Potential Pitfalls of Using Fractional Anisotropy, Axial Diffusivity, and Radial Diffusivity as Biomarkers of Cerebral White Matter Microstructure
Open accessAbstract
Fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD) are commonly used as MRI biomarkers of white matter microstructure in diffusion MRI studies of neurodevelopment, brain aging, and neurologic injury/disease. Some of the more frequent practices include performing voxel-wise or region-based analyses of these measures to cross-sectionally compare individuals or groups, longitudinally assess individuals or groups, and/or correlate with demographic, behavioral or clinical variables. However, it is now widely recognized that the majority of cerebral white matter voxels contain multiple fiber populations with different trajectories, which renders these metrics highly sensitive to the relative volume fractions of the various fiber populations, the microstructural integrity of each constituent fiber population, and the interaction between these factors. Many diffusion imaging experts are aware of these limitations and now generally avoid using FA, AD or RD (at least in isolation) to draw strong reverse inferences about white matter microstructure, but based on the continued application and interpretation of these metrics in the broader biomedical/neuroscience literature, it appears that this has perhaps not yet become common knowledge among diffusion imaging end-users. Therefore, this paper will briefly discuss the complex biophysical underpinnings of these measures in the context of crossing fibers, provide some intuitive “thought experiments” to highlight how conventional interpretations can lead to incorrect conclusions, and suggest that future studies refrain from using (over-interpreting) FA, AD, and RD values as standalone biomarkers of cerebral white matter microstructure.
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Written from the paper’s full text.
TL;DR
This paper argues that fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD) are problematic as standalone biomarkers of cerebral white matter microstructure because most white matter voxels contain crossing fibers. It uses thought experiments to show that conventional interpretations can lead to incorrect reverse inferences, and recommends avoiding over-interpretation of these metrics in isolation.
Key findings
- Most cerebral white matter voxels contain multiple fiber populations: the proportion is now thought to be at least 33% (Behrens et al., 2007) and more likely between 60 and 90% (Jeurissen et al., 2013), with many regions containing 3 or more intersecting fiber bundles.
- Because FA is a relative, unitless measure scaled between 0 and 1, it can paradoxically increase when one or more fiber bundles degenerate while others are preserved, or remain unchanged when all three eigenvalues increase proportionally despite net tissue loss; AD and RD are confounded for similar reasons in crossing-fiber voxels.
- Conventional reverse inferences from FA, AD, and RD can lead to Type I (false positive) and/or Type II (false negative) errors, and the paper states there is no way to control for these effects or estimate their prevalence.
- MD and Trace are proposed as more robust DTI metrics because they equally weight diffusion along all three tensor axes; advanced dMRI methods (DKI, NODDI, tensor-valued diffusion encoding/μFA, fixel-based analysis) and non-dMRI methods (T1w/T2w ratio, ihMT, myelin water imaging) are suggested as alternatives likely more robust to crossing fibers.
Methods
- Narrative/commentary review with no new empirical data or sample; it synthesizes prior literature and presents two intuitive “thought experiments.”
- The thought experiments use cartoon voxels containing three crossing fibers (vertical, horizontal, and through-plane) and corresponding diffusion tensor eigenvalues to illustrate how FA can increase despite net fiber reduction, or remain constant despite proportional tissue changes.
- Main analysis is conceptual and biophysical, supported by references to prior simulation and empirical work (e.g., Pierpaoli et al., 2001; Wheeler-Kingshott and Cercignani, 2009; Jeurissen et al., 2013).
Limitations
- The authors state that all MRI methods have limitations and that their list of alternatives is not comprehensive; they also note that they focused on FA for simplicity, though AD and RD are confounded similarly.
- The authors state there is no way to control for crossing-fiber effects or estimate the prevalence of Type I and Type II errors, which could even occur simultaneously in different brain regions.
- Evident from the text: this is not an original data study or systematic review, and the recommendations are conceptual rather than validated by new experiments in this paper.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
14 of 27 cited sources checked · 9 supported · 0 flagged · accuracy not yet measured
- Premisecitation
The majority of cerebral white matter voxels contain multiple fiber populations with different trajectories.
“it is now widely recognized that the majority of cerebral white matter voxels contain multiple fiber populations with different trajectories”
Quote in the paperSubstantial claim2 of 2 sources checked- cites Behrens et al., 2007 source
The Abstract says that multiple fibre populations occur across large regions of white matter. But Results (Local fitting of the complex fibre model; page unavailable) reports that only one third of voxels with fractional anisotropy above 0.1 supported more than one orientation in the 60-direction dataset. This supports the presence of multiple populations, not the claim that they occur in a majority of cerebral white matter voxels. (LLM, conf=0.88)
Experimental — accuracy not yet measured - cites Jeurissen et al., 2013 source
- Premisecitation
The proportion of cerebral white matter voxels containing complex fiber geometries and/or multiple fiber bundles in different orientations is more likely between 60 and 90%.
“and is more likely somewhere between 60 and 90% (Jeurissen et al., 2013)”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Jeurissen et al., 2013 source
- Premisecitation
Many cerebral white matter regions contain as many as 3 or more intersecting fiber bundles with different trajectories.
“with many regions thought to contain as many as 3 or more intersecting fiber bundles with different trajectories (Figure 1)”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Jeurissen et al., 2013 source
- Premisecitation
The proportion of cerebral white matter voxels containing complex fiber geometries and/or multiple fiber bundles in different orientations is at least 33%.
“the proportion of cerebral white matter voxels containing complex fiber geometries and/or multiple fiber bundles in different orientations (i.e., crossing fibers, kissing fibers, etc.) is now thought to be at least 33% (Behrens et al., 2007)”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Behrens et al., 2007 source
The Results (Local fitting of the complex fibre model) and Discussion report detectable complex fibre architecture in approximately one third of voxels with fractional anisotropy greater than 0.1 using a 60-direction dataset; page numbers are not supplied. This supports the approximate proportion, but not a lower bound of at least 33% for all cerebral white matter voxels. (LLM, conf=0.88)
Experimental — accuracy not yet measured
- Premisecitation
ihMT and MWI likely provide more myelin-specific information than can be obtained with more general measures based on DTI or T1w/T2w ratio metrics.
“in the case of ihMT and MWI likely also provide more myelin-specific information than can be obtained with more general measures based on DTI or T1w/T2w ratio metrics (Mädler et al., 2008; Ercan et al., 2018; Uddin et al., 2019).”
Quote in the paperSubstantial claim1 of 3 sources checked- cites Mädler et al., 2008 source
- cites Ercan et al., 2018 source
- cites Uddin et al., 2019 source
The Introduction calls MWF a myelin-specific measure and DTI metrics general measures of tissue microstructure; the Conclusions also say T1w/T2w ratios should not be interpreted as myelin-specific in subcortical structures. The paper mentions an earlier ihMTR comparison but does not establish that ihMT is more myelin-specific than DTI or T1w/T2w. Page numbers are unavailable in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measured
- Premisecitation
Even in regions with crossing fibers, many observed differences or changes in FA, AD and/or RD are likely to reflect actual anatomical and biophysical phenomena, as long as other pitfalls in acquisition and analysis have been avoided.
“as pointed out by Jeurissen et al. (2013), even regions with crossing fibers, many observed differences/changes in FA, AD and/or RD are likely to reflect actual anatomical and biophysical phenomena, as long as other pitfalls in the acquisition and analysis have been avoided (Jones and Cercignani, 2010).”
Quote in the paperSubstantial claim1 of 2 sources checked- cites Jeurissen et al., 2013 source
In “Implications for DTI” (page not provided), the paper says that, if known analysis pitfalls are avoided, an observed FA change is highly likely to reflect a true underlying biophysical phenomenon—even though its meaning is ambiguous in crossing-fiber regions. It discusses axial and radial diffusivity as similarly difficult to interpret, but does not explicitly extend that likelihood statement to changes in AD or RD. (LLM, conf=0.86)
Experimental — accuracy not yet measured - cites Jones and Cercignani, 2010 source
- Premisecitation
Ex vivo MRI and histological studies of the macaque brain have revealed that white matter crossing fibers become even more apparent upon higher resolution and more detailed examination.
“ex vivo MRI and histological studies of the macaque brain have revealed that white matter crossing fibers become even more apparent upon higher resolution and more detailed examination”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Schilling et al., 2017 source
- Premisecitation
DKI data acquired using conventional hardware and reasonable scan times can resolve crossing fibers significantly better than conventional DTI.
“DKI data acquired using conventional hardware and reasonable scan times can resolve crossing fibers significantly better than conventional DTI (Glenn et al., 2016)”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Glenn et al., 2016 source
The Conclusion (page unavailable) says DKI directly resolves crossing fibers and improves tractography accuracy relative to DTI; the Methods report use of a 3T MRI system. However, the Discussion says this study’s scan time was relatively long compared with typical clinical protocols. It cites shorter DKI protocols as feasible but does not establish the same crossing-fiber performance at those scan times. (LLM, conf=0.88)
Experimental — accuracy not yet measured
- Interpretationcitation
The crossing fiber problem cannot be overcome by scanning at higher resolution to reduce partial volume effects.
“suggesting that this is a fundamental problem that cannot be overcome by scanning at higher resolution to reduce partial volume effects (Schilling et al., 2017).”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Schilling et al., 2017 source
- Premisecitation
Other quantitative MRI techniques such as calibrated T1w/T2w ratio mapping, inhomogeneous magnetization transfer (ihMT) imaging, and T2 relaxation-based myelin water imaging (MWI) are likely less affected by fiber orientation.
“other quantitative MRI techniques such as calibrated T1w/T2w ratio mapping (Ganzetti et al., 2014; Uddin et al., 2018), inhomogeneous magnetization transfer (ihMT) imaging (Manning et al., 2017; Swanson et al., 2017), and T2 relaxation-based myelin water imaging MWI (Prasloski et al., 2012; Lee et al., 2018) are likely less affected by fiber orientation”
Quote in the paperSubstantial claim1 of 3 sources checked - Premisecitation
Increased diffusion anisotropy in the presence of Wallerian degeneration due to chronic lacunar infarcts has been reported.
“In fact, increased diffusion anisotropy in the presence of Wallerian degeneration due to chronic lacunar infarcts has been reported (Pierpaoli et al., 2001)”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Pierpaoli et al., 2001 source
- Interpretationreasoning
Disproportionate atrophy or degradation of one or more fiber bundles, along with relative preservation of other fiber bundles, could result in an increase in FA despite an actual decrease in local fiber density, myelination, etc.
“disproportionate atrophy or degradation of one or more fiber bundles – along with the relative preservation of other fiber bundle(s) – could result in a seemingly paradoxical increase in FA, despite an actual decrease in local fiber density, myelination, etc., (Figure 2A).”
Quote in the paperSubstantial claim - Interpretationcitation
The conventional interpretation that higher FA values generally reflect greater white matter density is problematic in the presence of crossing fibers.
“this makes the conventional “if some is good, more is better” interpretation of FA values much more problematic than originally thought.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Tuch et al., 2003 source
- Rests on an assumption: The conventional interpretation that higher FA values generally reflect greater white matter density is widely held.
- Interpretationreasoning
FA, AD, and RD are highly sensitive to the relative volume fractions of the various fiber populations, the microstructural integrity of each constituent fiber population, and the interaction between these factors.
“which renders these metrics highly sensitive to the relative volume fractions of the various fiber populations, the microstructural integrity of each constituent fiber population, and the interaction between these factors.”
Quote in the paperSubstantial claim - Interpretationcitation
AD and RD are confounded for similar reasons as FA in the presence of crossing fibers.
“For simplicity, we have focused on FA in our examples, but it should be noted that AD and RD are confounded for similar reasons.”
Quote in the paperSubstantial claim - Interpretationreasoning
Biophysical interpretations of FA, AD, and RD values are problematic in the presence of white matter fiber crossings.
“These examples hopefully highlight how biophysical interpretations of FA, AD, and RD values are problematic in the presence of white matter fiber crossings”
Quote in the paperSubstantial claim - Interpretationreasoning
FA values could decrease due to a disproportionate increase in one or more of the non-dominant fiber bundles, for example during neurodevelopment, neuroplasticity and/or neural repair.
“the opposite effect can also be true – where FA values could decrease due to a disproportionate increase in one or more of the non-dominant fiber bundles (e.g., during neurodevelopment, neuroplasticity and/or neural repair).”
Quote in the paperSubstantial claim - Interpretationreasoning
Interpreting FA, AD, and RD to reflect similarity, stability, differences or changes in tissue microstructure can potentially lead to Type I (false positive) and/or Type II (false negative) errors.
“how interpreting them to reflect similarity, stability, differences or changes in tissue microstructure can potentially lead to Type I (false positive) and/or Type II (false negative) errors.”
Quote in the paperSubstantial claim - Interpretationreasoning
Real differences in tissue microstructure could be missed if the underlying fiber bundles in a given region differ in the same way or change at the same rate, relative to each other.
“real differences could be missed if the underlying fiber bundles in a given region differ in the same way or change at the same rate, relative to each other.”
Quote in the paperSubstantial claim - Premisenone given
Anytime there is a proportional change in the three eigenvalues (λ1, λ2, and λ3), FA will remain constant.
“Indeed, anytime there is a proportional change in the three eigenvalues (λ1, λ2, and λ3), FA will remain constant.”
Quote in the paperSubstantial claim - Premisecitation
Certain DKI metrics such as kurtosis fractional anisotropy (KFA) and quantitative kurtosis tensor measures (e.g., radial tensor kurtosis) are likely more robust to crossing fibers.
“certain DKI metrics such as kurtosis fractional anisotropy (KFA) and quantitative kurtosis tensor measures (e.g., radial tensor kurtosis) are likely more robust to crossing fibers (Hansen et al., 2016; Hansen and Jespersen, 2016).”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Hansen et al., 2016
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites Hansen and Jespersen, 2016
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationreasoning
MD or Trace values are likely the most robust and interpretable DTI metrics for drawing reverse inferences about underlying tissue characteristics.
“we would suggest that either the MD (i.e., mean apparent diffusion along the three tensor dimensions; MD = [λ1 + λ2 + λ3]/3) or the Trace (i.e., total apparent diffusion along the three tensor dimensions; Trace = [λ1 + λ2 + λ3]) values are likely the most robust and interpretable metrics for drawing reverse inferences about the underlying tissue characteristics.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Genc et al., 2017 source
- Interpretationreasoning
In both of the theoretical thought experiments outlined in Figure 2, MD and Trace values would increase as expected.
“In both of the theoretical thought experiments outlined in Figure 2, MD and Trace values would increase as expected (owing to less restricted diffusion within the voxel).”
Quote in the paperSubstantial claim - Interpretationreasoning
If a multiple sclerosis lesion or traumatic brain injury damages all underlying fiber bundles within a particular region equally, FA will appear unchanged despite potentially significant alterations in the underlying tissues.
“if a multiple sclerosis (MS) lesion or traumatic brain injury damages all of the underlying fiber bundles within a particular region equally (e.g., increased membrane permeability and decreased myelination leading to an equal reduction in λ1, λ2, and λ3), FA will appear unchanged despite potentially significant alterations in the underlying tissues (Figure 2B).”
Quote in the paperSubstantial claim - Premisecitation
Empirical findings show that MD is more closely related to neurite density than FA, AD or RD measures.
“This is also supported by empirical findings that MD is more closely related to neurite density than FA, AD or RD measures (Genc et al., 2017).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Genc et al., 2017 source
- Interpretationreasoning
Increases, decreases, and even stable FA, AD, and RD measures become difficult, if not impossible, to interpret in terms of underlying tissue microstructural properties such as fiber diameter, fiber density, membrane permeability, and myelination.
“increases, decreases, and indeed even stable FA, AD, and RD measures become difficult (if not impossible) to interpret in terms of the other underlying tissue microstructural properties (e.g., fiber diameter, fiber density, membrane permeability, myelination, etc.).”
Quote in the paperSubstantial claim - Interpretationreasoning
Not all previous interpretations using FA, AD, and RD are necessarily wrong, especially in cases where the findings have been corroborated using other imaging metrics.
“This does not, however, mean that all of the previous interpretations using these metrics are necessarily wrong – especially in cases where the findings have been corroborated using other imaging metrics.”
Quote in the paperSubstantial claim - Interpretationreasoning
In certain white matter regions with very few crossing fibers, such as the corpus callosum, corticospinal tract, and spinal cord, the interpretation of FA, AD, and RD is more straightforward.
“there are obviously certain white matter regions with very few crossing fibers (e.g., corpus callosum, corticospinal tract, spinal cord, etc.), where the interpretation of these metrics is more straightforward.”
Quote in the paperSubstantial claim - Interpretationreasoning
During neurodevelopment, FA could appear constant if all constituent fiber bundles mature at the same rate.
“the opposite effect could also be true during neurodevelopment, where FA could appear constant if the all of the constituent fiber bundles were to mature at the same rate (i.e., equal increase in λ1, λ2, and λ3).”
Quote in the paperSubstantial claim - Interpretationreasoning
There is no way of controlling for these effects or estimating the prevalence of Type I and Type II errors inherent in any reverse inferences between FA, AD or RD and the underlying tissue microstructure.
“Therefore, there is no way of controlling for these effects or estimating the prevalence of Type I and Type II errors (which could even occur simultaneously in different brain regions) inherent in any reverse inferences between FA, AD or RD and the underlying tissue microstructure.”
Quote in the paperSubstantial claim - Interpretationreasoning
Equal FA values do not necessarily reflect equivalent underlying tissue composition in the presence of crossing fibers.
“one might erroneously infer that equal FA values reflect equivalent underlying tissue composition. However, as in the example above, this is not necessarily the case in the presence of crossing fibers either.”
Quote in the paperSubstantial claim
- May combine two claims. 5
- Classified as probably not a substantial claim. 3
May combine two claims.
PremisecitationChanges in AD can induce spurious alterations in RD and vice versa in voxels containing crossing fibers.
“changes in AD can induce spurious alterations in RD and vice versa in voxels containing crossing fibers (Wheeler-Kingshott and Cercignani, 2009)”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Wheeler-Kingshott and Cercignani, 2009 source
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
InterpretationcitationModeling individual fibers at the sub-voxel level could lead to more sensitive measurements and more detailed understandings about tissue degeneration in various disorders.
“Modeling individual fibers at the sub-voxel level like this could lead to more sensitive measurements and more detailed understandings about tissue degeneration in various disorders (Finkelstein et al., 2021).”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Finkelstein et al., 2021 source
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationFixel-based analysis approaches have been proposed to characterize a specific fiber population within a voxel (i.e., a "fixel").
“recent fixel-based analysis approaches have been proposed to characterize a specific fiber population within a voxel (i.e., a “fixel”) (Raffelt et al., 2017).”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Raffelt et al., 2017 source
May combine two claims.
InterpretationreasoningMD and Trace values are theoretically more robust to multiple fiber populations and/or complex fiber geometries than FA, AD, and RD, and are likely the best DTI indicators of how tissues constrain diffusion within a voxel.
“As a result, MD and Trace values are theoretically more robust to multiple fiber populations and/or complex fiber geometries, and are likely the best DTI indicators of how tissues are constraining diffusion within a voxel.”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationNODDI can estimate neurite density in dendrites and axons, as well as orientation dispersion.
“NODDI can estimate neurite density in dendrites and axons, as well as orientation dispersion (i.e., fanning of neurites), which are factors that contribute to, but are distinct from DTI-based FA measures.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Zhang et al., 2012 source
Probably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningStrong biophysical interpretations and conclusions should not be drawn using FA, AD or RD measurements alone.
“it suggests that strong biophysical interpretations and conclusions should not be drawn using FA, AD or RD measurements alone”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationOrientation dispersion estimates the angular variability between neurites and provides a better measure than FA in regions with fanning or crossing fibers.
“Orientation dispersion estimates the angular variability between neurites and provides a better measure than FA in regions with fanning or crossing fibers.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Zhang et al., 2012 source
The paper relies on this without support.
FA, AD, and RD are commonly used as MRI biomarkers of white matter microstructure in diffusion MRI studies of neurodevelopment, brain aging, and neurologic injury/disease.
“Fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD) are commonly used as MRI biomarkers of white matter microstructure in diffusion MRI studies of neurodevelopment, brain aging, and neurologic injury/disease.”
The paper relies on this without support.
Many diffusion imaging end-users continue to apply and interpret FA, AD, and RD as standalone biomarkers of white matter microstructure, and this has not become common knowledge among them.
“based on the continued application and interpretation of these metrics in the broader biomedical/neuroscience literature, it appears that this has perhaps not yet become common knowledge among diffusion imaging end-users.”
The paper relies on this without support.
The conventional interpretation that higher FA values generally reflect greater white matter density is widely held.
“the conventional thinking was that all else being equal, higher FA values generally reflected greater white matter density”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Figure 2A labels the λ1 increase as both large and small
MinorThe Figure 2 caption assigns λ1 in part A to both the bold arrow representing a relatively large increase and the thin arrows representing a relatively small increase.
“Bold arrow represents a relatively large increase (λ1 in part A)”
“thin arrows represent a relatively small increase (λ1 and λ2 in part A)”
Tissue damage is said to both decrease and increase the eigenvalues
MinorThe text's example says damage causes an equal reduction in all three eigenvalues, while its description of Figure 2B says the eigenvalues increase proportionally under the same scenario.
“if a multiple sclerosis (MS) lesion or traumatic brain injury damages all of the underlying fiber bundles within a particular region equally (e.g., increased membrane permeability and decreased myelination leading to an equal reduction in λ1, λ2, and λ3), FA will appear unchanged”
“all three fibers experience the same amount of tissue damage, such that all three eigenvalues (λ1, λ2, and λ3) are increased proportionally”
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“…crossing fibers, kissing fibers, etc.) is now thought to be at least 33% (Behrens et al., 2007), and is more likely somewhere between 60 and 90% (Jeurissen et al., 2013) – with many regions thought to contain as many as 3 or more intersecting fiber bundles with different trajectories ( Figure 1 )…”
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“In fact, increased diffusion anisotropy in the presence of Wallerian degeneration due to chronic lacunar infarcts has been reported (Pierpaoli et al., 2001), where degeneration of motor pathways in the rostral pons caused the transverse pontine fibers to become the dominant pathway – ultimately…”
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Artifacts and pitfalls in diffusion MRI
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“regions with crossing fibers, many observed differences/changes in FA, AD and/or RD are likely to reflect actual anatomical and biophysical phenomena, as long as other pitfalls in the acquisition and analysis have been avoided (Jones and Cercignani, 2010).”
BackgroundDiffusion MRI of complex neural architecture.
Tuch, D. S., Reese, T. G., Wiegell, M. R., & Wedeen, V. J. (2003) · Neuron · doi:10.1016/s0896-6273(03)00758-x · Cited by 698 · see citing works
Investigating white matter fibre density and morphology using fixel-based analysis.
Raffelt, D. A., Tournier, J.-D., Smith, R. E., Vaughan, D. N., Jackson, G., Ridgway, G. R., & Connelly, A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.09.029 · Cited by 620 · see citing works
Diffusion MRI fiber tractography of the brain
Jeurissen, B., Descoteaux, M., Mori, S., & Leemans, A. (2019) · NMR in Biomedicine · doi:10.1002/nbm.3785 · Cited by 574 · cited 2× · see citing works
“, 2003) tractography approaches (Maier-Hein et al., 2017; Jeurissen et al., 2019), and (3) indirectly measure brain function (Le Bihan et al.”
BackgroundWhite matter characterization with diffusional kurtosis imaging
Fieremans, E., Jensen, J. H., & Helpern, J. A. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.06.006 · Cited by 561 · cited 1× · see citing works
“…(Beaulieu, 2002; Alexander et al., 2019); axial kurtosis, radial kurtosis, mean kurtosis, maximum directional kurtosis, axonal water fraction (Fieremans et al., 2011; Henriques et al., 2021); neurite orientation dispersion, neurite density index, isotropic volume fraction (Zhang et al.,…”
BackgroundImaging brain microstructure with diffusion MRI: practicality and applications
Alexander, D. C., Dyrby, T. B., Nilsson, M., & Zhang, H. G. (2017) · NMR in Biomedicine · doi:10.1002/nbm.3841 · Cited by 423 · cited 2× · see citing works
“…(1) derive several different quantitative measures [e.g., fractional anisotropy, axial diffusivity, radial diffusivity, mean diffusivity (Beaulieu, 2002; Alexander et al., 2019); axial kurtosis, radial kurtosis, mean kurtosis, maximum directional kurtosis, axonal water fraction (Fieremans et al.,…”
BackgroundLebel, C., Treit, S., & Beaulieu, C. (2019) · NMR in Biomedicine · doi:10.1002/nbm.3778 · Cited by 379 · cited 1× · see citing works
“It is therefore not surprising that researchers are leveraging these techniques to study diffusion changes associated with neurodevelopment (Lebel et al., 2019), brain aging (Beck et al., 2021), traumatic brain injury (Hutchinson et al., 2018), and variety of neurodegenerative disorders (Goveas et…”
BackgroundThe “wet mind”: Water and functional neuroimaging
Bihan, D. L. (2007) · Physics in Medicine and Biology · doi:10.1088/0031-9155/52/7/r02 · Cited by 320 · cited 1× · see citing works
“…map the brain’s white matter pathways using deterministic (Mori et al., 1999) and/or probabilistic (Behrens et al., 2003) tractography approaches (Maier-Hein et al., 2017; Jeurissen et al., 2019), and (3) indirectly measure brain function (Le Bihan et al., 2006b; Le Bihan, 2007; Abe et al., 2017).”
Direct and fast detection of neuronal activation in the human brain with diffusion MRI
Le Bihan, D., Urayama, S.-I., Aso, T., Hanakawa, T., & Fukuyama, H. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0600644103 · Cited by 272 · cited 2× · see citing works
“…of dMRI comes a number of complications, caveats and limitations – and indeed, some excellent articles have been written about common pitfalls associated with the acquisition, analysis and interpretation of dMRI data (Le Bihan et al., 2006a; Jones and Cercignani, 2010; Jones et al., 2013).”
BackgroundRapid whole cerebrum myelin water imaging using a 3D GRASE sequence
Prasloski, T., Rauscher, A., MacKay, A. L., Hodgson, M., Vavasour, I. M., Laule, C., & Mädler, B. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.06.064 · Cited by 268 · see citing works
Vollmar, C., O’Muircheartaigh, J., Barker, G. J., Symms, M. R., Thompson, P. J., Kumari, V., Duncan, J. S., Richardson, M. P., & Koepp, M. J. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.03.046 · Cited by 261 · see citing works
Szczepankiewicz, F., Lasič, S., van Westen, D., Sundgren, P. C., Englund, E. M., Westin, C., Ståhlberg, F., Lätt, J., Topgaard, D., & Nilsson, M. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.09.057 · Cited by 245 · see citing works
Design and Validation of Diffusion MRI Models of White Matter
Jelescu, I. O., & Budde, M. D. (2017) · Frontiers in Physics · doi:10.3389/fphy.2017.00061 · Cited by 240 · cited 1× · see citing works
“Additionally, there have been several excellent in-depth reviews published on the biophysical interpretations of diffusion MRI signals (Beaulieu, 2002; Jelescu and Budde, 2017; Jelescu et al., 2020).”
BackgroundIs diffusion anisotropy an accurate monitor of myelination?
Mädler, B., Drabycz, S. A., Kolind, S. H., Whittall, K. P., & MacKay, A. L. (2008) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2008.01.047 · Cited by 227 · see citing works
Whole brain myelin mapping using T1- and T2-weighted MR imaging data.
Ganzetti, M., Wenderoth, N., & Mantini, D. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00671 · Cited by 225 · cited 1× · see citing works
“Beyond dMRI approaches, other quantitative MRI techniques such as calibrated T1w/T2w ratio mapping (Ganzetti et al., 2014; Uddin et al., 2018), inhomogeneous magnetization transfer (ihMT) imaging (Manning et al., 2017; Swanson et al., 2017), and T2 relaxation-based myelin water imaging MWI…”
MethodBeck, D., de Lange, A.-M. G., Maximov, I. I., Richard, G., Andreassen, O. A., Nordvik, J. E., & Westlye, L. T. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2020.117441 · Cited by 209 · see citing works
Szczepankiewicz, F., van Westen, D., Englund, E. M., Westin, C., Ståhlberg, F., Lätt, J., Sundgren, P. C., & Nilsson, M. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.07.038 · Cited by 169 · see citing works
Challenges for biophysical modeling of microstructure
Jelescu, I., Palombo, M., Bagnato, F. R., & Schilling, K. G. (2020) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2020.108861 · Cited by 165 · cited 1× · see citing works
“Additionally, there have been several excellent in-depth reviews published on the biophysical interpretations of diffusion MRI signals (Beaulieu, 2002; Jelescu and Budde, 2017; Jelescu et al., 2020).”
BackgroundThe role of diffusion MRI in neuroscience
Assaf, Y., Johansen‐Berg, H., & Thiebaut de Schotten, M. (2019) · NMR in Biomedicine · doi:10.1002/nbm.3762 · Cited by 158 · see citing works
Uddin, M. N., Figley, T. D., Solar, K. G., Shatil, A. S., & Figley, C. R. (2019) · Scientific Reports · doi:10.1038/s41598-019-39199-x · Cited by 151 · cited 2× · see citing works
“…Lee et al., 2018) are likely less affected by fiber orientation; and in the case of ihMT and MWI likely also provide more myelin-specific information than can be obtained with more general measures based on DTI or T1w/T2w ratio metrics (Mädler et al., 2008; Ercan et al., 2018; Uddin et al., 2019).”
BackgroundSelf-citationNeurite density index is sensitive to age related differences in the developing brain
Genc, S., Malpas, C. B., Holland, S. K., Beare, R., & Silk, T. J. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.01.023 · Cited by 119 · cited 1× · see citing works
“This is also supported by empirical findings that MD is more closely related to neurite density than FA, AD or RD measures (Genc et al., 2017).”
BackgroundClinical Applications of Diffusion Tensor Imaging
Lerner, A., Mogensen, M. A., Kim, P. E., Shiroishi, M. S., Hwang, D., & Law, M. (2013) · World Neurosurgery · doi:10.1016/j.wneu.2013.07.083 · Cited by 113 · cited 1× · see citing works
“The popularity of diffusion MRI (dMRI) has increased dramatically over the past couple of decades, and it is now commonly used for a wide range of clinical and research applications (Lerner et al., 2014; Assaf et al., 2019).”
BackgroundDiffusion MRI and the detection of alterations following traumatic brain injury
Hutchinson, E. B., Schwerin, S. C., Avram, A., Juliano, S. L., & Pierpaoli, C. M. (2017) · Journal of Neuroscience Research · doi:10.1002/jnr.24065 · Cited by 108 · cited 1× · see citing works
“, 2021), traumatic brain injury (Hutchinson et al., 2018), and variety of neurodegenerative disorders (Goveas et al.”
BackgroundDiffusion-MRI in neurodegenerative disorders
Goveas, J., O’Dwyer, L., Mascalchi, M., Cosottini, M., Diciotti, S., De Santis, S., Passamonti, L., Tessa, C., Toschi, N., & Giannelli, M. (2015) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2015.04.006 · Cited by 104 · see citing works
Can increased spatial resolution solve the crossing fiber problem for diffusion MRI?
Schilling, K., Gao, Y., Janve, V., Stepniewska, I., Landman, B. A., & Anderson, A. W. (2017) · NMR in Biomedicine · doi:10.1002/nbm.3787 · Cited by 102 · see citing works
Szczepankiewicz, F., Sjölund, J., Ståhlberg, F., Lätt, J., & Nilsson, M. (2019) · PLoS ONE · doi:10.1371/journal.pone.0214238 · Cited by 99 · see citing works
Uddin, M. N., Figley, T. D., Marrie, R. A., Figley, C. R., & for the CCOMS Study Group (2018) · NMR in Biomedicine · doi:10.1002/nbm.3868 · Cited by 84 · cited 1× · see citing works
“Beyond dMRI approaches, other quantitative MRI techniques such as calibrated T1w/T2w ratio mapping (Ganzetti et al., 2014; Uddin et al., 2018), inhomogeneous magnetization transfer (ihMT) imaging (Manning et al., 2017; Swanson et al., 2017), and T2 relaxation-based myelin water imaging MWI…”
MethodSelf-citationFast imaging of mean, axial and radial diffusion kurtosis
Hansen, B., Shemesh, N., & Jespersen, S. N. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.08.022 · Cited by 75 · cited 1× · see citing works
“…significantly better than conventional DTI (Glenn et al., 2016), and certain DKI metrics such as kurtosis fractional anisotropy (KFA) and quantitative kurtosis tensor measures (e.g., radial tensor kurtosis) are likely more robust to crossing fibers (Hansen et al., 2016; Hansen and Jespersen, 2016).”
BackgroundKurtosis fractional anisotropy, its contrast and estimation by proxy
Hansen, B., & Jespersen, S. N. (2016) · Scientific Reports · doi:10.1038/srep23999 · Cited by 74 · cited 1× · see citing works
“…significantly better than conventional DTI (Glenn et al., 2016), and certain DKI metrics such as kurtosis fractional anisotropy (KFA) and quantitative kurtosis tensor measures (e.g., radial tensor kurtosis) are likely more robust to crossing fibers (Hansen et al., 2016; Hansen and Jespersen, 2016).”
BackgroundWater diffusion closely reveals neural activity status in rat brain loci affected by anesthesia
Abe, Y., Tsurugizawa, T., & Bihan, D. L. (2017) · PLoS Biology · doi:10.1371/journal.pbio.2001494 · Cited by 74 · see citing works
Diffusional Kurtosis Imaging in the Diffusion Imaging in Python Project
Henriques, R. N., Correia, M., Marrale, M., Huber, E., Kruper, J., Koudoro, S., Yeatman, J. D., Garyfallidis, E., & Rokem, A. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.675433 · Cited by 68 · cited 2× · see citing works
“…et al., 2019); axial kurtosis, radial kurtosis, mean kurtosis, maximum directional kurtosis, axonal water fraction (Fieremans et al., 2011; Henriques et al., 2021); neurite orientation dispersion, neurite density index, isotropic volume fraction (Zhang et al., 2012; Faiyaz et al., 2021);…”
BackgroundThe physical mechanism of “inhomogeneous” magnetization transfer MRI.
Manning, A. P., Chang, K. L., MacKay, A. L., & Michal, C. A. (2017) · Journal of Magnetic Resonance · doi:10.1016/j.jmr.2016.11.013 · Cited by 67 · see citing works
Laguna, P. A. L., Combes, A. J. E., Streffer, J. R., Einstein, S. G., Timmers, M., Williams, S., & Dell’Acqua, F. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102168 · Cited by 59 · cited 2× · see citing works
“For example, the aforementioned intra-scanner and inter-scanner repeatability studies acquired test-retest scans over a short interval from the same healthy control participants (Vollmar et al., 2010; Luque Laguna et al., 2020).”
BackgroundGlenn, G. R., Kuo, L. W., Chao, Y. P., Lee, C. Y., Helpern, J. A., & Jensen, J. H. (2016) · American Journal of Neuroradiology · doi:10.3174/ajnr.a4714 · Cited by 58 · cited 2× · see citing works
“…DKI data acquired using conventional hardware and reasonable scan times can resolve crossing fibers significantly better than conventional DTI (Glenn et al., 2016), and certain DKI metrics such as kurtosis fractional anisotropy (KFA) and quantitative kurtosis tensor measures (e.g., radial…”
BackgroundMolecular, dynamic, and structural origin of inhomogeneous magnetization transfer in lipid membranes
Swanson, S. D., Malyarenko, D. I., Fabiilli, M. L., et al. (2016) · Magnetic Resonance in Medicine · doi:10.1002/mrm.26210 · Cited by 57 · cited 1× · see citing works
“…T1w/T2w ratio mapping (Ganzetti et al., 2014; Uddin et al., 2018), inhomogeneous magnetization transfer (ihMT) imaging (Manning et al., 2017; Swanson et al., 2017), and T2 relaxation-based myelin water imaging MWI (Prasloski et al., 2012; Lee et al., 2018) are likely less affected by fiber…”
BackgroundErcan, E., Varma, G., Mädler, B., Dimitrov, I. E., Pinho, M. C., Xi, Y., Wagner, B. C., Davenport, E. M., Maldjian, J. A., Alsop, D. C., et al. (2018) · Magnetic Resonance in Medicine · doi:10.1002/mrm.27211 · Cited by 40 · cited 1× · see citing works
“…Lee et al., 2018) are likely less affected by fiber orientation; and in the case of ihMT and MWI likely also provide more myelin-specific information than can be obtained with more general measures based on DTI or T1w/T2w ratio metrics (Mädler et al., 2008; Ercan et al., 2018; Uddin et al., 2019).”
Inter-Vendor Reproducibility of Myelin Water Imaging Using a 3D Gradient and Spin Echo Sequence
Lee, L. E., Ljungberg, E., Shin, D., Figley, C. R., Vavasour, I. M., Rauscher, A., Cohen‐Adad, J., Li, D. K. B., Traboulsee, A. L., MacKay, A. L., et al. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00854 · Cited by 38 · cited 1× · see citing works
“…transfer (ihMT) imaging (Manning et al., 2017; Swanson et al., 2017), and T2 relaxation-based myelin water imaging MWI (Prasloski et al., 2012; Lee et al., 2018) are likely less affected by fiber orientation; and in the case of ihMT and MWI likely also provide more myelin-specific information…”
BackgroundSelf-citationSingle‐shell NODDI using dictionary‐learner‐estimated isotropic volume fraction
Faiyaz, A., Doyley, M. M., Schifitto, G., Zhong, J., & Uddin, M. N. (2021) · NMR in Biomedicine · doi:10.1002/nbm.4628 · Cited by 18 · cited 1× · see citing works
“…et al., 2011; Henriques et al., 2021); neurite orientation dispersion, neurite density index, isotropic volume fraction (Zhang et al., 2012; Faiyaz et al., 2021); etc.] that reflect slightly different aspects of tissue microstructure, (2) non-invasively map the brain’s white matter pathways…”
BackgroundSelf-citationFinkelstein, A., Faiyaz, A., Weber, M. T., Qiu, X., Uddin, M. N., Zhong, J., & Schifitto, G. (2021) · Frontiers in Neurology · doi:10.3389/fneur.2021.725059 · Cited by 15 · see citing works
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Key, M. N., & Szabo‐Reed, A. N. (2023) · Nutrients · doi:10.3390/nu15112495 · Cited by 85 · see citing works
“A region with a high preference for a particular orientation will have a high FA value, indicating highly organized fibers, a sign of intact white matter tracts [32].”
BackgroundGong, Z., Bilgel, M., Kiely, M., Triebswetter, C., Ferrucci, L., Resnick, S. M., Spencer, R. G. S., & Bouhrara, M. (2023) · Alzheimer s & Dementia · doi:10.1002/alz.12968 · Cited by 75 · see citing works
“53,54 Therefore, the association between myelin content and cognition has not been directly established.”
An ALE meta-analytic review of musical expertise
Criscuolo, A., Pando-Naude, V., Bonetti, L., Vuust, P., & Brattico, E. (2022) · Scientific Reports · doi:10.1038/s41598-022-14959-4 · Cited by 55 · see citing works
Hinojosa, C. A., George, G. C., & Ben‐Zion, Z. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02558-w · Cited by 60 · see citing works
“The pattern of diffusivity can be computed using scalar measures such as fractional anisotropy (FA), whereby lower FA values illustrate a reduced axonal packing density [42].”
Gimbel, B. A., Anthony, M. E., Ernst, A. M., Roediger, D. J., de Water, E., Eckerle, J. K., Boys, C. J., Radke, J. P., Mueller, B. A., Fuglestad, A. J., et al. (2022) · Journal of Neurodevelopmental Disorders · doi:10.1186/s11689-022-09470-w · Cited by 36 · see citing works
“, NODDI) that allows for greater specificity with regard to white matter microstructure compared to traditional diffusion tensor imaging metrics such as FA and MD [54, 67].”
BackgroundMicrostructural brain abnormalities, fatigue, and cognitive dysfunction after mild COVID-19
Silva, L. S., da Costa, B. A., Aventurato, Í. K., João, R. B., de Campos, B. M., de Brito, M. R., Bechelli, J. F., Silva, L. C. S., dos Santos, A. F., Alvim, M. K. M., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-52005-7 · Cited by 38 · see citing works
“However, the significance and interpretation of higher values of AD in the absence of alterations of other diffusivities requires extreme caution, due to the complexity of biophysical properties of these metrics 71 .”
BackgroundEarly life cold and heat exposure impacts white matter development in children
Granés, L., Essers, E., Ballester, J., Petricola, S., Tiemeier, H. W., Iñiguez, C., Soriano‐Mas, C., & Guxens, M. (2024) · Nature Climate Change · doi:10.1038/s41558-024-02027-w · Cited by 35 · see citing works
“Second, MD could be a more robust measure when inferring the underlying tissue characteristics of white matter as compared with FA 40 .”
Human brain changes after first psilocybin use
Lyons, T., Spriggs, M., Kerkelä, L., Rosas, F. E., Roseman, L., Mediano, P. A. M., Timmermann, C., Oestreich, L., Pagni, B. A., Zeifman, R. J., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-71962-3 · Cited by 30 · see citing works
“However, interpretation of the axial diffusivity changes is complex, not least due to crossing fi ber-related confounds 43 .”
Lin, C., Frigerio, I., Bol, J. G. J. M., Bouwman, M. M. A., Wesseling, A. J., Dahl, M. J., Rozemüller, A. J. M., van der Werf, Y. D., Pouwels, P. J. W., van de Berg, W. D. J., et al. (2024) · Translational Neurodegeneration · doi:10.1186/s40035-024-00400-5 · Cited by 30 · see citing works
Frontostriatal and limbic contributions to cognitive decline in Parkinson's disease
Brown, G., Hakun, J. G., Lewis, M. M., Jesus, S. D., Du, G., Eslinger, P. J., Kong, L., & Huang, X. (2022) · Journal of Neuroimaging · doi:10.1111/jon.13045 · Cited by 28 · see citing works
Khan, A. F., & Iturria‐Medina, Y. (2024) · Translational Psychiatry · doi:10.1038/s41398-024-03073-w · Cited by 28 · see citing works
“Linking these biomarkers, which are often non-speci fi c to pathophysiology [191, 192], to mechanistic pathways requires information about features such as gene expression and Fig.”
BackgroundMarrie, R. A., Patel, R., Figley, C. R., Kornelsen, J., Bolton, J. M., Graff, L. A., Mazerolle, E. L., Helmick, C. A., Uddin, M. N., Figley, T. D., et al. (2022) · Frontiers in Neurology · doi:10.3389/fneur.2022.910014 · Cited by 26 · see citing works
“Choice of Diffusion Metric It is increasingly recognized that a large proportion of white matter fiber tracts have complex architecture including crossing fibers such that variations in DTI measures do not necessarily reflect variations in structural integrity of myelin or axons (55, 56).”
BackgroundSelf-citationLitwin, T., Rędzia-Ogrodnik, B., Antos, A., Przybyłkowski, A. M., Członkowska, A., & Bembenek, J. P. (2024) · Brain Sciences · doi:10.3390/brainsci14070727 · Cited by 25 · see citing works
“FA quantifies the variability of particle movements in space and correlates with tissue structure coherence [30,31].”
BackgroundMethodInfluentialPušnik, L., Serša, I., Umek, N., Cvetko, E., & Snoj, Ž. (2023) · Frontiers in Physiology · doi:10.3389/fphys.2023.1070227 · Cited by 20 · see citing works
“We hypothesize that the fibers crossing between the fascicles contributed to high fascicular coefficients of variation within the same fascicle and explain why no difference was observed (Figley et al., 2022).”
BackgroundLiu, Y., Jiang, Y., Du, W., Gao, B., Gao, J., Hu, S., Song, Q., Wang, W., & Miao, Y. (2024) · Scientific Reports · doi:10.1038/s41598-023-50768-z · Cited by 20 · see citing works
“This might be due to FA is a relative measure, the quantitative information might be inaccurate in the presence of crossing fibers 44 .”
BackgroundThanaraju, A., Marzuki, A. A., Chan, J. K., Wong, K. Y., Phon-Amnuaisuk, P., Vafa, S., Chew, J., Chia, Y. C., & Jenkins, M. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2024.105716 · Cited by 19 · see citing works
Self-citationThe impact of resective epilepsy surgery on the brain network: evidence from post-surgical imaging
Sainburg, L. E., Englot, D. J., & Morgan, V. L. (2025) · Brain · doi:10.1093/brain/awaf026 · Cited by 19 · see citing works
“57 Functionally, multiple FDG-PET studies have found increases in glucose metabolism after TLE surgery distant from the resection site, 16,24-26 which may indicate compensation for the activity of the resected area.”
Individual variation in the chimpanzee arcuate fasciculus predicts vocal and gestural communication
Hecht, E. E., Vijayakumar, S., Becker, Y., & Hopkins, W. D. (2025) · Nature Communications · doi:10.1038/s41467-025-58784-5 · Cited by 12 · see citing works
“…whether individual variation in AG behavior was associated with AF volume, asymmetry, and fractional anisotropy (FA), a scalar measure of white matterthat can re fl ect myelination and/or tract coherence, although it is not a comprehensive index of all aspects of white matter microstructure 30 .”
Whole-brain white matter variation across childhood environments
Carozza, S., Kletenik, I., Astle, D. E., Schwamm, L. H., & Dhand, A. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2409985122 · Cited by 19 · see citing works
“Second, we employed metrics derived from a tractography method that is robust to crossing fibers ( 44 , 45 ), which has been considered a confounding factor in FA analyses ( 46 ).”
Marchi, N. A., Daneault, V., André, C., Martineau‐Dussault, M., Baril, A., Thompson, C., Montplaisir, J. Y., Gilbert, D., Lorrain, D., Boré, A., et al. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.13833 · Cited by 16 · see citing works
“27 However,conventionaldiffusiontensorimaging(DTI)measures have limitations, such as partial volume effects, which may be particularly important in the AD continuum where atrophy leads to ex vacuo increases of cerebrospinal fluid and free water (FW) in brain tissue.”
BackgroundLeptin bioavailability and markers of brain atrophy and vascular injury in the middle age
Charisis, S., Short, M. I., Bernal, R., Kautz, T. F., Trevino, H., Mathews, J. J., Velarde, A. G., Muhammad, J. A. S., Luckey, A. M., Aslam, A., et al. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.13879 · Cited by 15 · see citing works
“…density, lower membrane permeability, greater myelination, etc.), due to the presence of crossing fibers throughout the majority of cerebral WM. 58 Notably, PSMD performs even better than traditional MD by leveraging skeletonization to focus the analysis of MD on the main fiber tracts,…”
BackgroundInfluentialAlzheimer's and vascular disease classification using regional texture biomarkers in FLAIR MRI.
Chan, K., Fischer, C. E., Maralani, P., Black, S., Moody, A., & Khademi, A. (2023) · NeuroImage: Clinical · doi:10.1016/j.nicl.2023.103385 · Cited by 14 · see citing works
“Recent studies on dMRI imaging show FA values are highly variable and conclusions about tissue integrity should be made with caution (Figley et al., 2022).”
BackgroundFaiyaz, A., Doyley, M. M., Schifitto, G., et al. (2023) · Frontiers in Neurology · doi:10.3389/fneur.2023.1168833 · Cited by 15 · see citing works
“Diffusion tensor imaging (DTI), a widely used dMRI approach, has been shown to be sensitive to pathological changes in the brain (5).”
BackgroundSelf-citationMahmoodi, A. L., Landers, M. J. F., Rutten, G.-J. M., & Brouwers, H. B. (2023) · Cancers · doi:10.3390/cancers15143631 · Cited by 14 · see citing works
Structural connectivity changes in focal epilepsy: Beyond the epileptogenic zone
Barrios‐Martinez, J., Almast, A., Lin, I., Youssef, A., Aung, T., Fernandes-Cabral, D. T., Yeh, F.-C. F., Chang, Y., Mettenburg, J., Modo, M., et al. (2024) · Epilepsia · doi:10.1111/epi.18175 · Cited by 15 · see citing works
Advanced MRI Methods for Diagnosis and Monitoring of Multiple Sclerosis ( MS )
Elahi, R., Taremi, S., Najafi, A., Karimi, H., Asadollahzadeh, E., Sajedi, S. A., Rad, H. S., & Sahraian, M. A. (2025) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.29817 · Cited by 14 · see citing works
“Current evidence increasingly suggests that FA, along with AD and RD, should not be interpreted as isolated biomarkers of WM integrity [97].”
Heijmans, M., Wolters, A. F., Temel, Y., et al. (2022) · Journal of Parkinson s Disease · doi:10.3233/jpd-223349 · Cited by 13 · see citing works
“However, due to the presence of many crossing fibers in the white matter, FA can also increase as a result of disproportionate degradation of one or more of these fiber bundles, even despite an actual decrease in local fiber density and myelination [10, 37].”
BackgroundAge of onset of obsessive-compulsive disorder differentially affects white matter microstructure
Vriend, C., de Joode, N. D., Pouwels, P., Liu, F., Otaduy, M., Pastorello, B., Robertson, F. C., Ipser, J., Lee, S., Hezel, D. M., et al. (2024) · Molecular Psychiatry · doi:10.1038/s41380-023-02390-8 · Cited by 13 · see citing works
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Structural connectivity of an interoception network in schizophreniaPreprint
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“…restriction of water diffusion in white matter (i.e., fiber packing density, axon diameter, directional organization, myelination, and membrane permeability; Beaulieu, 2002), caution should be taken when interpreting higher FA as indicating higher microstructural integrity (Figley et al., 2022).”
BackgroundUddin, M. N., Singh, M. V., Faiyaz, A., Szczepankiewicz, F., Nilsson, M., Boodoo, Z. D., Sutton, K. R., Tivarus, M. E., Zhong, J., Wang, L., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-80372-8 · Cited by 13 · see citing works
“Utilizing micrometer-scale displacement of tissue water, diffusion MRI (dMRI) can noninvasively detect microstructural abnormalities in the brain 15–18 .”
Self-citationExercise habits and mental health: Exploring the significance of multimodal imaging markers
Yao, Z., Hsieh, S., & Yang, M. (2023) · Progress in brain research · doi:10.1016/bs.pbr.2023.11.001 · Cited by 11 · see citing works
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“…at the microstructural level from FA, because its interpretation is limited in areas containing complex white matter fiber geometries or multiple crossing fibers, 53 we demonstrate that areas of WMH regression follow a different trajectory over time than other white matter areas for FA, MD, and T1.”
Ghaderi, S., Mohammadi, S., & Fatehi, F. (2024) · World Neurosurgery · doi:10.1016/j.wneu.2024.07.037 · Cited by 11 · see citing works
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“The DTI metrics analyzed in our research (FA, MD, RD and AD) reflect different aspects of WM properties (Winston 2012; Winklewski et al. 2018; Martinez-Heras et al. 2021; Figley et al. 2022).”
InfluentialSelf-citationZhao, H., Rong, B., Gao, G., Zhou, M., Huang, J., Tu, N., Bu, L., Xiao, L., & Wang, G. (2024) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2024.1364786 · Cited by 10 · see citing works
Fislage, M., Winzeck, S., Woodrow, R., Lammers‐Lietz, F., Stamatakis, E. A., Correia, M., Preller, J., Feinkohl, I., Hendrikse, J., Pischon, T., et al. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.13749 · Cited by 11 · see citing works
Papini, M. G., Avila, A. N., Fitzgerald, M., & Hellewell, S. C. (2024) · Journal of Neurotrauma · doi:10.1089/neu.2024.0039 · Cited by 10 · see citing works
“105,109,114 History of concussion or repetitive had injury was found to affect both blood and dMRI biomarkers of WM. Henry et al. found elevated FA in the corticospinal tract in athletes with a history of ‡ 3 concussions with concurrent changes to MD within the corpus callosum.”
Kucikova, L., Xiong, X., Reinecke, P., Madden, J., Jackson, E., Tappin, O., Huang, W., Dounavi, M., & Su, L. (2024) · Ageing Research Reviews · doi:10.1016/j.arr.2024.102510 · Cited by 10 · see citing works
Early life adversity and white matter microstructural organization—a systematic review
Mitchell, O., Roddy, D. W., & Connaughton, M. (2025) · Brain Imaging and Behavior · doi:10.1007/s11682-025-01007-8 · Cited by 10 · see citing works
“These issues can result in incorrectly identified tracts, partial volume errors, and inaccurate measurements (Figley et al., 2022; Keihaninejad et al., 2012).”
Lewis, L., Corcoran, M., Cho, K. I. K., Kwak, Y., Hayes, R. A., Larsen, B., & Jalbrzikowski, M. (2023) · Schizophrenia · doi:10.1038/s41537-023-00411-7 · Cited by 9 · see citing works
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“White matter integrity is influenced by a number of pathophysiological mechanisms and disruptions to white matter organization can arise from any of these mechanisms (Figley et al., 2022; Lagopoulos et al., 2013).”
Gudberg, C., Stevelink, R., Douaud, G., Wulff, K., Lazari, A., Fleming, M. K., & Johansen‐Berg, H. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.745014 · Cited by 9 · see citing works
“While specific biological interpretation of diffusion metrics can be challenging, particularly in regions of crossing fibers (Figley et al., 2022), our findings suggest a consistent picture of altered white matter microstructure across widely distributed regions.”
BackgroundThe role of brain white matter in depression resilience and response to sleep interventions
Bresser, T., Leerssen, J., Hölsken, S., Groote, I. R., Foster‐Dingley, J. C., van den Heuvel, M. P., & Someren, E. J. W. V. (2023) · Brain Communications · doi:10.1093/braincomms/fcad210 · Cited by 9 · see citing works
“More sophisticated diffusion analysis methods are becoming available.(18,60) Improved reproducibility, better inference and increased sensitivity and specificity, especially in regions with crossing fibres, may be expected from these more sophisticated methods and by using multiple modalities.”
InfluentialAsturias, A., Knoblauch, T., Rodriguez, A., Vanier, C. H., Tohic, C. L., Barrett, B., Eisenberg, M., Gibbert, R., Zimmerman, L., Parikh, S., et al. (2023) · Frontiers in Neuroimaging · doi:10.3389/fnimg.2023.1153115 · Cited by 9 · see citing works
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“One hypothesis is that it may not be associated with the changes in myelination, but fiber density and directional organization changes during brain development [58].”
BackgroundStructural connectivity in recovery after coma: Connectome atlas approach
Pozeg, P., Alemán‐Gómez, Y., Jöhr, J., Muresanu, D. F., Pincherle, A., Ryvlin, P., Hagmann, P., Diserens, K., & Dunet, V. (2023) · NeuroImage Clinical · doi:10.1016/j.nicl.2023.103358 · Cited by 10 · see citing works
“(Figley et al., 2022) this drawback can be improved with modern diffusion techniques that are more robust to crossing fibers.”
BackgroundAssessing quantitative MRI techniques using multimodal comparisons
Carter, F., Anwander, A., Johnson, M., Goucha, T., Adamson, H., Friederici, A., Lutti, A., Gauthier, C., Weiskopf, N., Bazin, P., et al. (2022) · bioRxiv · doi:10.1371/journal.pone.0327828 · Cited by 5 · see citing works
“The relatively low correlation in WM is also likely to be partially driven by the mismatch between the tensor-based simplification of WM architecture as FA, which best represents a single fibre population, and the reality that most regions include multiple fibre populations [76].”
Schrempft, S., Trofimova, O., Künzi, M., Ramponi, C., Lutti, A., Kherif, F., Latypova, A., Vollenweider, P., Marques-Vidal, P. M., Preisig, M., et al. (2024) · Journal of Neuroscience · doi:10.1523/jneurosci.1231-23.2024 · Cited by 8 · see citing works
The Impact of Insufficient Sleep on White Matter Development in Late Childhood and Early Adolescence
Santos, J. P. L., Soehner, A. M., Ladouceur, C. D., & Versace, A. (2024) · Journal of Adolescent Health · doi:10.1016/j.jadohealth.2024.10.007 · Cited by 9 · see citing works
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“This may be due to the degeneration of one or more fiber bundles in crossing fibers, resulting in a paradoxical increase in FA T compared to controls (Figley et al., 2021).”
Lewandowska, P., Bajada, C., Mysak, Y., Domagalik, A., Kossowski, B., Baumbach, C., Kaczmarek-Majer, K., Degórska, A., Skotak, K., Sitnik-Warchulska, K., et al. (2025) · Human Brain Mapping · doi:10.1002/hbm.70306 · Cited by 5 · see citing works
“Other direction-specific metrics (e.g., axial and radial diffusivity) were not included, as their interpretation can be more ambiguous in whole-brain averages across heterogeneous tissue types and regions with complex fiber architecture (Figley et al. 2022).”
Buthmann, J. L., Uy, J. P., Miller, J. G., Yuan, J. P., Coury, S. M., Ho, T. C., & Gotlib, I. H. (2024) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2024.101368 · Cited by 7 · see citing works
“…one metric of white matter tract structural organization (i.e., FA); scientists have called for the analysis of multiple measures before drawing strong conclusions about tract organization (Figley et al., 2022), and it is important that our findings be replicated and extended in future studies.”
BackgroundRetzler, C., Hallam, G., Johnson, S. J., & Retzler, J. (2023) · Research on Child and Adolescent Psychopathology · doi:10.1007/s10802-023-01065-w · Cited by 7 · see citing works
“The factors identified here can provide a starting point for less exploratory approaches in future studies, and be used in combination with other brain imaging metrics as suggested by Figley et al. (2022).”
MethodZhao, X., You, M., Ren, W., Ji, L., Liu, Y., & Lu, M. (2025) · Frontiers in Neurology · doi:10.3389/fneur.2025.1467578 · Cited by 7 · see citing works
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Characterisation of the neonatal brain using myelin-sensitive magnetisation transfer imaging
Blesa, M., Vaher, K., York, E. N., Galdi, P., Sullivan, G., Stoye, D. Q., Hall, J., Corrigan, A. E., Quigley, A. J., Waldman, A. D., et al. (2023) · Imaging Neuroscience · doi:10.1162/imag_a_00017 · Cited by 8 · see citing works
White matter brain age as a biomarker of cerebrovascular burden in the ageing brain
Du, J., Pan, Y., Jiang, J., Lam, B. C. P., Thalamuthu, A., Chen, R., Tsang, I. W.-H., Sachdev, P. S., & Wen, W. X. (2024) · European Archives of Psychiatry and Clinical Neuroscience · doi:10.1007/s00406-024-01758-3 · Cited by 8 · see citing works
“While some diffusion weighted imaging (DWI) measures such as fractional anisotropy (FA) and mean diffusivity (MD) can be used to examine the WM microstructural integrity, they typically capture distinct physiological properties [4].”
Brain health measurement: a scoping review
Lee, A. H. Y., Shah, S., Atha, K., Indoe, P., Mahmoud, N., Niblett, G., Pradhan, V., Roberts, N. W., Malouf, R. S., & Topiwala, A. (2024) · BMJ Open · doi:10.1136/bmjopen-2023-080334 · Cited by 10 · see citing works
“24 Another drawback of using MRI as a key method of measuring brain health is its significant cost.”
MethodKwiatkowski, A., Weidler, C., Habel, U., Coverdale, N. S., Hirad, A. A., Manning, K. Y., Rauscher, A., Bazarian, J. J., Cook, D. J., Li, D. K. B., et al. (2024) · Human Brain Mapping · doi:10.1002/hbm.26811 · Cited by 8 · see citing works
“Figley et al. (2022) have highlighted that in the presence of crossing fibers, which is the case in most WM voxels, FA can remain stable even with significant alterations in the underlying tissue composition.”
Lewis, C. J., Chipman, S. I., Johnston, J. M., et al. (2025) · Journal of Neurology · doi:10.1007/s00415-025-13091-3 · Cited by 7 · see citing works
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“The FA metric is influenced by a number of factors (Beaulieu, 2002; Curran et al., 2016); however, greater FA is generally interpreted as representing greater structural coherence of white matter tracts (but see Figley et al., 2022 for a more thorough discussion).”
BackgroundAsymmetries and hemispheric interaction in the auditory system of elderly people
Angenstein, N. (2024) · Frontiers in Neuroimaging · doi:10.3389/fnimg.2023.1320989 · Cited by 6 · see citing works
Spatola, G., Triebkorn, P., Richieri, R., Baunez, C., Farisse, J., Cretol, A., Guedj, E., Jirsa, V., & Régis, J. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e34699 · Cited by 6 · see citing works
“The disadvantage of this approach however is that DTI cannot account for multiple crossing fibres within a voxel, which may lead to overlooking or wrong interpretation of changes [20,21].”
BackgroundYi, X., Wang, Y., Shen, L., Fu, Y. H. H., Xiao, Q., Zhang, J., Ding, J., Jiang, F., Zhang, Z., Grecucci, A., et al. (2025) · Brain Imaging and Behavior · doi:10.1007/s11682-025-01046-1 · Cited by 6 · see citing works
“Although fractional anisotropy is the most commonly used metric in DTI studies, other DTI parameters including radial diffusivity (RD) and axial diffusivity (AD) may also provide meaningful diffusivity information of WM fiber tract integrity and efficiency (Figley et al., 2021 ).”
InfluentialGimbel, B. A., Roediger, D. J., Ernst, A. M., Anthony, M. E., de Water, E., Rockhold, M. N., Mueller, B. A., Mattson, S. N., Jones, K. L., Riley, E. P., et al. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1172010 · Cited by 6 · see citing works
“Using novel diffusion modeling approaches may identify potential white matter microstructural abnormalities with greater specificity (Sherbaf et al., 2019; Figley et al., 2021).”
BackgroundLim, L. L., Talozzi, L., & Howells, H. (2024) · BMC Psychiatry · doi:10.1186/s12888-024-05759-3 · Cited by 8 · see citing works
“Finally, given the presence of crossing fibres, we caution against over-interpreting the conventional DTI-based metrics (FA, AD and RD values) as standalone biomarkers of WM microstructure [97].”
BackgroundExplaining recovery from coma with multimodal neuroimaging
Pozeg, P., Jöhr, J., Prior, J. O., Diserens, K., & Dunet, V. (2024) · Journal of Neurology · doi:10.1007/s00415-024-12591-y · Cited by 7 · see citing works
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Aging Brain from a Lifespan Perspective
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“25 It has also been reported that MD could be more closely related to neurite density than FA, AD, or RD measures.”
D’Antonio, F., Teghil, A., Boccia, M., Gabrielli, G. B., Giulietti, G., Conti, D., Suppa, A., Fabbrini, A., Fiorelli, M., Caramia, F., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-65536-w · Cited by 5 · see citing works
“For instance, a selective loss of unidirectional fibers in a brain area with crossing fibers can lead to an increase in FA, while the loss of fibers in a white matter region characterized by only one direction (such as the corpus callosum) can result in a decrease in FA 57,58 .”
Gilliam, J. R., Sahu, P. K., Vendemia, J. M. C., & Silfies, S. P. (2024) · PLoS ONE · doi:10.1371/journal.pone.0309344 · Cited by 5 · see citing works
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“28 Based on conventional interpretation, we could infer that higher FA value might reflect increased number of microstructural tissue element, however, due to the presence of crossing fibers, disproportionate atrophy (while preservation of other fiber bundles) could result in increase of FA,…”
Diffusion tensor imaging after COVID-19 infection: A systematic review
Nelson, B. K., Farah, L. N., Saint, S. A., Song, C., Field, T. S., Sossi, V., Stoessl, A. J., Wellington, C. L., Honer, W. G., Lang, D. J., et al. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121150 · Cited by 6 · see citing works
“…are derived based on the principal direction of diffusion in a voxel and in the case of crossing fibres within a voxel, disproportionate degeneration of fibre bundles in one direction can lead to increased anisotropy even in the presence of damage to the white matter tracts (Figley, et al., 2022).”
Coexisting depression and frailty as an accelerated aging phenotype of late-life depression
Taylor, W. D. (2023) · International Psychogeriatrics · doi:10.1017/s1041610223000170 · Cited by 5 · see citing works
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“Higher FA values suggest better connectivity (connectivity with more directed diffusion) and in addition, facilitate communication between brain areas important for learning and memory [26, 50].”
BackgroundMethod9-HODE associates with thalamic atrophy and predicts white matter damage in multiple sclerosis
Fung, W. H., van Lingen, M. R., Broos, J. Y., Lam, K., van Dam, M., Fung, W. K., Noteboom, S., Koubiyr, I., de Vries, H. E., Jasperse, B., et al. (2024) · Multiple Sclerosis and Related Disorders · doi:10.1016/j.msard.2024.105946 · Cited by 6 · see citing works
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“Composite measures also capture the multidimensionality of brain morphology and may therefore be more sensitive to abnormalities therein than any of their components alone 45,46 .”
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Longitudinal Identification of Pre‐Lesional Tissue in Multiple Sclerosis With Advanced Diffusion MRI
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Quantitative diffusion imaging and genotype‐by‐sex interactions in a rat model of Alexander disease
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“23 With NODDI, however, that we specifically see decreased neurite density and orientation dispersion of neurites is consistent both with previous histological analyses of our AxD rat model 10 and with characteristic microstructural Tabular summary of the significant effects of the Alexander…”
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“As crossing fibers are widespread in the brain [43], [44], both micro-and macro-structural abnormalities detected by DNT in one WM tract could still be due, partly or fully, to differences in another fiber population, even when accounting for bundle geometry.”
Bettcher, B. M., Lopez‐Paniagua, D., Wang, Y., McConnell, B. V., Coughlan, C. M., Carlisle, T. C., Thaker, A. A., Lippitt, W., Filley, C. M., Pelak, V. S., et al. (2024) · Brain Behavior & Immunity - Health · doi:10.1016/j.bbih.2024.100834 · Cited by 3 · see citing works
McMaster, E. M., Newlin, N. R., Cho, C. Y., Rudravaram, G., Saunders, A. M., Krishnan, A. R., Remedios, L. W., Kim, M. E., Xu, H., Schilling, K. G., et al. (2025) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2025.110539 · Cited by 3 · see citing works
“The effect of spatial sampling on image-based brain map approximations can be observed in the microstructure [9].”
- Authors
- Figley, C. R., Uddin, M. N., Wong, K., Kornelsen, J., Puig, J., & Figley, T. D.
- Affiliations
- Health Sciences Centre (CA)
- University of Rochester Medicine (US)
- University of Manitoba (CA)
- Corresponding author
- Figley, Chase R.
- Venue
- Frontiers in Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2022-01-14
- Volume
- 15
- Pages
- 799576-799576
- ISSN
- 1662-453X (ISSN-L)
- PMC
- PMC8795606
- OpenAlex
- W4205639396
- Semantic Scholar
- be9d91e96b10025ed794c12fd9c9656efe11d15a
- Cited by
- 253
- References
- 54
- Citations by year
- 2026: 66 · 2025: 68 · 2024: 76 · 2023: 33 · 2022: 10
- FWCI
- 24.05
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Neuroscience (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Health Sciences Centre Foundation
- Research Manitoba
- Natural Sciences and Engineering Research Council of Canada
- 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
- fractional anisotropy, axial diffusivity, radial diffusivity, white matter microstructure, diffusion MRI, crossing fibers, MRI biomarkers
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.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-18.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-18.
Cited sources are checked by gpt-6-sol.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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