4.7 Article

Beyond noise to function: reframing the global brain activity and its dynamic topography

Journal

COMMUNICATIONS BIOLOGY
Volume 5, Issue 1, Pages -

Publisher

NATURE PORTFOLIO
DOI: 10.1038/s42003-022-04297-6

Keywords

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Funding

  1. Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions [2022SHIBS0003]
  2. National Natural Science Foundation of China [32201129]
  3. Shenzhen University
  4. European Union [785907]
  5. Ministry of Science and Technology of China
  6. National Key R&D Program of China [2016YFC1306700]
  7. EJLB-Michael Smith Foundation
  8. Canada Institute of Health Research (CIHR)
  9. NFRF, uOBMRI Team grant
  10. Hangzhou Normal University

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This article reviews the neural basis and functions of the global signal in the brain, showing its close coupling with physiological signals and its role in modulating arousal level, organizing a dynamic topography, and coordinating different forms of cognition. The findings suggest the need to go beyond considering the global signal as noise and embrace a dual-layer model with background and surface layers.
How global and local activity interact with each other is a common question in complex systems like climate and economy. Analogously, the brain too displays 'global' activity that interacts with local-regional activity and modulates behavior. The brain's global activity, investigated as global signal in fMRI, so far, has mainly been conceived as non-neuronal noise. We here review the findings from healthy and clinical populations to demonstrate the neural basis and functions of global signal to brain and behavior. We show that global signal (i) is closely coupled with physiological signals and modulates the arousal level; and (ii) organizes an elaborated dynamic topography and coordinates the different forms of cognition. We also postulate a Dual-Layer Model including both background and surface layers. Together, the latest evidence strongly suggests the need to go beyond the view of global signal as noise by embracing a dual-layer model with background and surface layer.

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