Disentangling Multispectral Functional Connectivity With Wavelets

• Authors: Jacob C.W. Billings, Emory University; Garth J. Thompson, Georgia Institute of Technology; Wenju Pan, Emory University; Matthew E. Magnuson, Georgia Institute of Technology; Alessio Medda, Georgia Tech Res Inst; Shella Keilholz, Emory University
• Journal Title: Frontiers in Neuroscience
• Volume: Volume 12, Number NOV
• Publisher: Frontiers Media | 2018-11-06, Pages 812-812
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.3389/fnins.2018.00812
• Subjects: Biology, Neuroscience; Engineering, Biomedical
• Keywords: Science & Technology; Life Sciences & Biomedicine; Neurosciences; Neurosciences & Neurology; resting state; functional magnetic resonance imaging; functional connectivity; wavelet packet transform; mutual information; clustering; RESTING-STATE; HUMAN BRAIN; FMRI; NETWORKS; SIGNAL; MRI; DYNAMICS; CORTEX; DECOMPOSITION; FLUCTUATIONS

Abstract

The field of brain connectomics develops our understanding of the brain's intrinsic organization by characterizing trends in spontaneous brain activity. Linear correlations in spontaneous blood-oxygen level dependent functional magnetic resonance imaging (BOLD-fMRI) fluctuations are often used as measures of functional connectivity (FC), that is, as a quantity describing how similarly two brain regions behave over time. Given the natural spectral scaling of BOLD-fMRI signals, it may be useful to represent BOLD-fMRI as multiple processes occurring over multiple scales. The wavelet domain presents a transform space well suited to the examination of multiscale systems as the wavelet basis set is constructed from a self-similar rescaling of a time and frequency delimited kernel. In the present study, we utilize wavelet transforms to examine fluctuations in whole-brain BOLD-fMRI connectivity as a function of wavelet spectral scale in a sample (N = 31) of resting healthy human volunteers. Information theoretic criteria measure relatedness between spectrally-delimited FC graphs. Voxelwise comparisons of between-spectra graph structures illustrate the development of preferential functional networks across spectral bands.