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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Streamline Density Normalization: A Robust Approach to Mitigate Bundle Variability in Multi-Site Diffusion MRI.

Yixue Feng¹, Yuhan Shuai¹, Julio E Villalón-Reina¹

¹Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States.

Biorxiv : the Preprint Server for Biology

|September 2, 2025

Summary

This summary is machine-generated.

Streamline Density Normalization (SDNorm) reduces variability in diffusion MRI tractometry. This method improves the consistency of microstructure analysis, enhancing reproducibility in large-scale neuroimaging studies.

Keywords:

bundle segmentation tractography tractometry

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Area of Science:

Neuroimaging
Computational Neuroscience
Biomedical Engineering

Background:

Tractometry enables quantitative analysis of brain tissue microstructure using diffusion MRI.
Variability in tractography and bundle segmentation processing parameters impacts reproducibility.
Inconsistent streamline reconstructions and sampling affect the reliability of tractometry results.

Purpose of the Study:

To introduce Streamline Density Normalization (SDNorm), a novel method to reduce variability in white matter bundle reconstructions.
To enhance the reproducibility and robustness of tractometry analysis across diverse neuroimaging studies.

Main Methods:

SDNorm is a supervised, two-step method employing linear regression for streamline weighting.
It matches individual subject bundles to a template streamline density map.
Iterative pruning using effective Streamline Point Density (eSPD) achieves target density.

Main Results:

SDNorm significantly reduces variability in streamline density across different bundles and acquisition protocols.
The method improves consistency in along-tract microstructure profiles.
SDNorm provides metrics for automated bundle quality control.

Conclusions:

SDNorm enhances the reproducibility and robustness of white matter bundle reconstruction.
This method is suitable for large-scale and multi-site neuroimaging studies with heterogeneous data.
SDNorm facilitates more reliable quantitative analysis of brain microstructure.