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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
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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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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
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA 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
“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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA 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
“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]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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
“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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes? Based: unclearNone 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
“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.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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
Load-bearingStrength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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
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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.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
“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]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
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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.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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 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
Strength:boosted· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably 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]
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- Not matchedcites [31]
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- 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.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
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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
“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
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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
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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.
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
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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
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
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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
“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
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
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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.
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
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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.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
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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
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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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably 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
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably 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
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably 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
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✗ Based: failsProbably 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
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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)
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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
“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)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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)
Load-bearingStrength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably 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
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably 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.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Load-bearingStrength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably 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
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Flagged first, then by citations
Research works printed but not linked: 6
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- Farquharson S, 2013, Beyond Diffusion Tensor Imaging Response
- Holdsworth SJ, Pulse sequences and hardware considerations for brain diffusion MRI, NMR Biomed
Cited by
Sorted by citations
Items 301–400 of 572
Deciphering convection enhanced delivery: A paradigm shift for circumventing glioblastoma multiforme
Sikder, A., Pasupuleti, V., Khairnar, P., Phatale, V., Amulya, E., Wagh, S. S., Negi, M., Asthana, A., Raghuvanshi, R. S., Khatri, D. K., et al. (2024) · Journal of Drug Delivery Science and Technology · doi:10.1016/j.jddst.2024.106398 · Cited by 4
Smullen, D., Bagshaw, A. P., Shalev, L., Tsafrir, S., Kolodny, T., & Mevorach, C. (2025) · Scientific Reports · doi:10.1038/s41598-025-02326-y · Cited by 4
“Tractography relies on the single-voxel dMRI signal to infer the trajectories of white matter bundles connecting distant regions of the brain, and can be used to assess certain microstructural properties of these bundles (18), including fractional anisotropy (FA), and diffusivity along various axes (axial diffusivity…”
A Comparison of Skeletal Muscle Diffusion Tensor Imaging Tractography Seeding Methods
Damon, B. M., Guzman, R. A. P., Lockard, C. A., & Zhou, X. (2025) · NMR in Biomedicine · doi:10.1002/nbm.70163 · Cited by 4
“These errors are similar to the partial volume artifacts between white and gray matter that can occur in white matter tractography based on functionally derived seeding regions [33].”
Varadarajan, D., & Haldar, J. P. (2018) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2018.8363680 · Cited by 4
“The ability to infer fiber orientations from diffusion MRI (dMRI) data enables noninvasive in vivo mapping of important biological fiber pathways (a process known as diffusion tractography) [1], which in turn can provide important information for clinical applications and basic scientific research.”
Wu, Y., Hong, Y., Ahmad, S., & Yap, P. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87234-2_44 · Cited by 4
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., et al. (2022) · Utrecht University Repository (Utrecht University) · Cited by 4
Jandric, D., Parker, G. J. M., Haroon, H. A., Tomassini, V., Muhlert, N., & Lipp, I. (2022) · NeuroImage Clinical · doi:10.1016/j.nicl.2022.102995 · Cited by 4
“Tractography, which fits a diffusion tensor or alternative model at each voxel to trace the fibre orientation through the WM (Mori et al., 1999; Basser et al., 2000; Catani et al., 2002; Jeurissen et al., 2019), provides an alternative for obtaining anatomically accurate WM tracts.”
Robalo, B. M. D. B., Vlegels, N., Leemans, A., Reijmer, Y. D., & Biessels, G. J. (2022) · Brain and Behavior · doi:10.1002/brb3.2523 · Cited by 3
“Diffusion-weighted imaging (DWI) and fiber tractography enable us to map cerebral white matter pathways and reconstruct large-scale brain networks (Jeurissen et al., 2017).”
BackgroundSelf-citationGPU-accelerated connectome discovery at scale
Sreenivasan, V., Kumar, S., Pestilli, F., Talukdar, P. P., & Sridharan, D. (2022) · Nature Computational Science · doi:10.1038/s43588-022-00250-z · Cited by 4
“For each dataset, we used a four-compartment model to simulate the (1) inter-axonal, (2) intra-axonal, (3) gray matter (GM) and (4) cerebrospinal fluid (CSF) tissue response profiles.”
MethodNeuroimaging in Machine Learning for Brain Disorders
Burgos, N. (2023) · Neuromethods · doi:10.1007/978-1-0716-3195-9_8 · Cited by 4
Huang, X., Jiang, R., Peng, S., et al. (2023) · Open Life Sciences · doi:10.1515/biol-2022-0725 · Cited by 4
Zhao, Q., Holt, A. G., Spritzer, C. E., DeFrate, L. E., McNulty, A. L., & Wang, N. (2024) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-23-1355 · Cited by 4
Freire, I., Lopes, T., Afonso, S., & Pereira, D. J. (2024) · Neuroradiology · doi:10.1007/s00234-024-03362-7 · Cited by 6
Abnormal large-scale structural rich club organization in Leber's hereditary optic neuropathy
Zhang, J., Wang, L., Ding, H., Fan, K., Tian, Q., Liang, M., Sun, Z., Shi, D., & Qin, W. (2021) · NeuroImage Clinical · doi:10.1016/j.nicl.2021.102619 · Cited by 4
Diffusion MRI Connections in the Octopus Brain
Jacobs, R. E. (2022) · Experimental Neurobiology · doi:10.5607/en21047 · Cited by 4
CONN-NLM: A Novel CONNectome-Based Non-local Means Filter for PET-MRI Denoising
Sun, Z., Meikle, S. R., & Calamante, F. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.824431 · Cited by 4
“In particular, dMRI can investigate the white matter pathways of the brain through fibre tractography, allowing for comprehensive mapping of the brain structural connectivity networks (Jeurissen et al., 2019).”
BackgroundYoon, K., Archer, D. B., Clarke, M. A., Smith, S. A., Oğuz, İ., Cutter, G., Xu, J., & Bagnato, F. R. (2022) · Frontiers in Neurology · doi:10.3389/fneur.2022.811315 · Cited by 4
Editorial: Organization of the White Matter Anatomy in the Human Brain
Sarubbo, S., & Petit, L. (2019) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2019.00085 · Cited by 4
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. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.07.20.500818 · Cited by 4
“, 2021), which is the primary modality used to study the structural connections of the human brain in vivo (Jeurissen et al., 2019).”
BackgroundDeep Learning: A Primer for Neurosurgeons
Hong-xi, Y., Yuwen, C., Cheng, X., Fan, H., Wang, X., & Ge, Z. (2024) · Advances in experimental medicine and biology · doi:10.1007/978-3-031-64892-2_4 · Cited by 4
Hu, L., Zhong, Y.-Y., Wang, P., Liu, L., Cao, X., Sun, L., Cao, Q., Yang, L., Qian, Y., Wang, Y.-F., et al. (2025) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2025.02.028 · Cited by 6
Alms, C., & Eseonu, C. I. (2025) · Operative Neurosurgery · doi:10.1227/ons.0000000000001622 · Cited by 3
McMaster, E. M., Newlin, N. R., Cho, C. Y., Rudravaram, G., Saunders, A. M., Krishnan, A. R., Remedios, L. W., Kim, M. E., Xu, H., Schilling, K. G., et al. (2025) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2025.110539 · Cited by 3
“…variability can be attributed to the selected tractography algorithm, and results generated with classical FOD-based probabilistic methods [43] or deterministic approaches [13,44] could provide different insight regarding the phenomena driving the effect of scan-rescan variability in our results.”
Anisotropic light propagation in human brain white matter
Pini, E., Meo, D. D., Costantini, I., et al. (2025) · Neurophotonics · doi:10.1117/1.nph.12.4.045003 · Cited by 3
“14 However, anisotropy in diffuse optics remains largely disregarded, despite its potential impact on optical neuroimaging and optogenetics, with a lack of experimental and numerical validation for anisotropic scattering coefficients in human white matter.”
Brain-Behavior Associations: From Discovery to Clinical Application
Iannone, S., Samardzija, A., Simon, A. J., Parra, F., & Constable, R. T. (2026) · Biological Psychiatry · doi:10.1016/j.biopsych.2026.02.020 · Cited by 5
Automation of Cranial Nerve Tractography by Filtering Tractograms for Skull Base Surgery
Decroocq, M., Ligneris, M. D., Poquillon, T., Vincent, M., Aubert, M., Jacquesson, T., & Frindel, C. (2022) · Frontiers in Neuroimaging · doi:10.3389/fnimg.2022.838483 · Cited by 3
“This process named “tractography” can be repeated from different starting points in a region of interest to produce an estimate of the structural connections of this region—referred to as a “tractogram” (Jeurissen et al., 2019).”
BackgroundResultLitmanovitch, E., Geva, R., Leshem, A., Lezinger, M., Heyman, E., Gidron, M., Yarmolovsky, J., Sasson, E., Tal, S., & Rachmiel, M. (2023) · Frontiers in Endocrinology · doi:10.3389/fendo.2023.1141085 · Cited by 3
A Hybrid Routing Pattern in Human Brain Structural Network Revealed By Evolutionary Computation
Liang, Q., Ma, J., Chen, X., Lin, Q., Shu, N., Dai, Z., & Lin, Y. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3351907 · Cited by 4
“The turning-angle threshold was set so to ensure that fiber tracts did not exhibit excessive bending or twisting that are considered biologically implausible based on the nature of local smoothness of WM bundles [109], [110].”
BackgroundExploring the pathophysiology of restless leg syndrome: focus on white matter structure and function
Wang, Z., Li, Z., Wang, J., Gao, J., & Li, Y. (2024) · Postgraduate Medical Journal · doi:10.1093/postmj/qgae156 · Cited by 3
Fine-scale striatal parcellation using diffusion MRI tractography and graph neural networks
Gao, J., Liu, M., Qian, M., Tang, H., Wang, J., Ma, L., Li, Y., Dai, X., Wang, Z., Lu, F., et al. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103482 · Cited by 3
The tractographer’s dilemma: understanding sources of variability in tractography
Dell’Acqua, F., Leemans, A., Zhang, F., Rheault, F., Farquharson, S., & Luca, A. D. (2025) · Brain Structure and Function · doi:10.1007/s00429-025-02964-y · Cited by 3
“However, this can be challenging due to the complexity of the processing pipeline underlying fiber tractography (Jeurissen et al. 2019 ).”
Self-citationFree water elimination tractometry for aging brains
Chang, K., Burke, L., LaPiana, N., Howlett, B., Hunt, D., Dezelar, M., Andre, J. B., Curl, P. K., Ralston, J. D., Rokem, A., et al. (2025) · Imaging Neuroscience · doi:10.1162/imag.a.991 · Cited by 3
Tree representations of brain structural connectivity via persistent homology
Li, D., Nguyen, P. H., Zhang, Z., & Dunson, D. B. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1200373 · Cited by 3
“Applying tractography to the dMRI data, we can construct a “tractogram” of 3D trajectories of white fiber tracts (Jeurissen et al., 2019).”
BackgroundMethodStudy of structural network connectivity using DTI tractography in insomnia disorder
Rostampour, M., Gharaylou, Z., Rostampour, A., Shahbodaghy, F., Zarei, M., Fadaei, R., & Khazaie, H. (2023) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2023.111730 · Cited by 3
Filipiak, P., Sajitha, T. A., Shepherd, T. M., Clarke, K., Goldman, H. G., Placantonakis, D. G., Zhang, J., Chan, K. C., Boada, F. E., & Baete, S. H. (2023) · Magnetic Resonance in Medicine · doi:10.1002/mrm.29911 · Cited by 2
“3,46 Their reconstruction in vivo poses a similar challenge 3,40 to the mouse optic chiasm studied here.”
BackgroundMethodHori, H., Taira, T., Abe, K., & Hori, T. (2023) · Journal of neurosurgery · doi:10.3171/2023.8.jns231459 · Cited by 3
Polonara, G., Mascioli, G., Salvolini, U., Paggi, A., Manzoni, T., & Mara Fabri, C. (2023) · Symmetry · doi:10.3390/sym15122188 · Cited by 3
Constrained spherical deconvolution on diffusion-weighted images of dolphin brains
Gerussi, T., Graïc, J., Cozzi, B., et al. (2024) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2024.02.002 · Cited by 3
“However, DTI ’ s main drawback is that it is insensitive to complex fiber architectures, specifically because it is unable to identify crossing fibers which limits its application to study complex neural pathways [1,11,19].”
BackgroundDiffusion Tensor Imaging in Alzheimer’s Studies
Ruiz‐Rizzo, A. L., Finke, K., & Archila-Meléndez, M. E. (2024) · Methods in molecular biology · doi:10.1007/978-1-0716-3774-6_8 · Cited by 3
A Reproducible Pipeline for Parcellation of the Anterior Limb of the Internal Capsule
Wong, K. S., Braun, H. C., Liu, Z., Bullock, D. N., Palnitkar, T., Patriat, R., Chandrasekaran, J., Brenny, S., Johnson, M. D., Widge, A. S., et al. (2024) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2024.07.008 · Cited by 5
Mapping short association fibre connectivity up to V3 in the human brain in vivo
Attar, F. M., Kirilina, E. P., Haenelt, D., Trampel, R., Pine, K., Edwards, L., & Weiskopf, N. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae279 · Cited by 3
“A joint tracer injection and DWI tractography study performed in macaque brain showed the absence of false positive connections (Maier-Hein et al. 2017; Jeurissen et al. 2019) for path lengths ≤ 20 mm (Donahue et al. 2016).”
MethodFlinkenflügel, K., Borgers, T., Klug, M., Mummendey, M. M., Leehr, E. J., Meinert, S., Gruber, M., Repple, J., Kircher, T. T. J., Opel, N., et al. (2025) · Neuropsychopharmacology · doi:10.1038/s41386-025-02070-x · Cited by 5
Delineation of the trigeminal-lateral parabrachial-central amygdala tract in humans
Kaya, B., Khalil, O., Abssy, S. S., Cioffi, I., & Moayedi, M. (2025) · Imaging Neuroscience · doi:10.1162/imag_a_00567 · Cited by 3
“…participant had a total of eight tractograms: within one hemisphere, fibres were constructed in both directions (i.e., Origin- to-Target; Target- to-Origin) for each circuit to account for directional biases during acquisition ( Van Essen et al., 2014) and fibre fanning ( Jeurissen et al., 2019).”
Unsupervised Detection of White Matter Fiber Bundles with Stochastic Neural Networks
Dayan, M., Katsageorgiou, V., Dodero, L., et al. (2018) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2018.8451498 · Cited by 3
“Diffusion MRI (dMRI) offers the unique ability to reconstruct brain white matter (WM) tracts in-vivo [1].”
BackgroundMelis, M., Blommaert, J., Radwan, A., Smeets, A., der Gucht, K. V., Deprez, S., & Sunaert, S. G. (2023) · Mindfulness · doi:10.1007/s12671-023-02140-5 · Cited by 3
“Additionally, MBIs might impact CRCI more directly by inducing functional and structural brain changes in areas related to internal and external attention, awareness, emotion regulation, memory, and executive function (Fox et al., 2016; Gotink et al., 2016; Marchand, 2014; Melis et al., 2022; Mooneyham et al., 2016; S…”
InfluentialBrain network analyses in clinical neuroscience
Sokolov, A. A., Granziera, C., Fischi-Gómez, E., Preti, M. G., Ryvlin, P., Ville, D. V. D., & Friston, K. J. (2019) · Swiss Archives of Neurology Psychiatry and Psychotherapy · doi:10.4414/sanp.2019.03074 · Cited by 3
Non–invasive inference of information flow using diffusion MRI, functional MRI, and MEG
Deslauriers‐Gauthier, S., Costantini, I., & Rachid Deriche, B. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab95ec · Cited by 3
“Diffusion MRI allows us to map structural brain connectivity in vivo and non-invasively via tractography (Jeurissen et al., 2019).”
BackgroundFrontiers of microstructural imaging with diffusion MRI
Jensen, J. H. (2021) · Advances in magnetic resonance technology and applications · doi:10.1016/b978-0-12-816633-8.00007-7 · Cited by 3
The Case for Optimized Edge-Centric Tractography at Scale
Moon, J. Y., Mukherjee, P., Madduri, R., Markowitz, A. J., Cai, L. T., Palacios, E. M., Manley, G. T., & Bremer, P. (2022) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2022.752471 · Cited by 3
Processing the diffusion-weighted magnetic resonance imaging of the PING datasetPreprint
Al‐Sharif, N., St‐Onge, E., Theaud, G., Evans, A. C., & Descoteaux, M. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.24.396549 · Cited by 3
“These challenges consist of low spatial resolution, noisy acquisition, MRI gradient-related artifacts and fiber organisation ambiguity (crossing, kissing, fanning) [Jbabdi et al., 2015; Dell’Acqua and Tournier, 2019; Jeurissen et al., 2019].”
BackgroundMethodSelf-citationSurface-based tracking for short association fibre tractographyPreprint
Shastin, D., Genc, S., Parker, G. D., Koller, K., Tax, C. M. W., Evans, J., Hamandi, K., Gray, W. P., Jones, D. K., & Chamberland, M. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.05.07.443084 · Cited by 3
The Digital Twin in Neuroscience: From Theory to Tailored TherapyPreprint
Fekonja, L. S., Schenk, R., Schröder, E., et al. (2024) · doi:10.31234/osf.io/fnpcz · Cited by 3
Little, G. E., Poirier, C., Boré, A., Parent, M., Petit, L., & Descoteaux, M. (2025) · Human Brain Mapping · doi:10.1002/hbm.70245 · Cited by 3
“Although dMRI and tractography are typically considered techniques for studying the microstructure and connectivity of WM, previous work has demonstrated that anisotropy of the diffusion signal can also be detected in the cortical GM (Calamante et al. 2018; McNab et al. 2013; Truong et al. 2014).”
Self-citationT2 contrast variation in human brain at 7 T and its potential contributors
Wang, Y., van Gelderen, P., Donadieu, M., Liu, J., de Zwart, J. A., Zhou, J., Nair, G., Reich, D. S., & Duyn, J. H. (2025) · Imaging Neuroscience · doi:10.1162/imag.a.67 · Cited by 2
“A confounding factor with our fiber-tract specific R 2 analysis is the variation in fiber diameter and orientation within regions of interest ( Jeurissen et al., 2019).”
Vavassori, L., Rheault, F., Avesani, P., BENEDICTIS, A. D., Corsini, F., Annicchiarico, L., Zigiotto, L., Rozzanigo, U., Barbareschi, M., Petit, L., et al. (2025) · Communications Biology · doi:10.1038/s42003-025-08774-6 · Cited by 2
Rauland, A., Meisler, S. L., Alexander‐Bloch, A. F., Bagautdinova, J., Baller, E. B., Gur, R. E., Gur, R. C., Luo, A. C., Moore, T. M., Popovych, O. V., et al. (2025) · Human Brain Mapping · doi:10.1002/hbm.70429 · Cited by 2
Detailed connectomic cluster resource for white matter mapping from ultra-high-field diffusion MRI
Yip, H., He, Y., Xie, Y. S., Zhang, F., & Wu, Y. (2026) · NeuroImage · doi:10.1016/j.neuroimage.2026.121704 · Cited by 2
Diffusion tractography outside the brain: the road less travelled
Schilling, K. G., Teh, I., Cohen‐Adad, J., Dortch, R. D., Ibrahim, I., Wang, N., Damon, B. M., Cochran, R. L., & Leemans, A. (2026) · Brain Structure and Function · doi:10.1007/s00429-025-03062-9 · Cited by 2
Self-citationManifold-aware synthesis of high-resolution diffusion from structural imaging
Anctil-Robitaille, B., Théberge, A., Jodoin, P., Descoteaux, M., Desrosiers, C., & Lombaert, H. (2022) · Frontiers in Neuroimaging · doi:10.3389/fnimg.2022.930496 · Cited by 2
Self-citationHabas, C. (2021) · Bulletin de l Académie Nationale de Médecine · doi:10.1016/j.banm.2021.06.022 · Cited by 2
Civier, O., Sourty, M., & Calamante, F. (2023) · Scientific Reports · doi:10.1038/s41598-022-17213-z · Cited by 3
Verschuur, A. S., Tax, C. M. W., Boomsma, M. F., Carlson, H. L., van Wezel‐Meijler, G., King, R., Leemans, A., & Leijser, L. M. (2024) · Frontiers in Radiology · doi:10.3389/fradi.2024.1416672 · Cited by 3
Self-citationBounding tractogram redundancy
Persson, S., & Moreno, R. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1403804 · Cited by 2
Feng, Y., Xie, L., Wang, J., Tian, Q., He, J., Zeng, Q., & Gao, F. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120766 · Cited by 2
“GT offers improved agreement with the measured dMRI data as well as increased robustness to noise and imaging artifacts [35].”
Current challenges and opportunities for tractography
Rheault, F., Poulin, P., Caron, A. V., St‐Onge, E., Schilling, K. G., Petit, L., Dell’Acqua, F., Leemans, A., & Descoteaux, M. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-818894-1.00037-9 · Cited by 2
Self-citationDeterministic fiber tractography
Leemans, A., Dell’Acqua, F., & Descoteaux, M. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-818894-1.00029-x · Cited by 2
Self-citationDurantel, T., Girard, G., Caruyer, E., Commowick, O., & Coloigner, J. (2025) · PLoS ONE · doi:10.1371/journal.pone.0304449 · Cited by 2
“Modern methods have then sought to estimate the fiber dispersion estimation using probabilistic methods [5], [13], [14].”
den Bergh, B. R. H. V., Sleurs, C., Geusens, B., Emsell, L., Sunaert, S. G., & Billiet, T. (2025) · Brain and Cognition · doi:10.1016/j.bandc.2025.106319 · Cited by 3
Unified Framework for Oculomotor Nerve Reconstruction: Tractography‐Based Anatomical Assessment
Huang, J., Zeng, Q., Wu, Y., et al. (2025) · Journal of Neuroimaging · doi:10.1111/jon.70052 · Cited by 2
Radetz, A., & Groppa, S. (2021) · Neuromethods · doi:10.1007/978-1-0716-1213-2_3 · Cited by 2
Normal development of the brain: a survey of joint structural–functional brain studies
Namiranian, R., Malakshan, S. R., Moghaddam, H. A., Khadem, A. A., & Jafari, R. (2022) · Reviews in the Neurosciences · doi:10.1515/revneuro-2022-0017 · Cited by 4
Model Averaging and Bootstrap Consensus‐based Uncertainty Reduction in Diffusion MRI Tractography
Gruen, J., van der Voort, G., & Schultz, T. (2022) · Computer Graphics Forum · doi:10.1111/cgf.14724 · Cited by 2
“There is a substantial body of literature on algorithms for diffusion MRI tractography [JDML19], and many of them were first introduced in visualization venues [WKL99, ZDL03, HS05, SS08].”
BackgroundBrain fiber structure estimation based on principal component analysis and RINLM filter
Zhu, Y., & Wang, Y. (2023) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-023-02972-2 · Cited by 2
Multiscale cortical parcellation based on geodesic distance and hierarchical clustering
Prieto, Y., Molina, J., Otero, M., et al. (2023) · International Symposium on Medical Information Processing and Analysis (SIPAIM) · doi:10.1109/sipaim56729.2023.10373421 · Cited by 2
“These techniques include diffusion-weighted magnetic resonance imaging (dMRI) [3], [4] and resting-state functional MRI (rfMRI) [5].”
MethodA taxonomic guide to diffusion MRI tractography visualization tools
Laamoumi, M., Hendriks, T., & Chamberland, M. (2024) · NMR in Biomedicine · doi:10.1002/nbm.5267 · Cited by 2
“20 This review process will focus on visualization tools designed for tractography data and the related challenges.”
InfluentialParcellation-Free prediction of task fMRI activations from dMRI tractography
Khatami, M., Wehler, R., & Schultz, T. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102317 · Cited by 2
Tractography in the presence of white matter lesions in multiple sclerosisPreprint
Lipp, I., Parker, G. D., Tallantyre, E. C., Goodall, A., Grama, S., Patitucci, E., Heveron, P., Tomassini, V., & Jones, D. K. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/559708 · Cited by 2
“Anatomical localisation of white matter tracts in vivo currently relies on diffusion-weighted MRI and fibre tracking (Catani et al., 2002; Jeurissen and Leemans, 2017).”
BackgroundSyversen, I. F., Reznik, D., Witter, M. P., Kobro‐Flatmoen, A., Schröder, T. N., & Doeller, C. F. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.23.520976 · Cited by 2
What has brain diffusion MRI taught us about chronic pain: a narrative reviewPreprint
Bautin, P., Fortier, M.-A., Sean, M., Little, G. E., Martel, M., Descoteaux, M., Léonard, G., & Tétreault, P. (2023) · medRxiv · doi:10.1101/2023.03.03.23286579 · Cited by 2
Self-citationSeo, J., Lee, E., Kim, B., Kim, G., Joo, Y. Y., & Cha, J. (2026) · Imaging Neuroscience · doi:10.1101/2023.07.24.23293075 · Cited by 2
Personalized circuit modeling captures variation in cortical functional connectivityPreprint
Cooper, R. A., Demirtaş, M., Burt, J. B., Howell, A. M., Ji, J., Repovš, G., Sotiropoulos, S. N., Anticevic, A., & Murray, J. D. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.19.581085 · Cited by 2
Ellis, D. G., Warren, D. E., Garlinghouse, M. A., & Aizenberg, M. R. (2024) · Preprints.org · doi:10.20944/preprints202405.0739.v1 · Cited by 1
Maldegem, M. V., Vohryzek, J., Atasoy, S., Alnagger, N., Cardone, P., Bonhomme, V. L., Vanhaudenhuyse, A., Athena Demertzi, A., Jaquet, O., Bahri, M. A., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.20.599885 · Cited by 2
The human claustrum initiates networks for externally and internally driven task demandsPreprint
Stewart, B. W., Cormie, M. A., Keaser, M. L., Moayedi, M., Mathur, B. N., & Seminowicz, D. A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.26.661853 · Cited by 2
“For each connection (e.g., LCL-ACC), two tractograms were computed, one for each direction, to control for directional biases in dMRI acquisition (Van Essen et al., 2013) and fiber fanning (Jeurissen et al., 2019).”
Network comparisons and their applications in connectomics
D’Souza, N. S., & Venkataraman, A. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-85280-7.00009-9 · Cited by 1
Balestrino, M., Adriano, E., Alì, P. A., & Pardini, M. (2024) · Brain Sciences · doi:10.3390/brainsci14040337 · Cited by 1
“We used MRI tractography [19] in two patients (28 and 18 y.o., respectively) to investigate if creatine deficiency is accompanied by changes in the arcuate fasciculus of the left hemisphere.”
MethodIskov, N. B., Olsen, A. S., Madsen, K. H., et al. (2024) · Network Neuroscience · doi:10.1162/netn_a_00399 · Cited by 1
White‐Matter Structural Connectivity and Alzheimer's Disease: A Mendelian Randomization Study
Liu, S., & Geng, D. (2025) · Brain and Behavior · doi:10.1002/brb3.70286 · Cited by 2
Randomly COMMITting: Iterative Convex Optimization for Microstructure-Informed Tractography
Persson, S., Wan, X., & Rodrigo Moreno, A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-86920-4_5 · Cited by 1
Gramegna, L., Zoli, M., Sighinolfi, G., Carrozzi, A., Vornetti, G., Cantoni, E., Guaraldi, F., Asioli, S., Tonon, C., Manners, D., et al. (2025) · NeuroImage: Clinical · doi:10.1016/j.nicl.2025.103827 · Cited by 1
A peridynamic biochemo-mechanical model for Alzheimer’s Disease
Qiu, L., Wang, X., Wei, Z. C., & Tong, Q. (2025) · Engineering Analysis with Boundary Elements · doi:10.1016/j.enganabound.2025.106329 · Cited by 1
Chen, T., Wang, P., Gao, E., Bai, J., Zhao, G., Zhao, K., Zhao, G., Zhang, Y., Zhang, Y., Wang, M., et al. (2025) · Frontiers in Neuroscience · doi:10.3389/fnins.2025.1605786 · Cited by 1
“Diffusion tensor imaging (DTI) has gained widespread clinical application in non-invasively detecting microstructural alterations in the CST (Jeurissen et al., 2019; Yang et al., 2021; Henderson et al., 2020).”
Anterograde degeneration along the visual pathway following optic nerve injury: a review
Chapelle, A.-C. (2025) · Frontiers in Neurology · doi:10.3389/fneur.2025.1623798 · Cited by 1
“Using techniques such as DTI and VBM, researchers have observed decreased gray and white matter volumes in these regions (32, 33).”
InfluentialJellema, P. E. J., Kersbergen, K. J., Hoving, E. W., Lequin, M. H., van Baarsen, K. M., Lindner, T., Nieuwenhuis, W. P., Luca, A. D., & Wijnen, J. P. (2025) · Magnetic Resonance Materials in Physics Biology and Medicine · doi:10.1007/s10334-025-01297-5 · Cited by 1
“To address this, we acquired multi-shell dMRI data, which is more suited for reconstruction of multiple fiber populations (e.g., crossing fibers) within a single voxel [42].”
Whole-brain reconstruction of fiber tracts based on cytoarchitectonic organization
Zhang, Y., Song, T., Yang, C., Shen, Y. X., Yang, Y., Hu, X., Zhao, R., Wang, W., Wang, X., Liu, H., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02865-2 · Cited by 2
“3 and 4 and Supplementary Note 1).”
Dor, N. B., Sighinolfi, G., Rosetti, V., et al. (2026) · Life · doi:10.3390/life16010115 · Cited by 1
Exploring the 3D architecture of brain tissue using digital holographic microscopy
Scheidt, D., Arzola, A. V., García, L. D. C., et al. (2026) · Biomedical Optics Express · doi:10.1364/boe.578659 · Cited by 1
“In particular, crossing fibers are a common challenge in brain imaging, including diffusion MRI, where tractography algorithms attempt to infer fiber pathways to map connectivity between brain areas at the microscale [18,19].”
Cavallo, M., Ricchi, M., Axford, A., Yeung, K., McGing, J., Brizi, L., Tyler, D. J., Testa, C. M., & Grist, J. T. (2026) · Scientific Reports · doi:10.1038/s41598-026-38964-z · Cited by 1
Filipiak, P., Shepherd, T. M., Clarke, K., Ressa, G., Placantonakis, D. G., Boada, F. E., & Baete, S. H. (2026) · Magnetic Resonance in Medicine · doi:10.1002/mrm.70314 · Cited by 2
“Despite suboptimal outcomes, the variant of ODF-FP with a fixed low 𝜇 may help to streamline the fiber reconstruction FIGURE 8 Box plots of the relative overlap between tractography of (A) AF—Arcuate Fasciculus, (B) SLF3—Superior Longitudinal Fasciculus III, (C) FAT—Frontal Aslant Tract, and the cortical area activate…”
InfluentialUnilateral inferior olivary hypertrophy in a patient with primary progressive apraxia of speech
Hossain, N., Gatto, R. G., Ghayal, N. B., Dickson, D. W., Whitwell, J. L., & Josephs, K. A. (2026) · Neuroimage Reports · doi:10.1016/j.ynirp.2026.100352 · Cited by 1
“Therefore, DTI analysis allowed us to further explore the white matter microstructure related to speech and language, given that our patient initially presented with PPAOS (Jeurissen et al., 2019; Smits et al., 2014).”
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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