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Related Concept Videos

Magnetic Resonance Imaging01:24

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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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Brain Imaging01:14

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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.
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Related Experiment Video

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Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
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Methods and Considerations for Dynamic Analysis of Functional MR Imaging Data.

Jingyuan E Chen1, Mikail Rubinov2, Catie Chang3

  • 1Department of Radiology, Stanford University, 1201 Welch Road, Stanford, CA 94305, USA; Department of Electrical Engineering, Stanford University, 1201 Welch Road, Stanford, CA 94305, USA.

Neuroimaging Clinics of North America
|October 8, 2017
PubMed
Summary
This summary is machine-generated.

Researchers are exploring dynamic functional connectivity using functional MR imaging (fMRI) to understand brain states. Careful analysis is needed to distinguish neural signals from noise for reliable biomarkers.

Keywords:
DynamicsFunctional connectivityFunctional magnetic resonance imagingNetworksResting state

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Medical Imaging

Background:

  • Functional MR imaging (fMRI) is increasingly used to study spontaneous fluctuations in brain activity.
  • These fluctuations reflect dynamic changes in functional connectivity over seconds to minutes.
  • Understanding these transient brain states may reveal novel cognitive and clinical biomarkers.

Purpose of the Study:

  • To provide an overview of methodological and interpretational considerations for analyzing time-varying functional connectivity in fMRI.
  • To highlight the potential of dynamic functional connectivity as a source of biomarkers.
  • To emphasize the need for rigorous validation and statistical testing.

Main Methods:

  • Review of current methodologies in analyzing time-varying functional connectivity from fMRI data.
  • Discussion of challenges in differentiating neural and non-neural signal components.
  • Emphasis on the importance of robust statistical validation.

Main Results:

  • Dynamic functional connectivity analysis offers a promising avenue for understanding brain function.
  • Transient configurations of coordinated brain activity can be identified.
  • Distinguishing neural from non-neural signals is critical for valid interpretation.

Conclusions:

  • Time-varying functional connectivity analysis in fMRI is an emerging field with significant potential.
  • Methodological rigor, including signal differentiation and statistical validation, is paramount.
  • This approach may yield novel biomarkers for cognitive and clinical applications.