Available: 11/13/20, Expires: 11/30/21
The overall goal of the proposed research is to identify the specific brain networks that are vulnerable in Alzheimer’s disease (AD), and derive new accurate, precise, and robust connectomic imaging biomarkers for (especially preclinical) AD, which could improve diagnosis, disease staging, prediction, assessment of progression, and therapeutic efficacy. Information flows in the human brain through a complex set of structural and functional networks. The complete connectivity map among brain areas, i.e. the connectome, can help to better understand the vulnerability of the brain architecture and function to debilitating neurodegenerative diseases, such as AD, and to discover diagnostically and therapeutically important biomarkers.
Diffusion-weighted MRI (dMRI) is used to noninvasively quantify the structural brain networks. In this project, the student – who is familiar with Matlab and/or Python – will use several existing dMRI databases of AD patients and controls to derive new connectomic biomarkers and validate them through disease staging and correlation with clinical and genetic data on cross-sectional datasets, and via prognosis and prediction of conversion to AD on longitudinal datasets.
The student will act as research assistant performing connectivity computation experiments and statistical analysis. The student should be available for at least 8 hours/week in the lab and should have experience with coding, Matlab, and Python. The project PI is very flexible to meet with the student during the project.