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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
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]
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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]
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plaindirect- Asserted: passes — The authors put it forward as true.
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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.
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- 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.
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]
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boosteddirect- Asserted: passes — The authors put it forward as true.
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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
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
“By using the fODF as a propagator, tracking can be performed even in white matter regions with complex fiber architecture.32,36”
§ Diffusion MRI fiber tractography of the brain > 3.1 | Local fiber orientation estimation
What the sources say
- Not matchedcites [32]
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- Not matchedcites [36]
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plaindirect- 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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- 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
“As such, even advanced fiber-tracking approaches will still be subject to modeling errors.”
§ Diffusion MRI fiber tractography of the brain > 3.1 | Local fiber orientation estimation
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In the paper
“This means that distinctly different fiber configurations such as bending, fanning, crossing, and kissing of fibers can all give rise to the same MRI measurements, making it impossible to distinguish between these cases at the local voxel level.26”
§ Diffusion MRI fiber tractography of the brain > 3.1 | Local fiber orientation estimation
What the sources say
- Not matchedcites [26]
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plaindirect- Asserted: passes — The authors put it forward as true.
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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
In the paper
“Farquharson et al. found that roughly 98% of the 160 neurosurgical tractography studies published to date reported the use of DTI-based tractography.6”
§ Diffusion MRI fiber tractography of the brain > 3.1 | Local fiber orientation estimation
What the sources say
- Not matchedcites [6]
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In the paper
“For tractography methods based on high-order reconstruction algorithms, tracking is usually terminated when the local fiber densities as characterized by the fODF along the current tracking orientation fall below a certain threshold, as this indicates a low probability that the local fiber orientation is supported by the data.32,36,47”
§ Diffusion MRI fiber tractography of the brain > 3.4 | Seed point selection schemes
What the sources say
- Not matchedcites [32]
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- Not matchedcites [47]
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hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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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
In the paper
“Related to this issue is the fact that fiber tracking is typically non-commutative: when tracking from a seed to a target, it cannot be guaranteed that there will be a path back to the seed when launching tracking at the target.10”
§ Diffusion MRI fiber tractography of the brain > 5.3 | Practical consequences of integration errors and error propagation
What the sources say
- Not matchedcites [10]
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In the paper
“The former strategy has the advantage that the tracking is guaranteed to probe the whole of the white matter.”
§ Diffusion MRI fiber tractography of the brain > 3.4 | Seed point selection schemes
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In the paper
“The rationale behind this criterion is that regions of low FA tend to be associated with high uncertainty in the principal diffusion direction, and therefore a large potential error for the next streamline step.”
§ Diffusion MRI fiber tractography of the brain > 3.4 | Seed point selection schemes
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In the paper
“On the same data, however, they are unlikely to outperform fODF tracking approaches.95,98,99”
§ Diffusion MRI fiber tractography of the brain > 5.1 | Taking the path of least hindrance
What the sources say
- Not matchedcites [95]
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In the paper
“As demonstrated succinctly by Jones et al., 10 both deterministic and probabilistic fiber tracking are more likely to reconstruct tracks that are short and straight and that do not encounter regions of complex fiber configuration (such as crossing and fanning).”
§ Diffusion MRI fiber tractography of the brain > 5.4 | The slippery slope of quantification
What the sources say
- Not matchedcites Jones et al., 10
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In the paper
“Note, however, that this approach will typically result in an overdefinition of long large white matter bundles, as they are probed by many more seed points.[44][45][46]”
§ Diffusion MRI fiber tractography of the brain > 3.4 | Seed point selection schemes
What the sources say
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In the paper
“Probabilistic uODF tracking will provide an indication of how precise the fiber-tracking results are, with more reproducible fiber trajectories exhibiting higher visitation counts.”
§ Diffusion MRI fiber tractography of the brain > 5.2 | The distinct flavors of probabilistic fiber tracking
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In the paper
“Additionally, the overlapping of diffusion from different fiber populations is known to introduce a bias in the orientations of least hindrance.90,91”
§ Diffusion MRI fiber tractography of the brain > 5.1 | Taking the path of least hindrance
What the sources say
- Not matchedcites [90]
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In the paper
“However, this approach leads to much greater interpolation errors than approaches that perform a smooth interpolation between grid points 39 (see Figure 4).”
§ Diffusion MRI fiber tractography of the brain > 3.3 | Interpolation methods
What the sources say
- Not matchedcites [39]
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In the paper
“However, the local fiber orientation estimates are subject to errors due to imaging noise and artifacts, and also due to local model inaccuracies and streamline integration errors, all of which will be reflected in the final global fiber tracks.39,[63][64][65]”
§ Diffusion MRI fiber tractography of the brain > 4.1 | Deterministic versus probabilistic approaches
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plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“This technique has been successfully used to isolate many different fiber tracts and as such it is sometimes referred to as virtual dissection 48 (see Figure 6).”
§ Diffusion MRI fiber tractography of the brain > 3.6 | Virtual dissection
What the sources say
- Not matchedcites [48]
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plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Under the same circumstances (disregarding all tractography biases) and when additionally ignoring the effects of noise, the fODF tractography approach tries to assess the proportion of white matter fibers that reach Region B.”
§ Diffusion MRI fiber tractography of the brain > 5.2 | The distinct flavors of probabilistic fiber tracking
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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 simple issues prohibit straightforward quantification of both deterministic and probabilistic fiber-tracking results.”
§ Diffusion MRI fiber tractography of the brain > 5.4 | The slippery slope of quantification
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In the paper
“However, due to the nature of diffusion, the proportion of diffusion as a function of orientation (diffusion orientation distribution function, dODF) does not necessarily reflect the underlying fODF.89”
§ Diffusion MRI fiber tractography of the brain > 5.1 | Taking the path of least hindrance
What the sources say
- Not matchedcites [89]
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plain- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: Water molecules are most likely to diffuse along the fiber orientation, but diffusion along other, even perpendicular, orientations is still common.
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In the paper
“The main problem with global methods is that they rely on stochastic optimization procedures and consequently do not guarantee convergence to a globally optimal solution.74”
§ Diffusion MRI fiber tractography of the brain > FIGURE 7
What the sources say
- Not matchedcites [74]
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plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“These issues can only be addressed properly by the introduction of a suitable model for diffusion in white matter.89,92”
§ Diffusion MRI fiber tractography of the brain > 5.1 | Taking the path of least hindrance
What the sources say
- Not matchedcites [89]
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plaindirect- Asserted: passes — The authors put it forward as true.
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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
“When seeding from Region A and counting the tracks reaching Region B, disregarding all tractography biases, the uODF approach allows its users to assess the probability that Region B is on the dominant pathway.”
§ Diffusion MRI fiber tractography of the brain > 5.2 | The distinct flavors of probabilistic fiber tracking
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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
“When tracking a specific fiber tract, newcomers to the field are often surprised to see that streamline fiber-tracking results differ substantially based on where along the tract they place their seed points (see Figure 10 for an example in the cingulum bundle).”
§ Diffusion MRI fiber tractography of the brain > 5.3 | Practical consequences of integration errors and error propagation
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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
“With conventional tracking, significant numbers of fiber tracks are terminating prematurely within white matter (e.g. in regions of complex fiber architecture) or at the interface with cerebrospinal fluid (left column).”
§ Diffusion MRI fiber tractography of the brain > 5.4.2 | Connection strength(s)
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In the paper
“The tendency of fiber tracking to reach the crowns, rather than the walls, of the gyri is mostly caused by failing to account for ambiguous local geometries.”
§ Diffusion MRI fiber tractography of the brain > 6.2 | Tracking near the cortex
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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
“Unfortunately, using anatomical constraints does not necessarily mean that we can now rely on fiber tracking to provide connection strengths.”
§ 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.
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 the above issues of local modeling errors and ambiguous local geometries are closely tied to the spatial resolution available in dMRI.”
§ Diffusion MRI fiber tractography of the brain > 6.3 | Spatial resolution
boostedcorrelational- 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.
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
“Post-processing of this tractogram does not remedy these issues (middle column).”
§ 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.
In the paper
“Recent studies have shown that, by simply extending a commonly used white matter model with a gray matter and cerebrospinal fluid component, local fiber orientation estimation and subsequent fiber tracking within the cortical folds can be dramatically improved 47 (see Figure 13).”
§ 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.
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.
- 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, there is currently no clear experimental evidence to support the superiority of global tracking with respect to ambiguous geometries.”
§ 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: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says 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
“While rejecting biologically implausible connections avoids these clearly false connections, it is not guaranteed that this strategy will provide us uniquely with true connections.”
§ 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.
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
“This leads us to the inconvenient truth that current fiber-tracking techniques cannot be used to reliably quantify long-range structural connectivity.”
§ 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.
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, track-density maps typically appear to be of high signal-to-noise ratio (SNR), even when calculated from low-quality data.”
§ Diffusion MRI fiber tractography of the brain > 5.4.1 | Voxel-wise track counts
hedged- 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.
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, reliable tracking near the cortex and in the cortical folds is challenging.”
§ Diffusion MRI fiber tractography of the brain > 6.2 | Tracking near the cortex
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.
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
“Second, TDI is sensitive to the previously mentioned biases towards simple fiber trajectories, potentially artificially reducing fiber densities near high-curvature or high-complexity regions.”
§ Diffusion MRI fiber tractography of the brain > 5.4.1 | Voxel-wise track counts
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.
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, the fundamental problem remains: in general, there is no mechanism in place that will ensure that local virtual fiber counts are consistent with the actual local raw dMRI signal.”
§ Diffusion MRI fiber tractography of the brain > 5.4.1 | Voxel-wise track counts
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.
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
“Indeed, while filtering techniques or global tracking can significantly improve the quantitative characteristics of TDI, Calamante et al. recently demonstrated that apparent fiber densities obtained directly through local modeling, without any fiber tracking, are still vastly superior in terms of interand intra-subject variability compared with track-density estimates obtained with fiber tracking.”
§ Diffusion MRI fiber tractography of the brain > 5.4.1 | Voxel-wise track counts
What the sources say
- Not matchedcites Calamante et al.
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.
boostedcorrelational- 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.
- 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
“The biggest issue with the quantification of connection strengths lies in the well-known bias of tractography towards simple trajectories.”
§ Diffusion MRI fiber tractography of the brain > 5.4.2 | Connection strength(s)
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.
In the paper
“While this approach makes perfect sense in a true crossing configuration, in the case of kissing fibers or a sharp bend it will potentially result in both false-positive and false-negative trajectories.110”
§ Diffusion MRI fiber tractography of the brain > 6.1 | Proper treatment of ambiguous local geometries
What the sources say
- Not matchedcites [110]
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.
- 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
“Unfortunately, tractogram post-processing techniques or the concept of global tracking cannot come to the rescue this time, as they only address local track-density issues.”
§ 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.
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
“Conversely, short tracks will be sampled by only a limited number of seed points, artificially decreasing their local track counts.”
§ 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.
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
“Such simple, but very strong, biological priors have been shown to massively increase the plausibility of long-range fiber connections, 44,46,108 especially when used together with filtering techniques or global tracking that ensure that the fiber tractogram accurately predicts the raw dMRI signal.”
§ Diffusion MRI fiber tractography of the brain > 5.4.2 | Connection strength(s)
What the sources say
- Not matchedcites [44]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [46]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [108]
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.
- 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
“Note that this local support or continuity does not have to be based purely on the trajectories themselves (i.e.geometric), 26 but can also be based on the microstructural features underlying the current trajectory.111,112”
§ Diffusion MRI fiber tractography of the brain > 6.1 | Proper treatment of ambiguous local geometries
What the sources say
- Not matchedcites [111]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [112]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaincorrelational- 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.
- Not matched
In the paper
“Indeed, for local track densities to make any sense quantitatively, it should be possible to map them back onto the raw dMRI data using a model that relates the fibers to the dMRI signal with minimal residuals (up to a global scaling factor).45”
§ 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.
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.
- 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 is easy to see that, when seed points are distributed uniformly throughout the brain, longer tracks will be sampled by a larger number of seed points, artificially increasing their local track counts.”
§ Diffusion MRI fiber tractography of the brain > 5.4.1 | Voxel-wise track counts
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.
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 clear that filtering can significantly improve the quantitative characteristics of TDI (middle row).”
§ Diffusion MRI fiber tractography of the brain > 5.4.1 | Voxel-wise track counts
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.
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
“Failing to account for this asymmetry could lead to a fanning-out of the trajectories in both directions, potentially introducing false positives as fibers move away from the cortex.”
§ Diffusion MRI fiber tractography of the brain > 6.1 | Proper treatment of ambiguous local geometries
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.
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 inability of fiber tracking to properly penetrate the gyri can mostly be ascribed to large modeling errors in the local fiber orientation estimation as one approaches the gray matter.”
§ Diffusion MRI fiber tractography of the brain > 6.2 | Tracking near the cortex
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.
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 fact, a recent simulation study has shown that fiber tracking will produce non-existing fiber bundles (that were never part of the set of ground-truth fiber bundles) even with biological priors in place.13,109”
§ Diffusion MRI fiber tractography of the brain > 5.4.2 | Connection strength(s)
What the sources say
- Not matchedcites [13]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [109]
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.
- 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 order to prevent tracks from stopping prematurely, the only available mechanism is to introduce strong biologically realistic priors.”
§ Diffusion MRI fiber tractography of the brain > 5.4.2 | Connection strength(s)
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.
In the paper
“Indeed, as fibers enter the gyrus, the geometry of the cortical folds requires fairly sharp turns for them to cover the whole of the cortical surface and, as explained before, when faced with the option of continuing a straight path or taking a sharp turn, most fiber-tracking algorithms will favor the straight option.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.
- 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, there are some serious pitfalls that make track density an unreliable quantitative measure.”
§ Diffusion MRI fiber tractography of the brain > 5.4.1 | Voxel-wise track counts
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.
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 angular resolution of an acquisition scheme is determined by the diffusion weighting strength or b-value and by the number or unique diffusion encoding gradient orientations.”
§ Diffusion MRI fiber tractography of the brain > 6.4 | Angular resolution limits
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.
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
“One should be aware that, even using the state-ofthe-art techniques and high-quality data, many issues that could potentially bias the resulting”
§ Diffusion MRI fiber tractography of the brain > FIGURE 14
hedged- 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.
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 addition, large voxels will also cause larger discontinuities in the field of local fiber orientation estimates, complicating the process of distinguishing between different complex geometries.”
§ Diffusion MRI fiber tractography of the brain > 6.3 | Spatial resolution
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.
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 gazing at these stunning high-resolution visualizations, it is easy to forget that the actual resolution of the MRI data on which the tractograms are based is anything but high definition.”
§ Diffusion MRI fiber tractography of the brain > 6.3 | Spatial resolution
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.
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
“Out of 96 tractograms submitted to the ISMRM 2015 Tractography Challenge using state-of-the-art methods from 21 international groups, there were on average four false-positive bundles for every valid bundle recovered.”
§ Diffusion MRI fiber tractography of the brain > 6.5 | False positives
What the sources say
- Not matchedcites [12]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [13]
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.
- Not matched
In the paper
“While recent comparisons of tractography-created cortical connectomes with data from neuroanatomical tracers have shown that state-of-the-art tractography performs much better than chance, fiber tracking is still far from perfect in detecting neuroanatomical pathways and their corresponding connection strengths.126”
§ Diffusion MRI fiber tractography of the brain > FIGURE 14
What the sources say
- Not matchedcites [126]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaincorrelational- 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.
- 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
“While most methods did find the existing true bundles, it is striking to see how current methods produce a number of invalid connections that is much larger than the number of valid ones.”
§ Diffusion MRI fiber tractography of the brain > 6.5 | False positives
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.
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
“To put things into perspective, a typical become larger, their tissue or microstructural content will become more heterogeneous, requiring more complex models to extract the fiber orientations reliably.”
§ Diffusion MRI fiber tractography of the brain > 6.3 | Spatial resolution
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.
In the paper
“Given this b-value, the minimal number of unique diffusion encoding gradient orientations to fully characterize the angular frequency content (disregarding noise and artifacts) was estimated to be just 45.”
§ 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.
hedged- 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.
- 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
“This also means that, globally, many different track configurations can explain the dMRI signal very well.”
§ Diffusion MRI fiber tractography of the brain > 6.5 | False positives
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.
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 the angle between two fiber populations drops below a certain threshold, local fiber orientation models can no longer distinguish the distinct fiber populations and fiber trajectories will tend towards the average of the two populations, effectively creating an angular black hole.”
§ Diffusion MRI fiber tractography of the brain > 6.4 | Angular resolution limits
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.
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
“Locally, many different fiber configurations can lead to the same voxel-wise dMRI signal (branching, kissing, crossing, bottleneck, …).”
§ Diffusion MRI fiber tractography of the brain > 6.5 | False positives
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.
In the paper
“While recent comparisons of tractography-created cortical connectomes with data from neuroanatomical tracers have shown that state-of-the-art tractography performs much better than chance, fiber tracking is still far from perfect in detecting neuroanatomical pathways and their corresponding connection strengths.126”
§ Diffusion MRI fiber tractography of the brain > FIGURE 14
What the sources say
- Not matchedcites [126]
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.
- 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
“Even using the latest-and-greatest methods and with state-of-the-art data acquisition, fiber tracking is still limited when it comes to quantification and biological interpretation.”
§ Diffusion MRI fiber tractography of the brain > 7 | CONCLUSION
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.
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 mentioned earlier, tractography is an ill-posed problem.”
§ Diffusion MRI fiber tractography of the brain > 6.5 | False positives
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.
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
“While considered controversial by many researchers, 123,124 recent work by Tax et al. 125 proposed a mathematical framework to quantify the presence of sheet structure in the brain and demonstrated that several white matter regions indeed show a significant degree of sheet structure.”
§ Diffusion MRI fiber tractography of the brain > 6.6 | Sheetography
What the sources say
- Not matchedcites Tax et al.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [125]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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.
- 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 is important to realize that the spatial accuracy of fiber tracking is determined not only by the spatial resolution of the data, but also by what is known as angular resolution or ability to resolve small inter-fiber angles.”
§ Diffusion MRI fiber tractography of the brain > 6.4 | Angular resolution limits
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.
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
“Clearly, the issue can only be addressed by imposing anatomical constraints (right)”
§ Diffusion MRI fiber tractography of the brain > FIGURE 14
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. 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
plaindirect- Asserted: passes — The authors put it forward as true.
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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
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In the paper
“while the white matter”
§ Diffusion MRI fiber tractography of the brain > 2.3 | The virtual nature of fiber tracking
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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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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]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [37]
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- Not matchedcites [38]
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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]
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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
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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]
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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
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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
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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]
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]
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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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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.
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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
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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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Probably not a substantial claim. The paper’s conclusions may not depend on it.
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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
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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
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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
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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
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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.
hedgedconditional- Asserted: passes — The authors put it forward as true.
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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.
plain- Asserted: passes — The authors put it forward as true.
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It 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
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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
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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
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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
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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
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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
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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
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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.
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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
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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
“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.
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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
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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
“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)
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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
“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.
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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…”
MethodProbabilistic 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-citationTracking neuronal fiber pathways in the living human brain
Conturo, T. E., Lori, N. F., Cull, T. S., Akbudak, E., Snyder, A. Z., Shimony, J. S., McKinstry, R. C., Burton, H., & Raichle, M. E. (1999) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.96.18.10422 · Cited by 1,774 · cited 3×
“Most algorithms use trilinear interpolation, where the local fiber orientations are calculated as a weighted sum from the eight voxels nearest to the point of interest, with the weight of each neighboring voxel determined by its distance from the point of interest.(21) Some implementations perform trilinear interpolat…”
BackgroundMethod
Research works printed but not linked: 6
- Dell'Acqua F, Reconstructing fiber orientations with diffusion MRI, NMR Biomed
- Bells S, 2011, Tractometry—comprehensive multi‐modal quantitative assessment of white matter along specific tracts, Proc Int Soc Magn Reson Med, 19, 678
- Basser PJ, 1998, Fiber‐tractography via diffusion tensor MRI (DT‐MRI), Proc Int Soc Magn Reson Med, 6, 1226
- 10.1016/j.neuroimage.2008.05.002 · DOI
- 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
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.”
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-18, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
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