Disentangling Multispectral Functional Connectivity With Wavelets

0545816 - ÚI 2022 CH eng J - Journal Article
Billings, Jacob - Thompson, G. J. - Pan, W.J. - Magnuson, M.E. - Medda, A. - Keilholz, S.
Disentangling Multispectral Functional Connectivity With Wavelets.
Frontiers in Neuroscience. Roč. 12 (2018), č. článku 812. E-ISSN 1662-453X
Keywords : resting-state * human brain * fmri * networks * signal * mri * dynamics * cortex * decomposition * fluctuations * resting state * functional magnetic resonance imaging * functional connectivity * wavelet packet transform * mutual information * clustering
Impact factor: 3.648, year: 2018

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.
Permanent Link: http://hdl.handle.net/11104/0322461