Harmonized connectome resampling for variance in voxel sizes

Diffusion MRI (dMRI) fiber tractography presents exciting opportunities to deepen our knowledge of human brain connectivity and discover novel alterations in white matter. To date, there has been no comprehensive study characterizing the effect of dMRI voxel resolution on the resulting connectome for subject data. We assessed the statistical significance of graph measures derived from dMRI data by comparing connectomes from the same scans across different resolutions with 44 subjects (32 female) from the Human Connectome Project - Young Adult dataset (HCP-YA) with scan/rescan data (88 scans). We explored 15 isotropic and anisotropic resolutions, generated tractography and connectomes, and compared graph measures between each resolution and its nearest larger and smaller resolutions. Nearly all pairwise comparisons yielded statistically significant differences in graph measures (p ≤ 0.05, Wilcoxon Sign-Rank Test). Upon up sampling the 14 down sampled resolutions in 0.5 mm increments, we observed mitigation of the spatial sampling effect on both the tractography and the connectome's complex graph measures. To investigate translational impact, we resampled 22 subjects from HCP-YA to the resolutions of two major national studies and up-sampled this data back to 1 mm isotropic with different interpolation methods. Similarity in results improved with higher resolution, even after initial down-sampling. To ensure robust tractography and connectomes, resample data to 1 mm isotropic resolution.

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Institutions

Publication Details

Journal
Magnetic Resonance Imaging
Published
2025-05-19
DOI
https://doi.org/10.1016/j.mri.2025.110424
Citations
2
Primary Topic
Advanced Neuroimaging Techniques and Applications
Type
article
Field-Weighted Citation Impact
2.72

Funders

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article

Harmonized connectome resampling for variance in voxel sizes

Nancy R. Newlin, Derek B. Archer, François Rheault, Laurie E. Cutting et al.
2 citations
Magnetic Resonance Imaging
Advanced Neuroimaging Techniques and Applications
2.72
article

Harmonized connectome resampling for variance in voxel sizes

Nancy R. Newlin, Derek B. Archer, François Rheault, Laurie E. Cutting, Kurt G. Schilling, Lucas W. Remedios, Michael E. Kim, Jai Hoon Yoon, Bennett A. Landman, Aravind R. Krishnan, Gaurav Rudravaram, Hanliang Xu, Elyssa M. McMaster, Adam M Saunders
article en
2 citations

Abstract

Diffusion MRI (dMRI) fiber tractography presents exciting opportunities to deepen our knowledge of human brain connectivity and discover novel alterations in white matter. To date, there has been no comprehensive study characterizing the effect of dMRI voxel resolution on the resulting connectome for subject data. We assessed the statistical significance of graph measures derived from dMRI data by comparing connectomes from the same scans across different resolutions with 44 subjects (32 female) from the Human Connectome Project - Young Adult dataset (HCP-YA) with scan/rescan data (88 scans). We explored 15 isotropic and anisotropic resolutions, generated tractography and connectomes, and compared graph measures between each resolution and its nearest larger and smaller resolutions. Nearly all pairwise comparisons yielded statistically significant differences in graph measures (p ≤ 0.05, Wilcoxon Sign-Rank Test). Upon up sampling the 14 down sampled resolutions in 0.5 mm increments, we observed mitigation of the spatial sampling effect on both the tractography and the connectome's complex graph measures. To investigate translational impact, we resampled 22 subjects from HCP-YA to the resolutions of two major national studies and up-sampled this data back to 1 mm isotropic with different interpolation methods. Similarity in results improved with higher resolution, even after initial down-sampling. To ensure robust tractography and connectomes, resample data to 1 mm isotropic resolution.

Magnetic Resonance ImagingVol. 121
Université de Sherbrooke (CA), Vanderbilt University (US), Vanderbilt University Medical Center (US)
Vanderbilt University, Vanderbilt Institute for Clinical and Translational Research, McMaster University, Georgia Clinical and Translational Science Alliance, National Institutes of Health, Vanderbilt Kennedy Center, Vanderbilt University Medical Center, National Institute on Aging, National Cancer Institute, National Center for Advancing Translational Sciences
Openalex Percentile: Top 13%
Advanced Neuroimaging Techniques and Applications
2.72
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