Advisor: Dr. Deqiang Qiu (Georgia Institute of Technology and Emory University)

Committee Members: 

Dr. Stanislav Emelianov (Georgia Institute of Technology and Emory University)

Dr. Erin M. Buckley (Georgia Institute of Technology and Emory University)

Dr. James Lah (Emory University)

Dr. Shella Keilholz (Georgia Institute of Technology and Emory University)

  Advancing Magnetic Resonance Imaging Methods for Assessing Cerebrovascular Contributions to Alzheimer's Disease

Alzheimer’s Disease (AD) is the leading cause of dementia, affecting millions of people each year with limited treatment options. AD has long been thought to be caused by the accumulation of neurotoxic amyloid and tau compounds. Cerebrovascular dysfunction is now understood to also play a role in AD progression; although, the exact pathological mechanisms are unclear. Amyloid and tau, while informative, are assessed using invasive procedures and may not be sensitive to earliest disease pathology. In contrast, cerebrovascular dysfunction has been observed in people at risk of AD before amyloid/tau accumulation or cognitive decline. Reliable, noninvasive methods to assess cerebrovascular dysfunction are therefore necessary to better understand its role in AD and to develop diagnostic and therapeutic tools. Magnetic resonance imaging (MRI) offers a promising approach to assess cerebrovascular health comprehensively and noninvasively through its ability to quantify multiple cerebrovascular biomarkers with high spatial resolution. Moreover, MRI-derived cerebrovascular biomarkers together with recent advancements in deep learning could aid our understanding of AD progression and/or lead to the establishment of novel biomarkers. In this work, we develop MRI techniques that assess multiple cerebrovascular parameters and apply these techniques with deep learning methods to investigate cerebrovascular dysfunction patterns occurring in early AD.