Publication

Disentangling Multispectral Functional Connectivity With Wavelets

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Last modified
  • 05/21/2025
Type of Material
Authors
    Jacob C.W. Billings, Emory UniversityGarth J. Thompson, Georgia Institute of TechnologyWenju Pan, Emory UniversityMatthew E. Magnuson, Georgia Institute of TechnologyAlessio Medda, Georgia Tech Res InstShella Keilholz, Emory University
Language
  • English
Date
  • 2018-11-06
Publisher
  • Frontiers Media
Publication Version
Copyright Statement
  • Copyright © 2018 Billings, Thompson, Pan, Magnuson, Medda and Keilholz.
License
Final Published Version (URL)
Title of Journal or Parent Work
ISSN
  • 1662-4548
Volume
  • 12
Issue
  • NOV
Start Page
  • 812
End Page
  • 812
Grant/Funding Information
  • This work was supported by the Air Force Center of Excellence on Bio-Nano-Enabled Inorganic/Organic Nanostructures and Improved Cognition (BIONIC) at Georgia Institute of Technology; NIH R01NS078095-02 and R01NS078095-02S1; and by Professional Development Supports Funds provided by Laney Graduate School, Emory University
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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.
Author Notes
Keywords
Research Categories
  • Biology, Neuroscience
  • Engineering, Biomedical

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