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Author Notes:

The authors thank Derek Smith, Anzar Abbas, and Dr. Savannah Cookson for their comments and Dr. Shiyang Chen and Dr. Jacob Billings for their very helpful discussions.

We also thank Dr. Matt Bezdek, Dr. Wenju Pan, Dr. Garth Thompson, Amrit Kashyap, Prof. David Van Essen, Dr. Cesar Caballero-Gaudes, Dr. Bruce Crosson, and Alican Nalci for their one-time yet valuable comments.

Subjects:

Research Funding:

This work was funded by the NIH grants R01MH111416-01 and R01NS078095 and NSF grant BCS INSPIRE 1533260.

Keywords:

  • Science & Technology
  • Life Sciences & Biomedicine
  • Neurosciences
  • Neuroimaging
  • Radiology, Nuclear Medicine & Medical Imaging
  • Neurosciences & Neurology
  • RESTING-STATE FMRI
  • HUMAN CONNECTOME PROJECT
  • FUNCTIONAL CONNECTIVITY
  • SPATIOTEMPORAL DYNAMICS
  • PHYSIOLOGICAL NOISE
  • RESPONSE FUNCTION
  • NETWORK
  • FLUCTUATIONS
  • MODE
  • ANTICORRELATIONS

Quasi-periodic patterns of intrinsic brain activity in individuals and their relationship to global signal

Journal Title:

NeuroImage

Volume:

Volume 167

Publisher:

, Pages 297-308

Type of Work:

Article | Post-print: After Peer Review

Abstract:

Quasiperiodic patterns (QPPs) as reported by Majeed et al., 2011 are prominent features of the brain's intrinsic activity that involve important large-scale networks (default mode, DMN; task positive, TPN) and are likely to be major contributors to widely used measures of functional connectivity. We examined the variability of these patterns in 470 individuals from the Human Connectome Project resting state functional MRI dataset. The QPPs from individuals can be coarsely categorized into two types: one where strong anti-correlation between the DMN and TPN is present, and another where most areas are strongly correlated. QPP type could be predicted by an individual's global signal, with lower global signal corresponding to QPPs with strong anti-correlation. After regression of global signal, all QPPs showed strong anti-correlation between DMN and TPN. QPP occurrence and type was similar between a subgroup of individuals with extremely low motion and the rest of the sample, which shows that motion is not a major contributor to the QPPs. After regression of estimates of slow respiratory and cardiac induced signal fluctuations, more QPPs showed strong anti-correlation between DMN and TPN, an indication that while physiological noise influences the QPP type, it is not the primary source of the QPP itself. QPPs were more similar for the same subjects scanned on different days than for different subjects. These results provide the first assessment of the variability in individual QPPs and their relationship to physiological parameters.

Copyright information:

© 2017 Elsevier Inc.

This is an Open Access work distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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