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Related Experiment Video

Updated: Jun 23, 2026

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI)
06:26

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI)

Published on: November 27, 2019

A parametric approach to voxel-based meta-analysis.

Sergi G Costafreda1, Anthony S David, Michael J Brammer

  • 1Institute of Psychiatry, King's College London, UK. s.costafreda@iop.kcl.ac.uk

Neuroimage
|May 22, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a new parametric approach for voxel-based meta-analysis in functional neuroimaging, improving consistency quantification and signal detection across studies. The method offers better power and efficiency for neuroimaging research.

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Last Updated: Jun 23, 2026

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI)
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06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Area of Science:

  • Neuroimaging
  • Cognitive Neuroscience
  • Statistical Analysis

Background:

  • Meta-analysis in functional neuroimaging aims to synthesize findings across studies.
  • Quantifying regional cerebral response consistency is crucial for robust conclusions.
  • Existing methods may lack efficiency or strict statistical control.

Purpose of the Study:

  • To develop a parametric, spatially-based approach for voxel-based meta-analysis.
  • To enhance the detection of consistent activation 'signal' regions.
  • To provide a computationally efficient and statistically rigorous tool for neuroimaging meta-analysis.

Main Methods:

  • A parametric voxel-based meta-analysis method using spatial statistics was derived.
  • Voxel scores represent the proportion of studies with local activations.
  • Thresholding is based on comparing observed scores to a null hypothesis of random activation locations.

Main Results:

  • Simulations demonstrated strict control over false positive rates.
  • The proposed method showed increased statistical power compared to alternatives.
  • Significant gains in computational time were achieved.

Conclusions:

  • Parametric voxel-based meta-analysis is a powerful and practical tool.
  • The method effectively quantifies consistency and detects signal regions in neuroimaging.
  • Applicable to both fixed-effects and random-effects meta-analyses.