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Cited by 19Advanced Neuroimaging Techniques and ApplicationsFunctional Brain Connectivity StudiesComplex Network Analysis Techniques

Diffusion MRI tractography filtering techniques change the topology of structural connectomes

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Frigo, Matteo, Deslauriers-Gauthier, Samuel, Parker, Drew, Aziz Ould Ismail, Abdol, John Kim, Junghoon, Verma, Ragini, Deriche, Rachid·2020·Journal of Neural Engineering
DOI 10.1088/1741-2552/abc29bPMC PMC8053736
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Abstract

Abstract Objective. The use of non-invasive techniques for the estimation of structural brain networks (i.e. connectomes) opened the door to large-scale investigations on the functioning and the architecture of the brain, unveiling the link between neurological disorders and topological changes of the brain network. This study aims at assessing if and how the topology of structural connectomes estimated non-invasively with diffusion MRI is affected by the employment of tractography filtering techniques in structural connectomic pipelines. Additionally, this work investigates the robustness of topological descriptors of filtered connectomes to the common practice of density-based thresholding. Approach. We investigate the changes in global efficiency, characteristic path length, modularity and clustering coefficient on filtered connectomes obtained with the spherical deconvolution informed filtering of tractograms and using the convex optimization modelling for microstructure informed tractography. The analysis is performed on both healthy subjects and patients affected by traumatic brain injury and with an assessment of the robustness of the computed graph-theoretical measures with respect to density-based thresholding of the connectome. Main results. Our results demonstrate that tractography filtering techniques change the topology of brain networks, and thus alter network metrics both in the pathological and the healthy cases. Moreover, the measures are shown to be robust to density-based thresholding. Significance. The present work highlights how the inclusion of tractography filtering techniques in connectomic pipelines requires extra caution as they systematically change the network topology both in healthy subjects and patients affected by traumatic brain injury. Finally, the practice of low-to-moderate density-based thresholding of the connectomes is confirmed to have negligible effects on the topological analysis.

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  • Selection of tractography filtering strategies is important and has been shown to affect the analysis of the topology of brain networks and affect sensitivity and specificity when analyzing connectomes.

    “Selection of tractography filtering strategies is important and has been shown to affect the analysis of the topology of brain networks (Civier et al., 2019; Frigo et al., 2020; Yeh et al., 2016a) and affect sensitivity and specificity when analyzing the connectomes (Zalesky et al., 2016a).”

    From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Frigo et al., 2020

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Authors
Frigo, M., Deslauriers-Gauthier, S., Parker, D., Aziz Ould Ismail, A., John Kim, J., Verma, R., & Deriche, R.
Affiliations
  • Université Côte d'Azur (FR)
  • University of Pennsylvania (US)
Corresponding author
Frigo, Matteo
ORCID
Venue
Journal of Neural Engineering
Publisher
IOP Publishing
Published
2020-11-11
Volume
17
Issue
6
Pages
065002
ISSN
  • 1741-2552 (ISSN-L)
  • 1741-2560
DOI
10.1088/1741-2552/abc29b
PMC
PMC8053736
OpenAlex
W3093146773
Cited by
19
References
4
Citations by year
2026: 3 · 2025: 3 · 2024: 2 · 2023: 4 · 2022: 3 · 2021: 4
FWCI
1.34
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Neural Engineering (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • HAL (submittedVersion, other-oa)
  • OSF Preprints (OSF Preprints) (submittedVersion)
Funders
  • H2020 European Research Council (694665)
  • Center for Scientific Review (R01NS065980)
  • Pennsylvania Department of Health (PACT)
  • NIH Blueprint for Neuroscience Research (1U54MH091657)
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 › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
Keywords
structural connectome, diffusion MRI, brain network topology, traumatic brain injury, global efficiency, average path length, modularity, clustering coefficient
MeSH terms
Brain, Humans, Diffusion Tensor Imaging, Connectome

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