Diffusional kurtosis imaging of the developing brain
Open accessAbstract
BACKGROUND AND PURPOSE: Diffusional kurtosis imaging is an extension of DTI but includes non-Gaussian diffusion effects, allowing more comprehensive characterization of microstructural changes during brain development. Our purpose was to use diffusional kurtosis imaging to measure age-related microstructural changes in both the WM and GM of the developing human brain. MATERIALS AND METHODS: Diffusional kurtosis imaging was performed in 59 subjects ranging from birth to 4 years 7 months of age. Diffusion metrics, fractional anisotropy, and mean kurtosis were collected from VOIs within multiple WM and GM structures and subsequently analyzed with respect to age. Diffusional kurtosis tractography images at various stages of development were also generated. RESULTS: Fractional anisotropy and mean kurtosis both showed age-related increases in all WM regions, reflecting progression of diffusional anisotropy throughout development, predominantly in the first 2 years of life (eg, 70% and 157% increase in fractional anisotropy and mean kurtosis, respectively, from birth to 2 years for the splenium). However, mean kurtosis detected continued microstructural changes in WM past the fractional anisotropy plateau, accounting for more delayed isotropic changes (eg, 90% of maximum fractional anisotropy was reached at 5 months, whereas 90% of maximum mean kurtosis occurred at 18 months for the external capsule). Mean kurtosis may also provide greater characterization of GM maturation (eg, the putamen showed no change in fractional anisotropy but an 81% change in mean kurtosis from birth to 4 years 7 months). CONCLUSIONS: Mean kurtosis detects significant microstructural changes consistent with known patterns of brain maturation. In comparison with fractional anisotropy, mean kurtosis may offer a more comprehensive evaluation of age-related microstructural changes in both WM and GM and is potentially a valuable technique for studying brain development.
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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)”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Paydar et al., 2014
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Cites
The basis of anisotropic water diffusion in the nervous system—A technical review.
Beaulieu, C. (2002) · NMR in Biomedicine · doi:10.1002/nbm.782 · Cited by 4,653 · see citing works
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 · see citing works
White matter integrity, fiber count, and other fallacies: The do's and don'ts of diffusion MRI
Jones, D. K., Knösche, T. R., & Turner, R. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2012.06.081 · Cited by 2,479 · cited 1× · see citing works
“It has been proposed that axial diffusivity characterizes axonal architecture, while radial diffusivity reflects myelin integrity,(9,12,14) though such simplified interpretations should be regarded with caution.(15) From diffusion tensor eigenvalues, fractional anisotropy (FA), which is a primary index of diffusional…”
BackgroundQuantitative growth and development of human brain
Dobbing, J., & Sands, J. (1973) · Archives of Disease in Childhood · doi:10.1136/adc.48.10.757 · Cited by 1,442 · see citing works
Microstructural maturation of the human brain from childhood to adulthood
Lebel, C., Walker, L., Leemans, A., Phillips, L., & Beaulieu, C. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2007.12.053 · Cited by 1,398 · see citing works
Flagged first, then by citations
Research works printed but not linked: 27
- Brody BA, Kinney HC, Kloman AS, et al. Sequence of central nervous system myelination in infancy. I. An autopsy study of myelination. J Neuropathol Exp Neurol 1987;46:283-301
- Kinney HC, Brody BA, Kloman AS, et al. Sequence of central nervous system myelination in human infancy. II. Patterns of myelination in autopsied brains. J Neuropathol Exp Neurol 1988;47:217-34
- Deipolyi AR, Mukherjee P, Gill K, et al. Comparing microstructural and macrostructural development of the cerebral cortex in prema- ture newborns: diffusion tensor imaging versus cortical gyration. Neuroimage 2005;27:579 -86
- Takeda K, Nomura Y, Sakuma H, et al. MR assessment of normal brain development in neonates and infants: comparative study of T1-and diffusion-weighted images. J Comput Assist Tomogr 1997; 21:1-7
- Basser PJ, Jones DK. Diffusion-tensor MRI: theory, experimental design and data analysis: a technical review. NMR Biomed 2002; 15:456 -67
- Cheung MM, Hui ES, Chan KC, et al. Does diffusion kurtosis imag- ing lead to better neural tissue characterization? A rodent brain maturation study. Neuroimage 2009;45:386 -92
- Yoshida S, Oishi K, Faria AV, et al. Diffusion tensor imaging of nor- mal brain development. Pediatr Radiol 2013;43:15-27
- Basser PJ. Inferring microstructural features and the physiological state of tissues from diffusion-weighted images. NMR Biomed 1995;8:333-44
- Hu ¨ppi PS, Dubois J. Diffusion tensor imaging of brain develop- ment. Semin Fetal Neonatal Med 2006;11:489 -97
- Schimrigk SK, Bellenberg B, Schluter M. Diffusion tensor imaging- based fractional anisotropy quantification in the corticospinal tract of patients with amyotrophic lateral sclerosis using a probabi- listic mixture model. AJNR Am J Neuroradiol 2007;28:724 -30
- Veraart J, Poot DH, Van Hecke W, et al. More accurate estimation of diffusion tensor parameters using diffusion kurtosis imaging. Magn Reson Med 2011;65:138 -45
- Jensen JH, Helpern JA. MRI quantification of non-Gaussian water diffusion by kurtosis analysis. NMR Biomed 2010;23:698 -710
- Lu H, Jensen JH, Ramani A, et al. Three-dimensional characteriza- tion of non-Gaussian water diffusion in humans using diffusion kurtosis imaging. NMR Biomed 2006;19:236 -47
- Tournier JD, Yeh CH, Calamante F, et al. Resolving crossing fibers using constrained spherical deconvolution: validation using diffu- sion-weighted imaging phantom data. Neuroimage 2008;42:617-25
- Perrin M, Poupon C, Cointepas Y, et al. Fiber tracking in Q-ball fields using regularized particle trajectories. Inf Process Med Imaging 2005;19:52-63
- Jensen JH, Helpern JA, Ramani A, et al. Diffusional kurtosis imaging: the quantification of non-Gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med 2005; 53:1432-40
- De Santis S, Gabrielli A, Palombo M, et al. Non-Gaussian diffusion imaging: a brief practical review. Magn Reson Imaging 2011; 29:1410 -16
- Tabesh A, Jensen JH, Ardekani BA, et al. Estimation of tensors and tensor-derived measures in diffusional kurtosis imaging. Magn Reson Med 2011;65:823-36
- Lazar M, Jensen JH, Xuan L, et al. Estimation of the orientation distribution function from diffusional kurtosis imaging. Magn Reson Med 2008;60:774 -81
- Mori S, Crain BJ, Chacko VP, et al. Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann Neurol 1999;45:265-69
- Provenzale JM, Isaacson J, Chen S. Progression of corpus callosum diffusion-tensor imaging values during a period of signal changes consistent with myelination. AJR Am J Roentgenol 2012;198: 1403-08
- Sampaio RC and Truwit CL. Myelination of the developing human brain. In: Nelson CA, Luciana M, eds. Handbook of Developmental Cognitive Neuroscience. Cambridge, Massachusetts: MIT Press; 2001:35-44
- Mu ¨ller K, Homberg V, Lenard HG. Magnetic stimulation of motor cortex and nerve roots in children: maturation of cortico-motoneu- ronal projections. Electroencephalogr Clin Neurophysiol 1991;81: 63-70
- Nezu A, Kimura S, Uehara S, et al. Magnetic stimulation of motor cortex in children: maturity of corticospinal pathway and problem of clinical application. Brain Dev 1997;19:176 -80
- Hui ES, Cheung MM, Qi L, et al. Toward better MR characterization of neural tissues using directional diffusion kurtosis analysis. Neu- roimage 2008;42:122-34
- Gao Y, Zhang Y, Wong CS, et al. Diffusion abnormalities in tempo- ral lobes of children with temporal lobe epilepsy: a preliminary dif- fusional kurtosis imaging study and comparison with diffusion tensor imaging. NMR Biomed 2012;25:1369 -77
- Helpern JA, Adisetiyo V, Falangola MF, et al. Preliminary evidence of altered gray and white matter microstructural development in the frontal lobe of adolescents with attention-deficit hyperactivity disorder: a diffusional kurtosis imaging study. J Magn Reson Imag- ing 2011;33:17-23
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Zhang, 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 · see citing works
“myelination is a clear cause [332] while axonal packing and”
Jelescu, I. O., Veraart, J., Adisetiyo, V., Milla, S. S., Novikov, D. S., & Fieremans, E. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.12.009 · Cited by 202 · see citing works
Self-citationWhite 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 · see citing works
Reveley, C., Ye, F. Q., & Leopold, D. A. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00368 · Cited by 14 · see citing works
- Authors
- Paydar, A., Fieremans, E., Nwankwo, J. I., Lazar, M., Sheth, H. D., Adisetiyo, V., Helpern, J. A., Jensen, J. H., & Milla, S. S.
- Affiliations
- New York University (US)
- Medical University of South Carolina (US)
- Venue
- American Journal of Neuroradiology
- Published
- 2013-11-14
- Volume
- 35
- Issue
- 4
- Pages
- 808-814
- ISSN
- 0195-6108 (ISSN-L)
- 1936-959X
- PMC
- PMC7965814
- OpenAlex
- W2021404550
- Cited by
- 91
- References
- 5
- Citations by year
- 2026: 6 · 2025: 7 · 2024: 7 · 2023: 4 · 2022: 7 · 2021: 9 · 2020: 8 · 2019: 4 · 2018: 13 · 2017: 7 · 2016: 8 · 2015: 6 · 2014: 4 · 2012: 1
- FWCI
- 4.66
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- American Journal of Neuroradiology (publishedVersion, cc-by)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- diffusion kurtosis imaging, brain development, white matter maturation, mean kurtosis, fractional anisotropy, non-Gaussian diffusion, diffusion tractography, splenium, external capsule, putamen
- MeSH terms
- Brain, Brain Mapping, Child, Preschool, Female, Humans, Infant, Infant, Newborn, Male, Models, Neurological, Retrospective Studies, Anisotropy, Internal Capsule, Diffusion Tensor Imaging, White Matter, Gray Matter, External Capsule
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