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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.
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...
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Multiple Comparison Tests01:13

Multiple Comparison Tests

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Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
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Related Experiment Video

Updated: Dec 25, 2025

Exploring the Neural Correlates of Cognitive Reappraisal in Obsessive-Compulsive Disorder Using Task-based Functional Magnetic Resonance Imaging
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Exploring the Neural Correlates of Cognitive Reappraisal in Obsessive-Compulsive Disorder Using Task-based Functional Magnetic Resonance Imaging

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Multiple testing correction over contrasts for brain imaging.

Bianca A V Alberton1, Thomas E Nichols2, Humberto R Gamba1

  • 1Graduate Program in Electrical and Computer Engineering, Universidade Tecnológica Federal Do Paraná, Curitiba, PR, Brazil.

Neuroimage
|March 24, 2020
PubMed
Summary
This summary is machine-generated.

Correcting for multiple testing is crucial in brain imaging to prevent false positives. Permutation testing offers an exact and flexible solution for addressing multiplicity in neuroimaging analyses.

Keywords:
Brain imagingContrast correctionMultiple comparisonsMultiple testingPermutation tests

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Area of Science:

  • Neuroimaging
  • Statistical analysis
  • Brain mapping

Background:

  • Multiple testing is a significant challenge in neuroimaging due to high dimensionality (voxels/vertices).
  • Testing multiple hypotheses (contrasts) within a general linear model (GLM) necessitates correction for multiplicity.
  • Failure to correct for multiple comparisons inflates the rate of false positives, compromising study validity.

Purpose of the Study:

  • To address the multiple testing problem in brain imaging.
  • To evaluate and compare various statistical correction methods for neuroimaging data.
  • To identify the most suitable method for handling multiplicity in common brain imaging scenarios.

Main Methods:

  • Discussion and comparison of different statistical correction methods.
  • Application of methods to address multiplicity in general linear models for neuroimaging.
  • Evaluation of permutation testing as a correction strategy.

Main Results:

  • A classical, widely used correction method was found to be invalid for neuroimaging data.
  • Permutation testing demonstrated exactness and flexibility across various common imaging situations.
  • The study highlights the limitations of existing methods and advocates for permutation-based approaches.

Conclusions:

  • Correction for multiple testing is essential in neuroimaging to maintain statistical rigor.
  • Permutation testing is recommended as the optimal method for addressing multiplicity in brain imaging.
  • The findings provide guidance for researchers to improve the reliability of neuroimaging statistical analyses.