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Related Concept Videos

Brain Imaging01:14

Brain Imaging

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 Stimulation (TMS).

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Meta-analysis of neuroimaging data.

Hedy Kober1, Tor D Wager1

  • 1Department of Psychiatry, Yale University, New Haven, CT 06519.

Wiley Interdisciplinary Reviews. Cognitive Science
|September 21, 2013
PubMed
Summary

Neuroimaging meta-analyses integrate findings across studies to understand brain-behavior links. Techniques like kernel density analysis (KDA) and activation likelihood estimate (ALE) assess consistency and specificity in cognitive neuroscience.

Area of Science:

  • Cognitive Neuroscience
  • Neuroimaging

Background:

  • Integrating knowledge from numerous neuroimaging studies investigating psychological phenomena is challenging.
  • Meta-analyses offer a solution by synthesizing findings across studies, laboratories, and task variations.

Purpose of the Study:

  • To review techniques for synthesizing neuroimaging data to answer questions about consistency and specificity of brain-behavior associations.
  • To discuss the application of meta-analysis in cognitive neuroscience.

Main Methods:

  • Discussion of established and novel meta-analysis techniques.
  • Focus on kernel density analysis (KDA), activation likelihood estimate (ALE), and multilevel kernel density analysis (MKDA).

Main Results:

  • Meta-analysis enables consistent assessment of brain regions associated with psychological domains.

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  • These methods can address the specificity of brain activation across different cognitive tasks.

    • Conclusions:

    • Meta-analysis is crucial for integrating neuroimaging findings and understanding brain function.
    • Techniques like KDA, ALE, and MKDA are valuable tools for current and future cognitive neuroscience research.