Indiana University-Purdue University Indianapolis (IUPUI) / Alzheimer's disease (AD) is a neurodegenerative disorder characterized by gradual loss of brain functions, usually preceded by memory impairments. It has been widely affecting aging Americans over 65 old and listed as 6th leading cause of death. More importantly, unlike other diseases, loss of brain function in AD progression usually leads to the significant decline in self-care abilities. And this will undoubtedly exert a lot of pressure on family members, friends, communities and the whole society due to the time-consuming daily care and high health care expenditures. In the past decade, while deaths attributed to the number one cause, heart disease, has decreased 16 percent, deaths attributed to AD has increased 68 percent. And all of these situations will continue to deteriorate as the population ages during the next several decades. To prevent such health care crisis, substantial efforts have been made to help cure, slow or stop the progression of the disease. The massive data generated through these efforts, like multimodal neuroimaging scans as well as next generation sequences, provides unprecedented opportunities for researchers to look into the deep side of the disease, with more confidence and precision. While plenty of efforts have been made to pull in those existing machine learning and statistical models, the correlated structure and high dimensionality of imaging and genetics data are generally ignored or avoided through targeted analysis. Therefore their performances on imaging genetics study are quite limited and still have plenty to be improved. The primary contribution of this work lies in the development of novel prior knowledge-guided regression and association models, and their applications in various neurobiological problems, such as identification of cognitive performance related imaging biomarkers and imaging genetics associations. In summary, this work has achieved the following research goals: (1) Explore the multimodal imaging biomarkers toward various cognitive functions using group-guided learning algorithms, (2) Development and application of novel network structure guided sparse regression model, (3) Development and application of novel network structure guided sparse multivariate association model, and (4) Promotion of the computation efficiency through parallelization strategies.

Dementia of Alzheimer‟s Type is a degenerative neurocognitive disease accounting for majority of Dementia‟s. It affects millions of people worldwide and thousands of people in South Africa. Apart from the economic burden this illness places on the country, it has detrimental effects for those who provide care for individuals with this illness, who are mostly spouses. The spousal caregivers bears great financial, social and emotional burden which worsens as the disease progresses. The aim of this study is to phenomenologically explore and describe the lived experiences of spousal caregivers in caring for the spouses with Dementia of Alzheimer‟s Type. This South African study was therefore qualitative in nature and was conducted in the province of KwaZulu- Natal, in the city of Pietermaritzburg. Eight participants were interviewed using a semi-structured questionnaire. Data was analysed using interpretative phenomenological analysis (IPA). The main findings of this study are the negative emotional affects that the caregiving role creates. Caregiver stress and strain is experienced as well as the experiences of various losses including lack of intimacy and ruined expectations for the future as the disease progresses. The caregiving role also created negative implications for the social lives of caregivers and coping mechanisms were seen to be very important. / Psychology / M.A. (Psychology)