Machine Learning Based Autism Detection Using Brain Imaging

Autism Spectrum Disorder (ASD) is a group of heterogeneous developmental disabilities that manifest in early childhood. Currently, ASD is primarily diagnosed by assessing the behavioral and intellectual abilities of a child. This behavioral diagnosis can be subjective, time consuming, inconclusive, does not provide insight on the underlying etiology, and is not suitable for early detection. Diagnosis based on brain magnetic resonance imaging (MRI)—a widely used non- invasive tool—can be objective, can help understand the brain alterations in ASD, and can be suitable for early diagnosis. However, the brain morphological findings in ASD from MRI studies have been inconsistent. Moreover, there has been limited success in machine learning based ASD detection using MRI derived brain features. In this thesis, we begin by demonstrating that the low success in ASD detection and the inconsistent findings are likely attributable to the heterogeneity of brain alterations in ASD. We then show that ASD detection can be significantly improved by mitigating the heterogeneity with the help of behavioral and demographics information. Here we demonstrate that finding brain markers in well-defined sub-groups of ASD is easier and more insightful than identifying markers across the whole spectrum. Finally, our study focused on brain MRI of a pediatric cohort (3 to 4 years) and achieved a high classification success (AUC of 95%). Results of this study indicate three main alterations in early ASD brains: 1) abnormally large ventricles, 2) highly folded cortices, and 3) low image intensity in white matter regions suggesting myelination deficits indicative of decreased structural connectivity. Results of this thesis demonstrate that the meaningful brain markers of ASD can be extracted by applying machine learning techniques on brain MRI data. This data-driven technique can be a powerful tool for early detection and understanding brain anatomical underpinnings of ASD

Benefit Estimation of Water Quality Improvements in Bagmati River Using Choice Modeling

Environmental Economics and Policy,

Demand for Environmental Quality: Evidence on Drinking Waterfrom Kathmandu, Nepal

This paper examines the demand for environmental quality - clean drinking water in particular - in Kathmandu, Nepal. Water supply is inadequate, unreliable, low quality and not directly potable. Residents engage in several strategies to cope with unreliable and low quality water supplies. Some of the major strategies are: hauling, storing, boiling and filtering. A Report on the Water Survey of Kathmandu Valley 2005 suggested that over 45 per cent of households filter water to make it potable, while about 39 per cent of households boil water. Use of Uro Guard and the Solar Disinfection System (SODIS) are other purification methods. To date, there has been little empirical analysis of such purification behaviors. This paper investigates these purification behaviors and the factors influencing them. We consider different types of treatments as demand for environmental quality. Using the Water Survey of Kathmandu, we estimate the effect of education level of household head, exposure to the media, gender, caste, ethnicity and opinion of water quality on drinking water purification. Treatment costs are calculated from respondents’ answers on treatment types, market price and value of time. We also estimate expected willingness to pay for environmental quality from the average cost for different types of treatment. Moreover, the impact of education level of household head, exposure to media, gender, caste, and ethnicity on willingness to pay is also evaluated

Coping with unreliable water supplies and willingness to pay for improved water supplies in Kathmandu

This paper estimates both the households’ costs of coping with the problem of unreliable public water supplies and their willingness to pay (WTP) for improved water supplies in Kathmandu valley. Coping costs are calculated from respondents’ answers on averting behavior, market price and value of time. The willingness to pay for improved water supply is calculated using stated preference method, which is then compared with the value obtained from revealed preference method. This paper also discusses the effects of a household’s socio-economic characteristics on its coping costs and WTP for improved water supply