Volume 13 Supplement 1
Data driven analysis of low frequency spatio-temporal dynamics in resting state MRI (rsMRI) data
© Willis et al; licensee BioMed Central Ltd. 2012
Published: 16 July 2012
Resting state MRI (rsMRI), based on fluctuations in blood oxygenation level dependent (BOLD) signals, serves as a powerful tool to map networks of “functional connectivity” in the brain even in the absence of task activation or stimulation. The most popular analysis techniques for resting state networks involve region of interest (ROI) correlations or Independent Component Analysis (ICA) approaches where the networks are assumed to be undirected and static over the course of the several minute long scan. Recent studies by Majeed  and Chang , show that patterns of connectivity exhibit time-varying properties that change significantly over the course of a single scan. Interactions between different areas of the brain exhibit dynamic properties on the order of tens of seconds . This time scale closely corresponds to the temporal scale observed in cognitive processes suggesting that the dynamics of this “background activity” may influence behavior and/or perception. Characterizing and understanding these dynamics presents unique challenges in terms of signal analysis. We are currently optimizing a completely data driven analysis technique based on wavelet features of BOLD time series data.
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