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Toward a quantitative assessment of diffusion anisotropy
Abstract
Indices of diffusion anisotropy calculated from diffusion coefficients acquired in two or three perpendicular directions are rotationally variant. In living monkey brain, these indices severely underestimate the degree of diffusion anisotropy. New indices calculated from the entire diffusion tensor are rotationally invariant (RI). They show that anisotropy is highly variable in different white matter regions depending on the degree of coherence of fiber tract directions. In structures with a regular, parallel fiber arrangement, water diffusivity in the direction parallel to the fibers (D| ≈ 1400–1800 × 10−6 mm 2/s) is almost 10 times higher than the average diffusivity in directions perpendicular to them ((D + D⊥′)/2 ≈ 150–300 × 10−6 mm 2/s), and is almost three times higher than previously reported. In structures where the fiber pattern is less coherent (e.g., where fiber bundles merge), diffusion anisotropy is significantly reduced. However, RI anisotropy indices are still susceptible to noise contamination. Monte Carlo simulations show that these indices are statistically biased, particularly those requiring sorting of the eigenvalues of the diffusion tensor based on their magnitude. A new intervoxel anisotropy index is proposed that locally averages inner products between diffusion tensors in neighboring voxels. This “lattice” RI index has an acceptably low error variance and is less susceptible to bias than any other RI anisotropy index proposed to date.
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Cites
Le Bihan, D., Breton, E., Lallemand, D., Grenier, P., Cabanis, E., & Laval-Jeantet, M. (1986) · Radiology · doi:10.1148/radiology.161.2.3763909 · Cited by 3,912
Diffusion tensor MR imaging of the human brain
Pierpaoli, C., Jezzard, P., Basser, P. J., Barnett, A., & Di Chiro, G. (1996) · Radiology · doi:10.1148/radiology.201.3.8939209 · Cited by 2,664
Self-citation
Cited by
Items 101–145 of 145
Evolution of anisotropic diffusion in the developing rat corpus callosum
Voříšek, I., & Syková, E. (1997) · Journal of Neurophysiology · doi:10.1152/jn.1997.78.2.912 · Cited by 113
Gold, B. T., Powell, D. K., Andersen, A. H., & Smith, C. D. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.05.036 · Cited by 112
Diffusion tensor imaging in patients with adult chronic idiopathic hydrocephalus
Hattingen, E., Jurcoane, A., Melber, J., Blasel, S., Zanella, F. E., Neumann-Haefelin, T., & Singer, O. C. (2010) · Neurosurgery · doi:10.1227/01.neu.0000367801.35654.ec · Cited by 111
Sotiropoulos, S. N., Hernández-Fernández, M., Vu, A. T., Andersson, J. L., Moeller, S., Yacoub, E., Lenglet, C., Ugurbil, K., Behrens, T. E. J., & Jbabdi, S. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.04.014 · Cited by 110
O’Donoghue, S., Holleran, L., Cannon, D. M., & McDonald, C. (2017) · Journal of Affective Disorders · doi:10.1016/j.jad.2016.11.015 · Cited by 109
Imaging laminar structures in the gray matter with diffusion MRI
Assaf, Y. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2017.12.096 · Cited by 108
Bozzali, M., Giulietti, G., Basile, B., Serra, L., Spanò, B., Perri, R., Giubilei, F., Marra, C., Caltagirone, C., & Cercignani, M. (2012) · Human Brain Mapping · doi:10.1002/hbm.21287 · Cited by 107
Perlbarg, V., Puybasset, L., Tollard, E., Lehéricy, S., Benali, H., & Galanaud, D. (2009) · Human Brain Mapping · doi:10.1002/hbm.20817 · Cited by 107
Romero, M. J., Asensio, S., Palau, C., Sanchez, A., & Romero, F. J. (2010) · Psychiatry Research: Neuroimaging · doi:10.1016/j.pscychresns.2009.07.004 · Cited by 103
Diffusion tensor imaging of thalamus correlates with cognition in CADASIL without dementia
O’Sullivan, M., Singhal, S., Charlton, R., & Markus, H. S. (2004) · Neurology · doi:10.1212/01.wnl.0000113760.72706.d2 · Cited by 101
Disability in optic neuritis correlates with diffusion tensor-derived directional diffusivities
Naismith, R. T., Xu, J., Tutlam, N. T., Snyder, A., Benzinger, T., Shimony, J., Shepherd, J., Trinkaus, K., Cross, A. H., & Song, S.-K. (2009) · Neurology · doi:10.1212/01.wnl.0000335766.22758.cd · Cited by 102
Carballedo, A., Amico, F., Ugwu, I., Fagan, A. J., Fahey, C., Morris, D., Meaney, J. F., Leemans, A., & Frodl, T. (2012) · American Journal of Medical Genetics Part B: Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32060 · Cited by 95
Castellanos, F. X., Kelly, C., & Milham, M. P. (2009) · The Canadian Journal of Psychiatry · doi:10.1177/070674370905401003 · Cited by 88
Relationships between tissue microstructure and the diffusion tensor in simulated skeletal muscle
Berry, D. B., Regner, B., Galinsky, V., Ward, S. R., & Frank, L. R. (2018) · Magnetic Resonance in Medicine · doi:10.1002/mrm.26993 · Cited by 86
Zalesky, A. (2011) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2010.06.027 · Cited by 88
Diffusion tensor imaging study of subcortical gray matter in CADASIL
Molko, N., Pappata, S., Mangin, J. F., Poupon, C., Vahedi, K., Jobert, A., LeBihan, D., Bousser, M. G., & Chabriat, H. (2001) · Stroke · doi:10.1161/hs0901.094255 · Cited by 82
Westlye, L. T., Walhovd, K. B., Bjørnerud, A., Due-Tønnessen, P., & Fjell, A. M. (2009) · Cerebral Cortex · doi:10.1093/cercor/bhn084 · Cited by 80
Fiber tractography reveals disruption of temporal lobe white matter tracts in schizophrenia
Phillips, O. R., Nuechterlein, K. H., Clark, K. A., Hamilton, L. S., Asarnow, R. F., Hageman, N. S., Toga, A. W., & Narr, K. L. (2009) · Schizophrenia Research · doi:10.1016/j.schres.2008.10.019 · Cited by 80
Lynch, K. M., Cabeen, R. P., Toga, A. W., & Clark, K. A. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116672 · Cited by 79
Metzler-Baddeley, C., Foley, S., de Santis, S., Charron, C., Hampshire, A., Caeyenberghs, K., & Jones, D. K. (2017) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01127 · Cited by 78
Lawrence, K. E., Levitt, J. G., Loo, S. K., Ly, R., Yee, V., O’Neill, J., Alger, J., & Narr, K. L. (2013) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2013.01.010 · Cited by 77
Cardiorespiratory fitness is associated with white matter integrity in aging
Hayes, S. M., Salat, D. H., Forman, D. E., Sperling, R. A., & Verfaellie, M. (2015) · Annals of Clinical and Translational Neurology · doi:10.1002/acn3.204 · Cited by 75
“Diffusion tensor reconstruction (including the six components, three eigenvectors and three eigenvalues) were calculated for each voxel using a least-squares fit to the diffusion signal.(29) To remove non-brain voxels from the analysis, the low b volume was skull stripped using FSL’s brain extraction tool (BET), and t…”
Method(2003) · PubMed Central · Cited by 76
Hutchinson, E. B., Avram, A. V., Irfanoglu, M. O., Koay, C. G., Barnett, A. S., Komlosh, M. E., Özarslan, E., Schwerin, S. C., Juliano, S. L., & Pierpaoli, C. (2017) · Magnetic Resonance in Medicine · doi:10.1002/mrm.26575 · Cited by 73
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Song, L., Mishra, V., Ouyang, M., Peng, Q., Slinger, M., Liu, S., & Huang, H. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00561 · Cited by 71
Sahara, N., Perez, P. D., Lin, W.-L., Dickson, D. W., Ren, Y., Zeng, H., Lewis, J., & Febo, M. (2014) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2013.12.009 · Cited by 70
Schermuly, I., Fellgiebel, A., Wagner, S., Yakushev, I., Stoeter, P., Schmitt, R., Knickenberg, R. J., Bleichner, F., & Beutel, M. E. (2010) · European Psychiatry · doi:10.1016/j.eurpsy.2010.05.001 · Cited by 67
Caverzasi, E., Papinutto, N., Castellano, A., Zhu, A. H., Scifo, P., Riva, M., Bello, L., Falini, A., Bharatha, A., & Henry, R. G. (2016) · Journal of Neuroimaging · doi:10.1111/jon.12359 · Cited by 63
Chung, A. W., Seunarine, K. K., & Clark, C. A. (2016) · Human Brain Mapping · doi:10.1002/hbm.23328 · Cited by 63
Gogliettino, A. R., Potenza, M. N., & Yip, S. W. (2016) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2016.02.015 · Cited by 56
Gyebnár, G., Szabó, Á., Sirály, E., Fodor, Z., Sákovics, A., Salacz, P., Hidasi, Z., Csibri, É., Rudas, G., Kozák, L. R., et al. (2018) · Psychiatry Research: Neuroimaging · doi:10.1016/j.pscychresns.2017.10.007 · Cited by 49
O'Sullivan, M. (2010) · Stroke · doi:10.1161/strokeaha.110.595314 · Cited by 48
White matter biomarkers from fast protocols using axially symmetric diffusion kurtosis imaging
Hansen, B., Khan, A. R., Shemesh, N., Lund, T. E., Sangill, R., Eskildsen, S. F., Østergaard, L., & Jespersen, S. N. (2017) · NMR in Biomedicine · doi:10.1002/nbm.3741 · Cited by 46
Unique white matter microstructural patterns in ADHD presentations—a diffusion tensor imaging study
Svatkova, A., Nestrasil, I., Rudser, K., Goldenring Fine, J., Bledsoe, J., & Semrud‐Clikeman, M. (2016) · Human Brain Mapping · doi:10.1002/hbm.23243 · Cited by 44
“FA ranges from values close to 0 in the tissues with no boundaries for water movement—such as the cerebrospinal fluid (CSF) to values around 1 in highly anisotropic tissues such as corpus callosum with parallel and highly organized fiber structure [Pierpaoli and Basser, 1996].”
BackgroundMethodCavedo, E., Lista, S., Rojkova, K., Chiesa, P. A., Houot, M., Brueggen, K., Blautzik, J., Bokde, A. L. W., Dubois, B., Barkhof, F., et al. (2017) · Neuroscience · doi:10.1016/j.neuroscience.2017.05.048 · Cited by 40
De Marco, R., Ronen, I., Branzoli, F., Amato, M. L., Asllani, I., Colasanti, A., Harrison, N. A., & Cercignani, M. (2022) · Brain, Behavior, and Immunity · doi:10.1016/j.bbi.2021.10.005 · Cited by 40
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Cabeen, R. P., Bastin, M. E., & Laidlaw, D. H. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.11.061 · Cited by 37
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Jeon, T., Mishra, V., Uh, J., Weiner, M., Hatanpaa, K. J., White, C. L., Zhao, Y. D., Lu, H., Diaz-Arrastia, R., & Huang, H. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.05.082 · Cited by 34
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Prčkovska, V., Rodrigues, P., Puigdellivol Sanchez, A., Ramos, M., Andorra, M., Martinez‐Heras, E., Falcon, C., Prats‐Galino, A., & Villoslada, P. (2016) · Journal of Neuroimaging · doi:10.1111/jon.12298 · Cited by 24
Pujol, S., Cabeen, R., Sébille, S. B., Yelnik, J., François, C., Fernandez Vidal, S., Karachi, C., Zhao, Y., Cosgrove, G. R., Jannin, P., et al. (2017) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2016.00119 · Cited by 21
Mole, J. P., Fasano, F., Evans, J., Sims, R., Hamilton, D. A., Kidd, E., & Metzler-Baddeley, C. (2020) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2020.06.014 · Cited by 20
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Müller, H.-P., Roselli, F., Rasche, V., & Kassubek, J. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00734 · Cited by 12
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Metadata
- Authors
- Pierpaoli, C., & Basser, P. J.
- Venue
- Magnetic Resonance in Medicine
- Publisher
- Wiley
- Published
- 1996-12
- Volume
- 36
- Issue
- 6
- Pages
- 893-906
- ISSN
- 0740-3194 (ISSN-L)
- 1522-2594
- OpenAlex
- W2157035009
- Cited by
- 2,405
- References
- 2
- FWCI
- 27.59
- Type
- journal article
- Language
- en
- Open access
- closed
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