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Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
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Resting-state "physiological networks".

Jingyuan E Chen1, Laura D Lewis2, Catie Chang3

  • 1Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA.

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|March 8, 2020
PubMed
Summary
This summary is machine-generated.

Physiological fluctuations in brain activity can mimic neuronal networks detected via functional MRI (fMRI). These "physiological networks" are widespread and challenge current connectivity study assumptions, offering potential clinical biomarkers.

Keywords:
Global signal regressionHeart rateRespiratory variationResting state functional connectivityfMRI

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Area of Science:

  • Neuroscience
  • Physiology
  • Medical Imaging

Background:

  • Functional MRI (fMRI) studies often analyze brain connectivity.
  • Slow physiological changes can cause significant fMRI signal fluctuations.
  • These fluctuations can create structured spatial patterns resembling brain networks.

Purpose of the Study:

  • To investigate if physiological networks resemble neuronal networks.
  • To determine the extent of physiological influence on fMRI connectivity.
  • To assess the impact of physiological confounds on resting-state network identification.

Main Methods:

  • Analysis of a large dataset from the 3T Human Connectome Project (HCP).
  • Examination of fMRI data coupled with physiological recordings (respiratory and heart rate).
  • Use of synthesized data to model physiological contributions to connectivity.

Main Results:

  • Brain-wide, heterogeneous dynamics were tightly coupled to respiration and heart rate.
  • Physiologically-coupled fluctuations alone generated networks resembling known resting-state networks.
  • Physiological connectivity dominated overall connectivity estimates and was not removed by global signal regression.

Conclusions:

  • Physiological networks can mimic neuronal networks, challenging existing interpretations of fMRI connectivity.
  • Physiological confounds are not always localized or globally coherent, necessitating careful consideration in fMRI studies.
  • Physiological dynamics offer potential for novel clinical biomarkers complementary to neuronal network analysis.