Functional connectivity network analysis of alzheimer and mild cognitive impairment patients

Abstract

In our era, while the life span is expanding, neurodegenerative diseases, such as Alzheimer's disease (AD), pose a great threat upon the quality of life. In such a case, the best course of action would be to detect, modify or treat the pathologies before they become too severe. Since the main cause of AD is still unknown, further studies for possible biomarkers are needed. Therefore, in this study, the objective is to nd a distinctive agent for AD and mild cognitive impairment (MCI) from an optimized auditory oddball task fMRI data via functional connectivity analysis. In order to achieve that, a group ICA approach using temporal concatenation of the subject data is adopted. Since, there are no studies investigating functional connectivity of AD and MCI during an oddball task, especially via group ICA, this study can enrich the literature. As the results are concerned, in group comparisons, no signi cant di erences are found in spatial maps. On the other hand, there are promising ndings in temporal course analysis of the components such as the multiple regression outcomes. Therefore, our next aim will be to perform a longutidinal study including both resting state and task related data for nding a better biomarker.|Keywords : fMRI, Alzheimer's disease, mild cognitive impairment, independent component analysis, oddball paradigm.