Hierarchical microstructure informed tractography
Open accessAbstract
Background: Tractography uses diffusion magnetic resonance imaging to noninvasively infer the macroscopic pathways of white matter fibers and it is the only available technique to probe in vivo the structural connectivity of the brain. However, despite this unique and compelling ability and its wide range of possible neurological applications, tractography is still limited, lacks anatomical precision, and suffers from a serious sensitivity/specificity trade-off. For this reason, in the past few years, tractography postprocessing techniques have emerged and proved effective for improving the quality of the reconstructions. Among them, the Convex Optimization Modeling for Microstructure Informed Tractography formulation allows incorporating the anatomical prior that fibers are naturally organized in fascicles, and has obtained exceptional results in increasing the accuracy of the estimated tractograms. Methods: We propose an extension to this idea and introduce a multilevel grouping of the streamlines to capture the white matter arrangement in fascicles and subfascicles. We tested our proposed formulation in synthetic and in vivo data. Results: Our experiments show that using multiple levels allows considering information about the white matter organization more adequately and helps to improve further the accuracy of the resulting tractograms. Conclusion: This new formulation represents a further important step toward a more accurate structural connectivity estimation. Tractography is an invaluable tool for studying noninvasively the neuronal architecture of the brain, but recent studies have shown that the presence of a large number of false positives can significantly bias any connectivity analysis. Recently, a filtering technique called Convex Optimization Modeling for Microstructure Informed Tractography (COMMIT)-2 has proven particularly effective in dramatically reducing their incidence by considering the prior knowledge that white matter fibers are organized in fascicles. In this work, we propose an extension to this method, which allows us to increase further the anatomical accuracy of tractography reconstructions. Our new formulation represents an additional step forward toward a more veridical characterization of brain connectivity.
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Combining knowledge- and data-driven strategies significantly boosts filtering performance.
“It was recently demonstrated that the performance of filtering methods can be significantly boosted by combining knowledge- and data-driven strategies.”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Ocampo-Pineda et al., 2021
New filtering formulations that organize streamlines into groups and seek the minimum number of bundles using Group Lasso significantly improve anatomical accuracy of the connectome.
“This prior knowledge helps resolve some of the ambiguities present in the data and, as a consequence, allows significant improvement of the anatomical accuracy of the connectome (Ocampo-Pineda et al., 2021; Schiavi et al., 2020a).”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Ocampo-Pineda et al., 2021
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- Authors
- Ocampo-Pineda, M., Schiavi, S., Rheault, F., Girard, G., Petit, L., Descoteaux, M., & Daducci, A.
- Affiliations
- Department of Computer Science, University of Verona, Verona, Italy.
- University of Verona (IT)
- Université de Sherbrooke (CA)
- Centre Hospitalier Universitaire Vaudois (CH)
- Centre National de la Recherche Scientifique (FR)
- Corresponding author
- Daducci, Alessandro
- ORCID
- Venue
- Brain Connectivity
- Publisher
- SAGE Publications
- Published
- 2021-03-01
- Volume
- 11
- Issue
- 2
- Pages
- 75-88
- ISSN
- 2158-0014 (ISSN-L)
- 2158-0022
- OpenAlex
- W3124084750
- Cited by
- 19
- References
- 48
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 5 · 2023: 4 · 2022: 5 · 2021: 2
- FWCI
- 2.12
- Type
- journal article
- Language
- en
- Open access
- green
- hal.science
- Locations
- Brain Connectivity (publishedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion, other-oa)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- IRIS (submittedVersion)
- SERVAL (Université de Lausanne) (submittedVersion)
- Funders
- National Institutes of Health (1U54‐MH‐091657)
- McDonnell Center for Systems Neuroscience (1U54MH091657)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Neurology › Cerebrovascular and genetic disorders
- Keywords
- tractography, hierarchical microstructure, structural connectivity, diffusion MRI, anatomical priors, false positive reduction
- MeSH terms
- Brain, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Diffusion Magnetic Resonance Imaging, Diffusion Tensor Imaging, White Matter
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