Tractography: Where Do We Go from Here?
Open accessAbstract
Diffusion tractography offers enormous potential for the study of human brain anatomy. However, as a method to study brain connectivity, tractography suffers from limitations, as it is indirect, inaccurate, and difficult to quantify. Despite these limitations, appropriate use of tractography can be a powerful means to address certain questions. In addition, while some of tractography's limitations are fundamental, others could be alleviated by methodological and technological advances. This article provides an overview of diffusion magnetic resonance tractography methods with a focus on how future advances might address challenges in measuring brain connectivity. Parts of this review are somewhat provocative, in the hope that they may trigger discussions possibly lacking in a field where the apparent simplicity of the methods (compared to their functional magnetic resonance imaging counterparts) can hide some fundamental issues that ultimately hinder the interpretation of findings, and cast doubt as to what tractography can really teach us about human brain anatomy.
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CSD-based tractography solves some typical tractography errors, but false positive tract reconstructions may still arise from noisy peaks or from ambiguous fanning and bending fiber populations.
“This solves some of the typical tractography errors, but false positive tract reconstructions may still arise from noisy (spurious) peaks or from ambiguous fanning and bending fiber populations (Jbabdi and Johansen-Berg 2011).”
From Diffusion MRI-based cortical connectome reconstruction: dependency on tractography procedures and neuroanatomical characteristics (2018) · cites this paper as Jbabdi and Johansen-Berg 2011
In “Low level modelling” (page unavailable), the paper says spherical-deconvolution fibre models have improved tractography, while unmodelled fanning can produce false positives. It also describes errors associated with sharp bends and noise-related uncertainty. It does not specifically identify noisy or spurious peaks as a cause of false-positive reconstructions, or establish the claimed improvement specifically for CSD-based tractography. (LLM, conf=0.83)
Experimental — accuracy not yet measuredThe principle of inferring connectivity from local orientation fields can lead to problems as soon as pathways overlap, cross, branch, and have complex geometries.
“Conceptually, the principle of inferring connectivity from local orientation fields can lead to problems as soon as pathways overlap, cross, branch, and have complex geometries7, 35, 36.”
From Tractography-based connectomes are dominated by false-positive connections (2016) · cites this paper as [7]
These problems have been discussed in schematic representations or theoretical arguments but have not yet been quantified in brain imaging.
“Since the invention of diffusion tractography, these problems have been discussed in schematic representations or theoretical arguments7, 8, 37, but have not yet been quantified in brain imaging.”
From Tractography-based connectomes are dominated by false-positive connections (2016) · cites this paper as [7]
Some underlying ambiguities in tractography have been discussed using schematic representations and theoretical arguments.
“some of the underlying ambiguities in tractography have been discussed using schematic representations and theoretical arguments1, 7, 8, 37.”
From Tractography-based connectomes are dominated by false-positive connections (2016) · cites this paper as [7]
DT-based deterministic tractography is particularly hampered by voxels containing multiple fiber orientations such as crossing, bending or fanning fibers.
“This method is particularly hampered by voxels containing multiple fiber orientations such as crossing, bending or fanning fibers (Jbabdi and Johansen-Berg 2011).”
From Diffusion MRI-based cortical connectome reconstruction: dependency on tractography procedures and neuroanatomical characteristics (2018) · cites this paper as Jbabdi and Johansen-Berg 2011
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References 132 · Cited by 614
Research works printed but not linked: 4
- [12]Pajevic S Pierpaoli C Color schemes to represent the orientation of anisotropic tissues from diffusion tensor data: application to white matter fiber tract mapping in the human brain Magn Reson Med 2000 43 6 921 10861892
- [94]Callaghan PT Principles of Nuclear Magnetic Resonance Microscopy 1991 Oxford University Press
- [104]Alexander DC A general framework for experiment design in diffusion MRI and its application in measuring direct tissue-microstructure features Magn Reson Med 2008 60 2 439 48 18666109
- [116]Jbabdi S Imaging Structure and Ounction Diffusion MRI 2009 461 480 Academic Press San Diego
- Authors
- Jbabdi, S., & Johansen-Berg, H.
- Affiliations
- University of Oxford
- Venue
- Brain Connectivity
- Publisher
- SAGE Publications
- Published
- 2011-06
- Volume
- 1
- Issue
- 3
- Pages
- 169-183
- ISSN
- 2158-0014 (ISSN-L)
- 2158-0022
- PMC
- PMC3677805
- OpenAlex
- W2019307288
- Cited by
- 614
- References
- 132
- Citations by year
- 2026: 23 · 2025: 35 · 2024: 27 · 2023: 32 · 2022: 42 · 2021: 45 · 2020: 50 · 2019: 50 · 2018: 50 · 2017: 46 · 2016: 41 · 2015: 55 · 2014: 40 · 2013: 52 · 2012: 25
- FWCI
- 6.04
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Brain Connectivity (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Wellcome Trust (090955)
- Medical Research Council
- 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 › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- diffusion tractography, white matter tracts, diffusion MRI, human brain anatomy
- MeSH terms
- Animals, Brain, Brain Mapping, Humans, Nerve Net, Neural Pathways, Diffusion Tensor Imaging
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