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Cluster-extent based thresholding in fMRI analyses: pitfalls and recommendations.

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Liberal thresholds in neuroimaging (fMRI) studies lead to poor spatial specificity. This study recommends stricter primary thresholds (p < .001) for accurate fMRI analysis and interpretation.

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Area of Science:

  • Neuroimaging
  • Cognitive Neuroscience
  • Statistical Analysis

Background:

  • Cluster-extent thresholding is widely used for neuroimaging statistical maps.
  • It offers high sensitivity but low spatial specificity, limiting precise localization of effects.
  • Liberal primary thresholds (e.g., p < .01) are common, often due to software defaults.

Purpose of the Study:

  • To investigate the impact of liberal primary thresholds on functional magnetic resonance imaging (fMRI) studies.
  • To highlight the challenges in spatial specificity and interpretation caused by large significant clusters.
  • To propose improved analysis and reporting guidelines for fMRI research.

Main Methods:

  • Survey of 814 fMRI studies published in 2010-2011 to assess primary threshold usage.
  • Analysis of an fMRI dataset (N=33) to illustrate problems with liberal thresholds.
  • Simulations to demonstrate the effects of liberal thresholds on false positives and localization.

Main Results:

  • Liberal primary thresholds are prevalent in fMRI studies, influenced by software defaults.
  • Large clusters resulting from liberal thresholds obscure the precise location of significant findings.
  • Simulations confirm detrimental effects on false positives, localization accuracy, and interpretation.

Conclusions:

  • Setting a default lower limit for primary thresholds at p < .001 is recommended.
  • More stringent thresholds or voxel-wise correction methods are advised for high-powered studies.
  • Transparent reporting of spatial precision and consideration of alternative analysis methods are crucial.