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Disentangling Multispectral Functional Connectivity With Wavelets
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SYSNO ASEP 0545816 Document Type J - Journal Article R&D Document Type The record was not marked in the RIV Subsidiary J Článek ve WOS Title Disentangling Multispectral Functional Connectivity With Wavelets Author(s) Billings, Jacob (UIVT-O) SAI, ORCID, RID
Thompson, G. J. (US)
Pan, W.J. (US)
Magnuson, M.E. (US)
Medda, A. (US)
Keilholz, S. (US)Number of authors 6 Article number 812 Source Title Frontiers in Neuroscience
Roč. 12 (2018)Language eng - English Country CH - Switzerland 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 UT WOS 000449346900002 EID SCOPUS 85057187166 DOI 10.3389/fnins.2018.00812 Annotation 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. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2022
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