The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Toward accurate diagnosis of white matter pathology using diffusion tensor imaging
Open accessAbstract
Diffusion tensor imaging (DTI) has been widely applied to investigate injuries in the central nervous system (CNS) white matter (WM). However, the underlying pathological correlates of diffusion changes have not been adequately determined. In this study the coregistration of histological sections to MR images and a pixel‐based receiver operating characteristic (ROC) analysis were used to compare the axial (λ∥) and radial (λ⟂) diffusivities derived from DTI and histological markers of axon (phosphorylated neurofilament, SMI‐31) and myelin (Luxol fast blue (LFB)) integrity, respectively, in two different patterns of injury to mouse spinal cord (SC) WM. In contusion SC injury (SCI), a decrease in λ∥ matched the pattern of axonal damage with high accuracy, but λ⟂ did not match the pattern of demyelination detected by LFB. In a mouse model of multiple sclerosis (MS), λ⟂ and λ∥ did not match the patterns of demyelination or axonal damage, respectively. However, a region of interest (ROI) analysis suggested that λ⟂‐detected demyelination paralleled that observed with LFB, and λ∥ decreased in both regions of axonal damage and normal‐appearing WM (NAWM) as visualized by SMI‐31. The results suggest that directional diffusivities may reveal abnormalities that are not obvious with SMI‐31 and LFB staining, depending on the type of injury. Magn Reson Med 57:688–695, 2007. © 2007 Wiley‐Liss, Inc.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Dysmyelination revealed through MRI as increased radial (but unchanged axial) diffusion of water
Song, S.-K., Sun, S.-W., Ramsbottom, M. J., Chang, C., Russell, J., & Cross, A. H. (2002) · NeuroImage · doi:10.1006/nimg.2002.1267 · Cited by 2,582
Self-citationSong, S.-K., Sun, S.-W., Ju, W.-K., Lin, S.-J., Cross, A. H., & Neufeld, A. H. (2003) · NeuroImage · doi:10.1016/j.neuroimage.2003.07.005 · Cited by 1,738
Self-citationDemyelination increases radial diffusivity in corpus callosum of mouse brain
Song, S.-K., Yoshino, J., Le, T. Q., Lin, S.-J., Sun, S.-W., Cross, A. H., & Armstrong, R. C. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.01.028 · Cited by 1,658
Self-citationBjartmar, C., Wujek, J. R., & Trapp, B. D. (2003) · Journal of the Neurological Sciences · doi:10.1016/s0022-510x(02)00069-2 · Cited by 573
Detecting axon damage in spinal cord from a mouse model of multiple sclerosis
Kim, J. H., Budde, M. D., Liang, H.-F., Klein, R. S., Russell, J. H., Cross, A. H., & Song, S.-K. (2006) · Neurobiology of Disease · doi:10.1016/j.nbd.2005.09.009 · Cited by 235
Self-citation
Cited by
Brain atrophy in Alzheimer’s Disease and aging
Pini, L., Pievani, M., Bocchetta, M., Altomare, D., Bosco, P., Cavedo, E., Galluzzi, S., Marizzoni, M., & Frisoni, G. B. (2016) · Ageing Research Reviews · doi:10.1016/j.arr.2016.01.002 · Cited by 836
Age-related changes in grey and white matter structure throughout adulthood
Giorgio, A., Santelli, L., Tomassini, V., Bosnell, R., Smith, S., De Stefano, N., & Johansen-Berg, H. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.03.004 · Cited by 532
Voss, M. W., Heo, S., Prakash, R. S., Erickson, K. I., Alves, H., Chaddock, L., Szabo, A. N., Mailey, E. L., Wójcicki, T. R., White, S. M., et al. (2013) · Human Brain Mapping · doi:10.1002/hbm.22119 · Cited by 553
Topiwala, A., Allan, C. L., Valkanova, V., Zsoldos, E., Filippini, N., Sexton, C., Mahmood, A., Fooks, P., Singh-Manoux, A., Mackay, C. E., et al. (2017) · BMJ · doi:10.1136/bmj.j2353 · Cited by 455
Quantification of increased cellularity during inflammatory demyelination
Wang, Y., Wang, Q., Haldar, J. P., Yeh, F.-C., Xie, M., Sun, P., Tu, T.-W., Trinkaus, K., Klein, R. S., Cross, A. H., et al. (2011) · Brain · doi:10.1093/brain/awr307 · Cited by 384
Self-citationRadial diffusivity predicts demyelination in ex vivo multiple sclerosis spinal cords
Klawiter, E. C., Schmidt, R. E., Trinkaus, K., Liang, H.-F., Budde, M. D., Naismith, R. T., Song, S.-K., Cross, A. H., & Benzinger, T. L. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.01.007 · Cited by 364
Self-citationMultiple sclerosis normal‐appearing white matter: Pathology–imaging correlations
Moll, N. M., Rietsch, A. M., Thomas, S., Ransohoff, A. J., Lee, J., Fox, R., Chang, A., Ransohoff, R. M., & Fisher, E. (2011) · Annals of Neurology · doi:10.1002/ana.22521 · Cited by 297
White matter pathology isolates the hippocampal formation in Alzheimer's disease
Salat, D. H., Tuch, D. S., van der Kouwe, A. J. W., Greve, D. N., Pappu, V., Lee, S. Y., Hevelone, N. D., Zaleta, A. K., Growdon, J. H., Corkin, S., et al. (2010) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2008.03.013 · Cited by 279
Menzler, K., Belke, M., Wehrmann, E., Krakow, K., Lengler, U., Jansen, A., Hamer, H. M., Oertel, W. H., Rosenow, F., & Knake, S. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.11.029 · Cited by 237
Quantitative tract-based white matter development from birth to age 2 years
Geng, X., Gouttard, S., Sharma, A., Gu, H., Styner, M., Lin, W., Gerig, G., & Gilmore, J. H. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.03.057 · Cited by 212
McCandless, E. E., Piccio, L., Woerner, B. M., Schmidt, R. E., Rubin, J. B., Cross, A. H., & Klein, R. S. (2008) · The American Journal of Pathology · doi:10.2353/ajpath.2008.070918 · Cited by 206
Self-citationChang, E. H., Argyelan, M., Aggarwal, M., Chandon, T.-S. S., Karlsgodt, K. H., Mori, S., & Malhotra, A. K. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.11.068 · Cited by 186
Altered white matter microstructure in children with attention-deficit/hyperactivity disorder
Nagel, B. J., Bathula, D., Herting, M., Schmitt, C., Kroenke, C. D., Fair, D., & Nigg, J. T. (2011) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2010.12.003 · Cited by 178
Xie, M., Tobin, J. E., Budde, M. D., Chen, C.-I., Trinkaus, K., Cross, A. H., McDaniel, D. P., Song, S.-K., & Armstrong, R. C. (2010) · Journal of Neuropathology & Experimental Neurology · doi:10.1097/nen.0b013e3181e3de90 · Cited by 175
Self-citationYeh, F.-C., Vettel, J. M., Singh, A., Poczos, B., Grafton, S. T., Erickson, K. I., Tseng, W.-Y. I., & Verstynen, T. D. (2016) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005203 · Cited by 151
Quantitative MRI and ultrastructural examination of the cuprizone mouse model of demyelination
Thiessen, J. D., Zhang, Y., Zhang, H., Wang, L., Buist, R., Del Bigio, M. R., Kong, J., Li, X., & Martin, M. (2013) · NMR in Biomedicine · doi:10.1002/nbm.2992 · Cited by 148
Characterizing White Matter Damage in Rat Spinal Cord with Quantitative MRI and Histology
Kozlowski, P., Raj, D., Liu, J., Lam, C., Yung, A. C., & Tetzlaff, W. (2008) · Journal of Neurotrauma · doi:10.1089/neu.2007.0462 · Cited by 129
Quantitative tractography metrics of white matter integrity in diffusion-tensor MRI
Correia, S., Lee, S. Y., Voorn, T., Tate, D. F., Paul, R. H., Zhang, S., Salloway, S. P., Malloy, P. F., & Laidlaw, D. H. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.05.022 · Cited by 85
The Biological Basis of Diffusion Anisotropy
Beaulieu, C. (2014) · Diffusion MRI · doi:10.1016/b978-0-12-396460-1.00008-1 · Cited by 66
Brain neuroplasticity related to lateral ankle ligamentous injuries: A systematic review
Maricot, A., Dick, E., Walravens, A., Pluym, B., Lathouwers, E., De Pauw, K., Verschueren, J., Roelands, B., Meeusen, R., & Tassignon, B. (2023) · Sports Medicine · doi:10.1007/s40279-023-01834-z · Cited by 62
Characterization of brain lesions in a mouse model of progressive multiple sclerosis
Levy, H., Assaf, Y., & Frenkel, D. (2010) · Experimental Neurology · doi:10.1016/j.expneurol.2010.08.017 · Cited by 50
Gao, S., Chen, Y., Sang, F., Yang, Y., Xia, J., Li, X., Zhang, J., Chen, K., & Zhang, Z. (2019) · Diabetes · doi:10.2337/db19-0233 · Cited by 46
Heritability of the limbic networks
Budisavljevic, S., Kawadler, J. M., Dell'Acqua, F., Rijsdijk, F. V., Kane, F., Picchioni, M., McGuire, P., Toulopoulou, T., Georgiades, A., Kalidindi, S., et al. (2016) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsv156 · Cited by 46
Melloni, E. M. T., Poletti, S., Vai, B., Bollettini, I., Colombo, C., & Benedetti, F. (2019) · Journal of Affective Disorders · doi:10.1016/j.jad.2019.02.015 · Cited by 34
Whittaker, H. T., Zhu, S., Di Curzio, D. L., Buist, R., Li, X.-M., Noy, S., Wiseman, F. K., Thiessen, J. D., & Martin, M. (2018) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2018.03.010 · Cited by 12
Metadata
- Authors
- Budde, M. D., Kim, J. H., Liang, H., Schmidt, R. E., Russell, J. H., Cross, A. H., & Song, S.
- Venue
- Magnetic Resonance in Medicine
- Publisher
- Wiley
- Published
- 2007-03-27
- Volume
- 57
- Issue
- 4
- Pages
- 688-695
- ISSN
- 0740-3194 (ISSN-L)
- 1522-2594
- OpenAlex
- W1987293003
- Cited by
- 399
- References
- 5
- FWCI
- 21.20
- Type
- journal article
- Language
- en
- Open access
- bronze
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.
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.