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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.
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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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Quantity and quality: Normative open-access neuroimaging databases.

Scott Jie Shen Isherwood1, Pierre-Louis Bazin1,2, Anneke Alkemade1

  • 1Integrative Model-Based Cognitive Neuroscience Research Unit, University of Amsterdam, Amsterdam, The Netherlands.

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|March 11, 2021
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Summary
This summary is machine-generated.

This study compares neuroimaging databases, finding that image quality metrics like signal-to-noise ratio (SNR) decline with age in brain regions such as the caudate nuclei. Database selection requires careful consideration of image quality and contrast types for reliable research.

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Area of Science:

  • Neuroimaging
  • Radiology
  • Gerontology

Background:

  • Neuroimaging databases are crucial for research, but their suitability depends on image quality.
  • Quantitative assessment of image quality in normative and open-access databases is needed.

Purpose of the Study:

  • To compare twenty neuroimaging databases using signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR).
  • To evaluate database utility for visualizing deep brain structures and the types of inferences possible.
  • To assess age-related changes in image quality metrics.

Main Methods:

  • Quantitative comparison of T1-weighted (T1w) and T2-weighted (T2w) image contrasts.
  • Analysis of SNR and CNR across different brain regions and age groups.
  • Correlation analysis of scan time with SNR and spatial resolution.

Main Results:

  • SNR and CNR decline with age in the caudate nuclei and corpus callosum across multiple contrasts.
  • Ultra-high field MRI shows benefits for image quality.
  • Increased scan time positively correlated with SNR and negatively with spatial resolution.

Conclusions:

  • Image quality and contrast types are critical for selecting structural neuroimaging databases.
  • Age-related declines in SNR and CNR suggest complex structural changes.
  • Understanding database limitations is essential for valid neuroimaging research.