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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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Related Experiment Video

Updated: May 14, 2026

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI)
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Published on: November 27, 2019

What are neuroimaging meta-analytic procedures?

S Cortese1, F X Castellanos, S B Eickhoff

  • 1Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience, Child Study Center of the NYU Langone Medical Center, New York, NY, USA.

Epidemiology and Psychiatric Sciences
|February 14, 2013
PubMed
Summary

Activation likelihood estimation (ALE) meta-analyses combine neuroimaging study results. This method helps uncover novel findings not seen in individual studies, explained here for new researchers.

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Area of Science:

  • Neuroimaging
  • Cognitive Neuroscience
  • Data Analysis

Background:

  • Neuroimaging studies generate vast datasets.
  • Integrating findings across studies is crucial for robust conclusions.
  • Novel insights can emerge from meta-analyses.

Purpose of the Study:

  • To introduce Activation Likelihood Estimation (ALE) meta-analysis methods.
  • To provide a guide for researchers new to neuroimaging meta-analysis.
  • To explain how ALE integrates diverse neuroimaging results.

Main Methods:

  • ALE is a coordinate-based meta-analysis technique.
  • It models the spatial uncertainty of reported activation foci.
  • Statistical thresholds are applied to identify significant convergence of results.

Main Results:

  • ALE meta-analyses can reveal consistent patterns of brain activation across studies.
  • This approach enhances statistical power compared to single studies.
  • Novel findings may be identified through aggregated data.

Conclusions:

  • ALE offers a powerful framework for neuroimaging research synthesis.
  • Understanding ALE methods is valuable for researchers seeking to integrate neuroimaging data.
  • This technique facilitates the discovery of broader neural mechanisms.