CoChat
Sign Up
Cited by 101Advanced Neuroimaging Techniques and ApplicationsDementia and Cognitive Impairment ResearchFunctional Brain Connectivity Studies

Diffusion MRI Indices and Their Relation to Cognitive Impairment in Brain Aging: The Updated Multi-protocol Approach in ADNI3

Open access
Zavaliangos-Petropulu, Artemis, Nir, Talia M., Thomopoulos, Sophia I., Reid, Robert I., Bernstein, Matt A., Borowski, Bret, Jack Jr., Clifford R., Weiner, Michael W. +2 more·2019·Frontiers in Neuroinformatics
DOI 10.3389/fninf.2019.00002PMC PMC6390411

Published version of a preprint

Open this paper in CoChat — free accountOpen PDF

Abstract

Abstract Brain imaging with diffusion-weighted MRI (dMRI) is sensitive to microstructural white matter changes associated with brain aging and neurodegeneration. In its third phase, the Alzheimer’s Disease Neuroimaging Initiative (ADNI3) is collecting data across multiple sites and scanners using different dMRI acquisition protocols, to better understand disease effects. It is vital to understand when data can be pooled across scanners, and how the choice of dMRI protocol affects the sensitivity of extracted measures to differences in clinical impairment. Here, we analyzed ADNI3 data from 317 participants (mean age: 75.4±7.9 years; 143 men/174 women), who were each scanned at one of 47 sites with one of six dMRI protocols using scanners from three different manufacturers. We computed four standard diffusion tensor imaging (DTI) indices including fractional anisotropy (FA DTI ) and mean, radial, and axial diffusivity, and one FA index based on the tensor distribution function (FA TDF ), in 24 bilaterally averaged white matter regions of interest. We found that protocol differences significantly affected dMRI indices, in particular FA DTI . We ranked the diffusion indices for their strength of association with four clinical assessments. In addition to diagnosis, we evaluated cognitive impairment as indexed by three commonly used screening tools for detecting dementia and Alzheimer’s disease: the Alzheimer’s Disease Assessment Scale (ADAS-cog), the Mini-Mental State Examination (MMSE), and the Clinical Dementia Rating scale sum-of-boxes (CDR-sob). Using a nested random-effects model to account for protocol and site, we found that across all dMRI indices and clinical measures, the hippocampal-cingulum and fornix ( crus ) / stria terminalis regions most consistently showed strong associations with clinical impairment. Overall, the greatest effect sizes were detected in the hippocampal-cingulum and uncinate fasciculus for associations between axial or mean diffusivity and CDR-sob. FA TDF detected robust widespread associations with clinical measures, while FA DTI was the weakest of the five indices for detecting associations. Ultimately, we were able to successfully pool dMRI data from multiple acquisition protocols from ADNI3 and detect consistent and robust associations with clinical impairment and age.

AI snapshot

Written from the paper’s full text.

TL;DR

In ADNI3, diffusion MRI indices pooled across six acquisition protocols and 47 sites showed robust associations with age and cognitive impairment, despite significant protocol effects. The TDF-derived FA index (FA_TDF) was more sensitive than standard DTI FA, and axial/mean diffusivity in temporal WM tracts showed the strongest clinical associations.

Key findings

  • Across 317 participants (mean age 75.4 ± 7.9 years; 143 men/174 women), protocol differences significantly affected all five dMRI indices; FA_DTI was least stable (significant protocol differences in 81.9% of pairwise tests), while AxD_DTI was most stable (20.6%).
  • The hippocampal-cingulum (CGH) and fornix (crus)/stria terminalis (Fx/ST) most consistently showed strong associations with clinical impairment; the largest effects were in CGH and uncinate fasciculus (UNC) for axial/mean diffusivity with CDR-sob (AxD_DTI UNC: d = 1.344, p = 3.13 × 10⁻²³; MD_DTI UNC: d = 1.342, p = 3.47 × 10⁻²³).
  • FA_TDF detected more widespread clinical associations than FA_DTI: 69/72 tests (94.4%) vs 55/72 (76.4%) for cognitive measures, and 20/24 ROIs vs 3/24 ROIs for CN vs MCI diagnosis.
  • ComBat harmonization reduced significant pairwise protocol differences by 93.8%, with only the corticospinal tract still showing protocol differences; age associations remained significant after harmonization.

Methods

  • Baseline ADNI3 data available as of April 30, 2018: 381 scanned participants, 55 excluded after quality assurance, leaving 317 from 47 sites scanned with six dMRI protocols from GE, Philips, and Siemens scanners.
  • Four DTI indices (FA, mean, radial, axial diffusivity) and one TDF-based FA index were computed in 24 bilaterally averaged JHU atlas WM ROIs; analyses used nested random-effects regressions with protocol and site as nested random variables and FDR correction (q = 0.05).
  • Clinical associations were tested with ADAS-cog, MMSE, CDR-sob, and CN vs MCI diagnosis; AD participants (N = 22) were not compared across diagnostic groups due to limited and unevenly distributed sample.

Limitations

  • The paper states that sample sizes and demographics varied by protocol, complicating direct protocol comparisons, and that separating protocol differences from scanner-manufacturer differences is difficult; few AD participants were scanned, limiting diagnostic-group comparisons.
  • The paper notes that the cognitive assessments are proxy measures and acknowledges their limitations for indexing specific cognitive abilities.
  • Evident from the text: analyses are cross-sectional baseline data, so longitudinal predictive utility is not established.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.3389/fninf.2019.00002

Cites

114 of 115 references verified0 citation errors · 1 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

39 of 50 cited sources checked · 20 supported · 1 flagged · accuracy not yet measured

  • Study design and conductcitation

    Due to differences in age and sample size between protocols and phases, effect sizes could not be directly compared.

    “Due to differences in age and sample size between protocols and phases, effect sizes could not be directly compared (Button et al., 2013), but the directions of associations with age were largely consistent for ADNI2 and ADNI3 phases separately, and each ADNI3 protocol (Figures 1, 2).”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites Button et al., 2013 source

      The “Effect size” section (page unavailable) says standardized effect sizes allow estimates from different studies to be compared directly, contrary to the cited rationale that differing sample sizes prevent direct comparison. The paper does not address the particular age differences, protocols, or phases in the citing sentence. (LLM, conf=0.78)

      Checked by a reasoning model — may be wrong.

      Experimental — accuracy not yet measured
  • Premisecitation

    Differences in dMRI acquisition parameters, including vendor, voxel size, and angular resolution, are known to affect derived dMRI measures.

    “differences in dMRI acquisition parameters, including vendor, voxel size, and angular resolution, are known to affect derived dMRI measures (Alexander et al., 2001; Cercignani et al., 2003; Zhan et al., 2010; Zhu et al., 2011).”
    Quote in the paperSubstantial claim2 of 3 sources checked
    • cites Alexander et al., 2001 source

      The Imaging Experiments and Discussion (page not provided) show that voxel size and the orientation of diffusion-encoding axes affect derived trace and fractional-anisotropy measurements, particularly where partial-volume effects are significant. The paper uses one scanner vendor and does not establish an effect of vendor differences or angular resolution; rotating a fixed set of directions is not a test of angular resolution. (LLM, conf=0.88)

      Experimental — accuracy not yet measured
    • cites Cercignani et al., 2003
    • cites Zhan et al., 2010 source

      The Conclusions report that the number of diffusion gradients affects the signal-to-noise ratio of derived measures, supporting the angular-resolution part of the claim. They also say achievable SNR depends on the scanner and spatial resolution, but do not establish that vendor or voxel-size differences affect derived dMRI measures. No page numbers are available in our copy. (LLM, conf=0.86)

      Experimental — accuracy not yet measured
  • Premisecitation

    FATDF can be more sensitive to neurodegenerative disease-related WM abnormalities than FADTI across high- and low-angular resolution dMRI.

    “we also evaluated a modified measure of FA, derived from the tensor distribution function (FATDF; Leow et al., 2009) which can be more sensitive to neurodegenerative disease-related WM abnormalities than FADTI across high- and low-angular resolution dMRI (Nir et al., 2017).”
    Quote in the paperSubstantial claim2 of 2 sources checked
    • cites Leow et al., 2009 source
    • cites Nir et al., 2017 source
  • Interpretationown data

    Tensor limitations have likely confounded previous diffusion studies of cognitive decline that have found little or no effects with FA.

    “suggesting that tensor limitations have likely confounded previous diffusion studies of cognitive decline that have found little or no effects with FA (Acosta-Cabronero et al., 2010).”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites Acosta-Cabronero et al., 2010 source

      The Discussion (page not available in our copy) explains that FA can miss proportional changes in diffusion and reports that FA was less sensitive than absolute diffusivity measures in this Alzheimer’s disease study. It does not establish that this limitation confounded previous studies of cognitive decline that found little or no FA effect; the authors caution that its relevance beyond Alzheimer’s disease needs to be demonstrated. (LLM, conf=0.84)

      Experimental — accuracy not yet measured
  • Findingcitation

    As in prior analysis of ADNI2, FADTI was the least sensitive DTI measure.

    “As in our prior analysis of ADNI2 (Nir et al., 2013), FADTI was the least sensitive DTI measure.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites Nir et al., 2013 source
  • May combine two claims. 36
  • Classified as probably not a substantial claim. 22

  • May combine two claims.

    Premisecitation

    The TDF model addresses well-established limitations of the standard single-tensor diffusion model, which cannot resolve complex profiles of WM architecture such as crossing or mixing fibers, present in up to 90% of WM voxels.

    “The TDF model addresses well-established limitations of the standard single-tensor diffusion model—which cannot resolve complex profiles of WM architecture such as crossing or mixing fibers, present in up to 90% of WM voxels (Tournier et al., 2004; Descoteaux et al., 2007, 2009; Jeurissen et al., 2013).”
    Quote in the paperSubstantial claim2 of 3 sources checked
    • cites Tournier et al., 2004 source
    • cites Descoteaux et al., 2007 source

      The Introduction (page not available) describes DTI’s well-known limitations in regions with multiple fiber crossings, and Human Brain Data shows crossings recovered by Q-ball ODFs where DTI profiles are limited. This paper does not establish that crossing or mixing fibers occur in up to 90% of white-matter voxels, and it studies Q-ball imaging rather than the TDF model. (LLM, conf=0.88)

      Experimental — accuracy not yet measured
    • cites Jeurissen et al., 2013 source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    ComBat was one of the most effective methods for harmonizing DTI measures in a recent comparison of such techniques.

    “ComBat—originally developed to model and remove batch effects from genomic microarray data (Johnson et al., 2007)—was one of the most effective methods for harmonizing DTI measures in a recent comparison of such techniques (Fortin et al., 2017).”
    Quote in the paperNot a substantial claim1 of 2 sources checked
    • cites Johnson et al., 2007 source
    • cites Fortin et al., 2017 source

  • Classified as probably not a substantial claim.

    Premisecitation

    Many large-scale multi-site DTI studies have obtained consistent results even when acquisition protocols across sites are not harmonized in advance.

    “Although many large-scale multi-site DTI studies have obtained consistent results even when acquisition protocols across sites are not harmonized in advance (Jahanshad et al., 2013; Kochunov et al., 2014; Acheson et al., 2017; Kelly et al., 2018), differences in dMRI acquisition parameters, including vendor, voxel size, and angular resolution, are known to affect derived dMRI measures (Alexander”
    Quote in the paperSubstantial claim3 of 3 sources checked
    • cites Jahanshad et al., 2013 source
    • cites Kochunov et al., 2014 source
    • cites Acheson et al., 2017 source

      The Discussion (page not specified in our copy) says ENIGMA-DTI was developed to homogenize measurements across diverse imaging protocols and reports a consistent heritability pattern in several large population studies. But this paper’s own reproducibility study used one scanner and the same acquisition protocol (Methods; Conclusions). It does not establish that many multi-site studies obtained consistent results without harmonizing acquisition protocols in advance. (LLM, conf=0.82)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingown data

    Effect size was not correlated with ROI size, consistent with prior studies of other disorders.

    “Effect size was not correlated with ROI size (Supplementary Figure S13), consistent with prior studies of other disorders (Kelly et al., 2018).”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites Kelly et al., 2018 source

  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation

    ComBat uses an empirical Bayes framework to reduce unwanted variation in multi-site data due to differences in acquisition protocol, while preserving the desired biological variation in the data.

    “ComBat uses an empirical Bayes framework to reduce unwanted variation in multi-site data due to differences in acquisition protocol, while preserving the desired biological variation in the data (Fortin et al., 2017).”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites Fortin et al., 2017 source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    ADNI2 introduced dMRI as an additional approach for tracking AD progression.

    “ADNI’s second phase (ADNI2) introduced to the initiative the use of diffusion-weighted MRI (dMRI) as an additional approach for tracking AD progression (Jack et al., 2015).”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites Jack et al., 2015 source

  • May combine two claims.

    Premisecitation

    Lower FADTI and higher MDDTI are most frequently reported in studies of AD.

    “Lower FADTI and higher MDDTI are most frequently reported in studies of AD (Kavcic et al., 2008; Clerx et al., 2012; Nir et al., 2013; Maggipinto et al., 2017; Mayo et al., 2017)”
    Quote in the paperNot a substantial claim2 of 3 sources checked
    • cites Kavcic et al., 2008 source

      The Results (Linking Performance and DTI; page unavailable) identify decreased FA in posterior regions as distinguishing the AD and older-adult groups. But the DTI Analyses report no statistically significant group difference in either FA or mean diffusivity after correction. The paper does not establish that higher mean diffusivity is a frequent finding across AD studies. (LLM, conf=0.82)

      Experimental — accuracy not yet measured
    • cites Clerx et al., 2012 source
    • cites Nir et al., 2013 source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    dMRI has been used in numerous studies to understand the effects of AD on white matter microstructure and brain connectivity.

    “dMRI has since been used in numerous studies to understand the effects of AD on white matter (WM) microstructure and brain connectivity (Daianu et al., 2013a,b; Nir et al., 2013; Prasad et al., 2013).”
    Quote in the paperNot a substantial claim2 of 3 sources checked
    • cites Daianu et al., 2013a,b

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

    • cites Nir et al., 2013 source
    • cites Prasad et al., 2013 source

  • May combine two claims.

    Premisecitation

    The participants recruited for ADNI3 tend to be younger and healthier, on average, than those in ADNI2, as they were recruited with the intention of studying the transition from CN to AD.

    “The participants recruited for ADNI3 tend to be younger and healthier, on average, than those in ADNI2, as they were recruited with the intention of studying the transition from CN to AD (Jack et al., 2015).”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites Jack et al., 2015 source

      The ADNI 3 MRI section (page not available in our copy) describes planned scans and imaging aims, but does not compare ADNI 3 recruits with ADNI 2 recruits. Searches for younger or healthier participants and recruitment to study the transition from cognitively normal to AD found no passage supporting those claims. (LLM, conf=0.77)

      Experimental — accuracy not yet measured

  • May combine two claims.

    Interpretationcitation

    AxDDTI may be more sensitive to unspecific microscopic cellular loss earlier in the disease, perhaps making it more sensitive in the healthier participants of the ADNI3 dataset.

    “but AxDDTI may be more sensitive to unspecific microscopic cellular loss earlier in the disease (O’Dwyer et al., 2011), perhaps making it more sensitive in the healthier participants of the ADNI3 dataset.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites O’Dwyer et al., 2011 source

      The Results (Early detection of diffusional changes in MCI) report axial diffusivity (DA) differences in MCI, supporting sensitivity to early white-matter changes. The Introduction and Discussion relate DA changes to white-matter damage and possible axonal degeneration, but do not establish that DA is specifically sensitive to nonspecific microscopic cellular loss. The ADNI3 inference is the citing authors’ own extrapolation. (LLM, conf=0.84)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S2 (checkable).

    Premisecitation

    Improved harmonization of multi-site diffusion data is of great interest.

    “As a result, improved harmonization of multi-site diffusion data is of great interest (Grech-Sollars et al., 2015; Pohl et al., 2016; Palacios et al., 2017).”
    Quote in the paperNot a substantial claim3 of 3 sources checked
    • cites Grech-Sollars et al., 2015 source

      The Introduction identifies comparability of diffusion data from different scanners as an important question and discusses harmonization across sites. But the Discussion (> Volunteers; page unavailable) concludes that large multi-centre studies may be feasible without extensive protocol harmonization, rather than advocating improved harmonization. (LLM, conf=0.78)

      Experimental — accuracy not yet measured
    • cites Pohl et al., 2016 source
    • cites Palacios et al., 2017 source

  • May combine two claims.

    Premisecitation

    The DTI model cannot differentiate tissue compartments or crossing fibers, which are estimated to occur in up to 90% of white matter voxels at typical dMRI resolution.

    “The DTI model cannot differentiate these components or even crossing fibers (Tuch et al., 2002; Jbabdi et al., 2010), which are estimated to occur in up to 90% of WM voxels at the typical dMRI resolution (Descoteaux et al., 2009; Jeurissen et al., 2013).”
    Quote in the paperSubstantial claim1 of 3 sources checked
    • cites Tuch et al., 2002 source
    • cites Jbabdi et al., 2010 source
    • cites Descoteaux et al., 2009 source

      The Introduction says that many voxels contain multiple crossing or otherwise complex fiber populations at DTI acquisition resolution. Section 4.1 also reports voxels with two- and three-fiber crossings. Neither passage gives the cited estimate of up to 90% of white-matter voxels, so the numerical part attributed to this source is unsupported. (LLM, conf=0.86)

      Experimental — accuracy not yet measured

  • May combine two claims.

    Findingcitation

    Among the three cognitive assessments, CDR-sob showed the strongest correlations with dMRI indices, in line with prior ADNI brain imaging studies.

    “Among the three cognitive assessments, CDR-sob showed the strongest correlations with dMRI indices, in line with prior ADNI brain imaging studies (Hua et al., 2009; Nir et al., 2013).”
    Quote in the paperSubstantial claim2 of 2 sources checked
    • cites Hua et al., 2009 source

      The Results section (“Minimal sample size estimates and power analyses”; page unavailable) identifies CDR-SB as the best of the three clinical assessments, but ranks them by the sample sizes needed to detect change. It does not show that CDR-SB had the strongest correlations with dMRI indices; the imaging measure studied was tensor-based morphometry of structural MRI. (LLM, conf=0.83)

      Experimental — accuracy not yet measured
    • cites Nir et al., 2013 source

      The Discussion reports that CDR-sob was the most widely correlated of the three assessments with diffusivity in the MCI group, in both voxel-based and ROI analyses. It does not establish that CDR-sob had the strongest correlations with dMRI indices across the full cohort or across all dMRI measures, as the unqualified claim implies. Page numbers are unavailable in our copy. (LLM, conf=0.86)

      Experimental — accuracy not yet measured

  • May combine two claims.

    Premisecitation

    The CST ROI from the JHU WMPM atlas is limited to a small region in the inferior portion of the brain and has been shown to be the least reliable and reproducible ROI.

    “However, the CST ROI from the JHU WMPM atlas is limited to a small region in the inferior portion of the brain and has been shown to be the least reliable and reproducible ROI (Jahanshad et al., 2013; Acheson et al., 2017)”
    Quote in the paperSubstantial claim2 of 2 sources checked
    • cites Jahanshad et al., 2013 source

      The Methods (Region of interest extraction; page unavailable) identify the JHU white matter parcellation atlas, and the Results (Meta and mega analyses; page unavailable) describe the CST as small and, with the fornix, among the least heritable regions and difficult to register. The Discussion cautions that CST may not be a reliable endophenotype in multisite studies. These passages do not establish that its atlas ROI is confined to an inferior brain region or that it is the least reliable and reproducible ROI. (LLM, conf=0.86)

      Experimental — accuracy not yet measured
    • cites Acheson et al., 2017 source

      The Methods (page not provided) say the tract ROIs were derived from the JHU white matter parcellation atlas, and the Results report relatively poor CST reproducibility. But the paper does not describe the CST ROI as a small region confined to the inferior brain, and it does not rank CST as the least reproducible: in the adolescent results, the fornix had higher variance and a lower ICC. (LLM, conf=0.90)

      Experimental — accuracy not yet measured

  • May combine two claims.

    Premisecitation

    In healthy tissue with crossing fibers, the DTI model may show low FA; FADTI may paradoxically appear to increase in regions where crossing fibers deteriorate in neurodegenerative diseases such as AD.

    “In healthy tissue with crossing fibers, the DTI model may show low FA. FADTI may paradoxically appear to increase in regions where crossing fibers deteriorate in neurodegenerative diseases such as AD (Douaud et al., 2011).”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites Douaud et al., 2011 source

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Interpretationreasoning

    The discrepancy between our findings and Fortin et al. (2017) may be due to several differences between studies: ADNI3 includes more sites and protocols; the number of ROIs is far less than the number of participants; and the age effects in the elderly populations tested here are stronger than the effects tested in adolescents in Fortin et al. (2017).

    “This discrepancy between our findings and that of Fortin et al. (2017), may be due to several differences between studies: (1) ADNI3 includes more sites and protocols; (2) in contrast to the number of voxels, the number of ROIs is far less than the number of participants; and (3) the age effects in the elderly populations tested here are stronger than the effects tested in adolescents in Fortin et”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites Fortin et al. (2017) source

      Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

      Experimental — accuracy not yet measured

  • May combine two claims.

    Findingown data

    Each ADNI protocol showed directionally consistent associations in more than 89% of tests (24 ROIs * 5 dMRI indices), except for P36 which was consistent in 81%, but had the smallest sample size (N = 12).

    “Each ADNI protocol showed directionally consistent associations in more than 89% of tests (24 ROIs * 5 dMRI indices), except for P36 which was consistent in 81%, but had the smallest sample size (N = 12; Figure 2B; Supplementary Tables S5–S11).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    The directions of associations with age were largely consistent for ADNI2 and ADNI3 phases separately, and each ADNI3 protocol.

    “but the directions of associations with age were largely consistent for ADNI2 and ADNI3 phases separately, and each ADNI3 protocol (Figures 1, 2).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    FATDF and all three diffusivity indices were consistent in ≥96% of tests (24 ROIs * 8 protocols/phases), while FADTI was only consistent in 88% of tests.

    “FATDF and all three diffusivity indices were consistent in ≥96% of tests (24 ROIs * 8 protocols/phases), while FADTI was only consistent in 88% of tests.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    None of the associations in the unexpected direction were significant after multiple comparisons correction, and only two had a p ≤ 0.05.

    “None of the associations in the unexpected direction were significant after multiple comparisons correction, and only two had a p ≤ 0.05.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    FATDF detected robust widespread associations with clinical measures, while FADTI was the weakest of the five indices for detecting associations.

    “FATDF detected robust widespread associations with clinical measures, while FADTI was the weakest of the five indices for detecting associations.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Pairwise tests comparing mean age of CN participants scanned with each protocol showed significant differences between those scanned with S31 and two other protocols: GE54 and S31 (p = 0.026); P33 and S31 (p = 0.0037).

    “Pairwise tests comparing the mean age of CN participants scanned with each protocol also showed significant differences between those scanned with S31 and two other protocols: GE54 and S31 (p = 0.026); P33 and S31 (p = 0.0037).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Of the three cognitive measures, CDR-sob associations showed the largest effect sizes across dMRI indices (in the UNC followed by the CGH for all indices except FADTI).

    “Of the three cognitive measures, CDR-sob associations showed the largest effect sizes across dMRI indices (in the UNC followed by the CGH for all indices except FADTI)”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Compared to FADTI, FATDF showed larger effect sizes; the strongest FATDF associations were in the UNC with CDR-sob (d = −1.244; p = 1.39 × 10−20), followed by the CGH (d = −1.213; p = 8.86 × 10−20).

    “Compared to FADTI, FATDF showed larger effect sizes; across cognitive tests, the strongest FATDF associations were detected in the UNC with CDR-sob (d = −1.244; p = 1.39 × 10−20), followed by the CGH (d = −1.213; p = 8.86 × 10−20).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    After ComBat harmonization, significant protocol differences in dMRI indices were all but eliminated across ROIs, with significant differences detected only in the CST for each dMRI index.

    “After using ComBat to harmonize dMRI indices across protocols, ANCOVAs revealed that significant protocol differences in dMRI indices were all but eliminated across ROIs (Supplementary Figure S2A); significant protocol differences were detected only in the CST, for each of the dMRI indices.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    The UNC showed either the first or second largest effect size (alternating with CGH) across diffusivity indices and FATDF tests, but was significant only for cognitive measure associations with FADTI (three of four clinical tests).

    “The UNC showed either the first or second largest effect size (alternating with CGH) across diffusivity indices and FATDF tests, but was significant only for cognitive measure associations with FADTI (i.e., three of four clinical tests).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Greater cognitive impairment was associated with lower anisotropy and higher diffusivity.

    “Greater cognitive impairment was associated with lower anisotropy and higher diffusivity.”
    • Also stated in: § Cognitive Measure Associations With Pooled ADNI3 dMRI Indices · plain
      “higher diffusivity was associated with greater cognitive impairment. Lower (D) FADTI (FDR critical threshold p = 0.025) and (E) FATDF (FDR critical threshold p = 7.73 × 10−3) were also associated with greater impairment”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    FATDF was significant in 20 out of 24 ROIs, similar to diffusivity indices.

    “On the other hand, FATDF was significant in 20 out of 24 ROIs, similar to diffusivity indices.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    ANCOVAs revealed significant protocol differences for 22 ROIs for FADTI and FATDF, with the highest overall effect size in the anterior limb of the internal capsule and external capsule for FADTI (ALIC d = 0.648; EC d = 0.652).

    “ANCOVAs revealed significant protocol differences for 22 ROIs for FADTI and FATDF, with the highest overall effect size detected in the anterior limb of the internal capsule (ALIC) and the external capsule (EC) for FADTI (ALIC: d = 0.648; EC: d = 0.652).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    AxDDTI had the smallest effect size overall in the splenium of the corpus callosum (d = 0.106), and only 13 ROIs showed significant AxDDTI differences between protocols.

    “AxDDTI had the smallest effect size, overall, in the splenium of the corpus callosum (SCC; d = 0.106), and only 13 ROIs showed significant AxDDTI differences between protocols.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    In 14 of 15 tests, the CGH consistently showed one of the top two largest effect sizes, along with the UNC, which was top two in 12 of 15 tests.

    “In 14 of 15 tests, the CGH consistently showed one of the top two largest effect sizes (CGH FADTI association with CDR-sob was the third largest), along with the uncinate fasciculus (UNC), which was top two in 12 of 15 tests (while significant, cognitive associations with UNC FADTI never showed one of the largest effect sizes).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    In pairwise analyses, AxDDTI was the most stable index across protocols, with significant protocol differences in only 20.6% of pairwise tests, compared to FADTI, the most variable index, with significant protocol differences in 81.9% of tests.

    “In pairwise analyses, AxDDTI was the most stable index across protocols, as significant protocol differences were detected in only 20.6% of pairwise tests (24 ROIs * 21 pairwise tests), compared to FADTI, the most variable index, which showed significant protocol differences in 81.9% of tests (Figure 4B).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    FATDF was significant in 89 of 96 tests (92.7%) while FADTI was significant in 58 (60.4%).

    “FATDF was significant in 89 of the 96 tests (92.7%; 24 ROIs * 4 clinical tests), while FADTI was only significant in 58 (60.4%).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    FADTI showed the greatest significant differences between protocols and AxDDTI the fewest.

    “FADTI showed the greatest significant differences (largest d-values; dark red) between protocols and AxDDTI the fewest (dark green).”
    • Also stated in: § ADNI2 and ADNI3 Protocols in Cognitively Normal Participants · plain
      “AxDDTI was the most stable dMRI index across protocols, while FADTI was the least stable.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    The largest effect sizes across all tests were detected with AxDDTI (UNC: d = 1.344) and MDDTI (UNC: d = 1.342, p = 3.47 × 10−23).

    “the largest effect sizes across all tests were detected with AxDDTI (UNC: d = 1.344) and MDDTI (UNC: d = 1.342, p = 3.47 × 10−23).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    In a preliminary analysis, ComBat reduced cross-site differences in dMRI indices while preserving biological relationships with age in CN controls.

    “In a preliminary analysis, we showed that one harmonization method performed on these regional measures, ComBat, reduced cross-site differences in dMRI indices, while preserving biological relationships with age in CN controls.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    While associations with clinical impairment were detected throughout the white matter, the corticospinal tract most frequently showed the lowest effect sizes and was significant in only 3 of the 20 clinical tests.

    “While associations with clinical impairment were detected throughout the WM, the region that most frequently showed the lowest effect sizes and was significant in only 3 of the 20 clinical tests, was the corticospinal tract (CST).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Here, the number of significant ROIs and the magnitude of effect sizes were comparable for ComBat and nested random-effects model approaches.

    “Here, the number of significant ROIs and the magnitude of effect sizes were comparable for ComBat and nested random-effects model approaches.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    Pairwise tests comparing protocols used FDR to account for multiple comparisons.

    “As before, we used FDR to account for multiple comparisons.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Study design and conductown data

    Due to limited available sample size of AD participants (N = 22) and their uneven distribution across acquisition protocols, only groups of people with CN and MCI diagnoses were compared.

    “Due to the limited available sample size of AD participants (N = 22), and their uneven distribution across the acquisition protocols tested here, we compared only groups of people with CN and MCI diagnoses.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Study design and conductown data

    Multivariate random-effects linear regressions testing associations between five dMRI indices in each of 24 WM ROIs and three cognitive measures and diagnosis controlled for age, sex, and age*sex interactions as fixed effects, and modeled protocol and acquisition site as nested random variables.

    “Age, sex, and age*sex interactions were controlled for as fixed effects, and the protocol and acquisition site were modeled as nested random variables.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Study design and conductnone given

    For each dMRI index, FDR was used to correct for multiple comparisons across the 24 ROIs assessed.

    “For each dMRI index, we used FDR to correct for multiple comparisons across the 24 ROIs assessed.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    All DWI and T1-weighted images were visually checked for quality assurance.

    “All DWI and T1-weighted images were visually checked for quality assurance.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Study design and conductown data

    In CN participants from ADNI2 and ADNI3, ComBat was run on each dMRI index, including age, sex, age*sex, and information from all 24 ROIs to inform the statistical properties of the protocol effects.

    “In the CN participants from ADNI2 and ADNI3, we ran ComBat on each of the dMRI indices, including age, sex, age*sex, and information from all 24 ROIs to inform the statistical properties of the protocol effects.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Study design and conductown data

    In CN participants, multivariate random-effects linear regressions assessing associations between dMRI indices and age controlled for sex and age*sex interactions as fixed variables, and acquisition site as a random variable.

    “In CN participants, multivariate random-effects linear regressions were used to assess whether dMRI indices from each ADNI protocol individually were associated with age, controlling for sex and age*sex interactions as fixed variables, and acquisition site as a random variable.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Study design and conductcitation

    FDR was used to correct for 24 ROI tests (q = 0.05).

    “FDR was again used to correct for 24 ROI tests (q = 0.05; Benjamini and Hochberg, 1995).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Benjamini and Hochberg, 1995 source

  • Classified as probably not a substantial claim.

    Study design and conductcitation

    The false discovery rate (FDR) procedure was used to correct for multiple comparisons (q = 0.05) across the 24 ROIs assessed for each dMRI index.

    “We used the false discovery rate (FDR) procedure to correct for multiple comparisons (q = 0.05; Benjamini and Hochberg, 1995) across the 24 ROIs assessed for each dMRI index.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Benjamini and Hochberg, 1995 source

  • Classified as probably not a substantial claim.

    Study design and conductown data

    In CN participants, differences in dMRI indices between the seven ADNI protocols were tested using ANCOVAs adjusting for age, sex, and age*sex interactions as fixed variables, and acquisition site as a random variable.

    “In CN participants, we tested for significant differences in dMRI indices between the seven ADNI protocols using analyses of covariance (ANCOVAs), adjusting for age, sex, and age*sex interactions as fixed variables, and acquisition site as a random variable.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Study design and conductown data

    Of the 381 participants scanned to date, 55 were excluded after quality assurance, including ensuring complete clinical and demographic information and image-level quality control.

    “Of the 381 participants scanned to date, 55 were excluded after quality assurance: this included ensuring complete clinical and demographic information, and image-level quality control (removing scans with severe motion, missing volumes, or corrupt files).”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    To ensure sufficient statistical power to assess differences in data collected with different protocols, only protocols with complete available data for at least 10 participants at the time of download were evaluated.

    “To ensure sufficient statistical power to assess differences in data collected with different protocols, we evaluated only those protocols with complete available data for at least 10 participants at the time of download; we did not assess protocol GE36, for which scans from 9 of 12 participants passed quality assurance.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Study design and conductreasoning

    Protocol GE36 was not assessed because scans from 9 of 12 participants passed quality assurance.

    “we did not assess protocol GE36, for which scans from 9 of 12 participants passed quality assurance.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Study design and conductnone given

    Separating protocol differences from differences in scanner manufacturer is difficult.

    “Even so, separating protocol differences from differences in scanner manufacturer is difficult.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationreasoning

    ADNI2's greater divergence across dMRI indices may be due to its larger voxel size (2.7 mm3 vs. 2.0 mm3 isotropic voxels used in ADNI3).

    “perhaps due to the larger voxel size in ADNI2 (2.7 mm3 vs. 2.0 mm3 isotropic voxels used in ADNI3).”
    Quote in the paperNot a substantial claim
53 of 53

  • The paper relies on this without support.

    The clinical assessments (ADAS-cog, MMSE, CDR-sob) are valid proxy measures of cognitive impairment.

    “We refer to these tools as “cognitive measures”, but recognize the limitations of these assessments as proxy measures of specific cognitive abilities (Balsis et al., 2015).”

  • The paper relies on this without support.

    At the spatial resolutions now used, a single voxel typically captures partial volumes of different tissue compartments (intra- and extra-cellular, vascular, CSF, and myelin), each affecting water diffusion and the MR signal.

    “However, at the spatial resolutions now used, a single voxel typically captures partial volumes of different tissue compartments–e.g., the intra- and extra-cellular compartments, the vascular compartment, the CSF and myelin; each affects water diffusion and the MR signal.”
2 of 2

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.

  • Voxel edge lengths are labeled as volumes

    Minor

    The stated 2.7 mm value is a voxel edge length, not a volume of 2.7 mm3. A 2.7 × 2.7 × 2.7 mm voxel has a volume of 19.683 mm3; likewise, a 2.0 mm isotropic voxel has a volume of 8 mm3.

    Reported 2.7 · recomputed 19.683

  • Figure 1A is described as pooling different study phases

    Minor

    The results text introduces Figure 1A as showing associations after pooling ADNI2 and ADNI3, while the figure caption says it pools ADNI3 data only.

  • Reported CDR-sob scores are incompatible with the stated range

    Minor

    The paper says CDR-sob scores range from 0 to 3, but the P33 group has a mean of 1.31 and a standard deviation of 2.84. For 24 scores confined to that range, the maximum possible sample standard deviation at that mean is about 1.52.

3 of 3

Cites

Flagged first, then by citations

Research works printed but not linked: 1

  1. Cross-vendor and Cross-protocol harmonisation of diffusion MRI data: a comparative study TaxC. M. W. GrussuF. KadenE. NingL. RudrapatnaU. EvansJ. Proc. Jt. Annu. Meet.4712018

Cited by

Sorted by citations

Related

Authors
Zavaliangos-Petropulu, A., Nir, T. M., Thomopoulos, S. I., Reid, R. I., Bernstein, M. A., Borowski, B., Jack Jr., C. R., Weiner, M. W., Jahanshad, N., & Thompson, P. M.
Affiliations
  • University of Southern California (US)
  • Mayo Clinic (US)
  • University of California, San Francisco (US)
Corresponding author
Thompson, Paul M.
ORCID
Venue
Frontiers in Neuroinformatics
Publisher
Frontiers Media SA
Published
2019-02-19
Volume
13
Pages
2-2
ISSN
1662-5196 (ISSN-L)
DOI
10.3389/fninf.2019.00002
PMC
PMC6390411
OpenAlex
W2951488689
Semantic Scholar
326a05bc8846dc5712fa669a1b0296e583214063
Cited by
101
References
113
Citations by year
2026: 8 · 2025: 10 · 2024: 18 · 2023: 20 · 2022: 16 · 2021: 11 · 2020: 16 · 2019: 2
FWCI
6.74
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Neuroinformatics (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
  • PubMed (publishedVersion)
  • eScholarship (California Digital Library) (submittedVersion, cc-by)
  • Europe PMC (PubMed Central) (submittedVersion, cc-by)
  • PubMed Central (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • U.S. Department of Defense (U01 AG024904, W81XWH, W81XWH-12-2-, W81XWH-12, W81XWH1220012, AG024904)
  • Foundation for the National Institutes of Health (W81XWH-12-2-0012, P41 EB015922, R56AG058854, U01AG024904, Grant U01 AG024904, AG024904)
  • Alzheimer's Association (U01 AG024904, W81XWH-12-2-0012)
  • Bristol-Myers Squibb (U01 AG024904, AG024904, W81XWH-12-2-0012)
  • Eli Lilly and Company (U01 AG024904, AG024904, W81XWH-12-2-0012)
  • Pfizer (AG024904, W81XWH-12-2-0012, U01 AG024904)
  • Biogen (W81XWH-12-2-0012, U01 AG024904, AG024904)
  • F. Hoffmann-La Roche (W81XWH-12-2-0012, AG024904, U01 AG024904)
  • Meso Scale Diagnostics (U01 AG024904, AG024904, W81XWH-12-2-0012)
  • BioClinica (U01 AG024904, W81XWH-12-2-0012, AG024904)
  • Novartis Pharmaceuticals Corporation (W81XWH-12-2-0012, U01 AG024904, AG024904)
  • Northern California Institute for Research and Education (U01 AG024904, W81XWH-12-2-0012)
  • Eisai (W81XWH-12-2-0012, U01 AG024904, AG024904)
  • Servier (AG024904, W81XWH-12-2-0012, U01 AG024904)
  • H. Lundbeck A/S (U01 AG024904, AG024904, W81XWH-12-2-0012)
  • IXICO (AG024904, W81XWH-12-2-0012, U01 AG024904)
  • National Institutes of Health (3u01ag024904-10s2, W81XWH, W81XWH-12-2-0012, 1rf1ag051710-01, EB015922, U01-AG024904, P01AG026572, P41-EB015922, Grant U01 AG024904, R56 AG058854, AG024904, AG026572, 5u24ag021886-24)
  • Genentech (W81XWH-12-2-0012, AG024904, U01 AG024904)
  • Canadian Institutes of Health Research (U01 AG024904, R56AG058854, W81XWH-12-2-0012)
  • National Institute on Aging (U01-AG-024904, AG026572, RF1 AG051710, U24‐AG021886, R56 AG058854, W81XWH-12-2–0012, AG 024904, P01-AG026572)
  • National Institute of Biomedical Imaging and Bioengineering (W81XWH-12-2-0012, P41EB015922, U01 AG024904, AG024904)
  • Johnson and Johnson Pharmaceutical Research and Development (W81XWH-12-2-0012, U01 AG024904)
  • Janssen Alzheimer Immunotherapy Research And Development (AG024904, U01 AG024904, W81XWH-12-2-0012)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Keywords
diffusion MRI, brain aging, cognitive impairment, Alzheimer's disease, fractional anisotropy, mean diffusivity, axial diffusivity, radial diffusivity, white matter microstructure, fornix, uncinate fasciculus, dementia screening, Clinical Dementia Rating

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 ›

Are you an author, or do you see a problem with this page? Request a review