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On the replicability of diffusion weighted MRI-based brain-behavior models
Raviteja Kotikalapudi1,2,3, Balint Kincses4,5, Giuseppe Gallitto4,5
1Department of Neurology, University Medicine Essen, Essen, Germany. raviteja.kotikalapudi@uk-essen.de.
Communications Biology
|October 31, 2025
Summary
Brain-wide association studies (BWAS) using diffusion MRI connectomes show moderate replicability. Trait-like phenotypes are more replicable than state-like ones, with streamline connectomes offering the best results.
Area of Science:
- Neuroimaging
- Connectomics
- Biostatistics
Background:
- Replicability of brain-wide association studies (BWAS) using MRI is a growing concern.
- Previous studies focused on functional and anatomical MRI, leaving structural connectomes under-evaluated.
Purpose of the Study:
- To comprehensively evaluate the replicability of BWAS using various diffusion MRI-derived structural connectome metrics.
- To investigate factors influencing BWAS replicability, including phenotype type and effect size.
Main Methods:
- Utilized diffusion MRI data from Human Connectome Project (HCP) and Autism Brain Imaging Data Exchange (ABIDE) datasets.
- Assessed replicability of brain-phenotype associations using streamline counts, fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD).
- Categorized phenotypes as trait-like or state-like and analyzed the relationship between replicability, effect size, and sample size.
Main Results:
- 36% of brain-phenotype associations were replicable across datasets using at least one diffusion MRI metric with discovery sample sizes up to 425.
- Trait-like phenotypes demonstrated higher replicability (50%) compared to state-like phenotypes (19%).
- Streamline-based connectomes showed the highest replicability (29-42%), and replicability directly correlated with effect size, with larger effects (>5% variance) requiring smaller discovery sample sizes (<300).
Conclusions:
- Trait-like phenotypes exhibit good replicability with moderate sample sizes in BWAS using structural connectomes.
- BWAS models requiring very large sample sizes (>425) likely have limited practical relevance due to small effect sizes.
- Large sample sizes remain essential for robust explainability, fairness assessment, and generalizability of neuroimaging findings.
