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Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

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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Modeling the Functional Network for Spatial Navigation in the Human Brain
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Graph-based network analysis of resting-state functional MRI.

Jinhui Wang1, Xinian Zuo, Yong He

  • 1State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University Beijing, China.

Frontiers in Systems Neuroscience
|July 1, 2010
PubMed
Summary
This summary is machine-generated.

Resting-state functional MRI (R-fMRI) reveals the brain

Keywords:
brainfunctional MRIfunctional connectivitygraph theoryhuman connectomenetworkresting-statesmall-world

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Area of Science:

  • Neuroscience
  • Network Science
  • Medical Imaging

Background:

  • Resting-state functional MRI (R-fMRI) measures intrinsic brain activity.
  • R-fMRI uses blood oxygen level-dependent signals for high-resolution task-free brain assessment.
  • Brain activity is understood as a complex network with intra- and inter-regional connectivity.

Purpose of the Study:

  • To review recent advances in graph-based brain network analyses of R-fMRI signals.
  • To explore these analyses in both typical and atypical populations.
  • To highlight future research directions in resting-state brain network analysis.

Main Methods:

  • Graph theory applied to R-fMRI data.
  • Analysis of topological organization of functional brain networks.
  • Comparison across typical development, aging, and pathological conditions.

Main Results:

  • Human brain functional networks exhibit non-trivial topological properties.
  • The brain's intrinsic activity is organized as a small-world, efficient network.
  • Significant modularity and highly connected hub regions characterize brain networks.

Conclusions:

  • Graph-based R-fMRI network analysis uncovers system-level changes in the resting brain.
  • These network properties change across the lifespan and in disease.
  • This approach offers novel insights into the physiological mechanisms of brain function.