Fiber length profiling: a novel approach to structural brain organization
Open accessAbstract
There has been a recent increased interest in the structural connectivity of the cortex. However, an important feature of connectivity remains relatively unexplored; tract length. In this article, we develop an approach to characterize fiber length distributions across the human cerebral cortex. We used data from 76 participants of the Adult WU-Minn Human Connectome Project using probabilistic tractography. We found that connections of different lengths are not evenly distributed across the cortex. They form patterns where certain areas have a high density of fibers of a specific length while other areas have very low density. To assess the relevance of these new maps in relation to established characteristics, we compared them to structural indices such as cortical myelin content and cortical thickness. Additionally, we assessed their relation to resting state network organization. We noted that areas with very short fibers have relatively more myelin and lower cortical thickness while the pattern is inverted for longer fibers. Furthermore, the cortical fiber length distributions produce specific correlation patterns with functional resting state networks. Specifically, we find evidence that as resting state networks increase in complexity, their length profiles change. The functionally more complex networks correlate with maps of varying lengths while primary networks have more restricted correlations. We posit that these maps are a novel way of differentiating between 'local modules' that have restricted connections to 'neighboring' areas and 'functional integrators' that have more far reaching connectivity.
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The volume and length of a fiber tract can be computed directly from tractography and have been used as quantitative measures.
“Other quantitative measures can be computed directly from tractography, such as the volume and the length of a fiber tract (Bajada et al., 2019; Behrman-Lay et al., 2015; Catani et al., 2007; Chandio et al., 2020a; Colby et al., 2012a; Grinberg et al., 2018; Rheault et al., 2020a; Song et al., 2015; Yeatman et al., 2012), have also been used.”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Bajada et al., 2019
Section 2.4 (Length mapping; page not supplied) says the authors calculated streamline lengths directly from tractography track files and used them to create cortical tract-length maps. Our copy does not report computing fiber-tract volume as a quantitative measure; its references to volume in Section 2.3 concern image space and subject brain volume. (LLM, conf=0.88)
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- Authors
- Bajada, C. J., Schreiber, J., & Caspers, S.
- Affiliations
- Forschungszentrum Jülich (DE)
- Corresponding author
- Caspers, Svenja
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2019-02
- Volume
- 186
- Pages
- 164-173
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2898672092
- Cited by
- 55
- References
- 77
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 11 · 2023: 8 · 2022: 13 · 2021: 5 · 2020: 7 · 2019: 1
- FWCI
- 2.26
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- NeuroImage (publishedVersion, cc-by)
- PubMed (publishedVersion)
- OAR@UM (University of Malta) (submittedVersion, other-oa)
- JuSER (Forschungszentrum Jülich) (submittedVersion, other-oa)
- Funders
- the Helmholtz Association
- European Union’s Horizon 2020 Research and Innovation Programme (785907)
- NIH Institutes (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
- fiber length distribution, structural connectivity, cerebral cortex, tract length, probabilistic tractography, myelin content, cortical thickness, resting-state networks, network complexity, local modules, human connectome
- MeSH terms
- Adult, Cerebral Cortex, Humans, Nerve Fibers, Myelinated, Nerve Net, Diffusion Tensor Imaging, Connectome, White Matter
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