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Related Experiment Video

Updated: Feb 3, 2026

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

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

Published on: November 27, 2019

Multi-level block permutation.

Anderson M Winkler¹, Matthew A Webster¹, Diego Vidaurre²

¹Oxford Centre for Functional MRI of the Brain, University of Oxford, Oxford, UK.

|June 16, 2015

Summary

This summary is machine-generated.

Permutation tests offer exact control of false positives but can be limited by data dependence. This study introduces a hierarchical permutation method to preserve data structure, enabling valid inference in complex datasets like those from the Human Connectome Project.

Keywords:

General linear model Multiple regression Permutation inference Repeated measurements

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Last Updated: Feb 3, 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

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Area of Science:

Statistics
Computational Biology
Data Science

Background:

Permutation tests are valuable for controlling false positives and using non-standard statistics.
Global exchangeability is often violated in real-world data, such as paired or repeated measures, and family-based studies.
Standard permutation methods can invalidate the null distribution when dependence structures are present.

Purpose of the Study:

To develop a permutation inference method that accommodates complex data dependence structures.
To extend block-based exchangeability to a hierarchical, multi-level definition.
To ensure the validity of permutation tests in the presence of known data dependencies.

Main Methods:

Introduced a hierarchical definition of exchangeability for nested data blocks.
Utilized a subset of permutations that preserve the original joint distribution and dependence structure.
The method is compatible with heteroscedasticity and variance groups, and can combine permutations with sign-flipping.

Main Results:

Demonstrated that the hierarchical permutation approach avoids false positives in datasets with dependence.
Applied the method to real-world data from the Human Connectome Project (HCP).
Provided a software implementation for the proposed permutation strategy.

Conclusions:

The hierarchical permutation method effectively handles complex dependence structures in statistical testing.
This approach extends the applicability of permutation inference to a wider range of scientific datasets.
The method offers a robust solution for maintaining statistical validity in the presence of data dependencies.