Diagnosis of Alzheimer's disease using resting-state fMRI and graph theory.

BACKGROUND: The study of the brain network based on the basis of the resting-state functional Magnetic Resonance Imaging (fMRI) provided some promising results to investigate changes in connectivity among different brain regions because of Alzheimer's disease (AD).

OBJECTIVE: In addition, the graph theory has been utilized as an efficient tool in diagnosing Alzheimer and in finding the developed differences in the brain as the result of this disease.

METHODS: This study considers 16 areas of the brain, which play major role in the development of AD. Accordingly, the time series and the correlation matrix were yielded for each of these areas. Then, by using threshold we obtain functional connectivity from correlation matrices along with the brain graph parameter for Normal Controls and AD groups were obtained in order to compare the existing differences.

RESULTS: The differences of characteristics among healthy individuals and patients suffering from Alzheimer has been investigated in this study through the formation of brain graphs for 16 areas and the utilization of data on Normal Controls (13 persons) and patients suffering from Alzheimer (13 patients).

CONCLUSIONS: Some of the properties of the graph are the characteristic path length, the clustering coefficient, the local and global efficiency yield of ability to separate the two groups which may be used to diagnose Alzheimer.