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Cluster-Based Permutation Testing

The Cluster-Based Permutation Testing extension performs non-parametric statistical analysis to identify brain regions with significant differences in temporal interference (TI) stimulation fields between experimental groups. This method provides robust control of family-wise error rates when performing voxelwise comparisons across the brain.

Key Features

  • Non-parametric Statistics: No assumptions about data distribution
  • Family-wise Error Control: Cluster-level correction for multiple comparisons
  • BIDS Integration: Automatic data discovery and organization
  • Flexible Group Comparisons: Responder vs non-responder, treatment vs control
  • Parallel Processing: Multi-core support for fast computation
  • Comprehensive Output: Statistical maps, cluster analysis, and detailed reports

Theoretical Background

Why Cluster-Based Permutation Testing?

Traditional voxelwise statistical tests (like t-tests) performed at each brain voxel create a massive multiple comparisons problem. With ~1,000,000 voxels in a typical brain analysis, even a 5% false positive rate would yield 50,000 false discoveries.

Cluster-based permutation testing addresses this by:

  1. Performing voxelwise statistics (t-values)
  2. Forming clusters of adjacent significant voxels
  3. Using cluster statistics (mass or size) instead of individual voxels
  4. Building null distributions through random group permutations
  5. Controlling family-wise error at the cluster level

Method Implementation

The analysis follows the Maris & Oostenveld (2007) framework:

  1. Voxelwise Testing: Compute t-statistics at each voxel
  2. Cluster Formation: Threshold at p < cluster_threshold to form clusters
  3. Cluster Statistics: Calculate mass (sum of t-values) or size (voxel count)
  4. Permutation Testing: Randomly reassign subjects to groups 1,000+ times
  5. Null Distribution: Build distribution of maximum cluster statistics under null
  6. Significance Testing: Compare observed clusters to null distribution

User Interface

Subject Configuration

Configure experimental groups through an intuitive interface:

  • Subject-Simulation Pairs: Link each subject to their TI simulation
  • Group Classification: Assign subjects as “Responder” or “Non-Responder”
  • CSV Import/Export: Batch configuration management for reproducibility

Statistical Parameters

Test Configuration

  • Test Type: Unpaired (independent groups) or paired (repeated measures)
  • Alternative Hypothesis: Two-sided, greater than, or less than
  • Cluster Threshold: p-value for initial cluster formation (default: 0.05)
  • Cluster Statistic: “Mass” (t-value sum) or “Size” (voxel count)
  • Significance Level: cutoff for the permutation null distribution (default: 0.05)

Workflow Example

Responder vs Non-Responder Analysis

  1. Launch Extension: Settings → Extensions → “Cluster-Based Permutation Testing”
  2. Configure Subjects:
    • Subject 001: Simulation “HIPP_L”, Responder
    • Subject 002: Simulation “HIPP_L”, Non-Responder
    • … (10+ subjects total)
  3. Set Parameters:
    • Test type: Unpaired
    • Permutations: 1,000
    • Cluster statistic: Mass
  4. Run Analysis: This cann take a while depending on dataset size, number of permutations, and number of cores assigned

Output Structure

Results are saved to derivatives/ti-toolbox/stats/{analysis_name}/:

hippocampus_responders_vs_nonresponders/
├── significant_voxels_mask.nii.gz          # Binary mask of significant voxels
├── pvalues_map.nii.gz                      # P-value map (-log10 scale)
├── average_responders.nii.gz               # Group average for responders
├── average_non_responders.nii.gz           # Group average for non-responders
├── difference_map.nii.gz                   # Responders - Non-responders
├── permutation_null_distribution.pdf       # Null distribution visualization
├── cluster_size_mass_correlation.pdf       # Cluster statistics
├── analysis_summary.txt                    # Complete statistical report
├── permutation_details.txt                 # Detailed permutation log
├── analysis_TIMESTAMP.log                  # Processing log
└── config.json                             # Analysis configuration

Key Findings:

  • Significant cluster in left hippocampus (p = 0.008, 1,245 voxels)
  • Significant cluster in right hippocampus (p = 0.012, 987 voxels)
  • Trend-level cluster in left entorhinal cortex (p = 0.067, 615 voxels)

Permutation Null Distribution

Technical Details

Data Requirements

  • File Format: NIfTI files in MNI space
  • File Pattern: Default grey_{simulation_name}_TI_MNI_MNI_TI_max.nii.gz
  • Data Type: Field intensity values in V/m

CSV-Based Configuration

Create subjects.csv:

subject_id,simulation_name,response
001,HIPP_L,1
002,HIPP_L,0
003,HIPP_R,1
004,HIPP_R,0

References

  • Maris, E., & Oostenveld, R. (2007). Nonparametric statistical testing of EEG-and MEG-data. Journal of Neuroscience Methods, 164(1), 177-190.
  • Nichols, T. E., & Holmes, A. P. (2002). Nonparametric permutation tests for functional neuroimaging: a primer with examples. Human Brain Mapping, 15(1), 1-25.
  • Winkler, A. M., et al. (2014). Permutation inference for the general linear model. NeuroImage, 92, 381-397.