Clinical and Physiological Analysis of Very Long Apneas in Premature Infants

Apnea is common in premature infants, and in severe cases it may impair development. Data recorded during apnea events by hospital monitors at the University of Virginia Neonatal Intensive Care Unit (NICU) include EKG, chest impedance, and pulse oximetry signals. In previous work, an apnea detection algorithm was developed that filtered the cardiac artifact from the chest impedance signal to improve detection of apneas [1]. An unexpected result was the discovery that Very Long Apneas (VLAs) lasting more than 60 seconds are not rare. We use this findings in our research to provide new information about these apneas and to test a model describing the rate of decrease of blood oxygen in apneas of various lengths. We study 86 very long apneas, along with 285 shorter apneas (10 - 40 s duration), to analyze the properties of VLAs. We begin with a quantitative measure of the oxygen deficit or the heartbeat deficit resulting from the apnea, concluding that both are roughly proportional to the duration of the apnea. We observe that heart rate and oxygen saturation decrease much more slowly in a VLA than in a short apnea, and the initial oxygen saturation prior to VLAs is unusually high. This raises the question of whether babies are hyperventilating before a VLA. To answer this, we have analyzed respiration rates preceding apneas of various durations, and have shown that VLAs are associated with a significantly increased respiration rate immediately prior to the apnea. Lastly, we have used the theory provided by [2] to model the rate of decrease in oxygen saturation during individual apnea events. The resulting model confirms our observation that higher initial levels of oxygen saturation result in slower rates of decrease

Exploring the potential of real-time fMRI to change thought trajectories

Real-time functional magnetic resonance imaging (rt-fMRI) is a non-invasive imaging technique with the potential to help us understand and train both normal and abnormal brain functioning. As fMRI records whole-brain, spatially-localized, patterns of activity in response to stimuli, neurofeedback capitalizes on this data by adjusting stimuli to drive neural responses. When applied to the clinical domain, rt-fMRI can provide specific training to the region or cognitive process thought to be deviant from healthy subjects. Given the potential of rt-fMRI, exploration must be done to develop new methods and uncover inherent limitations of this technique. This thesis represents work done to realize the potential of rt-fMRI for driving specific cognitive changes. While the current application of rt-fMRI is primarily focused on the treatment of depression, the experimental techniques can similarly be applied to other clinical populations. Chapter 2 reviews differences in how depressed and control subjects allocate attention. Attention encapsulates many cognitive processes, from initial encoding to sustained maintenance of stimuli. The review highlights the variability in results that are dependent upon the task used. This motivates the exploration of different real-time experimental designs and tasks to improve mental health; two such projects are detailed in Chapters 3 and 4. The study described in Chapter 3 directly aims to reduce depression severity through attention training, while the experiments presented in Chapter 4 explore the possibility of modifying story interpretations. In parallel, we developed an open-source cloud-based software framework for real-time processing. Our pipeline and experiments can be deployed at other institutions, with the hope that this encourages growth of the rt-fMRI community. Altogether, the work presented in this thesis tests the feasibility of rt-fMRI to be used to alter thought through neurofeedback. The findings in the respective experiments help us to understand the capabilities of this technique, while the cloud software facilitates future use

"American business can assist [African] hands”: the Kennedy administration, US corporations, and the Cold War struggle for Africa

While there has been significant historical interest in President Kennedy’s approach to Africa, historians have not considered in-depth how American economic aid to Africa became tied to the expansion of US business involvement on the subcontinent. A close examination of these issues reveals that the Kennedy administration saw the US Agency for International Development (USAID)’s economic aid programs as a critical mechanism for the resolution of America’s balance of export payments problems, and that Kennedy administration officials worked assiduously to bring American corporate interests to bear on questions of African economic development. This essay argues that the Kennedy administration promoted and fostered an environment that encouraged increased American business investment in Africa. This contention emerges from an analysis of the evolution of Kennedy’s views on Africa, including his support for African nationalist aspirations and for economic development and education, and their impact on administration policy. We examine sources from the Kennedy administration and from the papers of G. Mennen Williams, Kennedy’s Undersecretary of State for Africa and in so doing, we argue that the Kennedy administration fostered an approach to Sub-Saharan African economic development that forged a robust relationship between government aid and American business investment. The Kennedy administration’s embrace of the principles of free enterprise heralded a major shift in US relations with Africa. This point is further underscored by our examination of the significant growth of US-headquartered multinational corporations’ investments in Africa during and immediately following Kennedy’s presidency

European nutrition and health report 2004

The European Nutrition and Health Report, funded by the European Commission, is the first report combining health and nutrition data from European countries. Thirteen countries of the European Union and Norway expressed their interest in participating in this project. These countries of the EU are Austria, Belgium, Denmark, Finland, France, Germany, Greece, Italy, Portugal, Spain, Sweden, the United Kingdom, and Hungary which was included in the very last minute replacing Ireland. Thus it is the first and only new member state of the EU included in a project which was designed to cover the European Union before May 2004. The Institute of Nutritional Sciences of the University of Vienna (Austria) acted as coordinating centre of this project under the supervision of Prof. Ibrahim Elmadfa. The main task of the participating countries was the collection of national data. These data were then forwarded to the coordinating centre. Where necessary, the data set was transferred into another format by the coordinating centre, which was responsible for the preparation of the final report. The main goals of this report were: the compilation of available food and nutrient intake and health data; the identification of major nutrition and health problems in the participating countries and the EU regions; the identification of inadequacies of data collected in the participating countries, which would make a comparability of the collected data difficult. This report should not only compile data from the participating European countries, but should be an impulse for future projects in the area of nutrition and health monitoring. It should serve as a basis for improvements and for the planning of such future projects. Further, it shows what still has to be done in order to obtain comparable and representative dat

BrainIAK: The Brain Imaging Analysis Kit

Functional magnetic resonance imaging (fMRI) offers a rich source of data for studying the neural basis of cognition. Here, we describe the Brain Imaging Analysis Kit (BrainIAK), an open-source, free Python package that provides computationally optimized solutions to key problems in advanced fMRI analysis. A variety of techniques are presently included in BrainIAK: intersubject correlation (ISC) and intersubject functional connectivity (ISFC), functional alignment via the shared response model (SRM), full correlation matrix analysis (FCMA), a Bayesian version of representational similarity analysis (BRSA), event segmentation using hidden Markov models, topographic factor analysis (TFA), inverted encoding models (IEMs), an fMRI data simulator that uses noise characteristics from real data (fmrisim), and some emerging methods. These techniques have been optimized to leverage the efficiencies of high-performance compute (HPC) clusters, and the same code can be seamlessly transferred from a laptop to a cluster. For each of the aforementioned techniques, we describe the data analysis problem that the technique is meant to solve and how it solves that problem; we also include an example Jupyter notebook for each technique and an annotated bibliography of papers that have used and/or described that technique. In addition to the sections describing various analysis techniques in BrainIAK, we have included sections describing the future applications of BrainIAK to real-time fMRI, tutorials that we have developed and shared online to facilitate learning the techniques in BrainIAK, computational innovations in BrainIAK, and how to contribute to BrainIAK. We hope that this manuscript helps readers to understand how BrainIAK might be useful in their research