Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review

Studies consistently observe significant age-related increases of FA and MD in widespread fiber tracts during postnatal growth in both cross-sectional and longitudinal designs.

“Despite focusing on different developmental ranges, these studies consistently observe significant age-related increases of FA and MD in widespread fiber tracts during postnatal growth in both cross-sectional and longitudinal population designs (Lebel and Deoni, 2018).”

• cites Lebel and Deoni, 2018 sourceDisputed

  Section 3.3 (Developmental Timing; page not provided) reports that FA increases with age, but MD decreases—the opposite of the claimed MD increase. It also discusses longitudinal developmental curves, so the disagreement is specifically about MD's direction of change. (LLM, conf=0.90)

  Checked by a reasoning model — may be wrong.

  Experimental — accuracy not yet measured

Knowledge- and data-driven filtering methods have not proved effective in discriminating between true and spurious connections in connectome networks.

“actually none proved effective in discriminating between true and spurious connections in the connectome networks (Maier-Hein et al., 2017; Schiavi et al., 2020a).”

• cites Maier-Hein et al., 2017 sourcePartly supported

  The Results (‘Tractograms contained more invalid than valid bundles’; page unavailable) report that tested tractography approaches produced more invalid than valid bundles, supporting the concern about spurious connections. But the Results (‘Statistical analysis of processing steps’; page unavailable) say filtering affected performance and one clustering approach achieved 92% validly connecting streamlines. The paper therefore does not establish that no knowledge- or data-driven filtering method proved effective at discrimination. (LLM, conf=0.85)

  Experimental — accuracy not yet measured
• cites Schiavi et al., 2020aNot matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Tractography results are sensitive to the choice of the underlying fiber tracking algorithm, which can affect subsequent quantitative analyses using the tractography data.

“It is important to note that tractography is known to be sensitive to the choice of the underlying fiber tracking algorithms, which can further affect any subsequent quantitative analyses using the tractography data.”

• cites Afzali et al., 2020 sourceCould not verify

  The Microstructure models > Effect of model fitting passage mentions combining global tractography with microstructure estimation, but does not compare fiber-tracking algorithms or their effects on analyses of tractography data. Searches for tractography and tracking algorithms found no passage in our copy addressing that specific claim; pages were not provided. (LLM, conf=0.72)

  Experimental — accuracy not yet measured
• cites Alexander, 2006 sourceNot checked — no full text of the source
• cites Panagiotaki et al., 2012 sourceNot checked — no full text of the source

Anatomically constrained tractography and similar methods constrain streamline propagation and termination using knowledge of where neuronal fibers locate, preventing streamlines that cannot represent biological connectivity.

“the Anatomically-Constrained Tractography (ACT) framework (Smith et al., 2012) and similar methods (Girard and Descoteaux, 2012; Lemkaddem et al., 2014; Yeh et al., 2017) ensure that both streamline propagation and termination are constrained based on knowledge of where neuronal fibers locate, e.g., connecting between gray matter areas via the white matter.”

• Also stated in: § Termination bias · plain

  “they do prevent the generation of streamlines that cannot possibly represent biological connectivity within the brain.”

• cites Smith et al., 2012 sourceNot checked — no full text of the source
• cites Girard and Descoteaux, 2012Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Lemkaddem et al., 2014 sourceSupported

First-order streamline tractography in curved bundles tends to underestimate curvature, producing erroneous trajectories.

“Possibly the earliest bias established for dMRI tractography was the fact that if the streamline algorithm simply takes a step of finite size in the direction of the local fiber orientation at the current vertex (a so-called “first-order” method), streamlines in curved bundles will tend to under-estimate the curvature, resulting in erroneous trajectories (Tournier et al., 2002).”

• cites Tournier et al., 2002 sourcePartly supported

  The Tracking Algorithm section describes finite steps taken in the local major-eigenvector direction. Model A reports that larger steps increase endpoint offset and that tracks can depart systematically from an ideal curved path, especially without interpolation. The copy does not establish the claimed direction of the bias—that these tracks tend to underestimate curvature. (LLM, conf=0.85)

  Experimental — accuracy not yet measured

Tractography techniques suffer from a large number of false positive streamlines that do not correspond to real anatomical bundles.

“In particular, it has been shown that tractography techniques suffer from a large number of false positives, i.e., streamlines that are reconstructed but do not correspond to real anatomical bundles”

• cites Maier-Hein et al., 2017 sourceSupported
• cites Thomas et al., 2014 sourcePartly supported

  The Discussion (page unavailable) reports that high-sensitivity tractography tends to have a high false-positive rate, supporting the concern about reconstructed pathways without anatomical support. But the paper evaluates false positives by anatomical region, not by counting individual streamlines, and says some methods and settings can achieve much lower false-positive rates. The claim overgeneralizes those findings to tractography techniques as a whole. (LLM, conf=0.85)

  Experimental — accuracy not yet measured

False-positive connections can severely bias estimation of connectivity.

“and that these erroneous connections can severely bias the estimation of connectivity (Drakesmith et al., 2015; Maier-Hein et al., 2017; Zalesky et al., 2016b).”

• cites Drakesmith et al., 2015 sourceSupported
• cites Maier-Hein et al., 2017 sourceSupported
• cites Zalesky et al., 2016bNot matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Tractography results can vary across the large set of available diffusion models and tractography methods.

“On one hand, tractography results may vary across the large set of available diffusion models (Afzali et al., 2020; Alexander, 2006; Panagiotaki et al., 2012; Yablonskiy and Sukstanskii, 2010) and tractography methods”

• cites Basser et al., 2000 sourceNot checked — no full text of the source
• cites Behrens et al., 2007 sourceSupported
• cites Descoteaux et al., 2008 sourceSupported

White matter fibers synapse in the gray matter.

“despite the fact that white matter fibers synapse in the gray matter (Daducci et al., 2016)”

• cites Daducci et al., 2016 sourceSupported

Tractography results can be sensitive to parameter choices within a given algorithm, such as seeding and stopping thresholds.

“On the other hand, tractography results can also be sensitive to the choice of parameters (such as seeding and stopping thresholds) within a certain algorithm (Chamberland et al., 2014; Côté et al., 2013; Gong et al., 2018; Moldrich et al., 2010; Xie et al., 2020).”

• cites Chamberland et al., 2014 sourceSupported
• cites Côté et al., 2013Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Gong et al., 2018 sourceSupported

One study indicates that streamline labeling has higher test-retest reproducibility than ROI-based segmentation.

“For automated tract identification, one study indicates that streamline labeling has higher test-retest reproducibility than ROI-based segmentation (Zhang et al., 2019)”

• cites Zhang et al., 2019 sourceSupported

Manual streamline selection has been shown highly reproducible for specific tracts and segmentation protocols using diffusion tensor tractography.

“Methods using manual streamline selection have been shown to be highly reproducible for specific tracts and segmentation protocols using the diffusion tensor tractography (Wakana et al., 2007)”

• cites Wakana et al., 2007 sourceSupported

Combining knowledge- and data-driven strategies significantly boosts filtering performance.

“It was recently demonstrated that the performance of filtering methods can be significantly boosted by combining knowledge- and data-driven strategies.”

• cites Ocampo-Pineda et al., 2021 sourceSupported
• cites Schiavi et al., 2020aNot matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

There is ongoing disagreement about whether filtering methods are beneficial or detrimental to the structural connectome.

“Research is still underway to determine the best method(s) for filtering, with some disagreement on the beneficial versus detrimental effect of these methods on the structural connectome (Buchanan et al., 2020; Civier et al., 2019; Drakesmith et al., 2015; Frigo et al., 2020; Schiavi et al., 2020a; Yeh et al., 2016a).”

• cites Buchanan et al., 2020 sourceSupported
• cites Civier et al., 2019 sourceSupported
• cites Drakesmith et al., 2015 sourcePartly supported

  The Introduction’s “Thresholding” section says removing weak connections can suppress false positives but can also remove genuine connections and bias network metrics; it calls the alternative of leaving graphs unthresholded contentious. This supports a beneficial-versus-detrimental trade-off for network thresholding, but the paper does not establish disagreement about filtering methods for the structural connectome generally. Page numbers were not provided in our copy. (LLM, conf=0.82)

  Experimental — accuracy not yet measured

New filtering formulations that organize streamlines into groups and seek the minimum number of bundles using Group Lasso significantly improve anatomical accuracy of the connectome.

“This prior knowledge helps resolve some of the ambiguities present in the data and, as a consequence, allows significant improvement of the anatomical accuracy of the connectome (Ocampo-Pineda et al., 2021; Schiavi et al., 2020a).”

• cites Ocampo-Pineda et al., 2021 sourceSupported
• cites Schiavi et al., 2020aNot matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Yuan and Lin, 2006 sourcePartly supported

  Section 2 (Group lasso; page not available) says group lasso selects groups of variables and encourages sparsity at the factor level. The paper does not address streamline filtering, bundles, connectomes, or improved anatomical accuracy; those application and outcome claims are unsupported by this source. (LLM, conf=0.84)

  Experimental — accuracy not yet measured

• AssumptionThe authors state this as an assumption: Fibers are naturally organized in bundles.
• AssumptionThe authors state this as an assumption: The number of bundles should be low to minimize the overall wiring cost.

The performance of sophisticated registration tools such as FSL and ANTs is limited by nontrivial factors.

“While there are many sophisticated tools to compute these registrations such as FSL (Jenkinson et al., 2012) and ANTs (Avants et al., 2009), the performance is limited by nontrivial factors.”

• cites Jenkinson et al., 2012 sourceSupported
• cites Avants et al., 2009 sourceNot checked — no full text of the source

A fine-scale white matter parcellation (over 2000 parcels) can be beneficial for machine learning and statistical analysis.

“studies have suggested a fine scale white matter parcellation (over 2000 parcels) can be beneficial for machine learning and statistical analysis (Liu et al., 2017; Zhang et al., 2018a) while a coarse scale parcellation (fewer than 200 parcels) can improve the connectome consistency across individuals (Osmanlıoğlu et al., 2020; Rodrigues et al., 2013).”

• cites Liu et al., 2017 sourcePartly supported

  The Discussion reports that finer-grained brain parcellation, at or approaching 2000 nodes, improved classification of consciousness states. The Methods describe statistical comparisons and classification, but specify a maximum of 2000 nodes, not over 2000. The paper describes anatomical-functional brain parcellation, not a white-matter parcellation. (LLM, conf=0.89)

  Experimental — accuracy not yet measured
• cites Zhang et al., 2018aNot matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Many factors, such as cell death, edema, gliosis, inflammation, change in myelination, increase in connectivity of crossing fibers, and increase in extracellular or intracellular water, may cause changes in FA.

“For example, many factors (e.g., cell death, edema, gliosis, inflammation, change in myelination, increase in connectivity of crossing fibers, increase in extracellular or intracellular water, etc.) may cause changes in FA (Assaf and Pasternak, 2008; Jones et al., 2013; Le Bihan and Johansen-Berg, 2012).”

• cites Assaf and Pasternak, 2008 sourceNot checked — no full text of the source
• cites Jones et al., 2013 sourceNot checked — no full text of the source
• cites Le Bihan and Johansen-Berg, 2012 sourcePartly supported

  The sections on diffusion anisotropy in white matter and white matter diseases (pages not provided) say that myelin, fiber arrangement, extracellular water and tissue orientation patterns can affect anisotropy, supporting part of the FA claim. The cell-size section links edema and shifts between intracellular and extracellular spaces to changes in ADC, not specifically FA. Our copy does not establish cell death, gliosis, inflammation, or increased connectivity of crossing fibers as causes of FA changes. (LLM, conf=0.86)

  Experimental — accuracy not yet measured

Animal studies using invasive tract-tracing techniques have shown good agreement between NOS and biological connectivity measured using ex vivo tract-tracing data.

“Several studies conducted on animals using invasive tract-tracing techniques have shown good agreement between the NOS and the biological connectivity as measured using ex vivo tract-tracing data (Delettre et al., 2019; Girard et al., 2020; van den Heuvel et al., 2015a).”

• cites Delettre et al., 2019 sourceSupported
• cites Girard et al., 2020 sourcePartly supported

  The Conclusion reports good agreement between ex-vivo macaque tractography and connections identified by histological tracing. But §4.5 says the comparison was of binary connectivity, not histological cell counts or connection strength; it therefore does not establish good quantitative agreement between the number of streamlines (NOS) and biological connectivity. (LLM, conf=0.85)

  Experimental — accuracy not yet measured
• cites van den Heuvel et al., 2015aNot matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

A coarse-scale parcellation (fewer than 200 parcels) can improve connectome consistency across individuals.

“studies have suggested a fine scale white matter parcellation (over 2000 parcels) can be beneficial for machine learning and statistical analysis (Liu et al., 2017; Zhang et al., 2018a) while a coarse scale parcellation (fewer than 200 parcels) can improve the connectome consistency across individuals (Osmanlıoğlu et al., 2020; Rodrigues et al., 2013).”

• cites Osmanlıoğlu et al., 2020 sourceSupported
• cites Rodrigues et al., 2013 sourceNot checked — no full text of the source

Various parcellation scales have been applied in different studies, ranging from tens to millions of white matter parcels.

“Various parcellation scales have been applied in different studies, ranging from tens to millions of white matter parcels (Besson et al., 2014; Liu et al., 2017; Osmanlıoğlu et al., 2020; Rodrigues et al., 2013; Zalesky et al., 2010; Zhang et al., 2018a).”

• cites Besson et al., 2014 sourceNot checked — no full text of the source
• cites Liu et al., 2017 sourcePartly supported

  The Materials and Methods section (page unavailable) reports parcellation at multiple scales, from 116 to 2,000 brain-network nodes. That supports the use of varying scales, but this paper does not establish the claimed range from tens to millions or describe those nodes as white matter parcels. (LLM, conf=0.88)

  Experimental — accuracy not yet measured
• cites Osmanlıoğlu et al., 2020 sourcePartly supported

  The Data and methods section (“Atlases and registrations”; page unavailable) reports structural and functional connectomes at three parcellation scales: 118, 218, and 318 regions. This supports the use of different scales, but the paper does not establish a range from tens to millions of white matter parcels; its reported regions are not described as white matter parcels. (LLM, conf=0.86)

  Experimental — accuracy not yet measured

The number of streamlines (NOS) is a widely used measure to quantify connectivity strength.

“The number of streamlines (NOS) is one widely used measure to quantify the connectivity strength (Hagmann et al., 2008b; Ingalhalikar et al., 2014; Roberts et al., 2016; Sporns et al., 2005; van Dellen et al., 2018).”

• cites Hagmann et al., 2008bNot matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Ingalhalikar et al., 2014 sourcePartly supported

  In Materials and Methods (page not provided), the paper uses the number of tracked fibers reaching a target region to calculate a connectivity weight and notes that similar measures appear in previous studies. Its measure is a scaled probability, however, not the raw number of streamlines; the passage therefore does not establish that NOS itself is widely used to quantify connectivity strength. (LLM, conf=0.82)

  Experimental — accuracy not yet measured
• cites Roberts et al., 2016 sourceNot checked — no full text of the source

NOS does not provide a truly quantitative measure of connection strength because of the difference in resolution between the measured dMRI signal and actual axon dimensions.

“However, because of the difference in resolution between the measured dMRI signal and actual axon dimensions (as described in Section 3.3: “Connection density biases”), many studies have emphasized that NOS does not provide a truly quantitative measure of connection strength (Jones, 2010; Jones et al., 2013; Sotiropoulos and Zalesky, 2019; Yeh et al., 2020).”

• cites Jones, 2010 sourceNot checked — no full text of the source
• cites Jones et al., 2013 sourceNot checked — no full text of the source
• cites Sotiropoulos and Zalesky, 2019 sourceSupported

Inter-subject connectome consistency may also be improved using other approaches such as consistency-based thresholding.

“We note that inter-subject connectome consistency may also be improved using other approaches, e.g., consistency-based thresholding (Baum et al., 2018; Roberts et al., 2017).”

• cites Baum et al., 2018 sourceSupported
• cites Roberts et al., 2017 sourceNot checked — no full text of the source

More advanced microstructural measures include inter- and intra-cellular signal fractions derived from DKI, Free Water modeling, NODDI, SMT, or AFD.

“More advanced measures include those reflecting inter- and intra-cellular signal fractions derived from Diffusion Kurtosis Imaging (DKI) (Jensen and Helpern, 2010), Free Water modeling (Pasternak et al., 2012; 2009), Neurite Orientation Dispersion and Density Imaging (NODDI) (Zhang et al., 2012), Spherical Mean Technique (SMT) (Kaden et al., 2016), or Apparent Fiber Density (AFD) (Raffelt et al.,”

• cites Jensen and Helpern, 2010 sourcePartly supported

  The Simple Models section (page unavailable) discusses intracellular and extracellular compartments and relates kurtosis to a compartment’s water fraction. It says, however, that DKI-derived diffusion and kurtosis measures do not uniquely determine the fractions: kurtosis provides only an upper bound for the fast-diffusing compartment’s fraction. The paper therefore supports DKI as an advanced measure sensitive to tissue structure, but not the claim that it yields inter- and intra-cellular signal fractions. (LLM, conf=0.86)

  Experimental — accuracy not yet measured
• cites Pasternak et al., 2012 sourcePartly supported

  The Free-Water Model section describes separate free-water and tissue signal contributions and identifies free water as extracellular. It does not identify the tissue contribution as an intra-cellular fraction, so the citation supports the Free Water example but not that more specific characterization. (LLM, conf=0.86)

  Experimental — accuracy not yet measured
• cites Zhang et al., 2012 sourceNot checked — no full text of the source

One widely used hypothesis-driven analysis assesses statistical differences of tracts of interest between groups using statistical group-wise comparison methods such as Student's t-test, ANOVA, or more advanced methods.

“Usually, given a quantitative measure of interest, a selected summary statistic (e.g. mean of the chosen microstructural measure) is extracted from the tract(s)-of-interest and is used to compute the level of group differences (e.g., the p-value) using statistical group-wise comparison methods such as Student’s t-test, ANOVA, or other more advanced statistical analysis methods (Habeck and Stern, 2”

• cites Habeck and Stern, 2010 sourcePartly supported

  The Toy Example section (page unavailable) describes a standard group comparison using a univariate T-test, supporting that part of the citation. It does not describe extracting mean microstructural measures from tracts of interest or comparing those summaries with ANOVA; its example compares voxels and PCA subject scores instead. (LLM, conf=0.84)

  Experimental — accuracy not yet measured
• cites Martí-Juan et al., 2020 sourcePartly supported

  Section 4.5 (p. 16) discusses statistical methods for detecting longitudinal MRI group differences and names ANOVA as a comparator. It does not describe hypothesis-driven comparisons of tracts of interest, extraction of a summary microstructural measure, or use of Student’s t-test or p-values. (LLM, conf=0.79)

  Experimental — accuracy not yet measured
• cites Ombao et al., 2016 sourceNot checked — no full text of the source

The diffusion or fiber model can be simultaneously estimated when performing tractography so that microstructural measures are directly computed at points along each streamline.

“Instead of using a pre-calculated microstructure image, the diffusion or fiber model can be simultaneously estimated when performing tractography so that the microstructural measures are directly computed at points along each streamline (Girard et al., 2017; Gong et al., 2018; Malcolm et al., 2010; Olszewski et al., 2017; Reddy and Rathi, 2016).”

• cites Girard et al., 2017 sourceNot checked — no full text of the source
• cites Gong et al., 2018 sourceSupported
• cites Malcolm et al., 2010 sourceSupported

Other scalar values generally interpreted as measures of connectivity strength include microstructural measures such as FA and R1.

“Other scalar values that are generally interpreted as measures of connectivity “strength” include microstructural measures, e.g., FA (Bathelt et al., 2017) and R1 (Boshkovski et al., 2020a).”

• cites Bathelt et al., 2017 sourceSupported
• cites Boshkovski et al., 2020aNot matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

The most popular scalar value for connectivity matrix analysis is the NOS, where higher values are considered to indicate stronger connectivity between pairs of gray matter regions.

“The most popular scalar value for connectivity matrix analysis is the NOS (Hagmann et al., 2008b; Ingalhalikar et al., 2014; Roberts et al., 2016; Sporns et al., 2005; van Dellen et al., 2018), where higher values are considered to indicate stronger connectivity between pairs of gray matter regions.”

• cites Hagmann et al., 2008bNot matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Ingalhalikar et al., 2014 sourcePartly supported

  Materials and Methods (page unavailable) describes a connectivity matrix derived from streamline counts, with higher connection weights treated as greater connectivity. It does not identify the number of streamlines (NOS) as the most popular scalar value; its own matrix entries are scaled pathway probabilities rather than raw streamline counts. (LLM, conf=0.84)

  Experimental — accuracy not yet measured
• cites Roberts et al., 2016 sourceNot checked — no full text of the source

One popular approach is to average data across corresponding points along each streamline and leverage a prototype, core, or mean streamline to enable statistical analysis comparing subjects or groups.

“One popular approach is to average data across corresponding points along each streamline in the pathway and leverage a prototype, core, or mean streamline to enable statistical analysis comparing subjects or groups (Colby et al., 2012b; Wang et al., 2016; Yeatman et al., 2012).”

• cites Colby et al., 2012bNot matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Wang et al., 2016 sourceNot checked — no full text of the source
• cites Yeatman et al., 2012 sourceSupported

Quantitative measures extracted for individual fiber pathways can be used to construct a structural connectivity matrix mapping the whole-brain network of structural connectivity between all pairs of gray matter regions.

“Quantitative measures extracted for individual fiber pathways can also be used to construct a structural connectivity matrix to map the whole-brain network of structural connectivity between all pairs of gray matter regions (Hagmann et al., 2008a; Sporns et al., 2005).”

• cites Hagmann et al., 2008aNot matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Sporns et al., 2005 sourcePartly supported

  In “Steps Toward the Human Connectome,” the paper proposes using probabilistic tractography of fiber pathways to produce an all-to-all structural connectivity matrix for the human brain. It specifies a voxel-wise probabilistic matrix, however, rather than a matrix between all pairs of gray-matter regions, and does not specify quantitative measures extracted for each individual pathway. (LLM, conf=0.84)

  Experimental — accuracy not yet measured

Many methods perform along-tract analysis to investigate the distribution of microstructural measures along the fiber pathway.

“Instead of using a single scalar value to summarize the entire pathway, many methods perform along-tract analysis to investigate the distribution of the microstructural measures along the fiber pathway (variously called “tractometry,” “profilometry,” and “tract-based morphometry”) (Bells et al., 2011b; O’Donnell et al., 2009; Yeatman et al., 2012).”

• cites Bells et al., 2011bNot matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites O’Donnell et al., 2009 sourceSupported
• cites Yeatman et al., 2012 sourceSupported

Hubs identified with centrality measures have high metabolic expenditures.

“Indeed, hubs identified with these measures have been found to entail high metabolic expenditures and be disproportionately implicated in brain disorders (Crossley et al., 2014; Fornito et al., 2015).”

• cites Crossley et al., 2014 sourceSupported
• cites Fornito et al., 2015 sourceNot checked — no full text of the source

In the postnatal stage, in vivo tractography offers a superior tool to chart spatiotemporal maturation patterns of reconstructed tracts using quantitative dMRI metrics from infancy to adolescence.

“In the postnatal stage, in vivo tractography offers a superior tool to chart the spatiotemporal maturation patterns of the reconstructed tracts by employing quantitative dMRI metrics from the infancy period (Dubois et al., 2014; Gilmore et al., 2018; Lebel and Deoni, 2018; Qiu et al., 2015; Vasung et al., 2019) to the adolescent stage (Lebel et al., 2019; Tamnes et al., 2018).”

• cites Dubois et al., 2014 sourceNot checked — no full text of the source
• cites Gilmore et al., 2018 sourcePartly supported

  The Introduction (White matter; page unavailable) says diffusion imaging visualizes major fibre tracts and measures their microstructure using AD, RD and FA. Structural development (White matter; page unavailable) describes how those measures change from infancy through later childhood and adolescence. The paper does not establish that in vivo tractography is a superior tool; the citing sentence assigns adolescence to other references. (LLM, conf=0.82)

  Experimental — accuracy not yet measured
• cites Lebel and Deoni, 2018 sourcePartly supported

  The Introduction (§2.2; page unavailable) says DTI provides quantitative diffusion metrics and allows reconstruction of fibre architecture through tractography. Developmental Timing (§3.3; page unavailable) uses tractography and FA values to describe maturation across childhood and adolescence, and the review also covers infancy. The copy does not establish that tractography is a superior tool for this purpose; the Conclusions instead describe DTI as powerful but limited. (LLM, conf=0.82)

  Experimental — accuracy not yet measured

Structural connectivity can be partitioned into modules (communities) that reflect clusters embedded in the brain's wiring.

“At the mesocale, structural connectivity can be partitioned into modules (also known as communities) that reflect the presence of clusters embedded in the brain’s wiring (Betzel, 2020; Sporns and Betzel, 2016).”

• cites Betzel, 2020 sourceSupported
• cites Sporns and Betzel, 2016 sourceSupported

Researchers have implemented dMRI tractography investigations across broad brain developmental stages from middle fetal stage to adulthood.

“In the past decade, researchers have implemented dMRI tractography investigations across broad brain developmental stages ranging from the middle fetal stage to adulthood (Cao et al., 2017; Dubois et al., 2014; Gilmore et al., 2018; Huang, 2010; Lebel and Deoni, 2018; Lebel et al., 2019; Qiu et al., 2015; Zhao et al., 2019b).”

• cites Cao et al., 2017 sourcePartly supported

  The section “Early Development of the Human Brain from a Connectome Perspective” describes studies using diffusion MRI from 20 postmenstrual weeks to age five, and the References list a fetal MR tractography study. The paper therefore supports investigation beginning around mid-gestation, but it does not describe a span of dMRI tractography investigations extending to adulthood; its discussion of adult tractography is a methodological comparison. (LLM, conf=0.87)

  Experimental — accuracy not yet measured
• cites Dubois et al., 2014 sourceNot checked — no full text of the source
• cites Gilmore et al., 2018 sourcePartly supported

  The White matter sections describe diffusion imaging of fibre tracts at birth and DTI studies of development through later childhood and adolescence; the References also list a DTI study spanning infancy to adulthood. Our copy does not document dMRI tractography investigations beginning in the middle fetal stage, so it does not support the full age range claimed. (LLM, conf=0.82)

  Experimental — accuracy not yet measured

The results of fetal tractography are consistent with classical histological studies.

“These results are consistent with classical histological studies (Mitter et al., 2015; Vasung et al., 2010).”

• cites Mitter et al., 2015 sourceSupported
• cites Vasung et al., 2010 sourceNot checked — no full text of the source

Node centrality measures provide a useful characterization of the integrative importance of individual brain regions.

“At the local level, node centrality measures provide a useful characterization of the integrative importance of individual brain regions (Sporns et al., 2007).”

• cites Sporns et al., 2007 sourceSupported

Hubs identified with centrality measures are disproportionately implicated in brain disorders.

“Indeed, hubs identified with these measures have been found to entail high metabolic expenditures and be disproportionately implicated in brain disorders (Crossley et al., 2014; Fornito et al., 2015).”

• cites Crossley et al., 2014 sourceSupported
• cites Fornito et al., 2015 sourceNot checked — no full text of the source

In the early postnatal stage, ongoing myelination is a clear cause of tract-related changes.

“In the early postnatal stage, ongoing myelination is a clear cause (Paydar et al., 2014)”

• cites Paydar et al., 2014 sourceSupported

After birth, the left anterior cingulate gyrus and left superior occipital gyrus become hubs in toddler brains.

“After birth, the left anterior cingulate gyrus and left superior occipital gyrus become hubs in toddler brains (Huang et al., 2015).”

• cites Huang et al., 2015 sourceSupported

Hubs expand into the inferior frontal and insula regions at term age.

“hubs expand into the inferior frontal and insula regions at term age (Ball et al., 2013; Van Den Heuvel et al., 2015b).”

• cites Ball et al., 2013 sourceCould not verify

  The Whole-Cortex Survey (page unavailable) mentions inferior frontal cortex and the insula as locations in diffusivity clusters, but does not identify network hubs or track their expansion. The Discussion (page unavailable) discusses cortical microstructural maturation and only speculates about its relationship to functional connectivity. Searches for hubs expanding into these regions at term found no supporting passage. (LLM, conf=0.72)

  Experimental — accuracy not yet measured
• cites Van Den Heuvel et al., 2015bNot matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

The centrality of the pre-cuneus and cuneus still increases before pre-adolescence.

“The centrality of the pre-cuneus and cuneus still increases before pre-adolescence (Huang et al., 2015; Yap et al., 2011).”

• cites Huang et al., 2015 sourceSupported
• cites Yap et al., 2011 sourcePartly supported

  The Results section on pediatric network organization reports high centrality for the precuneus and changes in nodal roles across the first two years of life (Fig. 9; no page given). It does not establish that centrality increases for either the precuneus or cuneus, or that such an increase continues up to pre-adolescence. (LLM, conf=0.83)

  Experimental — accuracy not yet measured

In later childhood and adolescence, tract-related changes seem attributable to changes in neurite density, particularly axonal packing.

“In later childhood and adolescence, results seem to be attributed to changes of neurite density, particularly axonal packing (Lebel and Deoni, 2018).”

• cites Lebel and Deoni, 2018 sourceSupported

Studies characterize typical nonlinear trajectories (piecewise, exponential, quadratic) of diffusion metrics for major tracts across large developmental ranges.

“Studies also characterize several typical nonlinear trajectories (piecewise, exponential and quadratic) of diffusion metrics for major tracts across large developmental ranges such as infancy to childhood (Pecheva et al., 2017; Reynolds et al., 2019; Stephens et al., 2020) or adolescence to adulthood (Chang et al., 2015; Lebel and Beaulieu, 2011).”

• cites Pecheva et al., 2017 sourcePartly supported

  The Methods and Results sections examine age-related diffusion metrics in major white-matter tracts of preterm infants. But the paper uses linear regression against post-menstrual age, not piecewise, exponential, or quadratic trajectories, and does not study a range from infancy to childhood. No page numbers are available in our copy. (LLM, conf=0.86)

  Experimental — accuracy not yet measured
• cites Reynolds et al., 2019 sourcePartly supported

  The Results (§3.1; page unavailable) report quadratic trajectories for FA and MD in several major tracts, supporting the nonlinear-trajectory claim. Reynolds et al. studied children aged 2–8 years and fitted linear and quadratic models; our copy does not support attributing piecewise or exponential models, or an infancy-to-childhood age span, to this study. (LLM, conf=0.84)

  Experimental — accuracy not yet measured
• cites Stephens et al., 2020 sourcePartly supported

  The Tract Trajectories section (page not provided) reports diffusion-metric trajectories for major tracts from birth to age 6, with much faster change in the first year than later years. The Statistical Analysis section (page not provided) says these trajectories were plotted from age-specific averages; our copy does not identify them as piecewise, exponential, or quadratic models. (LLM, conf=0.85)

  Experimental — accuracy not yet measured

In the fetal brain, tract-related changes are probably caused by pre-myelination phases such as proliferation and maturation of oligodendrocyte and progenitor cells.

“In the fetal brain, it is probably caused by pre-myelination phases such as proliferation and maturation of oligodendrocyte and progenitor cells, the glia that form the myelin sheath (Jakovcevski et al., 2009).”

• cites Jakovcevski et al., 2009 sourcePartly supported

  The sections on oligodendrocyte lineage progression and axonal signaling describe proliferating progenitors and the maturation of precursors into pre-myelinating oligodendrocytes in the fetal forebrain before myelination begins (page numbers unavailable). They do not establish that these processes cause the tract-related changes in the citing claim. (LLM, conf=0.84)

  Experimental — accuracy not yet measured

Early tractography studies found consistent aging-related changes in specific fiber bundles, including decreased FA, increased MD, and decreased tract volume.

“Early tractography studies usually estimated the aging effect on specific fiber bundles such as the corpus callosum (Sullivan et al., 2006), uncinate fasciculus (Hasan et al., 2009a), and fornix (Stadlbauer et al., 2008b), which show consistent changes including decreased FA, increased MD and decreased tract volume.”

• cites Sullivan et al., 2006 sourcePartly supported

  The Results report lower FA and higher ADC in older adults’ callosal fiber bundles; higher ADC supports the cited increase in mean diffusivity. They also report fewer identified fibers, but do not report decreased tract volume. The Discussion (p. 1035) says overall corpus callosum size did not differ by age. (LLM, conf=0.88)

  Experimental — accuracy not yet measured
• cites Hasan et al., 2009aNot matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Stadlbauer et al., 2008bNot matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Neonatal brains already show a structural hub distribution similar to adult brains.

“although neonatal brains already show similar structural hub distribution as adult brains”

• cites Yap et al., 2011 sourcePartly supported

  The Results sections on broad-scale networks and nodal roles report connection hubs in the neonatal network, but they do not compare the neonatal hub distribution with that of adults. The nodal-roles Results instead report substantial changes over the first two years; the Discussion’s reference to adult-like patterns concerns two-year-olds, not neonates. No page numbers were provided in our copy. (LLM, conf=0.83)

  Experimental — accuracy not yet measured

The age at which FA reaches its highest peak varies across different white matter tracts.

“the age when FA reaches its highest peak varies across different white matter tracts.”

• cites Hasan et al., 2010 sourceSupported
• cites 2009bNot matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Lebel et al., 2010 sourceNot checked — no full text of the source

Many refinements occur for the topological roles of each node after the neonatal period.

“many refinements occur for the topological roles for each node (Yap et al., 2011).”

• cites Yap et al., 2011 sourceSupported

Combining tractography atlases with intraoperative electrical stimulation has produced probabilistic atlases of white matter pathways and structural hubs defining a minimal common brain connectome with low inter-individual variability and low potential for post-lesional compensation.

“By combining tractography atlases with intraoperative electrical stimulation for functional mapping, researchers have constructed probabilistic atlases of white matter pathways and structural hubs that define a minimal common brain connectome with a low inter-individual variability and a low potential of post-lesional compensation (Herbet et al., 2016; Sarubbo et al., 2020).”

• cites Herbet et al., 2016 sourceNot checked — no full text of the source
• cites Sarubbo et al., 2020 sourcePartly supported

  Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

  Experimental — accuracy not yet measured

Loss of frontal hubs with aging may be driven by vulnerable prefrontal tracts.

“and loss of frontal hubs that may be driven by the vulnerable prefrontal tracts (Zhao et al., 2015).”

• cites Zhao et al., 2015 sourceSupported

In psychiatric surgery, advanced tractography facilitates combining brain connectomic analyses with neuromodulation interventions.

“In psychiatric surgery, advanced tractography facilitates the combination between brain connectomic analyses and neuromodulation interventions (Davidson et al., 2020; Horn et al., 2017; Riva-Posse et al., 2018)”

• cites Davidson et al., 2020 sourceSupported
• cites Horn et al., 2017 sourcePartly supported

  The Abstract > Interpretation and Subjects and Methods > Connectivity Estimation (page numbers unavailable) describe combining diffusion tractography and functional connectome analyses with deep brain stimulation. But the study tests this approach in Parkinson disease, not psychiatric surgery; its references to psychiatric applications do not establish the claim in that setting. (LLM, conf=0.87)

  Experimental — accuracy not yet measured
• cites Riva-Posse et al., 2018 sourceSupported

The brain connectomic concept benefits traditional surgical strategy by enabling a potential shift from local topography to network-guided "oncological disconnection surgery".

“The brain connectomic concept benefits traditional surgical strategy by a potential shift from focusing on local topography to a network-guided “oncological disconnection surgery” (Duffau, 2021; Hart et al., 2020).”

• cites Duffau, 2021 sourceNot checked — no full text of the source
• cites Hart et al., 2020 sourcePartly supported

  Section 3c (p. 9) describes connectomics as a holistic view of brain connectivity and discusses glioblastoma analyses that reveal surgical risks beyond standard structural imaging. The pre-operative glioma example (Figure 6 caption, p. 23) also illustrates network mapping. These passages support a potential move beyond local anatomy, but they do not propose a network-guided “oncological disconnection surgery.” (LLM, conf=0.85)

  Experimental — accuracy not yet measured

A recent large-sample study implicates global dimensions of variation in the human structural connectome in aging-related cognitive decline.

“a recent large sample study implicates the global dimensions of variation in the human structural connectome in aging-related cognitive decline (Madole et al., 2020).”

• cites Madole et al., 2020 sourceSupported

Brain regions within the default mode network display the latest maturation age of peak.

“brain regions within the default mode network, which is involved broadly in high-level cognition (Raichle, 2015; Vatansever et al., 2018; 2017), display the latest maturation age of peak.”

• cites Raichle, 2015 sourceNot checked — no full text of the source
• cites Vatansever et al., 2018 sourceSupported
• cites [2017]Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Lifespan changes of the connectome show good consistency with cognitive maturation.

“Lifespan changes of the connectome also show good consistency with cognitive maturation (Zhao et al., 2015).”

• cites Zhao et al., 2015 sourceSupported

Age-related declines in FA are primarily induced by declines in neurite density rather than changes in tract complexity.

“Results from a study that employ an advanced diffusion model (NODDI) indicate that the age-related declines in FA are primarily induced by declines in neurite density rather than changes in tract complexity (Cox et al., 2016).”

• cites Cox et al., 2016 sourcePartly supported

  The Results (General factors of white matter microstructure) report that most of the statistical mediation of the age–FA association occurred through neurite density rather than tract complexity. The paper says this suggests that declining FA *may* predominantly be explained by declining neurite density; the citing sentence’s “induced by” implies a stronger causal conclusion than the paper states. (LLM, conf=0.84)

  Experimental — accuracy not yet measured

The location and surrounding damage of MS lesions contribute more than the number and volume of lesions.

“researchers have found that the location and surrounding damages of lesions contribute more than the number and volume of lesions (Lipp et al., 2020).”

• cites Lipp et al., 2020 sourceSupported

Raw streamline count is not a quantitative measure of connection density.

“This limitation is often expressed as the mantra “streamline count is not quantitative.””

• Also stated in: § Connection density biases · boosted

  “highlights the inappropriateness of applying such a strong biological interpretation to raw streamline count.”

• cites Jones, 2010 sourceNot checked — no full text of the source
• cites Jones et al., 2013 sourceNot checked — no full text of the source

Diffusion MRI tractography enables in vivo reconstruction of the brain's white matter connections at macro scale.

“Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo reconstruction of the brain’s white matter connections at macro scale.”

• Also stated in: § Introduction · boosted

  “Diffusion magnetic resonance imaging (dMRI) tractography is an imaging method that uniquely enables in vivo reconstruction of the brain’s white matter connections at macro scale (Basser et al., 2000).”

• cites Basser et al., 2000 sourceNot checked — no full text of the source

The diffusion-weighted signal itself does not provide direct evidence of fiber terminations.

“indeed, the diffusion-weighted signal itself does not provide direct evidence of fiber terminations (Smith et al., 2020b).”

• cites Smith et al., 2020bNot matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Most quantitative tractography approaches share three main processing steps: tractography correction, segmentation, and quantification.

“For the methodology review, we organize the paper according to three main processing steps that are common across most approaches for quantitative analysis of tractography.”

There is no consensus on the best tractography algorithm.

“While many research studies have been performed to compare different tractography algorithms (Bastiani et al., 2012; Fillard et al., 2011; Petrov et al., 2017; Pujol et al., 2015; Sinke et al., 2018; Wilkins et al., 2015; Zhan et al., 2015), there is no consensus on “the best algorithm.””

• cites Bastiani et al., 2012 sourceNot checked — no full text of the source
• cites Fillard et al., 2011 sourceNot checked — no full text of the source
• cites Petrov et al., 2017Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

When a tractography bias is clearly demonstrated and a tailored mechanism addresses it, employing that mechanism is expected to improve biological accuracy of reconstruction even without direct validation.

“Where such an issue can be clearly demonstrated in exemplar data, and a tailored mechanism proposed that intrinsically addresses the issue, it is expected that the employment of such a mechanism in the reconstruction of empirical image data will lead to overall improved biological accuracy of the reconstruction (even in the absence of direct validation of such)”

A substantial proportion of streamlines from whole-brain tractography are unreasonable for quantification of white matter fiber connectivity.

“resulting in a substantial proportion of streamlines from whole-brain tractography being unreasonable for quantification of white matter fiber connectivity (Yeh et al., 2016a).”

• cites Yeh et al., 2016aNot matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

The capacity of thresholding to differentiate true from spurious connections remains debated.

“The capacity for thresholding to differentiate between true and spurious connections remains a debated topic in the field.”

Common termination criteria based on heuristic thresholds are not adequate to ensure biologically plausible streamline generation.

“These common termination criteria are therefore not adequate to ensure biologically plausible streamline generation”

• cites Smith et al., 2012 sourceNot checked — no full text of the source

Tractography segmentation enables cross-subject quantitative comparison of segmented white matter pathways.

“Critically, this step enables cross-subject quantitative comparison of the segmented white matter pathways; see (Smith et al., 2014) for a review of general considerations in multi-subject dMRI studies.”

• cites Smith et al., 2014 sourceNot checked — no full text of the source

Many streamline labeling methods use streamline-based registration to directly align individual tractography data to a tractography atlas with a reference tract segmentation.

“Many streamline labeling methods use streamline-based registration to directly align individual tractography data to a tractography atlas where a reference tract segmentation is available.”

There remains no consensus about the best methodology in quantitative analysis of tractography.

“We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the “best” methodology in quantitative analysis of tractography”

Validation of a tract identification method is difficult.

“First, given the fact that there is no ground truth in tractography, validation of a tract identification method is difficult.”

• AssumptionRests on an assumption: There is no ground truth in tractography.

Selection of different tract identification methods may require different image registration methods.

“The fifth point is that selection of different tract identification methods may require different image registration methods.”

Cortical-parcellation-based methods are more widely used because they enable construction of a connectivity matrix and its subsequent analysis using graph theory.

“The cortical-parcellation-based methods are more widely used as they enable construction of a connectivity matrix and its subsequent analysis using techniques from graph theory (as described in Section 5.6) (Bassett and Bullmore, 2017; Bullmore and Sporns, 2009a; Gong et al., 2009a; Ingalhalikar et al., 2014; Sporns et al., 2005; Yeh et al., 2016b; Zalesky et al., 2012).”

• cites Bassett and Bullmore, 2017Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Bullmore and Sporns, 2009aNot matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Gong et al., 2009aNot matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

The underlying tractography method can affect the consistency of methods that segment tractography streamline data.

“One factor that can affect the consistency of methods that segment tractography streamline data is the underlying tractography method that is employed.”

ROI-based tract identification methods usually require volumetric registration to align ROIs to dMRI space.

“The ROI-based methods usually require volumetric registration (e.g., between a population T1-weighted template and individual dMRI data) to align ROIs to the dMRI space.”

Compared to cortical-parcellation-based methods that focus on streamline terminal regions, fiber clustering methods leverage the full lengths of streamline trajectories.

“Compared to the cortical-parcellation-based methods that focus on streamline terminal regions, fiber clustering methods leverage the full lengths of the streamline trajectories.”

Currently, there is no consensus about which brain parcellation technique could be most useful.

“Currently, there is no consensus about which brain parcellation technique could be most useful (de Reus and Van den Heuvel, 2013; Yeh et al., 2020; Zalesky et al., 2012).”

• cites de Reus and Van den Heuvel, 2013 sourceNot checked — no full text of the source
• cites Yeh et al., 2020Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Zalesky et al., 2012 sourceNot checked — no full text of the source

Fiber clustering can be insensitive to streamlines diverging at the cortex if they pass through the same deep white matter regions.

“As a result, the cortical-parcellation-based methods can erroneously group streamlines that follow completely different trajectories through the white matter but nevertheless end up at the same gray matter endpoints, whereas fiber clustering can be insensitive to streamlines diverging at the cortex if they pass through the same deep white matter regions.”

Cortical-parcellation-based methods can erroneously group streamlines that follow completely different trajectories through the white matter but end up at the same gray matter endpoints.

“As a result, the cortical-parcellation-based methods can erroneously group streamlines that follow completely different trajectories through the white matter but nevertheless end up at the same gray matter endpoints, whereas fiber clustering can be insensitive to streamlines diverging at the cortex if they pass through the same deep white matter regions.”

Fiber clustering methods generally start with computation of pairwise streamline geometric similarities, followed by a computational clustering method to group similar streamlines into clusters.

“In general, fiber clustering methods start with a computation of pairwise streamline geometric similarities, followed by a computational clustering method to group similar streamlines into clusters.”

Measuring the distribution of microstructural measures along the fiber pathway can enable study of tissue microstructure in local regions along the fiber pathway.

“This can enable the study of the tissue microstructure in local regions along the fiber pathway.”

The most commonly adopted strategy for extraction of quantitative measures is to compute a scalar value as a summary statistic of the fiber pathway.

“The most commonly adopted strategy for extraction of quantitative measures is to compute a scalar value as a summary statistic of the fiber pathway.”

Microstructural measures can be computed along fiber pathways by computing a model in each voxel to derive a 3D microstructural map and then using tractography to define sampling locations.

“Usually, this is done by computing a model in each voxel to derive a 3D microstructural map (e.g., an FA image) and then using tractography to define the locations from which the values of this microstructural measure should be sampled.”

SIFT provides only the NOS adjusted based on voxel-wise spherical deconvolution.

“While in its original implementation SIFT (Smith et al., 2013) provides only the NOS adjusted based on voxel-wise spherical deconvolution, its evolution SIFT2 (Smith et al., 2015b) optimizes per-streamline cross-section multipliers to match a whole-brain tractogram to fixel-wise fiber densities and thus provides a value for each streamline that can be used (for example) to define the edge weights”

• cites Smith et al., 2013 sourceNot checked — no full text of the source

• AssumptionThe authors state this as an assumption: The microstructure properties corresponding to a single streamline (or a group of axons following the same trajectory) remain constant along its path on average at achievable resolution.

For microstructural measures, the mean measure within the fiber pathway is the most widely used summary statistic.

“For microstructural measures, the mean measure within the fiber pathway is the most widely used.”

This approach requires definition of a coordinate system, sampling framework, or surface-based representation to define how to sample the microstructural data of interest.

“This approach requires definition of a coordinate system, sampling framework, or surface-based representation to define how to sample the microstructural data of interest (Bells et al., 2011a; Chen et al., 2016b; Colby et al., 2012b; Corouge et al., 2006; Goodlett et al., 2009; Irimia et al., 2020; Jones et al., 2005; O’Donnell et al., 2009; Yeatman et al., 2012; Yeh, 2020).”

• cites Bells et al., 2011aNot matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Chen et al., 2016bNot matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Colby et al., 2012bNot matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Other approaches for tractography quantification leverage information from imaging modalities other than dMRI, such as T1-weighted imaging, myelin sensitive maps, and g-ratio.

“Other approaches for tractography quantification leverage information from imaging modalities other than dMRI, such as T1-weighted (T1w) imaging (Boshkovski et al., 2020a; Yeatman et al., 2014a), myelin sensitive maps (Lee et al., 2020; Mancini et al., 2020; Piredda et al., 2021) and g-ratio (Campbell et al., 2018).”

• cites Boshkovski et al., 2020aNot matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Yeatman et al., 2014aNot matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Lee et al., 2020 sourceNot checked — no full text of the source

Filtering methods can be used to perform microstructure-informed tractography with the aim to extract quantitative measures of white matter fiber pathways and reduce potential bias in the estimation of connectivity.

“The so called filtering methods discussed in Section 3.3 can also be used to perform “microstructure-informed” tractography with the aim to extract quantitative measures of white matter fiber pathways and reduce potential bias in the estimation of connectivity.”

• AssumptionThe authors state this as an assumption: The microstructure properties corresponding to a single streamline (or a group of axons following the same trajectory) remain constant along its path on average at achievable resolution.

Several factors not specifically reviewed in the paper (q-space sampling schemes, fiber orientation models, and tractography algorithms) affect most network measures derived from the connectivity matrix.

“Yeh et al., 2016a), several other factors (such as q-space sampling schemes, fiber orientation models, and tractography algorithms) that are not specifically reviewed in our paper, have also been shown to affect most network measures derived from the connectivity matrix.”

• cites Yeh et al., 2016aNot matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Myriad components of the processing pipeline can influence the resulting connectome matrix data, even down to the mechanism by which streamlines are assigned to parcels.

“Myriad components of the processing pipeline can also influence the resulting connectome matrix data, even down to the level of detail of the mechanism by which streamlines are assigned to those parcels (Yeh et al., 2019a).”

• cites Yeh et al., 2019aNot matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Multiple comparison correction methods that assume independence across data samples can be overly conservative when applied to tests on multiple tracts and/or quantitative measurements that are not independent.

“However, it is important to highlight that such multiple comparison correction methods, which assume independence across data samples, can be overly conservative when applied to tests performed on multiple tracts and/or quantitative measurements that are not independent.”

A widely used strategy after mapping microstructural measures along each point of streamlines is to analyze data along the tract profile.

“After mapping microstructural measures along each point of the streamlines along the fiber pathway, a widely used strategy is to analyze data along the tract profile.”

MD and FA are not mutually mathematically orthogonal measures.

“For example, MD and FA are not mutually mathematically orthogonal measures (Ennis and Kindlmann, 2006).”

• cites Ennis and Kindlmann, 2006 sourceNot checked — no full text of the source

The combination of structural and functional connectivity measures has been shown to increase predictive power.

“For example, the combination of structural and functional connectivity measures has been shown to increase predictive power (Whitfield-Gabrieli et al., 2016).”

• cites Whitfield-Gabrieli et al., 2016 sourceNot checked — no full text of the source

Graph measures can describe how the brain's structural wiring supports communication between distant ROIs and cognitive systems.

“At a global scale, graph measures can be used to describe how the brain’s structural wiring supports the communication of information between distant ROIs and cognitive systems (Avena-Koenigsberger et al., 2018; Mišić et al., 2015).”

• cites Avena-Koenigsberger et al., 2018 sourceNot checked — no full text of the source
• cites Mišić et al., 2015Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

In vivo fetal tractography has strong potential for future prenatal brain investigations.

“Due to recent advances in methodological progress such as motion correction algorithms, in vivo tractography on the fetal brain has become available and exhibit highly agreement with ex vivo and neuroanatomical studies (Christiaens et al., 2021; Khan et al., 2019), which indicates strong potential for future prenatal brain investigations.”

Advanced communication measures can elucidate how the organization of structural connectivity shapes the brain's functional dynamics.

“Nonetheless, recent work has shown that advanced communication measures can elucidate how the organization of structural connectivity shapes the brains functional dynamics (Goñi et al., 2014; Imms et al., 2021; Mišic et al., 2018; Wang et al., 2019).”

• cites Goñi et al., 2014Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Imms et al., 2021 sourceNot checked — no full text of the source
• cites Mišic et al., 2018Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Centrality measures are particularly useful for identifying hub regions that may play a central role in integrative brain function.

“Centrality measures find particular utility in identifying hub regions that may play a central role in integrative brain function (van den Heuvel and Sporns, 2013).”

• cites van den Heuvel and Sporns, 2013Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

The use of advanced communication measures enables the study of directional and asymmetric functional interactions from structural connectivity mapped with dMRI.

“Therefore, the use of advanced communication measures enables the study of directional and asymmetric functional interactions from structural connectivity mapped with dMRI.”

The anterior and posterior parts of tracts reach peak FA earlier than the central part, around 20 to 40 years old.

“In general, the anterior and posterior parts of tracts peak earlier than the central part of tracts, around 20 to 40 years old (Lebel et al., 2010), supporting a classical “last-in-first-out” theory.”

• cites Lebel et al., 2010 sourceNot checked — no full text of the source

The whole brain tract network is already highly efficient and specialized around term age.

“which reveals that the whole brain tract network is already highly efficient and specialized around term age.”

Axonal packing and decreasing water content may also contribute to early postnatal tract-related changes.

“while axonal packing and decreasing water content (Neil et al., 2002) may also make a contribution.”

• cites Neil et al., 2002 sourceNot checked — no full text of the source

Aging is associated with decreased inter-/intra-modularity strength.

“and decreased inter-/intra-modularity strength that may be due to the widespread degeneration of whole brain tracts (Li et al., 2020c; Wu et al., 2012a)”

• cites Li et al., 2020cNot matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Wu et al., 2012aNot matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

A common location of "eloquent" brain areas across patients is inaccurate, given functional compensation and individual variation.

“However, considering the potential for functional compensation and the presence of individual variation, a common location of “eloquent” areas across patients seems inaccurate (Southwell et al., 2017).”

• cites Southwell et al., 2017 sourceNot checked — no full text of the source

Increased water content along with healthy atrophy may cause increased MD at older ages.

“while an increased amount of water content along with healthy atrophy may cause increased MD (Moseley, 2002).”

• cites Moseley, 2002 sourceNot checked — no full text of the source

Studies indicate potential for individual quantitative tractography to generate neurosurgical prognostic markers at the brain-system level in the future.

“These studies indicate the potential of using individual quantitative tractography to generate neurosurgical prognostic markers at the brain system level in the future.”

Aging is associated with decreased network efficiency.

“including decreased network efficiency (Bi et al., 2021; Gong et al., 2009b; Li et al., 2020c; Madden et al., 2020; Shu et al., 2018b; Wu et al., 2012a)”

• cites Bi et al., 2021Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Gong et al., 2009bNot matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Li et al., 2020cNot matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Decreased inter-/intra-modularity strength with aging may be due to widespread degeneration of whole brain tracts.

“and decreased inter-/intra-modularity strength that may be due to the widespread degeneration of whole brain tracts (Li et al., 2020c; Wu et al., 2012a)”

• cites Li et al., 2020cNot matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Wu et al., 2012aNot matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Prior structural and functional skeleton atlases provide a whole-brain knowledge map of essential brain sub-networks that may help inform resection of certain neuronal circuits during tumor surgery.

“Such prior “structural and functional skeletons” provide a whole brain knowledge map of essential brain sub-networks, which may be helpful in informing the resection of certain neuronal circuits during tumor surgery (Duffau, 2021).”

• cites Duffau, 2021 sourceNot checked — no full text of the source

Overall connectome efficiency-metabolism coupling across brain regions significantly increases with aging.

“they found that the overall connectome efficiency-metabolism coupling across brain regions significantly increased with aging (Bi et al., 2021).”

• cites Bi et al., 2021Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Connectomic studies have shown that lexical access speed, related to return to professional work after awake surgery for low-grade glioma patients, may be completely disrupted only when the left inferior longitudinal fasciculus is damaged.

“For instance, connectomic studies have shown that the lexical access speed, which is related to the return to professional work after awake surgery for patients with low-grade gliomas, may be completely disrupted only when the left inferior longitudinal fasciculus (ILF) is damaged (Moritz-Gasser et al., 2012).”

• cites Moritz-Gasser et al., 2012 sourceNot checked — no full text of the source

Decreased axonal density and loss of fibers may cause a decline of FA values at older ages.

“decreased axonal density and loss of fibers may cause a decline of FA values”

• cites Moseley, 2002 sourceNot checked — no full text of the source

Technical improvements such as fiber filtering techniques can improve anatomical accuracy.

“While technical improvements (e.g., using fiber filtering techniques as introduced in Section 3.5) can improve anatomical accuracy”

There remains no consensus about the "best" methodology in quantitative analysis of tractography.

“there nevertheless remains no consensus about the “best” methodology in quantitative analysis of tractography”