Towards quantitative connectivity analysis: reducing tractography biases
Abstract
Diffusion MRI tractography is often used to estimate structural connections between brain areas and there is a fast-growing interest in quantifying these connections based on their position, shape, size and length. However, a portion of the connections reconstructed with tractography is biased by their position, shape, size and length. Thus, connections reconstructed are not equally distributed in all white matter bundles. Quantitative measures of connectivity based on the streamline distribution in the brain such as streamline count (density), average length and spatial extent (volume) are biased by erroneous streamlines produced by tractography algorithms. In this paper, solutions are proposed to reduce biases in the streamline distribution. First, we propose to optimize tractography parameters in terms of connectivity. Then, we propose to relax the tractography stopping criterion with a novel probabilistic stopping criterion and a particle filtering method, both based on tissue partial volume estimation maps calculated from a T1-weighted image. We show that optimizing tractography parameters, stopping and seeding strategies can reduce the biases in position, shape, size and length of the streamline distribution. These tractography biases are quantitatively reported using in-vivo and synthetic data. This is a critical step towards producing tractography results for quantitative structural connectivity analysis.
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Seeding only from gray matter avoids known tract-length connectivity biases.
“For the data used in this study, we took the approach of seeding only from the gray matter to avoid known tract-length connectivity biases (Girard et al., 2014; Jeurissen et al., 2019), and normalized the streamline counts by the volumes of each node-pair in order to better match the normalized connection density metric used in the Knox et al. model.”
From The role of spatial embedding in mouse brain networks constructed from diffusion tractography and tracer injections (2021) · cites this paper as Girard et al., 2014
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- Authors
- Girard, G., Whittingstall, K., Deriche, R., & Descoteaux, M.
- Affiliations
- Institut national de recherche en sciences et technologies du numérique (FR)
- Université de Sherbrooke (CA)
- Corresponding author
- Girard, Gabriel
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2014-09
- Volume
- 98
- Pages
- 266-278
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W1969637629
- Cited by
- 324
- References
- 52
- Citations by year
- 2026: 20 · 2025: 33 · 2024: 28 · 2023: 27 · 2022: 28 · 2021: 36 · 2020: 31 · 2019: 31 · 2018: 26 · 2017: 30 · 2016: 15 · 2015: 17 · 2014: 2
- FWCI
- 16.66
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- diffusion MRI tractography, streamline distribution, particle filter, seeding strategies, white matter bundles
- MeSH terms
- Algorithms, Brain, Brain Mapping, Humans, Image Interpretation, Computer-Assisted, Artifacts, Diffusion Magnetic Resonance Imaging, White Matter, Gray Matter
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