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Related Concept Videos

Group Design02:01

Group Design

The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to...

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Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI)
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Minimizing within-experiment and within-group effects in Activation Likelihood Estimation meta-analyses.

Peter E Turkeltaub1, Simon B Eickhoff, Angela R Laird

  • 1Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA. peter.turkeltaub@uphs.upenn.edu

Human Brain Mapping
|February 10, 2011
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Summary

Activation Likelihood Estimation (ALE) meta-analysis methods were optimized by modifying how experiments contribute and organizing datasets. These adjustments improve statistical rigor without significantly altering results, validating previous neuroimaging findings.

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Area of Science:

  • Neuroimaging
  • Cognitive Neuroscience
  • Data Analysis

Background:

  • Activation Likelihood Estimation (ALE) is a standard coordinate-based meta-analysis (CBMA) technique.
  • Previous modifications have enhanced ALE's statistical rigor.
  • Concerns exist regarding potential biases from within-experiment and within-group effects.

Purpose of the Study:

  • To optimize the Activation Likelihood Estimation (ALE) algorithm for coordinate-based meta-analysis (CBMA).
  • To evaluate modifications addressing within-experiment and within-group effects in ALE.
  • To validate the robustness of existing ALE meta-analysis findings.

Main Methods:

  • Developed a modified ALE algorithm to mitigate the influence of experiment size and focus proximity.
  • Introduced an alternative dataset organization to balance the impact of multiple experiments within subject groups.
  • Compared results from standard ALE with modified approaches.

Main Results:

  • Within-experiment effects accounted for only 2-3% of ALE values, with minimal impact on thresholded maps.
  • The alternate dataset organization reduced cumulative ALE values by 7-9%, altering cluster sizes and extents.
  • Overall differences between standard and modified ALE methods were minor.

Conclusions:

  • Modified ALE algorithm offers theoretical advantages for statistical rigor.
  • Alternate dataset organization provides a conservative approach for CBMA.
  • Combined modifications minimize biases, enhancing the reliability of ALE for representing independent findings.