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Updated: Mar 14, 2026

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
Published on: November 8, 2012
Diffusion MRI sampling schemes bias diffusion metrics and tractography
Ivanei Bramati1,2, Diego Szczupak2,3, Marina Carneiro Monteiro1
1Department of Brain Connectivity and Plasticity, D'Or Institute for Research and Education, Rio de Janeiro, Brazil.
Introduction:
Diffusion MRI is increasingly used to study white-matter architecture, but tractography and diffusion metrics can be biased by different sampling schemes. We assessed systematic differences across four common protocols-single-shell high-angular resolution diffusion imaging (HARDI), Siemens clinical multi-shell (Sms), diffusion spectrum imaging (DSI), and Human Connectome Project multi-shell (HCPms)-in healthy adults and individuals with corpus callosum dysgenesis (CCD).
Methods:
All data were acquired on a single 3 T scanner and processed uniformly to extract fractional anisotropy (FA), mean diffusivity (MD), effective contrast-to-noise ratio (eCNR), and orientation dispersion within the corpus callosum (CC), corona radiata (CR), and centrum semiovale (CSO). In controls, we measured tract volumes for CC, bilateral CR, anterior commissure (AC) and posterior commissure (PC), and streamline counts for AC and PC; in CCD, we quantified volumes of the Probst and sigmoid bundles.
Results:
Across participants, FA and MD showed moderate cross-scheme correlations for most ROIs, but matched means were rare (only Sms-HARDI in CC). eCNR and dispersion exhibited few cross-scheme correlations; however, means were similar for eCNR between Sms and HCPms and for dispersion among HARDI, DSI, and HCPms. Tract-based volumes correlated across Sms, DSI, and HCPms for CC in controls and for the right sigmoid and both Probst bundles in CCD. DSI and HCPms yielded similar volumes in all ROIs (controls and CCD). In controls, Sms volumes agreed with DSI/HCPms in CR, but were lower in CC and in all CCD ROIs. HARDI produced higher volumes in CC and bilateral CR in controls and in all CCD ROIs. For AC and PC in controls, tract-based means (volumes, streamlines, streamlines/volume) were consistent across schemes; nonetheless, correlations were limited-streamlines and streamlines/volume correlated for Sms, DSI, and HARDI in AC, and for DSI and HCPms in PC.
Discussion:
These findings demonstrate systematic differences in voxel-wise metrics and tractography outcomes from four diffusion-sampling schemes. In addition to qualitatively informing attempts to consolidate or contrast data across schemes, future work could explore regression-based harmonization-and other methods-to reduce residual bias and enable pooled analyses across diverse protocols.
Insights
Different diffusion MRI sampling schemes create systematic biases in white matter analysis. Harmonization methods are needed to combine data from diverse protocols for accurate brain architecture studies.
Area of Science:
- Neuroimaging
- Diffusion MRI
- White Matter Anatomy
Background:
- Diffusion MRI is vital for studying white matter architecture.
- Tractography and diffusion metrics can be influenced by varying sampling schemes.
- Understanding these differences is crucial for reliable data interpretation.
Purpose of the Study:
- To assess systematic differences in diffusion MRI metrics and tractography across four common protocols: single-shell HARDI, Siemens multi-shell (Sms), DSI, and HCP multi-shell (HCPms).
- To evaluate these differences in healthy adults and individuals with corpus callosum dysgenesis (CCD).
Main Methods:
- Acquired data on a 3T scanner and processed uniformly.
- Extracted fractional anisotropy (FA), mean diffusivity (MD), effective contrast-to-noise ratio (eCNR), and orientation dispersion.
- Measured tract volumes and streamline counts in specific white matter regions (CC, CR, CSO, AC, PC) and CCD-specific bundles (Probst, sigmoid).
Main Results:
- FA and MD showed moderate correlations across schemes, but matched means were infrequent.
- eCNR and dispersion had limited cross-scheme correlations, with some similarities between Sms/HCPms and HARDI/DSI/HCPms.
- Tract volumes correlated across Sms, DSI, and HCPms for CC (controls) and CCD bundles; DSI and HCPms showed consistent volumes.
- Sms volumes agreed with DSI/HCPms in CR but were lower in CC and CCD ROIs; HARDI produced higher volumes in CC, CR, and CCD ROIs.
- AC and PC tract metrics were consistent across schemes, but correlations varied.
Conclusions:
- Four diffusion-sampling schemes exhibit systematic differences in voxel-wise metrics and tractography outcomes.
- Findings inform efforts to consolidate or contrast data across schemes.
- Future research should explore harmonization methods to reduce bias and enable pooled analyses across diverse protocols.
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