The DOI matches its Crossref record, with a small difference in title or year; 126 of the 126 references it cites were found in a registry or an index; none of the 84 sources its claims cite is checked for support yet.
Diffusion MRI fiber tractography of the brain
Open accessAbstract
The ability of fiber tractography to delineate non-invasively the white matter fiber pathways of the brain raises possibilities for clinical applications and offers enormous potential for neuroscience. In the last decade, fiber tracking has become the method of choice to investigate quantitative MRI parameters in specific bundles of white matter. For neurosurgeons, it is quickly becoming an invaluable tool for the planning of surgery, allowing for visualization and localization of important white matter pathways before and even during surgery. Fiber tracking has also claimed a central role in the field of "connectomics," a technique that builds and studies comprehensive maps of the complex network of connections within the brain, and to which significant resources have been allocated worldwide. Despite its unique abilities and exciting applications, fiber tracking is not without controversy, in particular when it comes to its interpretation. As neuroscientists are eager to study the brain's connectivity, the quantification of tractography-derived "connection strengths" between distant brain regions is becoming increasingly popular. However, this practice is often frowned upon by fiber-tracking experts. In light of this controversy, this paper provides an overview of the key concepts of tractography, the technical considerations at play, and the different types of tractography algorithm, as well as the common misconceptions and mistakes that surround them. We also highlight the ongoing challenges related to fiber tracking. While recent methodological developments have vastly increased the biological accuracy of fiber tractograms, one should be aware that, even with state-of-the-art techniques, many issues that severely bias the resulting structural "connectomes" remain unresolved.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This review explains diffusion MRI fiber tractography, its technical steps, algorithm families, and common misinterpretations. It argues that tractography is valuable for qualitative white matter delineation but remains severely limited for quantitative connectivity and connectomics, even with state-of-the-art methods.
Key findings
- DTI-based tractography remains dominant in clinical neurosurgery: roughly 98% of 160 neurosurgical tractography studies reported DTI-based tractography, despite growing consensus that it should be deprecated in favor of higher-order models.
- Tractography is biased toward short, straight, simple trajectories; long, curving, or complex-fiber tracks are more likely to terminate prematurely, and whole-brain seeding overrepresents long bundles while underrepresenting short ones.
- In the ISMRM 2015 Tractography Challenge, 96 tractograms from 21 international groups contained on average four false-positive bundles for every valid bundle recovered.
- Practical resolution limits remain: clinical whole-brain diffusion MRI is limited to at best 2 mm³, angular resolution levels off around b = 3000 s/mm² with as few as 45 unique gradient orientations, and inter-fiber angles below 30° are rarely resolved.
Methods
- Narrative review of diffusion MRI fiber tractography, covering key concepts, terminology, local fiber orientation estimation, integration, interpolation, seeding, stopping criteria, virtual dissection, and algorithm families.
- No primary empirical sample is analyzed; the paper synthesizes prior literature and cites examples including 160 neurosurgical tractography studies, the ISMRM 2015 Tractography Challenge, simulation studies, and Human Connectome Project data.
- Main analysis is conceptual and technical: it compares deterministic vs probabilistic, local vs global, and streamline vs filtering/shortest-path approaches, and discusses common misconceptions about quantification.
Limitations
- The authors state their focus on challenges and limitations may appear pessimistic, but they intend it as a reflection on intrinsic tractography limitations and inspiration for next-generation algorithms.
- The paper notes that even advanced higher-order models are simplified approximations whose assumptions may not always hold, and that global methods rely on stochastic optimization without guaranteed convergence to a globally optimal solution.
- Evident from the text: this is a review, not a systematic meta-analysis or primary validation study; it also focuses on brain white matter tractography, with non-brain fibrous tissues mentioned only briefly.
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Claims the paper makes
84 Own interpretation · 46 Sources not found · 6 No basis given · 3 Own result
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In the paper
“Note that, as we move away from the seed point, the errors made by the nearest-neighbor interpolation accumulate”
§ Diffusion MRI fiber tractography of the brain > FIGURE 14
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In the paper
“Although all tractograms appear great, inspection of the tracking endpoints reveals numerous biologically implausible terminals (left).”
§ Diffusion MRI fiber tractography of the brain > FIGURE 14
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In the paper
“Given that a single white matter voxel can contain hundreds of thousands of fibers, as well as other microstructural tissue constituents, the mapping from diffusion signal to fiber orientations is ill posed.”
§ Diffusion MRI fiber tractography of the brain > 3.1 | Local fiber orientation estimation
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In the paper
“in contrast to several imaging techniques such as (polarized-)light microscopy 24 and electron microscopy, 25 where individual fibers can be visualized directly, dMRI can probe white matter fiber orientations only indirectly by looking at the associated average diffusion pattern of the water molecules.”
§ Diffusion MRI fiber tractography of the brain > 3.1 | Local fiber orientation estimation
What the sources say
- Not matchedcites [24]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [25]
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plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“different tracking approaches will produce very different tractograms given the same connectome”
§ Diffusion MRI fiber tractography of the brain > 2.3 | The virtual nature of fiber tracking
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In the paper
“Over the last decade, a range of so-called”
§ Diffusion MRI fiber tractography of the brain > 3.1 | Local fiber orientation estimation
What the sources say
- Not matchedcites [3]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [30]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [34]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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- The authors state this as an assumption: When a number of nerve fibers align along a common orientation, the diffusion of water molecules is hindered to a greater extent across this orientation than along it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Consequently, it should be clear that fiber tracking can do no more than to make an educated guess at the local fiber orientation(s).”
§ Diffusion MRI fiber tractography of the brain > 3.1 | Local fiber orientation estimation
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- The authors state this as an assumption: When a number of nerve fibers align along a common orientation, the diffusion of water molecules is hindered to a greater extent across this orientation than along it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While recent methodological developments have vastly increased the biological accuracy of fiber tractograms, one should be aware that, even with state-of-the-art techniques, many issues that severely bias the resulting structural”
§ Diffusion MRI fiber tractography of the brain > Abstract
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“However, the diffusion tensor is only capable of distinguishing a single fiber population per voxel (see Figure 2, left).”
§ Diffusion MRI fiber tractography of the brain > 3.1 | Local fiber orientation estimation
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In the paper
“Recent studies have shown that DTI-based tractography can result in systematically unreliable and clinically misleading information, while higher-order tractography approaches, using the same dMRI data, clearly demonstrate fiber trajectories that are biologically much more plausible.32,36”
§ Diffusion MRI fiber tractography of the brain > 3.1 | Local fiber orientation estimation
What the sources say
- Not matchedcites [32]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [36]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- The authors state this as an assumption: When a number of nerve fibers align along a common orientation, the diffusion of water molecules is hindered to a greater extent across this orientation than along it.
- Not matched
- It may state two separate claims as one. 49
- Probably not a substantial claim. 18
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Filtering the tractogram makes the distribution of endpoints more uniform, but does not prevent the many implausible terminals (middle).”
§ Diffusion MRI fiber tractography of the brain > FIGURE 14
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In particular, trajectories end prematurely in regions containing complex white matter configurations (green arrows) or at the cerebrospinal fluid interface (cyan arrows).”
§ Diffusion MRI fiber tractography of the brain > FIGURE 14
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“while the white matter”
§ Diffusion MRI fiber tractography of the brain > 2.3 | The virtual nature of fiber tracking
plaincorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Note that Euler integration is a first-order integration method and assumes that the orientation v(r i ) is constant at the length scale of step size Δ, which will make this method highly susceptible to overshoots in highly curved regions, especially for larger step sizes (see Figure 3).”
§ Diffusion MRI fiber tractography of the brain > 3.2 | Integration methods
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It may state two separate claims as one.
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In the paper
“Although there seems to be a growing consensus in the dMRI community that diffusion tensor tractography should be deprecated, 6,34,37,38 this wisdom does not seem to have been fully translated into clinical practice.”
§ Diffusion MRI fiber tractography of the brain > 3.1 | Local fiber orientation estimation
What the sources say
- Not matchedcites [6]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [34]
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- Not matchedcites [37]
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- Not matchedcites [38]
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hedged- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
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None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Higherorder numerical integration schemes, such as Runge-Kutta integration, take into account the variations of v between r i and r i + 1 and are much less susceptible to these integration errors.23,39”
§ Diffusion MRI fiber tractography of the brain > 3.2 | Integration methods
What the sources say
- Not matchedcites [23]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [39]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It should be clear at this point that, even when using higher-order integration methods, the sheer step-by-step nature of such a procedure makes streamline fiber tracking extremely susceptible to error propagation: indeed, even small local errors can accumulate along the way, causing tracks to veer off course and jump to unrelated adjacent tracts or to stop prematurely.”
§ Diffusion MRI fiber tractography of the brain > 3.2 | Integration methods
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Recent studies have shown that DTI-based tractography can result in systematically unreliable and clinically misleading information, while higher-order tractography approaches, using the same dMRI data, clearly demonstrate fiber trajectories that are biologically much more plausible.32,36”
§ Diffusion MRI fiber tractography of the brain > 3.1 | Local fiber orientation estimation
What the sources say
- Not matchedcites [32]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [36]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
boosteddirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It should be noted that even the most advanced higher-order models are still only models, i.e. simplified approximations of the physical reality, and that some of the assumptions behind these models might not always hold.”
§ Diffusion MRI fiber tractography of the brain > 3.1 | Local fiber orientation estimation
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“even the most advanced tracking algorithms are bound to produce only very crude approximations to the actual connectome.”
§ Diffusion MRI fiber tractography of the brain > 2.3 | The virtual nature of fiber tracking
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“In voxels of complex fiber architecture (e.g.crossing fiber populations or partial volume effect between adjacent fiber populations) the diffusion tensor is often a poor representation of the underlying fiber orientations, [28][29][30] causing false negatives, in which tracking can terminate prematurely, 31,32 or false positives, in which tracking can switch to an unrelated adjacent tract.32,33”
§ Diffusion MRI fiber tractography of the brain > 3.1 | Local fiber orientation estimation
What the sources say
- Not matchedcites [28]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [29]
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- Not matchedcites [30]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [31]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [32]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [33]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Indeed, it is easy to see that, as tracks grow longer, the chance of encountering local tractography errors as well as the extent of error accumulation increases, making it more likely for trajectories to terminate prematurely.”
§ Diffusion MRI fiber tractography of the brain > 5.4 | The slippery slope of quantification
hedgeddirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While this latter approach is particularly appealing as it allows for correlation analyses between structural and functional connectivity, it may suffer from ill-defined fiber orientations present in gray matter regions.”
§ Diffusion MRI fiber tractography of the brain > 3.4 | Seed point selection schemes
hedgedconditional- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“By treating the problem in a probabilistic fashion, it also becomes possible to track through regions of high uncertainty, where deterministic techniques would usually stop, acknowledging, however, that the probability of”
§ Diffusion MRI fiber tractography of the brain > 4.1 | Deterministic versus probabilistic approaches
hedgeddirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“As previously suggested by Behrens et al., 100 combining the two approaches could potentially provide both types of information simultaneously.”
§ Diffusion MRI fiber tractography of the brain > 5.2 | The distinct flavors of probabilistic fiber tracking
What the sources say
- Not matchedcites Behrens et al., 100
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedgedconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These local fiber orientations are subject to noise and model errors, resulting in a distribution of possible fiber orientations also known as the uODF.”
§ Diffusion MRI fiber tractography of the brain > 5.2 | The distinct flavors of probabilistic fiber tracking
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While this technique is very powerful, it relies heavily on prior anatomical knowledge.”
§ Diffusion MRI fiber tractography of the brain > 3.6 | Virtual dissection
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Specification of prior knowledge that is too strong might lead to inconsistencies with the actual data, potentially resulting in non-existent fiber trajectories.On the other hand, specification of prior knowledge that is too weak might lead to fiber tracks that perfectly match the underlying data, but that make little sense anatomically.”
§ Diffusion MRI fiber tractography of the brain > FIGURE 7
hedgedconditional- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These methods construct globally optimal pathways from a given seed point, which do not necessarily always align with the locally estimated fiber orientation.However, they do not consider the pathways from all seed points simultaneously, setting them apart from the purely global methods.”
§ Diffusion MRI fiber tractography of the brain > FIGURE 7
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is also easy to see that increased architectural complexity will increase the local fiber orientation uncertainty, resulting in fewer tracks making it from start to finish.”
§ Diffusion MRI fiber tractography of the brain > 5.4 | The slippery slope of quantification
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These errors are especially important in the context of streamline tractography, as measurement uncertainty can propagate.”
§ Diffusion MRI fiber tractography of the brain > 4.1 | Deterministic versus probabilistic approaches
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As a consequence, diffusion from closely aligned fiber orientations will be blurred together, implying that only a single fiber population is present.”
§ Diffusion MRI fiber tractography of the brain > 5.1 | Taking the path of least hindrance
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“TDI can provide maps with high anatomical contrast, and when combined with a super-resolution voxel grid can potentially resolve sub-voxel anatomical details. 102”
§ Diffusion MRI fiber tractography of the brain > 5.4.1 | Voxel-wise track counts
What the sources say
- Not matchedcites [101]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [102]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Both issues are not only of practical importance when trying to qualitatively reconstruct a particular tract; they are also a confounding factor when it comes to quantification of brain connectivity.”
§ Diffusion MRI fiber tractography of the brain > 5.3 | Practical consequences of integration errors and error propagation
plaincorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“It should be noted at this point that, while probabilistic uODF tracking gives an indication of the precision of the tracking result, it says nothing about accuracy: even the most reproducible fiber pathways can be highly inaccurate.10”
§ Diffusion MRI fiber tractography of the brain > 5.2 | The distinct flavors of probabilistic fiber tracking
What the sources say
- Not matchedcites [10]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In general, this problem has many solutions and selecting a suitable one requires prior knowledge about the expected properties of the trajectories such as local smoothness.”
§ Diffusion MRI fiber tractography of the brain > FIGURE 7
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, convincing results based on this combination have not been published to date.”
§ Diffusion MRI fiber tractography of the brain > 5.2 | The distinct flavors of probabilistic fiber tracking
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Typically, probabilistic tractography algorithms build on the deterministic streamline approach described in the previous section, and as such are subject to the same limitations.”
§ Diffusion MRI fiber tractography of the brain > 4.1 | Deterministic versus probabilistic approaches
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Global tracking promises a better stability with respect to noise and imaging artifacts and a better agreement with the actual dMRI data that was acquired.”
§ Diffusion MRI fiber tractography of the brain > FIGURE 7
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Another, lesser-known downside is that streamline tractograms are typically a very poor predictor of the dMRI data that was actually measured, resulting in fiber tracks that have little to no quantitative or biological meaning (this issue is further discussed in section 5.4).”
§ Diffusion MRI fiber tractography of the brain > 4.2 | Local versus global approaches
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Typically, these ROIs are defined by the user.This task requires anatomical knowledge and is subject to inter-operator variability.”
§ Diffusion MRI fiber tractography of the brain > 3.4 | Seed point selection schemes
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Note that whole-brain tracking can be achieved either by seeding from all white matter voxels or by seeding solely from those voxels that are close to the gray matter-white matter interface, as this is where fibers are known to originate from (see Figure 5).”
§ Diffusion MRI fiber tractography of the brain > 3.4 | Seed point selection schemes
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Local methods are fast and used widely but have important drawbacks.The most apparent is probably that minor errors in the local fiber orientations can accumulate and significantly affect the final result.”
§ Diffusion MRI fiber tractography of the brain > 4.2 | Local versus global approaches
hedgeddirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While these frameworks are often mathematically elegant, they are not widely adopted in software packages and their use in practical studies has been limited.”
§ Diffusion MRI fiber tractography of the brain > FIGURE 7
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The overrepresentation of long bundles can be reduced by seeding at the gray matter-white matter interface; however, it is not guaranteed that these trajectories will be able to cover the whole of the white matter, as tracks typically end prematurely due to errors.”
§ Diffusion MRI fiber tractography of the brain > 3.4 | Seed point selection schemes
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“There are many misconceptions that surround fiber tracking, and not recognizing its many technological pitfalls can easily lead to big mistakes, in particular when it comes to interpretation of fiber-tracking results.”
§ Diffusion MRI fiber tractography of the brain > FIGURE 7
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In a sense, this approach can be regarded as providing more biophysically meaningful tractograms, as it is driven by anatomical dispersion rather than noise characteristics and model inaccuracies.”
§ Diffusion MRI fiber tractography of the brain > 5.2 | The distinct flavors of probabilistic fiber tracking
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, by introducing anatomical constraints during fiber tracking, the resulting fiber trajectories no longer stop prematurely within white matter, but evenly cover the white matter-gray matter interface.”
§ Diffusion MRI fiber tractography of the brain > 5.4.2 | Connection strength(s)
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Also, when studying connection strengths for the entire brain (aka connectomics), whole-brain seeding will potentially overrepresent long fibers, which will result in biased connectivity matrices with little biophysical meaning.”
§ Diffusion MRI fiber tractography of the brain > 5.4.2 | Connection strength(s)
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“However, this approach does not necessarily yield accuracy, and some structures observed in TDI have been shown to be caused by noise rather than anatomy.103,104”
§ Diffusion MRI fiber tractography of the brain > 5.4.1 | Voxel-wise track counts
What the sources say
- Not matchedcites [103]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [104]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Finally, and most importantly, the seeding procedures associated with whole-brain tracking are known to cause overrepresentation of long fiber tracks and underrepresentation of short fiber tracks.45,105”
§ Diffusion MRI fiber tractography of the brain > 5.4.1 | Voxel-wise track counts
What the sources say
- Not matchedcites [45]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [105]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
In the paper
“While there is no information in the voxel-wise data to disambiguate these local geometries, it is possible to test whether they are supported by the local neighborhood and use this information to make better-informed decisions during fiber tracking.26”
§ Diffusion MRI fiber tractography of the brain > 6.1 | Proper treatment of ambiguous local geometries
What the sources say
- Not matchedcites [26]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
In the paper
“More specifically, fiber-tracking algorithms have a hard time penetrating into the gyri, and if they do manage they tend to propagate more often towards the crowns of the gyri than towards the gyral walls.88”
§ Diffusion MRI fiber tractography of the brain > 6.2 | Tracking near the cortex
What the sources say
- Not matchedcites [88]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“When adopting the fODF approach, at least in theory, we are closer to an actual measure for quantification; however, in practice we still have to curb our enthusiasm, as pretty much exactly the same limitations that plagued local track counts come into play.”
§ Diffusion MRI fiber tractography of the brain > 5.4.2 | Connection strength(s)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“This requirement can be met either by using a global tracking approach, 71,73 which satisfies this requirement by its very definition, or by applying filtering approaches that will remove or weight fiber tracks, such that the sum of their contributions fits the measured dMRI signal best.45,[74][75][76][77]”
§ Diffusion MRI fiber tractography of the brain > 5.4.1 | Voxel-wise track counts
What the sources say
- Not matchedcites [71]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [73]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [45]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“What is alarming is that these artificial bundles approximate true anatomical structures very well: they connect cortical areas and appear to be dense and well structured.”
§ Diffusion MRI fiber tractography of the brain > 5.4.2 | Connection strength(s)
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“First, as TDI is based on fiber tracking, it is very sensitive to noise, the accumulation of noise-induced errors, and model imperfections.”
§ Diffusion MRI fiber tractography of the brain > 5.4.1 | Voxel-wise track counts
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Matters become even more complicated in the case of fanning fiber configurations, as one should also take into account the polarity of the fanning.”
§ Diffusion MRI fiber tractography of the brain > 6.1 | Proper treatment of ambiguous local geometries
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In general, techniques that disambiguate local fiber geometries do not seem to have found their way into popular fiber-tracking software yet and remain inaccessible to most researchers.”
§ Diffusion MRI fiber tractography of the brain > 6.1 | Proper treatment of ambiguous local geometries
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“In theory, global tractography methods should already be able to resolve these local ambiguities by their very definition, as they search for the set of continuous trajectories that best explain the full dMRI data, in particular when they are combined with microstructural continuity priors.113”
§ Diffusion MRI fiber tractography of the brain > 6.1 | Proper treatment of ambiguous local geometries
What the sources say
- Not matchedcites [113]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, each of these geometries may require different decisions to be taken during track propagation, and failing to distinguish between these cases could adversely affect fiber-tracking outcomes.”
§ Diffusion MRI fiber tractography of the brain > 6.1 | Proper treatment of ambiguous local geometries
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“While most high-order local fiber models can provide high-quality fiber orientation estimates in voxels containing pure white matter, in voxels partially containing grey matter tissue these models may no longer be appropriate and are known to produce unreliable, noisy fiber orientation estimates.47”
§ Diffusion MRI fiber tractography of the brain > 6.2 | Tracking near the cortex
What the sources say
- Not matchedcites [47]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It should be clear that fiber tracking is a complicated endeavor involving many steps (local fiber orientation modeling, interpolation, integration/ propagation, seeding, masking, stopping criteria, etc.), each incurring potential errors.”
§ Diffusion MRI fiber tractography of the brain > 7 | CONCLUSION
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Such effects can potentially veer a track off course and create completely artificial fiber trajectories.”
§ Diffusion MRI fiber tractography of the brain > 6.4 | Angular resolution limits
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“By using a proper high-order model, by applying realistic anatomical priors, and by ensuring fidelity of the tractograms to the data, the biological accuracy can be vastly increased and one could potentially start to derive useful connectivity information.”
§ Diffusion MRI fiber tractography of the brain > FIGURE 14
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Without a proper high-order model, without anatomical constraints, and without enforcing fidelity of the fiber tracks to the raw dMRI data, quantifying connections using fiber tracking is the equivalent of opening Pandora's box: the resulting”
§ Diffusion MRI fiber tractography of the brain > FIGURE 14
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Experimental evidence suggests that the angular frequency content of the dMRI signal in the brain white matter levels off at a b-value of approximately b = 3000 s/mm 2 , suggesting that this b-value achieves the highest practically available angular resolution.122”
§ Diffusion MRI fiber tractography of the brain > 6.4 | Angular resolution limits
What the sources say
- Not matchedcites [122]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Increasing spatial resolution is challenging, as it typically goes hand in hand with either a significant drop in SNR or a significant increase in acquisition time, resulting in either poor-quality data or impractically long scan times.”
§ Diffusion MRI fiber tractography of the brain > 6.3 | Spatial resolution
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As a consequence, even with state-of-the-art high-angularresolution diffusion imaging and cutting-edge fiber orientation estimation techniques, inter-fiber angles below 30° are rarely resolved, and are a potential source of inaccurate and/or completely artifactual fiber trajectories.”
§ Diffusion MRI fiber tractography of the brain > 6.4 | Angular resolution limits
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The authors state this as an assumption.
When a number of nerve fibers align along a common orientation, the diffusion of water molecules is hindered to a greater extent across this orientation than along it.
“These models all rely on one fundamental assumption: when a number of nerve fibers align themselves along a common orientation, the diffusion of water molecules will be hindered to a greater extent across this orientation than along it.3”
The authors state this as an assumption.
Connection strength directly relates to the proportion of white matter fibers connecting different regions, with regions connected by a larger proportion of fibers deemed more strongly connected.
“While there are many possible definitions of connection strength, one typically assumes that it directly relates to the proportion of white matter fibers connecting different regions, with regions that are connected by a larger proportion of fibers deemed to be more strongly connected.”
The authors state this as an assumption.
Fiber densities and/or axon diameters remain consistent along their trajectory.
“For example, by assuming that fiber densities and/or axon diameters remain consistent along their trajectory, one can potentially disambiguate e.g.crossing and kissing fibers (assuming that each of the interacting fiber bundles exhibits a significantly distinct microstructural signature).”
The authors state this as an assumption.
Each of the interacting fiber bundles exhibits a significantly distinct microstructural signature.
“For example, by assuming that fiber densities and/or axon diameters remain consistent along their trajectory, one can potentially disambiguate e.g.crossing and kissing fibers (assuming that each of the interacting fiber bundles exhibits a significantly distinct microstructural signature).”
The paper relies on this without support.
Water molecules are most likely to diffuse along the fiber orientation, but diffusion along other, even perpendicular, orientations is still common.
“Although water molecules are most likely to diffuse along the fiber orientation, diffusion along other, even perpendicular, orientations is still common.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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 Jeurissen et al., 2019
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Tractography and network analysis both have their own limitations (Jeurissen et al., 2019;Sotiropoulos and Zalesky, 2019), which, in combination with the limitations of NODDI, means that caution is required when interpreting the data.”
From NODDI in clinical research (2020) · cites this paper as Jeurissen et al., 2019
The cited source says what the claim attributes to it.
In the citing paper
“The reconstructed streamlines are only simulated entities that do not correspond directly to nerve fibers (Jeurissen et al., 2019; Yeh et al., 2020)”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Jeurissen et al., 2019
Citations
Cites
Complex brain networks: graph theoretical analysis of structural and functional systems
Bullmore, E., & Sporns, O. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2575 · Cited by 11,820
Spin Diffusion Measurements: Spin Echoes in the Presence of a Time-Dependent Field Gradient
Stejskal, E. O., & Tanner, J. E. (1965) · The Journal of Chemical Physics · doi:10.1063/1.1695690 · Cited by 8,239 · cited 1×
“Diffusion MRI (dMRI) uses a specific type of MRI sequence that is sensitive to the random microscopic motion (or diffusion) of water molecules.(1) The coherent arrangement of fibers in brain tissue introduces a directional dependence to this motion, as water molecules are less hindered along the fibers than in their p…”
BackgroundThe WU-Minn Human Connectome Project: An overview
Van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T. E. J., Yacoub, E., & Ugurbil, K. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.041 · Cited by 5,651 · cited 1×
“Fiber tracking has also claimed a central role in the field of “connectomics,” a technique that builds and studies comprehensive maps of the complex network of connections within the brain,(7) and to which significant resources are allocated worldwide.(8,9) Despite its unique abilities and exciting applications, fiber…”
BackgroundEstimation of the Effective Self-Diffusion Tensor from the NMR Spin Echo
Basser, P. J., Mattiello, J., & Bihan, D. L. (1994) · Journal of Magnetic Resonance Series B · doi:10.1006/jmrb.1994.1037 · Cited by 3,656 · cited 1×
“To date, one of the most widely used models for characterizing fiber orientation in terms of measured diffusion signal is the diffusion tensor.(27) Diffusion tensor imaging (DTI) requires only a limited number of raw dMRI images to be acquired, and estimating the diffusion tensor typically requires modest computing re…”
MethodThree-dimensional tracking of axonal projections in the brain by magnetic resonance imaging
Mori, S., Crain, B. J., Chacko, V. P., & van Zijl, P. C. M. (1999) · Annals of Neurology · doi:10.1002/1531-8249(199902)45:2 · Cited by 3,513
Self-citationProbabilistic diffusion tractography with multiple fibre orientations: What can we gain?
Behrens, T. E. J., Berg, H. J., Jbabdi, S., Rushworth, M. F. S., & Woolrich, M. W. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2006.09.018 · Cited by 3,471
The Human Connectome: A Structural Description of the Human Brain
Sporns, O., Tononi, G., & Kötter, R. (2005) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0010042 · Cited by 3,260 · cited 1×
“The full wiring diagram of the brain, in all its complexity, is often called the connectome.(19,20) Capturing the full connectome can be regarded as the holy grail of fiber tractography (and perhaps even of the whole of neuroscience for that matter), as it will provide us with unprecedented insight into the brain's ar…”
BackgroundCharacterization and propagation of uncertainty in diffusion-weighted MR imaging
Behrens, T. E. J., Woolrich, M. W., Jenkinson, M., Johansen‐Berg, H., Nunes, R. G., Clare, S., Matthews, P. M., Brady, J. M., & Smith, S. M. (2003) · Magnetic Resonance in Medicine · doi:10.1002/mrm.10609 · Cited by 3,038 · cited 1×
“Brain regions that contain higher densities of the resulting trajectories are then deemed to have a higher probability of “connection” with the seed point.(66,67) Probabilistic streamline results are, therefore, often quantified by generating visitation count maps of the number of trajectories that traverse each voxel…”
BackgroundWhite matter integrity, fiber count, and other fallacies: The do's and don'ts of diffusion MRI
Jones, D. K., Knösche, T. R., & Turner, R. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2012.06.081 · Cited by 2,402
Fiber tracking: principles and strategies – a technical review
Mori, S., & van Zijl, P. C. M. (2002) · NMR in Biomedicine · doi:10.1002/nbm.781 · Cited by 2,019 · cited 1×
“These local fiber orientations can then be pieced together to infer long‐range pathways connecting distant regions of the brain, a process that is most often called fiber tracking or fiber tractography.(4) The ability of dMRI‐based fiber tractography to delineate the white matter fiber pathways of the brain non‐invasi…”
BackgroundSelf-citation
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Topographic organization of the human subcortex unveiled with functional connectivity gradients
Tian, Y., Margulies, D. S., Breakspear, M. J., & Zalesky, A. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-00711-6 · Cited by 808
The cingulum bundle: Anatomy, function, and dysfunction
Bubb, E. J., Metzler-Baddeley, C., & Aggleton, J. P. (2018) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2018.05.008 · Cited by 740
“Meanwhile, the problem of disentangling complex fibre architecture (Fischer et al., 2012; Jeurissen et al., 2017) helps to explain why the reconstructed cingulum sometimes appears to extend rostrally beyond the limit of the tract (Heilbronner and Haber, 2014), suggesting that streamlines have jumped into adjacent path…”
Zhang, F., Daducci, A., He, Y., Schiavi, S., Seguin, C., Smith, R. E., Yeh, C.-H., Zhao, T., & O’Donnell, L. J. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2021.118870 · Cited by 302
“not correspond directly to nerve fibers [223, 513], and basic diffusion metrics are only inferences based on local diffusion properties, which are not direct measures of tissue properties [11].”
BackgroundMethodQuantitative magnetic resonance imaging of brain anatomy and in vivo histology
Weiskopf, N., Edwards, L., Helms, G., Mohammadi, S., & Kirilina, E. P. (2021) · Nature Reviews Physics · doi:10.1038/s42254-021-00326-1 · Cited by 321
Kamiya, K., Hori, M., & Aoki, S. (2020) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2020.108908 · Cited by 283
“Tractography and network analysis both have their own limitations (Jeurissen et al., 2019; Sotiropoulos and Zalesky, 2019), which, in combination with the limitations of NODDI, means that caution is required when interpreting the data.”
Figley, C. R., Uddin, M. N., Wong, K., Kornelsen, J., Puig, J., & Figley, T. D. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.799576 · Cited by 253
“, 2003) tractography approaches (Maier-Hein et al., 2017; Jeurissen et al., 2019), and (3) indirectly measure brain function (Le Bihan et al.”
BackgroundBrain States and Transitions: Insights from Computational Neuroscience
Kringelbach, M. L., & Deco, G. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.108128 · Cited by 249
“…are not without problems, given that fiber tracking is complex and involves many steps inherently subject to potential errors from and decisions regarding local fiber orientation modeling, integration/propagation, interpolation, seeding, masking, and stopping criteria (Jeurissen et al., 2019).”
BackgroundBrain networks and their relevance for stroke rehabilitation
Guggisberg, A. G., Koch, P., Hummel, F. C., & Buetefisch, C. M. (2019) · Clinical Neurophysiology · doi:10.1016/j.clinph.2019.04.004 · Cited by 266
MRI of the Neonatal Brain: A Review of Methodological Challenges and Neuroscientific Advances
Dubois, J., Alison, M., Counsell, S. J., Hertz‐Pannier, L., Hüppi, P. S., & Benders, M. J. N. L. (2021) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.27192 · Cited by 226
“However, the ones based on simple diffusion models (eg, DTI that only considers a single fiber population per voxel) present major limitations, leading to biases such as false negatives (premature termination of a tract) or false positives (switch of a tract to a neighboring one).(99) It is only by using the most soph…”
Modelling white matter with spherical deconvolution: How and why?
Dell’Acqua, F., & Tournier, J. (2018) · NMR in Biomedicine · doi:10.1002/nbm.3945 · Cited by 223
Mapping Structural Connectivity Using Diffusion MRI : Challenges and Opportunities
Yeh, C., Jones, D. K., Liang, X., Descoteaux, M., & Connelly, A. (2021) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.27188 · Cited by 187
“, high curvature).(17,34) However, these criteria are often insufficient to warrant appropriate streamline termination and often cause streamlines to end within WM or cerebrospinal fluid (CSF), or even to propagate through and beyond the cortical GM; all such results are biologically implausible.”
BackgroundSelf-citationTheaud, G., Houde, J., Boré, A., Rheault, F., Morency, F. C., & Descoteaux, M. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116889 · Cited by 200
Self-citationOpportunities of connectomic neuromodulation
Horn, A., & Fox, M. D. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117180 · Cited by 187
Hernández-Fernández, M., Reguly, I. Z., Jbabdi, S., Giles, M. B., Smith, S. M., & Sotiropoulos, S. N. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.12.015 · Cited by 182
Mapping connectomes with diffusion MRI: deterministic or probabilistic tractography?
Sarwar, T., Ramamohanarao, K., & Zalesky, A. (2018) · Magnetic Resonance in Medicine · doi:10.1002/mrm.27471 · Cited by 190
The role of diffusion MRI in neuroscience
Assaf, Y., Johansen‐Berg, H., & Thiebaut de Schotten, M. (2019) · NMR in Biomedicine · doi:10.1002/nbm.3762 · Cited by 158
“There are many different methodological approaches to perform diffusion tractography, as reviewed elsewhere in this special issue.(37,89) From the neuroanatomical”
BackgroundMethodHenderson, F. C., Abdullah, K. G., Verma, R., & Brem, S. S. (2020) · Neurosurgical FOCUS · doi:10.3171/2019.11.focus19785 · Cited by 162
Chen, Y., Wang, Y., Song, Z., Fan, Y., Gao, T., & Tang, X. (2023) · Ageing Research Reviews · doi:10.1016/j.arr.2023.101911 · Cited by 152
Scale matters: The nested human connectome
Axer, M., & Amunts, K. (2022) · Science · doi:10.1126/science.abq2599 · Cited by 126
Brain simulation as a cloud service: The Virtual Brain on EBRAINS
Schirner, M., Domide, L., Perdikis, D., Triebkorn, P., Stefanovski, L., Pai, R. K., Prodan, P., Valean, B., Palmer, J., Langford, C., et al. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.118973 · Cited by 127
Common misconceptions, hidden biases and modern challenges of dMRI tractography
Rheault, F., Poulin, P., Caron, A. V., St‐Onge, E., & Descoteaux, M. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab6aad · Cited by 112
Self-citationStraathof, M., Sinke, M. R. T., Dijkhuizen, R. M., & Otte, W. M. (2018) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x18809547 · Cited by 109
“inferring the direction and trajectory of white matter tracts from the diffusion of water.(16,80) Recent research has demonstrated that these tractography-based structural networks do not always reflect the axonal projections between regions measured with neuronal tracers,(18) and are especially limited in reconstruct…”
BackgroundSchilling, K. G., Tax, C. M. W., Rheault, F., Landman, B. A., Anderson, A. W., Descoteaux, M., & Petit, L. (2021) · Human Brain Mapping · doi:10.1002/hbm.25697 · Cited by 102
“…et al., 2007; Descoteaux, Deriche, Knosche, & Anwander, 2009; Donahue et al., 2016; Dyrby et al., 2007; Dyrby, Innocenti, Bech, & Lundell, 2018; Jeurissen, Descoteaux, Mori, & Leemans, 2019; Knösche, Anwander, Liptrot, & Dyrby, 2015; Mani, Jacob, Guidon, Magnotta, & Zhong, 2015; Poulin,…”
Self-citationDiffusion Tensor Imaging Biomarkers to Predict Motor Outcomes in Stroke: A Narrative Review
Moura, L. M., Luccas, R., de Paiva, J. P. Q., Amaro, E., Leemans, A., Leite, C. D. C., Otaduy, M. C. G., & Conforto, A. B. (2019) · Frontiers in Neurology · doi:10.3389/fneur.2019.00445 · Cited by 102
“Tractography Tractography corresponds to the mathematical reconstruction of tracts (57, 58).”
BackgroundSelf-citationDiffusion-Weighted Imaging: Recent Advances and Applications
Martínez‐Heras, E., Grussu, F., Prados, F., Solana, E., & Llufriú, S. (2021) · Seminars in Ultrasound CT and MRI · doi:10.1053/j.sult.2021.07.006 · Cited by 117
Calamante, F. (2019) · Diagnostics · doi:10.3390/diagnostics9030115 · Cited by 98
“This type of streamlines fibre-tracking can be broadly divided into deterministic and probabilistic, depending on how the direction to progress at each step is selected [4].”
BackgroundMethodToward personalized medicine in connectomic deep brain stimulation
Hollunder, B., Rajamani, N., Siddiqi, S., Finke, C., Kühn, A. A., Mayberg, H. S., Fox, M., Neudorfer, C., & Horn, A. (2021) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2021.102211 · Cited by 89
Validation strategies for the interpretation of microstructure imaging using diffusion MRI
Dyrby, T. B., Innocenti, G. M., Bech, M., & Lundell, H. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.06.049 · Cited by 94
Tractostorm: The what, why, and how of tractography dissection reproducibility
Rheault, F., BENEDICTIS, A. D., Daducci, A., Maffei, C., Tax, C. M. W., Romascano, D., Caverzasi, E., Morency, F. C., Corrivetti, F., Pestilli, F., et al. (2020) · Human Brain Mapping · doi:10.1002/hbm.24917 · Cited by 95
“The set of all streamlines forms an object often called the tractogram (Catani & De Schotten, 2008; Jeurissen, Descoteaux, Mori, & Leemans, 2017).”
Self-citationGoddings, A., Roalf, D. R., Lebel, C. A., & Tamnes, C. K. (2021) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2021.101008 · Cited by 97
“Different approaches to tractography are covered in detail elsewhere (Jeurissen et al., 2019), but all use some way to represent brain white matter pathways as three-dimensional structures that can be used for quantitative analysis.”
Modeling Brain Dynamics in Brain Tumor Patients Using the Virtual Brain
Aerts, H., Schirner, M., Jeurissen, B., Roost, D. V., Achten, E., Ritter, P., & Marinazzo, D. (2018) · eNeuro · doi:10.1523/eneuro.0083-18.2018 · Cited by 86
“Although great advances have been made in diffusion weighted imaging acquisitions and tractography algorithms, it is known that DWI tractography underestimates the number of short-distance streamlines in favor of long fiber tracks (Jeurissen et al., 2017).”
BackgroundSelf-citationQuantitative streamlines tractography: methods and inter-subject normalisation
Smith, R. E., Raffelt, D. A., Tournier, J., & Connelly, A. (2022) · Aperture Neuro · doi:10.52294/apertureneuro.2022.2.neod9565 · Cited by 91
Girard, G., Caminiti, R., Battaglia-Mayer, A., St-Onge, E., Ambrosen, K. S., Eskildsen, S. F., Krug, K., Dyrby, T. B., Descoteaux, M., Thiran, J.-P., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117201 · Cited by 83
Self-citationBundle-specific tractography with incorporated anatomical and orientational priors
Rheault, F., St‐Onge, E., Sidhu, J., Maier-Hein, K. H., Tzourio‐Mazoyer, N., Petit, L., & Descoteaux, M. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.11.018 · Cited by 92
“Orientational priors: enhancing fiber orientations During tractography, streamlines are generated by taking steps using the fiber orientation distribution (FOD) to choose the next direction (Jeurissen et al., 2017).”
InfluentialSelf-citationTractography and machine learning: current state and open challenges
Poulin, P., Jörgens, D., Jodoin, P.-M., & Descoteaux, M. (2019) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2019.04.013 · Cited by 84
“In between the local and global methods is the category of shortest-pathmethods, including front evolution, simulated diffusion, geodesic, and graph-based approaches [1].”
BackgroundMethodSelf-citationYang, J. Y.-M., Yeh, C.-H., Poupon, C., & Calamante, F. (2021) · Physics in Medicine & Biology · doi:10.1088/1361-6560/ac0d90 · Cited by 80
Brain Tumor Characterization Using Radiogenomics in Artificial Intelligence Framework
Jena, B., Saxena, S., Nayak, G. K., Balestrieri, A., Gupta, N., Khanna, N. N., Laird, J. R., Kalra, M. K., Fouda, M. M., Saba, L., et al. (2022) · Cancers · doi:10.3390/cancers14164052 · Cited by 83
A Whole-Cortex Probabilistic Diffusion Tractography Connectome
Rosen, B. Q., & Halgren, E. (2021) · eNeuro · doi:10.1523/eneuro.0416-20.2020 · Cited by 82
“This approach reduces the overrepresentation of major bundles (Jeurissen et al., 2019), enables the automated assessment based on intersubject homology (Fischl et al., 1999), facilitates comparison to other cortical datasets, and is true to the anatomic nature of the cortical ribbon.”
BackgroundMethodInfluentialBoerwinkle, V. L., Foldes, S. T., Torrisi, S. J., et al. (2018) · Epilepsia · doi:10.1111/epi.14583 · Cited by 74
Attar, F. M., Kirilina, E. P., Haenelt, D., Pine, K., Trampel, R., Edwards, L., & Weiskopf, N. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa049 · Cited by 64
“each hemisphere to investigate (as a first-order approximation) the potential bias of streamline tractography in our study toward the detection of short streamlines (Jones 2010; Jbabdi and Johansen-Berg 2011; Ercsey-Ravasz et al. 2013; Donahue et al. 2016; Schilling et al. 2018; Jeurissen et al. 2019).”
BackgroundMethodConnections of the human orbitofrontal cortex and inferior frontal gyrus
Heather Hsu, C.-C., Rolls, E. T., Huang, C.-C., Chong, S. T., Zac Lo, C.-Y., Feng, J., & Lin, C.-P. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa160 · Cited by 67
“2010), whereas most tracking algorithms favor the straight option (turning through an angle of less than 60◦) (Jeurissen et al. 2019).”
BackgroundAtlas-based data integration for mapping the connections and architecture of the brain
Leergaard, T. B., & Bjaalie, J. G. (2022) · Science · doi:10.1126/science.abq2594 · Cited by 68
Roine, T., Jeurissen, B., Perrone, D., Aelterman, J., Philips, W. R., Sijbers, J., & Leemans, A. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.10.009 · Cited by 68
Self-citationOldham, S., Arnatkevičiūtė, A., Smith, R. E., Tiego, J., Bellgrove, M. A., & Fornito, A. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117252 · Cited by 63
Genetic architecture of the structural connectome
Wainberg, M., Forde, N. J., Mansour, S., Kerrebijn, I., Medland, S. E., Hawco, C. S., & Tripathy, S. J. (2024) · Nature Communications · doi:10.1038/s41467-024-46023-2 · Cited by 67
Structural connectome and connectivity lateralization of the multimodal vestibular cortical network
Indovina, I., Bosco, G., Riccelli, R., Maffei, V., Lacquaniti, F., Passamonti, L., & Toschi, N. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117247 · Cited by 63
Anatomy and physiology of word-selective visual cortex: from visual features to lexical processing
Caffarra, S., Karipidis, I. I., Yablonski, M., & Yeatman, J. D. (2021) · Brain Structure and Function · doi:10.1007/s00429-021-02384-8 · Cited by 69
Schilling, K. G., Tax, C. M. W., Rheault, F., Hansen, C. B., Yang, Q., Yeh, F.-C. F., Cai, L. Y., Anderson, A. W., & Landman, B. A. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118451 · Cited by 66
The digital twin in neuroscience: from theory to tailored therapy
Fekonja, L. S., Schenk, R., Schröder, E., Tomasello, R., Tomšič, S., & Picht, T. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1454856 · Cited by 66
Bedini, M., & Baldauf, D. (2021) · European Journal of Neuroscience · doi:10.1111/ejn.15393 · Cited by 60
“…in the last decades have enabled the detailed mapping of the brain’s architecture and structural connectivity of this species (Barbas & Pandya, 1989; Felleman & Van Essen, 1991; Kötter, 2004; Markov et al., 2014), in humans, this knowledge is predominantly based on dMRI (Jeurissen et al., 2019).”
BackgroundFiber length profiling: a novel approach to structural brain organization
Bajada, C. J., Schreiber, J., & Caspers, S. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2018.10.070 · Cited by 55
“This is an open access a possible pathways that axons might follow to connect to different brain regions (white matter tracts); this method is called tractography (Jeurissen et al., 2017).”
Delettre, C., Messé, A., Dell, L.-A., Foubet, O., Heuer, K., Larrat, B., Meriaux, S., Mangin, J.-F., Reillo, I., de Juan Romero, C., et al. (2019) · Network Neuroscience · doi:10.1162/netn_a_00098 · Cited by 55
Nath, V., Schilling, K. G., Parvathaneni, P., Huo, Y., Blaber, J. A., Hainline, A. E., Baraković, M., Romascano, D., Rafael‐Patiño, J., Frigo, M., et al. (2019) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.26794 · Cited by 52
Self-citationComputational Models in Electroencephalography
Glomb, K., Cabral, J., Cattani, A., Mazzoni, A., Raj, A., & Franceschiello, B. (2021) · Brain Topography · doi:10.1007/s10548-021-00828-2 · Cited by 60
Tractography in the presence of multiple sclerosis lesions
Lipp, I., Parker, G. D., Tallantyre, E. C., Goodall, A., Grama, S., Patitucci, E., Heveron, P., Tomassini, V., & Jones, D. K. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2019.116471 · Cited by 51
“The reconstruction of individual tracts is based on combining prior anatomical knowledge of the tract location with diffusion MRI-based evidence about fi bre orientations in the imaging voxels.”
Advancements in diffusion MRI tractography for neurosurgery
Kamagata, K., Andica, C., Uchida, W., Takabayashi, K., Saito, Y., Lukies, M., Hagiwara, A., Fujita, S., Akashi, T., Wada, A., et al. (2024) · Investigative Radiology · doi:10.1097/rli.0000000000001015 · Cited by 50
Functionnectome as a framework to analyse the contribution of brain circuits to fMRI
Nozais, V., Forkel, S. J., Foulon, C., Petit, L., & de Schotten, M. T. (2021) · Communications Biology · doi:10.1038/s42003-021-02530-2 · Cited by 53
Imaging Alzheimer's genetic risk using diffusion MRI: A systematic review
Harrison, J. R., Bhatia, S., Tan, Z. X., Mirza-Davies, A., Benkert, H., Tax, C. M. W., & Jones, D. K. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102359 · Cited by 50
Filtering in tractography using autoencoders (FINTA)
Legarreta, J. H., Petit, L., Rheault, F., Theaud, G., Lemaire, C., Descoteaux, M., & Jodoin, P. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102126 · Cited by 53
“Tractography filtering is currently approached based on five main patterns: (i) streamline geometry features; (ii) region-of-interest-driven streamline inclusion and exclusion; (iii) clustering; (iv) connectivity-based; and (v) diffusion signal mapping or forward models (Jeurissen et al., 2019; Sotiropoulos and Zalesk…”
BackgroundMethodSelf-citationSchilling, K. G., Gao, Y., Stepniewska, I., Janve, V. A., Landman, B. A., & Anderson, A. W. (2018) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2018.09.004 · Cited by 48
“A common strategy following probabilistic tractography is to threshold the streamline count to a certain percent of its maximum value (typically 5%), as the voxels containing few streamlines are considered to have a higher uncertainty of connection to the seed region [52].”
BackgroundHe, J., Zhang, F., Xie, G., Yao, S., Feng, Y., Bastos, D. C. A., Yogesh Rathi, A., Makris, N., Kikinis, R., Golby, A. J., et al. (2021) · Human Brain Mapping · doi:10.1002/hbm.25472 · Cited by 43
DeepTract: A Probabilistic Deep Learning Framework for White Matter Fiber Tractography
Benou, I., & Raviv, T. R. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32248-9_70 · Cited by 44
Schilling, K. G., Archer, D. B., Rheault, F., Lyu, I., Huo, Y., Cai, L. Y., Bunge, S. A., Weiner, K. S., Gore, J. C., Anderson, A. W., et al. (2023) · Brain Structure and Function · doi:10.1007/s00429-023-02642-x · Cited by 50
Mitigating gyral bias in cortical tractography via asymmetric fiber orientation distributions
Wu, Y., Hong, Y., Feng, Y., Shen, D. G., & Yap, P. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101543 · Cited by 43
Abós, A., Baggio, H. C., Segura, B., Campabadal, A., Uribe, C., Giraldo, D. M., Pérez‐Soriano, A., Muñoz, E., Compta, Y., Junqué, C., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-52829-8 · Cited by 44
Classifyber, a robust streamline-based linear classifier for white matter bundle segmentation
Bertò, G., Bullock, D. N., Astolfi, P., Hayashi, S., Zigiotto, L., Annicchiarico, L., Corsini, F., BENEDICTIS, A. D., Sarubbo, S., Pestilli, F., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117402 · Cited by 46
Román, C., Hernández, C., Figueroa, M. E., Houenou, J., Poupon, C., Mangin, J., & Guevara, P. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119550 · Cited by 44
Yamanbaeva, G., Schaub, A.-C., Schneider, E., Schweinfurth, N., Kettelhack, C., Doll, J. P. K., Mählmann, L., Brand, S., Beglinger, C., Borgwardt, S., et al. (2023) · Journal of Affective Disorders · doi:10.1016/j.jad.2022.12.142 · Cited by 45
Nozais, V., Forkel, S. J., Petit, L., Talozzi, L., Corbetta, M., de Schotten, M. T., & Joliot, M. (2023) · Communications Biology · doi:10.1038/s42003-023-05107-3 · Cited by 41
Meyer's loop tractography for image-guided surgery depends on imaging protocol and hardware
Chamberland, M., Tax, C. M. W., & Jones, D. K. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.08.021 · Cited by 39
“Additionally, it is worth noting that, although underestimating the loop when compared to other protocols, the Std-40 mT/m measurements derived from single-shell CSD estimated a smaller ML-TP distance than most previously-published DTI studies (Figs.”
BackgroundThe role of diffusion tractography in refining glial tumor resection
Vanderweyen, D., Theaud, G., Sidhu, J., Rheault, F., Sarubbo, S., Descoteaux, M., & Fortin, D. (2020) · Brain Structure and Function · doi:10.1007/s00429-020-02056-z · Cited by 41
Self-citationDynamic primitives of brain network interaction
Schirner, M., Kong, X., Yeo, B. T. T., Deco, G., & Ritter, P. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.118928 · Cited by 42
An overview of mild traumatic brain injuries and emerging therapeutic targets
Bielanin, J. P., Metwally, S. A. H., Paruchuri, S. S., & Sun, D. (2023) · Neurochemistry International · doi:10.1016/j.neuint.2023.105655 · Cited by 41
“…- uODF (uncertainty of orientation distribution function) and fODF (fiber orientation distribution function) of white matter tractography - still have limitations, either over-or underrepresenting fibers, especially when studying connections across the entire brain (Jeurissen et al., 2019).”
BackgroundNonparametric D-R1-R2 distribution MRI of the living human brain
Martin, J., Reymbaut, A., Schmidt, M. A., Doerfler, A., Uder, M., Laun, F. B., & Topgaard, D. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118753 · Cited by 35
“In combination with tractography (Basser et al., 2000; Correia et al., 2008; Girard et al., 2014; Jeurissen et al., 2019), one could theoretically obtain fiber bundle-specific diffusion and relaxation metrics across the whole brain.”
Structural and Functional Connectivity Substrates of Cognitive Impairment in Multiple Sclerosis
Zhang, J., Cortese, R., Stefano, N. D., & Giorgio, A. (2021) · Frontiers in Neurology · doi:10.3389/fneur.2021.671894 · Cited by 38
“Measuring SC Diffusion MRI is a type of sequence that is sensitive to the random microscopic motion of water molecules (18), thus providing information on the microstructure of WM fiber tracts noninvasively.”
BackgroundMethodRamanoël, S., York, E. N., Petit, M. L., Lagrené, K., Habas, C., & Arleo, A. (2019) · Frontiers in Neural Circuits · doi:10.3389/fncir.2019.00069 · Cited by 41
“More precisely, this technique measures the general diffusion of water molecules related to barriers and obstacles imposed by the arrangement of fibers and cell membranes in brain tissue (for a detailed description of diffusion-weighted imaging methods, see Jones et al., 2013; Wandell, 2016; Jeurissen et al., 2017).”
MethodPietrasik, W., Cribben, I., Olsen, F., Huang, Y., & Malykhin, N. V. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116675 · Cited by 36
“The various limitations presented here, such as the partial volume effect and crossing fiber problem, could, to some extent, be mitigated by applying mor e sophisticated DTI tractography protocols than the single tensor DTI utilized in this study (Jeurissen et al., 2019).”
Surface-based tracking for short association fibre tractography
Shastin, D., Genc, S., Parker, G. D., Koller, K., Tax, C. M. W., Evans, J., Hamandi, K., Gray, W., Jones, D. K., & Chamberland, M. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119423 · Cited by 43
Cesare, G. D., Pinardi, C., Carapelli, C., Caruana, F., Marchi, M., Gerbella, M., & Rizzolatti, G. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy095 · Cited by 34
White matter network alterations in patients with depersonalization/derealization disorder
Sierk, A., Daniels, J. K., Manthey, A., Kok, J. G., Leemans, A., Gaebler, M., Lamke, J.-P., Kruschwitz, J., & Walter, H. (2018) · Journal of Psychiatry and Neuroscience · doi:10.1503/jpn.170110 · Cited by 36
Self-citationBoukadi, M., Marcotte, K., Bedetti, C., Houde, J., Désautels, A., Deslauriers‐Gauthier, S., Chapleau, M., Boré, A., Descoteaux, M., & Brambati, S. M. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.01055 · Cited by 34
Self-citationWei, Y., Bresser, T., Wassing, R., Stoffers, D., Someren, E. J. W. V., & Foster‐Dingley, J. C. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.102019 · Cited by 36
A half-century of innovation in technology—preparing MRI for the 21st century
Börnert, P., & Norris, D. G. (2020) · British Journal of Radiology · doi:10.1259/bjr.20200113 · Cited by 34
The expanding horizons of network neuroscience: From description to prediction and control
Srivastava, P., Fotiadis, P., Parkes, L., & Bassett, D. S. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119250 · Cited by 38
“Indeed, over the past few decades, diffusion-weighted imaging (DWI) has evolved into one of the cardinal neuroimaging methodologies to non-invasively capture white matter organization and the microstructural integrity of the brain ( Le Bihan and Johansen-Berg, 2012 ).”
Global Alterations of Whole Brain Structural Connectome in Parkinson’s Disease: A Meta-analysis
Zuo, C., Suo, X., Lan, H., Pan, N., Wang, S., Kemp, G. J., & Gong, Q. (2022) · Neuropsychology Review · doi:10.1007/s11065-022-09559-y · Cited by 35
“Tractography, a key method in ‘connectomics’, infers continuity of white matter tracts from voxel to voxel (Jeurissen et al., 2019).”
BackgroundMethodThe Superoanterior Fasciculus (SAF): A Novel White Matter Pathway in the Human Brain?
Dávid, S., Heemskerk, A. M., Corrivetti, F., de Schotten, M. T., Sarubbo, S., Corsini, F., BENEDICTIS, A. D., Petit, L., Viergever, M. A., Jones, D. K., et al. (2019) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2019.00024 · Cited by 32
“A conceptual limitation of FT (Jeurissen et al., 2017) is the inability to resolve the functionality and directionality of anatomical links, where the existence of a physical connection is necessary to complete the cortical functions.”
BackgroundSelf-citationEarly Detection of Cerebral Palsy Using Sensorimotor Tract Biomarkers in Very Preterm Infants
Parikh, N. A., Hershey, A., & Altaye, M. (2019) · Pediatric Neurology · doi:10.1016/j.pediatrneurol.2019.05.001 · Cited by 33
Advances in computational and statistical diffusion MRI
O’Donnell, L. J., Daducci, A., Wassermann, D., & Lenglet, C. (2017) · NMR in Biomedicine · doi:10.1002/nbm.3805 · Cited by 32
“17 , 18 Despite the large number of available methods (see Jeurissen et al. 19 for a comprehensive review), the reconstructed pathways are not truly quantitative and reflect the actual white matter structure only indirectly.”
Cognitive Training in Young Patients With Traumatic Brain Injury: A Fixel-Based Analysis
Verhelst, H., Giraldo, D. L., Linden, C. V., Vingerhoets, G., Jeurissen, B., & Caeyenberghs, K. (2019) · Neurorehabilitation and neural repair · doi:10.1177/1545968319868720 · Cited by 32
Self-citationRAN training in dyslexia: Behavioral and brain correlates
Stappen, C. V., Dricot, L., & Reybroeck, M. V. (2020) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2020.107566 · Cited by 38
Vavassori, L., Sarubbo, S., & Petit, L. (2021) · Brain Structure and Function · doi:10.1007/s00429-021-02265-0 · Cited by 27
Image registration: Maximum likelihood, minimum entropy and deep learning
Sedghi, A., O’Donnell, L. J., Kapur, T., Miller, E. G., Mousavi, P., & Wells, W. M. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101939 · Cited by 31
“We choose 80 cases from the original dataset and create a new dataset by choosing T2 MRI from one subject and T1 MRI from another subject.”
BackgroundInfluentialDirect Structural Connections between Auditory and Visual Motion-Selective Regions in Humans
Gurtubay-Antolín, A., Battal, C., Maffei, C., Rezk, M., Mattioni, S., Jovicich, J., & Collignon, O. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.1552-20.2021 · Cited by 31
Schilling, K. G., Archer, D. B., Yeh, F.-C. F., Rheault, F., Cai, L. Y., Shafer, A. T., Resnick, S. M., Hohman, T. J., Jefferson, A. L., Anderson, A. W., et al. (2023) · Aging Brain · doi:10.1016/j.nbas.2023.100067 · Cited by 31
“This is largely because of the limitations of diffusion MRI fiber tractography [21–23], which is the primary methodology used to non-invasively study the white matter connections [24].”
MethodSkibbe, H., Rachmadi, M. F., Nakae, K., Gutierrez, C. E., Hata, J., Tsukada, H., Poon, C., Schlachter, M., Doya, K., Majka, P., et al. (2023) · PLoS Biology · doi:10.1371/journal.pbio.3002158 · Cited by 29
“Such data are important to further study how fiber tracking techniques and their parameters affect the comparison with tracer data as well as their influence on the resulting connectomes [33,37].”
BackgroundGerhardt, J., Sollmann, N., Hiepe, P., Kirschke, J. S., Meyer, B., Krieg, S. M., & Ringel, F. A. (2019) · Clinical Neurology and Neurosurgery · doi:10.1016/j.clineuro.2019.105387 · Cited by 29
Striatal topographical organization: Bridging the gap between molecules, connectivity and behavior
Basile, G. A., Bertino, S., Bramanti, A., Ciurleo, R., Anastasi, G. P., Milardi, D., & Cacciola, A. (2021) · European Journal of Histochemistry · doi:10.4081/ejh.2021.3284 · Cited by 29
Gatto, R. G., Martin, P. R., Ali, F., Clark, H. M., Duffy, J. R., Utianski, R. L., Botha, H., Machulda, M. M., Dickson, D. W., Josephs, K. A., et al. (2022) · NeuroImage Clinical · doi:10.1016/j.nicl.2022.103030 · Cited by 29
“An additional advantage of dMRI is that it allows the mapping of specific tracts that roughly represent white matter bundles (Jeurissen et al., 2019).”
Insula-cingulate structural and functional connectivity: an ultra-high field MRI study
Cormie, M. A., Kaya, B., Hadjis, G. E., Mouseli, P., & Moayedi, M. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhad244 · Cited by 30
Recent advances of deep learning in psychiatric disorders
Chen, L., Xia, C., & Sun, H. (2020) · Precision Clinical Medicine · doi:10.1093/pcmedi/pbaa029 · Cited by 28
Metadata
- Authors
- Jeurissen, B., Descoteaux, M., Mori, S., & Leemans, A.
- Affiliations
- University of Antwerp (BE)
- Université de Sherbrooke (CA)
- Kennedy Krieger Institute (US)
- Utrecht University (NL)
- Corresponding author
- Jeurissen, Ben
- Venue
- NMR in Biomedicine
- Publisher
- Wiley
- Published
- 2017-09-25
- Volume
- 32
- Issue
- 4
- Pages
- e3785-e3785
- ISSN
- 0952-3480 (ISSN-L)
- 1099-1492
- OpenAlex
- W2758113150
- Semantic Scholar
- dba9b3e437792c297753d84a35092ea06ac599ea
- Cited by
- 574
- References
- 119
- Citations by year
- 2026: 63 · 2025: 84 · 2024: 72 · 2023: 70 · 2022: 75 · 2021: 78 · 2020: 64 · 2019: 42 · 2018: 24 · 2017: 2
- FWCI
- 25.00
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- NMR in Biomedicine (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- Utrecht University Repository (Utrecht University) (submittedVersion)
- Funders
- Fonds Wetenschappelijk Onderzoek (12M3116N)
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (VIDI Grant 639.072.411)
- Natural Sciences and Engineering Research Council of Canada (Discovery Grant)
- 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 MRI, fiber tractography, connectomics, structural connectome, neurosurgical planning, structural bias
- MeSH terms
- Algorithms, Brain, Humans, Image Processing, Computer-Assisted, Terminology as Topic, Diffusion Tensor Imaging, Connectome
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