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Description
Parkinson's disease is a neurodegenerative disorder in the central nervous system that affects a host of daily activities and involves a variety of symptoms; these include tremors, slurred speech, and rigid muscles. It is the second most common movement disorder globally. In Stage 3 of Parkinson's, afflicted individuals begin to develop an abnormal gait pattern known as freezing of gait (FoG), which is characterized by decreased step length, shuffling, and eventually complete loss of movement; they are unable to move, and often results in a fall. Surface electromyography (sEMG) is a diagnostic tool to measure electrical activity in the muscles to assess overall muscle function. Most conventional EMG systems, however, are bulky, tethered to a single location, expensive, and primarily used in a lab or clinical setting. This project explores an affordable, open-source, and portable platform called Open Brain-Computer Interface (OpenBCI). The purpose of the proposed device is to detect gait patterns by leveraging the surface electromyography (EMG) signals from the OpenBCI and to help a patient overcome an episode using haptic feedback mechanisms. Previously designed devices with similar intended purposes utilize accelerometry as a method of detection as well as audio and visual feedback mechanisms in their design.
ContributorsAnantuni, Lekha (Author) / McDaniel, Troy (Thesis director) / Tadayon, Arash (Committee member) / Harrington Bioengineering Program (Contributor) / School of Human Evolution and Social Change (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Abstract My documentary is about the concussion detection study with Arizona State Football, Translational Genomics Research Institute (TGen), Riddell and the Barrow Neurological Institute. Football players voluntarily participate in the study that aims to identify a biomarker released from the brain to identify if a player has suffered from a concussion. The study uses blood, urine and saliva samples, along with head impact data from Riddell's Sideline Response System. The study is also focusing on the impact of sub-concussive hits and the effects. According to the Barrow Neurological Institute, 84% of respondents believe concussions are "a serious medical condition," and a third of Valley parents will not let their children play football. I interviewed an ASU football player who participated in the study and found out about his experiences with concussions. The severity of concussions has received a lot of attention in recent years, and this study hopes to mitigate concussions symptoms and the fear of concussions. According to the 2015 NFL Health and Safety Report, since 2012 the NFL reported concussions were down by 35%. I interviewed the TGen leaders of the study and the neurologist at the Barrow Concussion and Brain Injury center involved in the study to find out how they plan to find a biomarker and use it to develop an objective way to diagnose concussions. An example of a possible objective test is a mouthguard that changes from clear to blue after a player sustained a hit that resulted in a concussion. The 2015-2016 ASU football season marked the study's third year of research. At the time of my documentary, the study had no timeline to release data.
ContributorsSeki, Katryna Marie (Author) / Lodato, Mark (Thesis director) / Kurland, Brett (Committee member) / Walter Cronkite School of Journalism and Mass Communication (Contributor) / School of Politics and Global Studies (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
It is unknown which regions of the brain are most or least active for golfers during a peak performance state (Flow State or "The Zone") on the putting green. To address this issue, electroencephalographic (EEG) recordings were taken on 10 elite golfers while they performed a putting drill consisting of hitting nine putts spaced uniformly around a hole each five feet away. Data was collected at three time periods, before, during and after the putt. Galvanic Skin Response (GSR) measurements were also recorded on each subject. Three of the subjects performed a visualization of the same putting drill and their brain waves and GSR were recorded and then compared with their actual performance of the drill. EEG data in the Theta (4 \u2014 7 Hz) bandwidth and Alpha (7 \u2014 13 Hz) bandwidth in 11 different locations across the head were analyzed. Relative power spectrum was used to quantify the data. From the results, it was found that there is a higher magnitude of power in both the theta and alpha bandwidths for a missed putt in comparison to a made putt (p<0.05). It was also found that there is a higher average power in the right hemisphere for made putts. There was not a higher power in the occipital region of the brain nor was there a lower power level in the frontal cortical region during made putts. The hypothesis that there would be a difference between the means of the power level in performance compared to visualization techniques was also supported.
ContributorsCarpenter, Andrea (Co-author) / Hool, Nicholas (Co-author) / Muthuswamy, Jitendran (Thesis director) / Crews, Debbie (Committee member) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The purpose of this study is to analyze the stereotypes surrounding four wind instruments (flutes, oboes, clarinets, and saxophones), and the ways in which those stereotypes propagate through various levels of musical professionalism in Western culture. In order to determine what these stereotypes might entail, several thousand social media and blog posts were analyzed, and direct quotations detailing the perceived stereotypical personality profiles for each of the four instruments were collected. From these, the three most commonly mentioned characteristics were isolated for each of the instrument groups as follows: female gender, femininity, and giggliness for flutists, intelligence, studiousness, and demographics (specifically being an Asian male) for clarinetists, quirkiness, eccentricity, and being seen as a misfit for oboists, and overconfidence, attention-seeking behavior, and coolness for saxophonists. From these traits, a survey was drafted which asked participating college-aged musicians various multiple choice, opinion scale, and short-answer questions that gathered how much they agree or disagree with each trait describing the instrument from which it was derived. Their responses were then analyzed to determine how much correlation existed between the researched characteristics and the opinions of modern musicians. From these results, it was determined that 75% of the traits that were isolated for a particular instrument were, in fact, recognized as being true in the survey data, demonstrating that the stereotypes do exist and seem to be widely recognizable across many age groups, locations, and levels of musical skill. Further, 89% of participants admitted that the instrument they play has a certain stereotype associated with it, but only 38% of people identify with that profile. Overall, it was concluded that stereotypes, which are overwhelmingly negative and gendered by nature, are indeed propagated, but musicians do not appear to want to identify with them, and they reflect a more archaic and immature sense that does not correlate to the trends observed in modern, professional music.
ContributorsAllison, Lauren Nicole (Author) / Bhattacharjya, Nilanjana (Thesis director) / Ankeny, Casey (Committee member) / School of Life Sciences (Contributor) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Social impact bonds (SIBs) are a multi-year contract between social service providers, the government, and private investors. The three parties agree on a specific outcome for a societal issue. Investors provide capital required for the service provider to operate the project. The service provider then delivers the service to the target population. The success of the project is evaluated by outside party. If the target outcome is met, the government repays the investors at a premium. Nonprofit service providers can only serve a small community as they lack the funding to scale their programs and their reliance on government funding and philanthropy leads to a lot of time focused on raising money in the short-term and inhibits them from evolving their programs and projects for long-term strategic success. Government budgets decline but social problems persist. These contracts share risk between the government and the investors and allow governments to test out programs and alleviate taxpayer burdens from unsuccessful social service programs. Arizona has a severe homelessness problem. Nightly, 6000 people are homeless in Maricopa County. In a given year, over 32,000 individuals were homeless, composed of single adults, families, children, and veterans. Homelessness is not only a debilitating and difficult experience for those who experience it, but also has considerable economic costs on society. Homeless individuals use a number of government programs beyond emergency shelters, and these can cost taxpayers billions of dollars per year. Rapid rehousing was a successful intervention model that the state has been heavily investing in the last few years. This thesis aimed to survey the Arizona climate and determine what barriers were present for enacting an SIB for homelessness. The findings showed that although there are many competent stakeholder groups, lack of interest and overall knowledge of SIBs prevented groups from taking responsibility as the anchor for such a project. Additionally, the government and nonprofits had good partnerships, but lacked relationships with the business community and investors that could propel an SIB. Finally, although rapid rehousing can be used as a successful intervention model, there are not enough years of proven success to justify the spending on an SIB. Additionally, data collection for homelessness programming needs to be standardized between all relevant partners. The framework for an SIB exists in Arizona, but needs a few more years of development before it can be considered.
ContributorsAhmed, Fabeeha (Author) / Desouza, Kevin (Thesis director) / Lucio, Joanna (Committee member) / School of Politics and Global Studies (Contributor) / Department of Economics (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The following paper consists of a review of sovereign debt sustainability economics and IMF debt sustainability frameworks, as well as a historical case study of Greece and a variable suggestion for the IMF to improve baseline assumptions. The purpose of this paper is to review the current methodology of perceiving debt and improve upon it in the face of an increasingly indebted global economy. Thus, this paper suggests the IMF adopt the variable calculated in Reinhart and Rogoff (2009) as a new benchmark for determining debt sustainability of market access countries. Through an exploration of the most recent Greek crisis, as well as modern Greek financial and political history, the author of this paper contends the IMF should reduce the broadness of the MAC DSA, as it will make for better debt sustainability projections and assumptions in implementing debt program policy.
ContributorsJennings, Zane Phillips (Author) / Mendez, Jose (Thesis director) / Roberts, Nancy (Committee member) / Economics Program in CLAS (Contributor) / School of Politics and Global Studies (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Alternative currencies have a long and varied history, in which Bitcoin is the latest chapter. The pseudonymous Satoshi Nakamoto created Bitcoin as an implementation of the concept of a cryptocurrency, or a decentralized currency based on the principles of cryptography. Since its creation in 2008, Bitcoin has had a fairly tumultuous existence that limited its adoption. Wide price fluctuations occurred as the appeal of free money by running a piece of computer software drove people to purchase expensive hardware, and high-profile scandals cast Bitcoin as an unstable currency well-suited primarily for purchasing illicit materials. Consumer confidence in the currency was extremely low, and businesses were extremely hesitant to accept a currency that could easily lose half (or more) of its value overnight. However, recent years have seen the currency begin to stabilize as businesses and mainstream investors have begun to accept and support it. Alternative cryptocurrencies, titled "altcoins," have also been created to fill market niches that Bitcoin was not addressing. Governmental intervention, a concern of many following the currency, has been surprisingly restrained and has actually contributed to its stability. The future of Bitcoin looks very bright as it carries the dream of the alternative currency forward into the 21st century.
ContributorsReardon, Brett (Co-author) / Burke, Ryan (Co-author) / Happel, Stephen (Thesis director) / Boyes, William (Committee member) / School of Politics and Global Studies (Contributor) / Department of Information Systems (Contributor) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The goal of our study is to identify socio-economic risk factors for depressive disorder and poor mental health by statistically analyzing survey data from the CDC. The identification of risk groups in a particular demographic could aid in the development of targeted interventions to improve overall quality of mental health in the United States. In our analysis, we studied the influences and correlations of socioeconomic factors that regulate the risk of developing Depressive Disorders and overall poor mental health. Using the statistical software STATA, we ran a regression model of selected independent socio-economic variables with the dependent mental health variables. The independent variables of the statistical model include Income, Race, State, Age, Marital Status, Sex, Education, BMI, Smoker Status, and Alcohol Consumption. Once the regression coefficients were found, we illustrated the data in graphs and heat maps to qualitatively provide visuals of the prevalence of depression in the U.S. demography. Our study indicates that the low-income and under-educated populations who are everyday smokers, obese, and/or are in divorced or separated relationships should be of main concern. A suggestion for mental health organizations would be to support counseling and therapeutic efforts as secondary care for those in smoking cessation programs, weight management programs, marriage counseling, or divorce assistance group. General improvement in alleviating poverty and increasing education could additionally show progress in counter-acting the prevalence of depressive disorder and also improve overall mental health. The identification of these target groups and socio-economic risk factors are critical in developing future preventative measures.
ContributorsGrassel, Samuel (Co-author) / Choueiri, Alexi (Co-author) / Choueiri, Robert (Co-author) / Goegan, Brian (Thesis director) / Holter, Michael (Committee member) / Sandra Day O'Connor College of Law (Contributor) / School of Molecular Sciences (Contributor) / School of Politics and Global Studies (Contributor) / Economics Program in CLAS (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Introduction: There are 350 to 400 pediatric heart transplants annually according to the Pediatric Heart Transplant Database (Dipchand et al. 2014). Finding appropriate donors can be challenging especially for the pediatric population. The current standard of care is a donor-to-recipient weight ratio. This ratio is not necessarily a parameter directly indicative of the size of a heart, potentially leading to ill-fitting allografts (Tang et al. 2010). In this paper, a regression model is presented - developed by correlating total cardiac volume to non-invasive imaging parameters and patient characteristics – for use in determining ideal allograft fit with respect to total cardiac volume.
Methods: A virtual, 3D library of clinically-defined normal hearts was compiled from reconstructed CT and MR scans. Non-invasive imaging parameters and patient characteristics were collected and subjected to backward elimination linear regression to define a model relating patient parameters to the total cardiac volume. This regression model was then used to retrospectively accept or reject an ‘ideal’ donor graft from the library for 3 patients that had undergone heart transplantation. Oversized and undersized grafts were also transplanted to qualitatively analyze virtual transplantation specificity.
Results: The backward elimination approach of the data for the 20 patients rejected the factors of BMI, BSA, sex and both end-systolic and end-diastolic left ventricular measurements from echocardiography. Height and weight were included in the linear regression model yielding an adjusted R-squared of 82.5%. Height and weight showed statistical significance with p-values of 0.005 and 0.02 respectively. The final equation for the linear regression model was TCV = -169.320+ 2.874h + 3.578w ± 73 (h=height, w=weight, TCV= total cardiac volume).
Discussion: With the current regression model, height and weight significantly correlate to total cardiac volume. This regression model and virtual normal heart library provide for the possibility of virtual transplant and size-matching for transplantation. The study and regression model is, however, limited due to a small sample size. Additionally, the lack of volumetric resolution from the MR datasets is a potentially limiting factor. Despite these limitations the virtual library has the potential to be a critical tool for clinical care that will continue to grow as normal hearts are added to the virtual library.
Methods: A virtual, 3D library of clinically-defined normal hearts was compiled from reconstructed CT and MR scans. Non-invasive imaging parameters and patient characteristics were collected and subjected to backward elimination linear regression to define a model relating patient parameters to the total cardiac volume. This regression model was then used to retrospectively accept or reject an ‘ideal’ donor graft from the library for 3 patients that had undergone heart transplantation. Oversized and undersized grafts were also transplanted to qualitatively analyze virtual transplantation specificity.
Results: The backward elimination approach of the data for the 20 patients rejected the factors of BMI, BSA, sex and both end-systolic and end-diastolic left ventricular measurements from echocardiography. Height and weight were included in the linear regression model yielding an adjusted R-squared of 82.5%. Height and weight showed statistical significance with p-values of 0.005 and 0.02 respectively. The final equation for the linear regression model was TCV = -169.320+ 2.874h + 3.578w ± 73 (h=height, w=weight, TCV= total cardiac volume).
Discussion: With the current regression model, height and weight significantly correlate to total cardiac volume. This regression model and virtual normal heart library provide for the possibility of virtual transplant and size-matching for transplantation. The study and regression model is, however, limited due to a small sample size. Additionally, the lack of volumetric resolution from the MR datasets is a potentially limiting factor. Despite these limitations the virtual library has the potential to be a critical tool for clinical care that will continue to grow as normal hearts are added to the virtual library.
ContributorsSajadi, Susan (Co-author) / Lindquist, Jacob (Co-author) / Frakes, David (Thesis director) / Ryan, Justin (Committee member) / Harrington Bioengineering Program (Contributor) / School of International Letters and Cultures (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Glioblastoma multiforme (GBM) is a malignant, aggressive and infiltrative cancer of the central nervous system with a median survival of 14.6 months with standard care. Diagnosis of GBM is made using medical imaging such as magnetic resonance imaging (MRI) or computed tomography (CT). Treatment is informed by medical images and includes chemotherapy, radiation therapy, and surgical removal if the tumor is surgically accessible. Treatment seldom results in a significant increase in longevity, partly due to the lack of precise information regarding tumor size and location. This lack of information arises from the physical limitations of MR and CT imaging coupled with the diffusive nature of glioblastoma tumors. GBM tumor cells can migrate far beyond the visible boundaries of the tumor and will result in a recurring tumor if not killed or removed. Since medical images are the only readily available information about the tumor, we aim to improve mathematical models of tumor growth to better estimate the missing information. Particularly, we investigate the effect of random variation in tumor cell behavior (anisotropy) using stochastic parameterizations of an established proliferation-diffusion model of tumor growth. To evaluate the performance of our mathematical model, we use MR images from an animal model consisting of Murine GL261 tumors implanted in immunocompetent mice, which provides consistency in tumor initiation and location, immune response, genetic variation, and treatment. Compared to non-stochastic simulations, stochastic simulations showed improved volume accuracy when proliferation variability was high, but diffusion variability was found to only marginally affect tumor volume estimates. Neither proliferation nor diffusion variability significantly affected the spatial distribution accuracy of the simulations. While certain cases of stochastic parameterizations improved volume accuracy, they failed to significantly improve simulation accuracy overall. Both the non-stochastic and stochastic simulations failed to achieve over 75% spatial distribution accuracy, suggesting that the underlying structure of the model fails to capture one or more biological processes that affect tumor growth. Two biological features that are candidates for further investigation are angiogenesis and anisotropy resulting from differences between white and gray matter. Time-dependent proliferation and diffusion terms could be introduced to model angiogenesis, and diffusion weighed imaging (DTI) could be used to differentiate between white and gray matter, which might allow for improved estimates brain anisotropy.
ContributorsAnderies, Barrett James (Author) / Kostelich, Eric (Thesis director) / Kuang, Yang (Committee member) / Stepien, Tracy (Committee member) / Harrington Bioengineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05