Alzheimer’s disease is a major cause of dementia. Its diagnosis requires accurate biomarkers that are sensitive to disease stages. Neuroimaging techniques such as PET and MRI have been used as imaging biomarkers. However, it is virtually impossible to visually detect a slight decrease in regional cerebral blood flow or glucose metabolism in early stages of the disease. Moreover, visual inspection is susceptible to other factors like subjectivity and experience of the physician. On the other hand, voxel-based representations of neuroimagery can be used to perform both standardization and data-driven analysis. Computerized methods can also improve the speed of diagnosis with no compromise of accuracy and facilitate accurate diagnosis in cases where there is a lack of access to an experienced physician.
During my graduate studies, I focused on predictive modeling of Alzheimer’s disease using the variations of high-dimensional neuroimaging data. Simply, I regarded the induction of a classifier as the creation of a computational biomarker for disease staging. In this regard, I investigated clever utilizations of a wide range of machine learning algorithms in order to get the best of them, given the challenges of working with the scarce and high-dimensional neuroimaging data. I also strove for simplicity and comprehensibility in these predictive models, which is highly valuable for clinical decision-making.
- Data-driven Prognosis of Alzheimer’s Disease (Software Prototype), Patient Early Health Collaboration Project, 03/01/08 – 12/31/09, Funding from GE Healthcare, PIs: Vijay V. Raghavan and C. H. Chu, Laboratory for Internet Computing, CACS, UL Lafayette. Research Assistant: Murat Seçkin Ayhan
- Towards creating a Large and Scalable In-Memory Database Management System, State of Louisiana, Governor’s Information Technology Initiative Project, 07/01/07 – 07/31/09, PI: Vijay V. Raghavan, Laboratory for Internet Computing, CACS, UL Lafayette. Research Assistant: Murat Seçkin Ayhan
- Evliya Çelebi: A Middleware for Geographical Information, TÜBİTAK-SOBAG, 105K040, 2005, PI: Hayri Sever, Project completed at the Department of Computer Engineering, Baskent University, Research Assistant: Murat Seçkin Ayhan