide management according to status of autoregulation. In the future CPA-guided management should be tested in prospective studies

Objectives To examine the associations of Intimate partner violence (IPV) with stress-related sleep disturbance (measured using the Ford Insomnia Response to Stress Test [FIRST]) and poor sleep quality (measured using the Pittsburgh Sleep Quality Index [PSQI]) during early pregnancy. Methods This cross-sectional study included 634 pregnant Peruvian women. In-person interviews were conducted in early pregnancy to collect information regarding IPV history, and sleep traits. Adjusted odds ratios (aOR) and 95% confidence intervals (95%CIs) were calculated using logistic regression procedures. Results Lifetime IPV was associated with a 1.54-fold increased odds of stress-related sleep disturbance (95% CI: 1.08–2.17) and a 1.93-fold increased odds of poor sleep quality (95% CI: 1.33–2.81). Compared with women experiencing no IPV during lifetime, the aOR (95% CI) for stress-related sleep disturbance associated with each type of IPV were: physical abuse only 1.24 (95% CI: 0.84–1.83), sexual abuse only 3.44 (95%CI: 1.07–11.05), and physical and sexual abuse 2.51 (95% CI: 1.27–4.96). The corresponding aORs (95% CI) for poor sleep quality were: 1.72 (95% CI: 1.13–2.61), 2.82 (95% CI: 0.99–8.03), and 2.50 (95% CI: 1.30–4.81), respectively. Women reporting any IPV in the year prior to pregnancy had increased odds of stress-related sleep disturbance (aOR = 2.07; 95% CI: 1.17–3.67) and poor sleep quality (aOR = 2.27; 95% CI: 1.30–3.97) during pregnancy. Conclusion Lifetime and prevalent IPV exposures are associated with stress-related sleep disturbance and poor sleep quality during pregnancy. Our findings suggest that sleep disturbances may be important mechanisms that underlie the lasting adverse effects of IPV on maternal and perinatal health.

Making sense of anguish is an important process leading to personal growth, development, and overall meaning making. Today's quarterlife students (students between the ages of 20-25) may face a variety of traumatic life events that influence how they grow as individuals and are able to move forward from these experiences. I examine several topics that many quarterlife students experience during these challenging years. As a gay identified individual, I examine aspects of `coming out' and the reluctance of blooming into the individual that I wanted to be. I examine the impact of resenting those individuals who may have hurt you and the ultimate growth that results from the pursuit of forgiveness. I then examine the importance of not sacrificing who you are within romantic relationships, and how being in abusive relationships can inhibit one's ability to be happy. Lastly, I close with how despite all of the traumatic experiences one must overcome, we all have the ability to be happy and construct positive meaning from such times of anguish. Written within a scholarly personal narrative methodology, my thesis examines several generational life events that have the potential to cause anguish, and how one can harness personal growth and meaning making from traumatic past experiences.

Traumatic Brain Injury (TBI) and its resulting pathophysiology have been extensively examined before. However, little is known in the area of pre-injury factors that influence vulnerability to and recovery from TBI. The current study examined the effects of pre-injury chronic nicotine exposure on Morris water maze performance, following TBI in adolescent rats. Fifteen days prior to lateral fluid percussion injury (FPI), adolescent rats (30 days old) were implanted with osmotic mini-pumps filled with nicotine (4.5mg/kg/day) or saline. Half the rats received lateral fluid percussion injury and half received sham injury. Animals were assessed for cognitive recovery in the Morris water maze on post-injury days (PID) 11 through 15. The MWM results indicated no significant differences between injured animals infused with chronic nicotine and injured animals infused with saline.

The overall goal of this research is to develop methods and algorithms to investigate the severity of Traumatic brain injury (TBI) and to estimate the intracranial pressure (ICP) level non-invasively. Brain x-ray computed tomography (CT) images and artificial intelligence methods are employed to estimate the level of ICP. Fully anisotropic complex wavelet transform features are proposed to extract directional textural features from brain images. Different feature selection and classification methods are tested to find the optimal feature vector and estimate the ICP using support vector regression. By using systematic feature extraction, selection and classification, promising results on ICP estimation are achieved. The results also indicate the reliability of the proposed algorithm. In the following, case-based finite element (FE) models are extracted from CT images using Matlab, Solidworks, and Ansys software tools. The ICP estimation obtained from image analysis is used as an input to the FE modeling to obtain stress/strain distribution over the tissue. Three in-plane modeling approaches are proposed to investigate the effect of ICP elevation on brain tissue stress/strain distribution. Moreover, the effect of intracranial bleeding on ICP elevation is studied in 2-D modeling. A mathematical relationship between the intracranial pressure and the maximum strain/stress over the brain tissue is obtained using linear regression method. In the following, a 3-D model is constructed using 3 slices of brain CT images. The effect of increased ICP on the tissue deformation is studied. The results show the proposed framework can accurately simulate the injury and provides an accurate ICP estimation non-invasively. The results from this study may be used as a base for developing a non-invasive procedure for evaluating ICP using FE methods.

In the past decades, technical advances have allowed for the collection and storage of more types and larger quantities of medical data. The increase in the volume of existing medical data has increased the need for processing and analyzing such data. Medical data holds information that is invaluable for diagnostic as well as treatment planning purposes. Presently, a large portion of the data is not optimally used towards medical decisions because information contained in the data is inaccessible through simple human inspection, or traditional computational methods. In the field of trauma medicine, where caregivers are frequently confronted with situations where they need to make rapid decisions based on large amounts of information, the need for reliable, fast and automated computational methods for decision support systems is stringent. Such methods could process and analyze, in a timely fashion, all available medical data and provide caretakers with recommendations/predictions for both patient diagnostic and treatment planning. Presently however, even extracting features that are known to be useful for diagnosis, like presence and location of hemorrhage and fracture, is not easily achievable in automatic manner. Trauma is the main cause of death among Americans age 40 and younger; hence, it has become a national priority. A computer-aided decision making system capable of rapidly analyzing all data available for a patient and forming reliable recommendations for physicians can greatly impact the quality of care provided to patients. Such a system would also reduce the overall costs involved in patient care as it helps in optimizing the decisions, avoiding unnecessary procedures, and customizing treatments for individual patients. Among different types of trauma with a high impact on the lives of Americans, traumatic pelvic injuries, which often occur in motor vehicle accidents and in falls, have had a tremendous toll on both human lives and healthcare costs in the United States. The present project has developed automated computational methods and algorithms to analyze pelvic CT images and extract significant features describing the severity of injuries. Such a step is of great importance as every CT scan consists of tens of slices that need to be closely examined. This method can automatically extract information hidden in CT images and therefore reduce the time of the examination. The method identifies and signals areas of potential abnormality and allows the user to decide upon the action to be taken (e.g. further examination of the image and/or area and neighboring images in the scan). The project also initiates the design of a system that combines the features extracted from biomedical signals and images with information such as injury scores, injury mechanism and demographic information in order to detect the presence and the severity of Traumatic Pelvic Injuries and to provide recommendations for diagnosis and treatment. The recommendations are provided in form of grammatical rules, allowing physicians to explore the reasoning behind these assessments.

In recent decades, more types and quantities of medical data have been collected due to advanced technology. A large number of significant and critical information is contained in these medical data. High efficient and automated computational methods are urgently needed to process and analyze all available medical data in order to provide the physicians with recommendations and predictions on diagnostic decisions and treatment planning. Traumatic pelvic injury is a severe yet common injury in the United States, often caused by motor vehicle accidents or fall. Information contained in the pelvic Computed Tomography (CT) images is very important for assessing the severity and prognosis of traumatic pelvic injuries. Each pelvic CT scan includes a large number of slices. Meanwhile, each slice contains a large quantity of data that may not be thoroughly and accurately analyzed via simple visual inspection with the desired accuracy and speed. Hence, a computer-assisted pelvic trauma decision-making system is needed to assist physicians in making accurate diagnostic decisions and determining treatment planning in a short period of time. Pelvic bone segmentation is a vital step in analyzing pelvic CT images and assisting physicians with diagnostic decisions in traumatic pelvic injuries. In this study, a new hierarchical segmentation algorithm is proposed to automatically extract multiplelevel bone structures using a combination of anatomical knowledge and computational techniques. First, morphological operations, image enhancement, and edge detection are performed for preliminary bone segmentation. The proposed algorithm then uses a template-based best shape matching method that provides an entirely automated segmentation process. This is followed by the proposed Registered Active Shape Model (RASM) algorithm that extracts pelvic bone tissues using more robust training models than the Standard ASM algorithm. In addition, a novel hierarchical initialization process for RASM is proposed in order to address the shortcoming of the Standard ASM, i.e. high sensitivity to initialization. Two suitable measures are defined to evaluate the segmentation results: Mean Distance and Mis-segmented Area to quantify the segmentation accuracy. Successful segmentation results indicate effectiveness and robustness of the proposed algorithm. Comparison of segmentation performance is also conducted using both the proposed method and the Snake method. A cross-validation process is designed to demonstrate the effectiveness of the training models. 3D pelvic bone models are built after pelvic bone structures are segmented from consecutive 2D CT slices. Automatic and accurate detection of the fractures from segmented bones in traumatic pelvic injuries can help physicians detect the severity of injuries in patients. The extraction of fracture features (such as presence and location of fractures) as well as fracture displacement measurement, are vital for assisting physicians in making faster and more accurate decisions. In this project, after bone segmentation, fracture detection is performed using a hierarchical algorithm based on wavelet transformation, adaptive windowing, boundary tracing and masking. Also, a quantitative measure of fracture severity based on pelvic CT scans is defined and explored. The results are promising, demonstrating that the proposed method not only capable of automatically detecting both major and minor fractures, but also has potentials to be used for clinical applications.

There is convincing evidence that demonstrates traumatic stress and aggressive behavior are highly related among adolescents. The evidence is less clear regarding the direction of this relation. The purpose of this study was to examine the reciprocal longitudinal relations between physical aggression and traumatic stress among a predominantly African American sample of middle school students. Support was found for traumatic stress predicting increased levels of physical aggression across the winter to the spring of the sixth grade for boys and across all waves from the fall of the seventh grade to the fall of the eighth grade for both boys and girls. Conversely, physical aggression during the winter of the sixth grade predicted a decrease in traumatic stress in the spring of the sixth grade for both boys and girls. These findings suggest that interventions may need to incorporate skills that are aligned with trauma-informed care practices in order to reduce traumatic stress and physical aggression among adolescents.

There is a growing body of research in support of the relationship between self-compassion and psychological health (Neff, Kirkpatrick & Rude, 2007). However, studies are limited on the malleability of self-compassion specifically within clinical populations, and its influence on psychiatric symptom reduction (Raes, 2011). This study therefore aimed to explore self-compassion, through examination of the underlying components (self-kindness, common humanity and mindfulness), and their impact on maladaptive functioning, specifically for participants diagnosed with PTSD. The study used an exploratory mixed methods design, with eight participants recruited by their attendance at two PTSD CBT/IPT (with self-compassion) group treatment interventions. Participants were adults, with a diagnosis of PTSD and a history of recurrent or current depression. Participants attended a group interview and two therapists who facilitated each treatment group were also interviewed. Participants completed quantitative measures pre and post intervention; Self-Compassion Scale (Neff, 2003a) and Clinician-Administered PTSD Scale (Blake et al., 1995), and session data was recorded using the PTSD checklist (PCL-C; Weathers, Litz, Huska & Keane, 1994) and the Session Feedback Form (AccEPT Primary Care Psychological Therapies Service). The findings demonstrate the importance that individuals place on self-compassion in their PTSD recovery, particularly in relation to self-kindness and the generation of positive self-belief and a compassionate inner-voice. The study also indicates that a clinically significant change in self-compassion is possible for individuals with PTSD, supporting the malleability of self-compassion within clinical populations.