Multiscale functional networks in human resting state functional MRI

Recent advent of fast imaging techniques for MRI application allow whole brain coverage with sub-second resolution, opening the door for new data-driven computational techniques that can harvest the information contained in the data. This paper examines the use of wavelet based spectral decomposition and hierarchical clustering for resting state functional MRI. Wavelet packets naturally enable short time spectral decomposition with minimal temporal window lengths across multiple frequency ranges, while hierarchical clustering is used for organizing broadband and filtered fMRI data into functional network. This method was applied to human group data from five volunteers from the 1000 Functional Connectomes database.