The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Atlas-based whole brain white matter analysis using large deformation diffeomorphic metric mapping: Application to normal elderly and Alzheimer's disease participants
Open accessAbstract
The purpose of this paper is to establish single-participant white matter atlases based on diffusion tensor imaging. As one of the applications of the atlas, automated brain segmentation was performed and the accuracy was measured using Large Deformation Diffeomorphic Metric Mapping (LDDMM). High-quality diffusion tensor imaging (DTI) data from a single-participant were B0-distortion-corrected and transformed to the ICBM-152 atlas or to Talairach coordinates. The deep white matter structures, which have been previously well documented and clearly identified by DTI, were manually segmented. The superficial white matter areas beneath the cortex were defined, based on a population-averaged white matter probability map. The white matter was parcellated into 176 regions based on the anatomical labeling in the ICBM-DTI-81 atlas. The automated parcellation was achieved by warping this parcellation map to normal controls and to Alzheimer’s disease patients with severe anatomical atrophy. The parcellation accuracy was measured by a kappa analysis between the automated and manual parcellation at 11 anatomical regions. The kappa values were 0.70 for both normal controls and patients while the inter-rater reproducibility was 0.81 (controls) and 0.82 (patients), suggesting “almost perfect” agreement. A power analysis suggested that the proposed method is suitable for detecting FA and size abnormalities of the white matter in clinical studies.
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Cites
The Measurement of Observer Agreement for Categorical Data
Landis, J. R., & Koch, G. G. (1977) · Biometrics · doi:10.2307/2529310 · Cited by 81,148
Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007) · Behavior Research Methods · doi:10.3758/bf03193146 · Cited by 65,971
A Voxel-Based Morphometric Study of Ageing in 465 Normal Adult Human Brains
Good, C. D., Johnsrude, I. S., Ashburner, J., Henson, R. N. A., Friston, K. J., & Frackowiak, R. S. J. (2001) · NeuroImage · doi:10.1006/nimg.2001.0786 · Cited by 4,561
Rapid appearance and local toxicity of amyloid-β plaques in a mouse model of Alzheimer’s disease
Meyer-Luehmann, M., Spires-Jones, T. L., Prada, C., Garcia-Alloza, M., de Calignon, A., Rozkalne, A., Koenigsknecht-Talboo, J., Holtzman, D. M., Bacskai, B. J., & Hyman, B. T. (2008) · Nature · doi:10.1038/nature06616 · Cited by 987
Spatial transformations of diffusion tensor magnetic resonance images
Alexander, D. C., Pierpaoli, C., Basser, P. J., & Gee, J. C. (2001) · IEEE Transactions on Medical Imaging · doi:10.1109/42.963816 · Cited by 610 · cited 1×
“In this study, we used Gee and Alexander's method (Alexander et al., 2001; Xu et al., 2003), which is one of the simplest and, currently, most widely used methods.”
Diffusion Tensor Imaging and Axonal Tracking in the Human Brainstem
Stieltjes, B., Kaufmann, W. E., van Zijl, P. C. M., Fredericksen, K., Pearlson, G. D., Solaiyappan, M., & Mori, S. (2001) · NeuroImage · doi:10.1006/nimg.2001.0861 · Cited by 492
Self-citationVariational problems on flows of diffeomorphisms for image matching
Dupuis, P., Grenander, U., & Miller, M. I. (1998) · Quarterly of Applied Mathematics · doi:10.1090/qam/1632326 · Cited by 447
Self-citationFellgiebel, A., Wille, P., Müller, M. J., Winterer, G., Scheurich, A., Vucurevic, G., Schmidt, L. G., & Stoeter, P. (2004) · Dementia and Geriatric Cognitive Disorders · doi:10.1159/000077817 · Cited by 298
Ringman, J. M., O'Neill, J., Geschwind, D., Medina, L., Apostolova, L. G., Rodriguez, Y., Schaffer, B., Varpetian, A., Tseng, B., Ortiz, F., et al. (2007) · Brain · doi:10.1093/brain/awm102 · Cited by 290
Axonopathy in an APP/PS1 transgenic mouse model of Alzheimer’s disease
Wirths, O., Weis, J., Szczygielski, J., Multhaup, G., & Bayer, T. A. (2006) · Acta Neuropathologica · doi:10.1007/s00401-006-0041-4 · Cited by 130
Cited by
Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces
Dalca, A. V., Balakrishnan, G., Guttag, J., & Sabuncu, M. R. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.07.006 · Cited by 467
Effectiveness of regional DTI measures in distinguishing Alzheimer’s disease, MCI, and normal aging
Nir, T. M., Jahanshad, N., Villalon-Reina, J. E., Toga, A. W., Jack, C. R., Weiner, M. W., & Thompson, P. M. (2013) · NeuroImage: Clinical · doi:10.1016/j.nicl.2013.07.006 · Cited by 348
Self-citationFibre-specific white matter reductions in Alzheimer’s disease and mild cognitive impairment
Mito, R., Raffelt, D., Dhollander, T., Vaughan, D. N., Tournier, J.-D., Salvado, O., Brodtmann, A., Rowe, C. C., Villemagne, V. L., & Connelly, A. (2018) · Brain · doi:10.1093/brain/awx355 · Cited by 309
Neuropsychiatric symptoms in Alzheimer's disease: what might be associated brain circuits?
Rosenberg, P. B., Nowrangi, M. A., & Lyketsos, C. G. (2015) · Molecular Aspects of Medicine · doi:10.1016/j.mam.2015.05.005 · Cited by 302
Self-citationZhang, F., Daducci, A., He, Y., Schiavi, S., Seguin, C., Smith, R. E., Yeh, C.-H., Zhao, T., & O’Donnell, L. J. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2021.118870 · Cited by 302
“Currently, popularly used brain ROI atlases include those provided in Freesurfer [117, 143], MNI–ICBM152 [286, 297, 320, 319], and JHU-DTI [477, 476] (see [184] for a review of more atlases).”
Metadata
- Authors
- Oishi, K., Faria, A., Jiang, H., Li, X., Akhter, K., Zhang, J., Hsu, J. T., Miller, M. I., van Zijl, P. C. M., Albert, M., Lyketsos, C. G., Woods, R., Toga, A. W., Pike, G. B., Rosa-Neto, P., Evans, A., Mazziotta, J., & Mori, S.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2009-06
- Volume
- 46
- Issue
- 2
- Pages
- 486-499
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2092497652
- Cited by
- 575
- References
- 10
- FWCI
- 13.86
- Type
- journal article
- Language
- en
- Open access
- green
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- Oishi, K., Faria, A., Jiang, H., Li, X., Akhter, K., Zhang, J., Hsu, J. T., Miller, M. I., van Zijl, P. C. M., Albert, M., Lyketsos, C. G., Woods, R., Toga, A. W., Pike, G. B., Rosa-Neto, P., Evans, A., Mazziotta, J., & Mori, S. (2009). Atlas-based whole brain white matter analysis using large deformation diffeomorphic metric mapping: Application to normal elderly and Alzheimer's disease participants. NeuroImage. https://doi.org/10.1016/j.neuroimage.2009.01.002
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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.
