Seed-based and data-driven analyses of default mode network connectivity measures in dementia

Abstract

Functional neuroimaging and its applications to neurodegenerative diseases and mental illnesses have created an enlarging area of interest that varying lines of research ranging from molecular biology to engineering contribute to. Among them, Alzheimer's disease has a critical importance by causing the largest number of dementia cases. Recently, mild and subjective cognitive impairments have also been associated with Alzheimer's as possible indicators of cognitive decline. Using resting-state fMRI to investigate functional connectivity measures and detect any abnormality within and between networks have yielded promising results that disclose information about the nature of the diseases. The objective of this thesis is to use varying resting-state fMRI methods to di erentiate between SCI, MCI and AD patients by investigating the changes within Default Mode Network (DMN). The obtained results indicate that the changes within the functional connectivity measures among DMN components can be detected independent of the method of choice, and the measures of connectivity di er among groups. Subsequent research would aim for detection of possible bio-markers that are present through several stages and nding a common framework where metrics obtained from di erent methods can be compared.