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Author Notes:

Correspondence: Alessio Medda alessio.medda@gtri.gatech.edu Shella Keilholz shella.keilholz@bme.gatech.edu

All authors designed the study.

JB conducted the analysis with advice from all authors, especially AM and SK.

JB authored the manuscript with input and revisions provided by all authors.

Each author has given final approval of the manuscript's publication and agrees to be accountable for all aspects of the work.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Subjects:

Research Funding:

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

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

Disentangling Multispectral Functional Connectivity With Wavelets

Tools:

Journal Title:

Frontiers in Neuroscience

Volume:

Volume 12, Number NOV

Publisher:

, Pages 812-812

Type of Work:

Article | Final Publisher PDF

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.

Copyright information:

Copyright © 2018 Billings, Thompson, Pan, Magnuson, Medda and Keilholz.

This is an Open Access work distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
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