Clustering techniques applied to MRI and fMRI data of the human brain at 7 Tesla

Human brain function depends on interactions between functionally specialized brain regions. One of the most challenging problems in neuroscience today is the detection of such functional networks that are characterized by both integration and segregation. In recent years there has been increasing evidence that low-frequency fluctuations are not only a major source of variation in fMRI data of the human brain, but may contain information about cognitive networks that are specific to the overall task domain without being time locked to stimulus onsets. This opens a new avenue into the analysis of networks. In this talk, model-free clustering techniques that harvest the low-frequency part of the fMRI signal at 3T and 7T will be presented. Special focus will be placed on spectral clustering, eigenvector centrality mapping and connectivity concordance mapping