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One diffusion acquisition and different white matter models: How does microstructure change in human early development based on WMTI and NODDI?
Open accessAbstract
White matter microstructural changes during the first three years of healthy brain development are characterized using two different models developed for limited clinical diffusion data: White Matter Tract Integrity (WMTI) metrics from Diffusional Kurtosis Imaging (DKI) and Neurite Orientation Dispersion and Density Imaging (NODDI). Both models reveal a non-linear increase in intra-axonal water fraction and in tortuosity of the extra-axonal space as a function of age, in the genu and splenium of the corpus callosum and the posterior limb of the internal capsule. The changes are consistent with expected behavior related to myelination and asynchrony of fiber development. The intra- and extracellular axial diffusivities as estimated with WMTI do not change appreciably in normal brain development. The quantitative differences in parameter estimates between models are examined and explained in the light of each model's assumptions and consequent biases, as highlighted in simulations. Finally, we discuss the feasibility of a model with fewer assumptions.
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“The pathology changes at different MS stages may also be attributed to the decoupling between intra-axonal and extra-axonal diffusivity, which may also affect the calculated NODDI metrics and need substantial investigation in further studies (Jelescu et al. 2015; Jelescu et al. 2016).”
From Neurite orientation dispersion and density imaging of mouse brain microstructure (2019) · cites this paper as Jelescu et al. 2015
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“In contrast, ODI in WM remains almost stable during the first two decades of life (Genc et al., 2017;Jelescu et al., 2015;Lynch et al., 2020;Mah et al., 2017).”
From NODDI in clinical research (2020) · cites this paper as Jelescu et al., 2015
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“The pathology changes at different MS stages may also be attributed to the decoupling between intra-axonal and extra-axonal diffusivity, which may also affect the calculated NODDI metrics and need substantial investigation in further studies (Jelescu et al. 2015; Jelescu et al. 2016).”
From Neurite orientation dispersion and density imaging of mouse brain microstructure (2019) · cites this paper as Jelescu et al. 2015
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“When applied to clinical studies, the use of different constraints sometimes yields results that agree in their relative trends (Huber et al., 2019;Jelescu et al., 2015;Kamiya et al., 2017), but such agreement is not always guaranteed and is not proof of validity.”
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“In WM, NDI has a positive correlation with age, but the increase is non-linear and more rapid during early life (Dean et al., 2016;Jelescu et al., 2015;Lynch et al., 2020) (Fig. 9B).”
From NODDI in clinical research (2020) · cites this paper as Jelescu et al., 2015
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Citations
Cites
Regression and time series model selection in small samples
HURVICH, C. M., & TSAI, C.-L. (1989) · Biometrika · doi:10.1093/biomet/76.2.297 · Cited by 6,586
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 4,628
Basser, P. J., & Pierpaoli, C. (1996) · Journal of Magnetic Resonance, Series B · doi:10.1006/jmrb.1996.0086 · Cited by 4,306
NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain
Zhang, H., Schneider, T., Wheeler-Kingshott, C. A., & Alexander, D. C. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.03.072 · Cited by 3,288
Characterization and propagation of uncertainty in diffusion-weighted MR imaging
Behrens, T. E. J., Woolrich, M. W., Jenkinson, M., Johansen‐Berg, H., Nunes, R. G., Clare, S., Matthews, P. M., Brady, J. M., & Smith, S. M. (2003) · Magnetic Resonance in Medicine · doi:10.1002/mrm.10609 · Cited by 3,038
Jensen, J. H., Helpern, J. A., Ramani, A., Lu, H., & Kaczynski, K. (2005) · Magnetic Resonance in Medicine · doi:10.1002/mrm.20508 · Cited by 2,488
Regional Development of the Brain in Early Life
(1968) · Archives of Disease in Childhood · doi:10.1136/adc.43.229.388 · Cited by 2,107
Wyatt, J. S., Delpy, D. T., Cope, M., Wray, S., & Reynolds, E. O. R. (1986) · The Lancet · doi:10.1016/s0140-6736(86)90467-8 · Cited by 875
Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain
Assaf, Y., & Basser, P. J. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.03.042 · Cited by 856
Dubois, J., Dehaene-Lambertz, G., Kulikova, S., Poupon, C., Hüppi, P. S., & Hertz-Pannier, L. (2014) · Neuroscience · doi:10.1016/j.neuroscience.2013.12.044 · Cited by 851
Cited by
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Items 101–200 of 202
Neurobiological underpinnings of rapid white matter plasticity during intensive reading instruction
Huber, E., Mezer, A., & Yeatman, J. D. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118453 · Cited by 28
Quasi-diffusion magnetic resonance imaging (QDI): A fast, high b-value diffusion imaging technique
Barrick, T. R., Spilling, C. A., Ingo, C., Madigan, J. B., Isaacs, J. D., Rich, P. M., Jones, T. L., Magin, R. L., Hall, M. G., & Howe, F. A. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116606 · Cited by 26
Parvathaneni, P., Nath, V., Blaber, J. A., Schilling, K. G., Hainline, A. E., Mojahed, E., Anderson, A. W., & Landman, B. A. (2018) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2018.03.004 · Cited by 25
Zhou, Z., Tong, Q., Zhang, L., Ding, Q., Lu, H. J., Jonkman, L. E., Yao, J., He, H., Zhu, K., & Zhong, J. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117313 · Cited by 28
Prasad, J. D., van de Looij, Y., Gunn, K. C., Ranchhod, S. M., White, P. B., Berry, M. J., Bennet, L., Sizonenko, S. V., Gunn, A. J., & Dean, J. M. (2020) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2020.12.006 · Cited by 24
A biomimetic tumor tissue phantom for validating diffusion‐weighted MRI measurements
McHugh, D. J., Zhou, F., Wimpenny, I., Poologasundarampillai, G., Naish, J. H., Cristinacce, P. L. H., & Parker, G. J. M. (2017) · Magnetic Resonance in Medicine · doi:10.1002/mrm.27016 · Cited by 24
Heritability of complex white matter diffusion traits assessed in a population isolate
Kochunov, P., Fu, M., Nugent, K. L., Wright, S. N., Du, X. M., Muellerklein, F., Morrissey, M., Eskandar, G., Shukla, D. K., Jahanshad, N., et al. (2015) · Human Brain Mapping · doi:10.1002/hbm.23047 · Cited by 26
Quantitative MRI in leukodystrophies
Stellingwerff, M. D., Pouwels, P. J. W., Roosendaal, S. D., Barkhof, F., & van der Knaap, M. S. (2023) · NeuroImage Clinical · doi:10.1016/j.nicl.2023.103427 · Cited by 23
Filter‐probe diffusion imaging improves spinal cord injury outcome prediction
Skinner, N. P., Lee, S., Kurpad, S. N., Schmit, B. D., Muftuler, L. T., & Budde, M. D. (2018) · Annals of Neurology · doi:10.1002/ana.25260 · Cited by 23
Chung, S., Chen, J., Li, T., Wang, Y., & Lui, Y. W. (2022) · American Journal of Neuroradiology · doi:10.3174/ajnr.a7522 · Cited by 20
The effects of puberty and sex on adolescent white matter development: A systematic review
Piekarski, D. J., Colich, N. L., & Ho, T. C. (2023) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2023.101214 · Cited by 25
Coronado‐Leija, R., Abdollahzadeh, A., Lee, H., Coelho, S., Ades‐Aron, B., Liao, Y., Salo, R. A., Tohka, J., Sierra, A., Novikov, D. S., et al. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00212 · Cited by 19
Self-citationDiffusion MR Imaging in Mild Traumatic Brain Injury
Borja, M. J., Chung, S., & Lui, Y. W. (2017) · Neuroimaging Clinics of North America · doi:10.1016/j.nic.2017.09.009 · Cited by 19
Characterizing intra‐axonal water diffusion with direction‐averaged triple diffusion encoding MRI
Jensen, J. H., & Helpern, J. A. (2018) · NMR in Biomedicine · doi:10.1002/nbm.3930 · Cited by 19
Coelho, S., Pozo, J. M., Costantini, M., Highley, J. R., Mozumder, M., Simpson, J. E., Ince, P. G., & Frangi, A. F. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.06.040 · Cited by 23
Probing Tissue Microarchitecture of the Baby Brain via Spherical Mean Spectrum Imaging
Huynh, K. M., Xu, T., Wu, Y., Wang, X., Chen, G., Wu, H., Thung, K.-H., Lin, W., Shen, D. G., & Yap, P. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3001175 · Cited by 19
Novello, L., Henriques, R. N., Ianuș, A., Feiweier, T., Shemesh, N., & Jovicich, J. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119137 · Cited by 21
Evaluating kurtosis‐based diffusion MRI tissue models for white matter with fiber ball imaging
Jensen, J. H., McKinnon, E. T., Glenn, G. R., & Helpern, J. A. (2017) · NMR in Biomedicine · doi:10.1002/nbm.3689 · Cited by 18
Shen, Y., Zhao, X. W., Wang, K., Sun, Y., Zhang, X., Wang, C., Yang, Z., Feng, Z., & Zhang, X. (2024) · Academic Radiology · doi:10.1016/j.acra.2023.12.023 · Cited by 18
Luca, A. D., Ianuș, A., Leemans, A., Palombo, M., Shemesh, N., Zhang, H. G., Alexander, D. C., Nilsson, M., Froeling, M., Biessels, G. J., et al. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118367 · Cited by 19
Sepehrband, F., O’Brien, K., & Barth, M. (2017) · Magnetic Resonance in Medicine · doi:10.1002/mrm.26608 · Cited by 16
Advanced Diffusion MRI of the Visual System in Glaucoma: From Experimental Animal Models to Humans
Mendoza, M., Shotbolt, M., Faiq, M. A., Parra, C., & Chan, K. C. (2022) · Biology · doi:10.3390/biology11030454 · Cited by 16
Genomic kinship construction to enhance genetic analyses in the human connectome project data
Kochunov, P., Donohue, B., Mitchell, B. D., Ganjgahi, H., Adhikari, B. M., Ryan, M. C., Medland, S. E., Jahanshad, N., Thompson, P. M., Blangero, J., et al. (2018) · Human Brain Mapping · doi:10.1002/hbm.24479 · Cited by 14
Self-citationDrobyshevsky, A., Synowiec, S., Goussakov, I. V., Lu, J. N., Gascoigne, D. A., Aksenov, D. P., & Yarnykh, V. L. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.119974 · Cited by 13
Fritz, F. J., Poser, B. A., & Roebroeck, A. F. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118285 · Cited by 15
Kline, J. E., Dudley, J., Illapani, V. S. P., Li, H., Kline‐Fath, B. M., Tkach, J. A., He, L., Yuan, W., & Parikh, N. A. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119727 · Cited by 15
Dhiman, S., Hickey, R. E., Thorn, K. E., Moss, H. G., McKinnon, E. T., Adisetiyo, V., Ades‐Aron, B., Jensen, J. H., & Benitez, A. M. (2024) · Journal of Visualized Experiments · doi:10.3791/66397 · Cited by 13
Self-citationMani, M., Yang, B., Bathla, G., Magnotta, V. A., & Jacob, M. (2021) · Magnetic Resonance in Medicine · doi:10.1002/mrm.29095 · Cited by 13
Dimond, D., Heo, S., Ip, A., Rohr, C. S., Tansey, R. C., Graff, K., Dhollander, T., Smith, R. E., Lebel, C. A., Dewey, D., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117168 · Cited by 15
Mark, I. T., Wren-Jarvis, J., Xiao, J., Cai, L. T., Parekh, S. A., Bourla, I., Lazerwitz, M. C., Rowe, M. A., Marco, E. J., & Mukherjee, P. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1136424 · Cited by 12
Willis, H. E., Caron, B. A., Cavanaugh, M. R., Starling, L., Ajina, S., Pestilli, F., Tamietto, M., Huxlin, K. R., Watkins, K. E., & Bridge, H. (2024) · Brain Communications · doi:10.1093/braincomms/fcae323 · Cited by 12
Tsuchida, A., Laurent, A., Crivello, F., Petit, L., Pepe, A., Beguedou, N., Debette, S., Tzourio, C., & Mazoyer, B. M. (2021) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2021.692152 · Cited by 15
Optimizing Filter-Probe Diffusion Weighting in the Rat Spinal Cord for Human Translation
Budde, M. D., Skinner, N. P., Muftuler, L. T., Schmit, B. D., & Kurpad, S. N. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00706 · Cited by 11
Saadani‐Makki, F., Hagmann, C. F., Balédent, O., & Makki, M. (2018) · Journal of Neuroscience Research · doi:10.1002/jnr.24359 · Cited by 11
Borghesani, V., Cheng, W., Watson, C., Bouhali, F., Caverzasi, E., Battistella, G., Bogley, R., Yabut, N. A., Deleon, J., Miller, Z., et al. (2021) · Journal of Neuroimaging · doi:10.1111/jon.12892 · Cited by 11
Niu, Y., Camacho, M. C., Schilling, K. G., & Humphreys, K. L. (2025) · Brain Structure and Function · doi:10.1007/s00429-025-03007-2 · Cited by 11
Population‐based Bayesian regularization for microstructural diffusion MRI with NODDIDA
Mozumder, M., Pozo, J. M., Coelho, S., & Frangi, A. F. (2019) · Magnetic Resonance in Medicine · doi:10.1002/mrm.27831 · Cited by 10
Grand, Q. L., Tsuchida, A., Koch, A., Imtiaz, M. A., Aziz, N. A., Vigneron, C., Zago, L., Lathrop, M. G., Dubrac, A., Couffinhal, T., et al. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02604-7 · Cited by 16
Neurite orientation and dispersion density imaging: clinical utility, efficacy, and role in therapy
Sone, D. (2019) · Reports in Medical Imaging · doi:10.2147/rmi.s194083 · Cited by 9
Li, X., Li, M., Wang, M., Wu, F., Liu, H., Sun, Q., Zhang, Y., Liu, C., Jin, C., & Yang, J. (2021) · Human Brain Mapping · doi:10.1002/hbm.25689 · Cited by 9
Microstructural maturation of white matter tracts in encephalopathic neonates
Kansagra, A. P., Mabray, M. C., Ferriero, D. M., Barkovich, A. J., Xu, D., & Hess, C. P. (2016) · Clinical Imaging · doi:10.1016/j.clinimag.2016.05.009 · Cited by 9
Beck, D., de Lange, A. G., Maximov, I. I., Richard, G., Andreassen, O. A., Nordvik, J. E., & Westlye, L. T. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.04.21.053850 · Cited by 10
Guerreri, M., Epstein, S. C., Azadbakht, H., & Zhang, H. G. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-34048-2_1 · Cited by 9
Sun, H., Xue, B., Peng, M., Ma, H., Yu, B., Hou, Y., & Guo, Q. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102389 · Cited by 8
Periventricular gradients in NAWM abnormalities differ in MS, NMOSD and MOGAD
Sun, J., Xu, S., Tian, D., Duan, Y., Xu, X., Lv, S. J., Cao, G., Shi, F., Chard, D., Barkhof, F., et al. (2023) · Multiple Sclerosis and Related Disorders · doi:10.1016/j.msard.2023.104732 · Cited by 7
Oliveira, R., Pelentritou, A., Domenicantonio, G. D., Marzia De Lucia, E., & Lutti, A. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.874023 · Cited by 7
Gondová, A., Neumane, S., Arichi, T., & Dubois, J. (2025) · Human Brain Mapping · doi:10.1002/hbm.70186 · Cited by 8
Anisotropy Across Fields and Scales
Özarslan, E., Schultz, T., Zhang, E., & Fuster, A. (2021) · Mathematics and visualization · doi:10.1007/978-3-030-56215-1 · Cited by 6
Coors, A., Boenniger, M. M., Santos, M. L. S., Lohner, V., Koch, A., Ettinger, U., Aziz, N. A., & Breteler, M. M. B. (2025) · Neurology · doi:10.1212/wnl.0000000000210278 · Cited by 6
Morrison, J., Ayonrinde, O., Care, A. S., Clarke, G. D., Darby, J., David, A., Dean, J., Hooper, S., Kitchen, M., Macgowan, C., et al. (2020) · Journal of Developmental Origins of Health and Disease · doi:10.1017/s2040174420000884 · Cited by 6
Pierre, W. C., Zhang, E., Londoño, I., Leener, B. D., Lesage, F., & Lodygensky, G. A. (2022) · Behavioural Brain Research · doi:10.1016/j.bbr.2022.113884 · Cited by 5
Characterization of neurite and soma organization in the brain and spinal cord with diffusion MRI
Schilling, K. G., Palombo, M., Witt, A. A., O’Grady, K. P., Pizzolato, M., Landman, B. A., & Smith, S. A. (2025) · Imaging Neuroscience · doi:10.1162/imag.a.111 · Cited by 5
Comparison of NODDI and spherical mean signal for measuring intra-neurite volume fraction
Li, H., Nikam, R. M., Kandula, V. V. R., Chow, H. M., & Choudhary, A. K. (2018) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2018.11.021 · Cited by 4
Lu, P., Baraković, M., Weigel, M., Rahmanzadeh, R., Galbusera, R., Schiavi, S., Daducci, A., Rosa, F. L., Cuadra, M. B., Sandkühler, R., et al. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.647535 · Cited by 4
Diffusion Kurtosis Imaging maps neural damage in the EAE model of multiple sclerosis
Chuhutin, A., Hansen, B., Wlodarczyk, A., Owens, T., Shemesh, N., & Jespersen, S. N. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2019.116406 · Cited by 3
Jun, Y., Liu, Q., Gong, T., Cho, J., Fujita, S., Yong, X., Liao, C., Schmidt, M. E., Nasr, S., Jaimes, C. E., et al. (2026) · Medical Image Analysis · doi:10.1016/j.media.2026.104058 · Cited by 3
Chung, S., Bacon, T. E., Rath, J. F., Alivar, A., Coelho, S., Amorapanth, P. X., Fieremans, E., Novikov, D. S., Flanagan, S. R., Bacon, J. H., et al. (2024) · American Journal of Neuroradiology · doi:10.3174/ajnr.a8213 · Cited by 3
Self-citationBrain network analyses in clinical neuroscience
Sokolov, A. A., Granziera, C., Fischi-Gómez, E., Preti, M. G., Ryvlin, P., Ville, D. V. D., & Friston, K. J. (2019) · Swiss Archives of Neurology Psychiatry and Psychotherapy · doi:10.4414/sanp.2019.03074 · Cited by 3
Frontiers of microstructural imaging with diffusion MRI
Jensen, J. H. (2021) · Advances in magnetic resonance technology and applications · doi:10.1016/b978-0-12-816633-8.00007-7 · Cited by 3
Schilliger, Z., Pavan, T., Alemán‐Gómez, Y., Steullet, P., Céléreau, E., Binz, P., Celen, Z., Piguet, C., Merglen, A., Hagmann, P., et al. (2025) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2025.01.026 · Cited by 3
Self-citationChung, S., Fieremans, E., Rath, J. F., & Lui, Y. W. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-823036-7.00026-8 · Cited by 2
Self-citationHu, B., Laurent Younes, B., Bu, X., Liu, C.-F., Ratnanather, J. T., Paulsen, J. S., Georgiou‐Karistianis, N., Miller, M. I., Ross, C. A., & Faria, A. V. (2023) · NeuroImage Clinical · doi:10.1016/j.nicl.2023.103493 · Cited by 2
Comparison of different neurite density metrics with brain asymmetry evaluation
Maximov, I. I., & Westlye, L. T. (2023) · Zeitschrift für Medizinische Physik · doi:10.1016/j.zemedi.2023.07.003 · Cited by 2
Oliviero, S., & Gratta, C. D. (2021) · Magnetic Resonance Materials in Physics Biology and Medicine · doi:10.1007/s10334-020-00899-5 · Cited by 2
Schilling, K. G., Palombo, M., O’Grady, K. P., Combes, A. J. E., Anderson, A. W., Landman, B. A., & Smith, S. A. (2022) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2022.07.015 · Cited by 2
Multimodal MRI: Applications to early brain development in infants
Dubois, J. (2021) · Advances in magnetic resonance technology and applications · doi:10.1016/b978-0-12-816633-8.00017-x · Cited by 2
Correlation between diffusion kurtosis and NODDI metrics in neonates and young children
Ahmed, S., Wang, Z., Chia, J. M., & Rollins, N. K. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2217197 · Cited by 1
Tractography: Applications to neurodevelopment, aging, and plasticity
Lebel, C. A., Salat, D. H., & Yeatman, J. D. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-818894-1.00009-4 · Cited by 1
Sandgaard, A. D., & Jespersen, S. N. (2025) · NMR in Biomedicine · doi:10.1002/nbm.70004 · Cited by 1
Yoon, C. D., & Dean, D. C. (2025) · Autism Research · doi:10.1002/aur.70122 · Cited by 3
Mohyee, R. A., Elliott, B. L., Pike, M. R., Smith, E., Kring, A. M., Olson, I. R., Breen, E. C., Cohn, B. A., Cirillo, P. M., Krigbaum, N. Y., et al. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2420188122 · Cited by 1
Roebroeck, A. F. (2021) · Springer eBooks · doi:10.1007/978-3-030-59797-9_6 · Cited by 1
Measuring Microstructural Features Using Diffusion MRI
Shemesh, N. (2020) · Advances in magnetic resonance technology and applications · doi:10.1016/b978-0-12-817057-1.00024-x · Cited by 1
Single Encoding Diffusion MRI: A Probe to Brain Anisotropy
Jallais, M., & Wassermann, D. (2021) · Mathematics and visualization · doi:10.1007/978-3-030-56215-1_8 · Cited by 1
Karahan, E., Costigan, A. G., Graham, K., Lawrence, A., & Zhang, J.-X. (2018) · bioRxiv · doi:10.1101/473660 · Cited by 3
Guerrero, J. M., Adluru, N., Bendlin, B. B., Goldsmith, H. H., Schaefer, S. M., Davidson, R. J., Kecskemeti, S. R., Zhang, H. G., & Alexander, A. L. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/630541 · Cited by 1
Fritz, F. J., Poser, B. A., & Roebroeck, A. F. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.15.098269 · Cited by 1
Soma and Neurite Density MRI (SANDI) of the in-vivo mouse brainPreprint
Ianuș, A., Carvalho, J., Fernandes, F. F., Cruz, R., Chavarrías, C., Palombo, M., & Shemesh, N. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.08.11.455923 · Cited by 1
Alsameen, M. H., Gong, Z., Qian, W., Kiely, M., Triebswetter, C., Bergeron, C. M., Cortina, L. E., Faulkner, M. E., Laporte, J. P., & Bouhrara, M. (2023) · medRxiv · doi:10.1101/2023.03.06.23286818 · Cited by 1
Asadi, S., Spencer, A. P. C., Alemán‐Gómez, Y., Jedynak, M., Canales-Rodríguez, E. J., Pavan, T., Mullier, E., de Wouters, L., Rigoni, I., Lajous, H., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.28.685017 · Cited by 1
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George, J. (2017) · The University of Queensland · doi:10.14264/uql.2017.917
Acuff, H. E. (2018) · D-Scholarship@Pitt (University of Pittsburgh)
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Harms, R. (2019) · doi:10.26481/dis.20191025rh
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Goubran, M., Mills, B. D., Georgiadis, M., Karimpoor, M., Mouchawar, N., Sami, S., Dennis, E. L., Akers, C. S., Mitchell, L. A., Boldt, B. M., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.04.01.486632
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Pizzini, F. B., & Talenti, G. (2023) · Springer eBooks · doi:10.1007/978-3-031-10909-6_15
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Villalón‐Reina, J. E., Nir, T. M., Nourollahimoghadam, E., Dhinagar, N. J., Jahanshad, N., Thompson, P. M., & Henriques, R. N. (2023) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc40787.2023.10340067
Dhiman, S., Hickey, R. E., Thorn, K. E., Moss, H. G., McKinnon, E. T., Adisetiyo, V., Ades‐Aron, B., Jensen, J. H., & Benitez, A. M. (2024) · doi:10.3791/66397-v
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Farooq, H., Pisharady, P. K., & Lenglet, C. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13480-7.00006-5
Gondová, A., Neumane, S., Arichi, T., & Dubois, J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.09.612067
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Novikov, D. S., Fieremans, E., & Lee, H. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-818894-1.00031-8
Self-citationHakhu, S., Hu, L. S., Beeman, S. C., & Sadleir, R. (2025) · Frontiers in Radiology · doi:10.3389/fradi.2025.1492479
Albertson, D. N., Tracy, D. K., Joyce, D. W., & Shergill, S. S. (2025) · Cambridge University Press eBooks · doi:10.1017/9781009028967.016
Metadata
- Authors
- Jelescu, I. O., Veraart, J., Adisetiyo, V., Milla, S. S., Novikov, D. S., & Fieremans, E.
- Affiliations
- NYU Langone Health (US)
- Corresponding author
- Jelescu, Ileana O.
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2015-02
- Volume
- 107
- Pages
- 242-256
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC4300243
- OpenAlex
- W2005552408
- Cited by
- 202
- References
- 45
- Citations by year
- 2026: 2 · 2025: 13 · 2024: 11 · 2023: 13 · 2022: 14 · 2021: 18 · 2020: 27 · 2019: 21 · 2018: 29 · 2017: 27 · 2016: 15 · 2015: 12
- FWCI
- 14.08
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (R01-NS088040, R21-NS081230)
- Raymond and Beverly Sackler Laboratories for Convergence of Physical, Engineering, and Biomedical Sciences
- Alzheimer Drug Discovery Foundation
- Belgian Science Policy Office (P7/11)
- King Baudouin Foundation
- Belgian American Educational Foundation
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- white matter microstructure, early brain development, white matter tract integrity, NODDI, diffusion kurtosis imaging, corpus callosum, internal capsule, myelination, axial diffusivity, model bias, parameter estimation
- MeSH terms
- Aging, Axons, Computer Simulation, Corpus Callosum, Female, Humans, Infant, Infant, Newborn, Male, Models, Neurological, Myelin Sheath, Nerve Fibers, Myelinated, Neurites, Internal Capsule, Diffusion Magnetic Resonance Imaging, White Matter
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