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Related Concept Videos

Brain Imaging01:14

Brain Imaging

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Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
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Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
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Resting-State fMRI: Emerging Concepts for Future Clinical Application.

Shiori Amemiya1, Hidemasa Takao1, Osamu Abe1

  • 1Department of Radiology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan.

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Summary
This summary is machine-generated.

Resting-state functional magnetic resonance imaging (rsfMRI) reveals the global signal is a dominant component, not just noise. This global signal offers diagnostic biomarkers and challenges current network analysis assumptions.

Keywords:
blood oxygenation level dependencycerebrovascular perfusionhemodynamic response functionresting-state functional magnetic resonance imagingspatiotemporal organization

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Area of Science:

  • Neuroimaging
  • Systems Neuroscience
  • Clinical Neuroscience

Background:

  • Resting-state functional magnetic resonance imaging (rsfMRI) identifies brain networks from spontaneous neural activity.
  • rsfMRI is valuable in clinical settings due to its ease of implementation and short scan times.
  • The global signal in rsfMRI has been historically overlooked, often considered physiological noise.

Purpose of the Study:

  • To re-evaluate the significance of the global signal in rsfMRI.
  • To explore the potential of the global signal as a diagnostic biomarker.
  • To challenge conventional rsfMRI analysis paradigms.

Main Methods:

  • Review of recent studies on rsfMRI global signal spatiotemporal analyses.
  • Analysis of the global signal's contribution to overall rsfMRI signal variance.
  • Investigation of the relationship between the global signal and resting-state networks (RSNs).

Main Results:

  • The global signal is the dominant component in rsfMRI, explaining most brain signal variance.
  • The global signal contains rich information about local hemodynamics, serving as a potential diagnostic biomarker.
  • Spatiotemporal analyses demonstrate a fundamental association between the global signal and RSN organization.

Conclusions:

  • The global signal is a critical component of rsfMRI, not merely a nuisance.
  • Understanding the global signal can lead to novel diagnostic biomarkers for clinical applications.
  • New conceptual frameworks for rsfMRI analysis are needed, incorporating the global signal's role.