| dc.description.abstract | Functional magnetic resonance imaging (fMRI) is widely used to study the characteristics of the human brain activities. Most of the analysis methods are based on voxels. Some summary statistics characterizing the temporal response in each voxel are computed and represented in the spatial domain by a brain map for visual inspection or additional inference (see, e.g., Bandettini et al., 1993; Worsley and Friston, 1995; Xiong et al., 1996; and Lange and Zeger, 1997). This method usually relies on some model and assumption about the fMRI acquisition, e.g., concerning the stimulus, the haemodynamic response, among others. This work attempts to explore the avalanches of resting-state fMRI via K-means clustering, the co-activation pattern (CAP) analysis, Markov chains and sliding window principle components analysis (PCA) in Alzheimer’s disease (AD) and cognitive normal (CN) subjects. The K-means clustering is used to establish states for the Markov chain. Different brain activation patterns among Alzheimer’s disease (AD) and cognitive normal (CN) subjects can be found according to the analysis. These findings suggest that functional neuroimaging can be used as a method of identifying pre-clinical Alzheimer’s disease. | |
