CoChat
Sign Up
Cited by 115Advanced Neuroimaging Techniques and ApplicationsFetal and Pediatric Neurological DisordersAdvanced MRI Techniques and Applications

Fetal brain growth portrayed by a spatiotemporal diffusion tensor MRI atlas computed from in utero images

Open access
Khan, Shadab, Vasung, Lana, Marami, Bahram, Rollins, Caitlin K., Afacan, Onur, Ortinau, Cynthia M., Yang, Edward, Warfield, Simon K. +1 more·2019·NeuroImage
DOI 10.1016/j.neuroimage.2018.08.030PMC PMC6289660
Open this paper in CoChat — free accountOpen PDF

Abstract

Altered structural fetal brain development has been linked to neuro-developmental disorders. These structural alterations can be potentially detected in utero using diffusion tensor imaging (DTI). However, acquisition and reconstruction of in utero fetal brain DTI remains challenging. Until now, motion-robust DTI methods have been employed for reconstruction of in utero fetal DTIs. However, due to the unconstrained fetal motion and permissible in utero acquisition times, these methods yielded limited success and have typically resulted in noisy DTIs. Consequently, atlases and methods that could enable groupwise studies, multi-modality imaging, and computer-aided diagnosis from in utero DTIs have not yet been developed. This paper presents the first DTI atlas of the fetal brain computed from in utero diffusion-weighted images. For this purpose an algorithm for computing an unbiased spatiotemporal DTI atlas, which integrates kernel-regression in age with a diffeomorphic tensor-to-tensor registration of motion-corrected and reconstructed individual fetal brain DTIs, was developed. Our new algorithm was applied to a set of 67 fetal DTI scans acquired from healthy fetuses each scanned at a gestational age between 21 and 39 weeks. The neurodevelopmental trends in the fetal brain, characterized by the atlas, were qualitatively and quantitatively compared with the observations reported in prior ex vivo and in utero studies, and with results from imaging gestational-age equivalent preterm infants. Our major findings revealed early presence of limbic fiber bundles, followed by the appearance and maturation of projection pathways (characterized by an age related increase in FA) during late 2nd and early 3rd trimesters. During the 3rd trimester association fiber bundles become evident. In parallel with the appearance and maturation of fiber bundles, from 21 to 39 gestational weeks gradual disappearance of the radial coherence of the telencephalic wall was qualitatively identified. These results and analyses show that our DTI atlas of the fetal brain is useful for reliable detection of major neuronal fiber bundle pathways and for characterization of the fetal brain reorganization that occurs in utero. The atlas can also serve as a useful resource for detection of normal and abnormal fetal brain development in utero.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.neuroimage.2018.08.030

Cites

8 of 8 references verified0 citation errors · 0 not checkable

Statistics

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.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • In vivo tractography on the fetal brain has become available and exhibits high agreement with ex vivo and neuroanatomical studies.

    “Due to recent advances in methodological progress such as motion correction algorithms, in vivo tractography on the fetal brain has become available and exhibit highly agreement with ex vivo and neuroanatomical studies (Christiaens et al., 2021; Khan et al., 2019), which indicates strong potential for future prenatal brain investigations.”

    From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Khan et al., 2019

    The Introduction (Fetal DTI; page unavailable) reports that motion-corrected in utero DTI enabled fetal tractography. The Conclusion (page unavailable) says the authors’ atlas portrayed developmental trends seen in prior ex vivo and in utero studies, but it does not establish high agreement specifically between in vivo tractography and ex vivo or neuroanatomical studies. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 76

  1. Adluru N, Zhang H, Fox AS, Shelton SE, Ennis CM, Bartosic AM, Oler JA, Tromp DPM, Zakszewski E, Gee JC, Kalin NH, Alexander AL, 2012 A diffusion tensor brain template for Rhesus Macaques. Neuroimage 59, 306– 318. https://doi.org/10.1016/j.neuroimage.2011.07.029 21803162 PMC3195880 · DOI
  2. Alexander AL, Lee JE, Lazar M, Field AS, 2007 Diffusion tensor imaging of the brain. Neurotherapeutics 4, 316– 29. https://doi.org/10.1016/j.nurt.2007.05.011 17599699 PMC2041910 · DOI · Paper page
  3. Arsigny V, Fillard P, Pennec X, Ayache N, 2006 Log-Euclidean metrics for fast and simple calculus on diffusion tensors. Magn. Reson. Med 56, 411– 421. https://doi.org/10.1002/mrm.20965 16788917 · DOI · Paper page
  4. Ball G, Srinivasan L, Aljabar P, Counsell SJ, Durighel G, Hajnal JV, Rutherford MA, Edwards AD, 2013 Development of cortical microstructure in the preterm human brain. Proc. Natl. Acad. Sci 110, 9541– 9546. https://doi.org/10.1073/pnas.1301652110 23696665 PMC3677430 · DOI · Paper page
  5. Bourgeois JP, Jastreboff PJ, Rakic P, 1989 Synaptogenesis in visual cortex of normal and preterm monkeys: evidence for intrinsic regulation of synaptic overproduction. Proc. Natl. Acad. Sci. U. S. A 86, 4297– 301. https://doi.org/10.1073/pnas.86.11.4297 2726773 PMC287439 · DOI
  6. Bourgeois JP, Rakic P, 1993 Changes of synaptic density in the primary visual cortex of the macaque monkey from fetal to adult stage. J. Neurosci 13, 2801– 2820. 8331373 PMC6576672
  7. Brown CJ, Miller SP, Booth BG, Andrews S, Chau V, Poskitt KJ, Hamarneh G, 2014 Structural network analysis of brain development in young preterm neonates. Neuroimage 101, 667– 680. https://doi.org/10.1016/j.neuroimage.2014.07.030 25076107 · DOI · Paper page
  8. Bui T, Daire J-L, Chalard F, Zaccaria I, Alberti C, Elmaleh M, Garel C, Luton D, Blanc N, Sebag G, 2006 Microstructural development of human brain assessed in utero by diffusion tensor imaging. Pediatr. Radiol 36, 1133– 1140. https://doi.org/10.1007/s00247-006-0266-3 16960686 · DOI
  9. Calabrese E, Badea A, Coe CL, Lubach GR, Shi Y, Styner MA, Johnson GA, 2015 A diffusion tensor MRI atlas of the postmortem rhesus macaque brain. Neuroimage 117, 408– 416. https://doi.org/10.1016/j.neuroimage.2015.05.072 26037056 PMC4512905 · DOI
  10. Chan WY, Yew DT, 1998 Apoptosis and Bcl-2 oncoprotein expression in the human fetal central nervous system. Anat. Rec 252, 165– 175. https://doi.org/10.1002/(SICI)1097-0185(199810)252:2<165::AID-AR2>3.0.CO;2-2 9776071 · DOI
  11. Clowry GJ, 2007 The dependence of spinal cord development on corticospinal input and its significance in understanding and treating spastic cerebral palsy. Neurosci. Biobehav. Rev https://doi.org/10.1016/j.neubiorev.2007.04.007 17544509 · DOI
  12. Counsell SJ, Ball G, Pandit A, Edwards AD, 2014 Chapter 13 – Diffusion Imaging in the Developing Brain, Second Edi. ed, Diffusion MRI: From Quantitative Measurement to In vivo Neuroanatomy. Elsevier https://doi.org/10.1016/B978-0-12-396460-1.00013-5 · DOI
  13. Davis BC, Fletcher PT, Bullitt E, Joshi S, 2010 Population shape regression from random design data. Int. J. Comput. Vis 90, 255– 266. https://doi.org/10.1007/s11263-010-0367-1 · DOI
  14. Dean DC, Planalp EM, Wooten W, Adluru N, Kecskemeti SR, Frye C, Schmidt CK, Schmidt NL, Styner MA, Goldsmith HH, Davidson RJ, Alexander AL, 2017 Mapping White Matter Microstructure in the One Month Human Brain. Sci. Rep 7, 9759 https://doi.org/10.1038/s41598-017-09915-6 28852074 PMC5575288 · DOI
  15. Dubois J, Dehaene-Lambertz G, Kulikova S, Poupon C, Hüppi PS, Hertz-Pannier L, 2014 The early development of brain white matter: A review of imaging studies in fetuses, newborns and infants. Neuroscience https://doi.org/10.1016/j.neuroscience.2013.12.044 24378955 · DOI · Paper page
  16. Dubois J, Kostovic I, Judas M, 2015 Development of structural and functional connectivity, Brain mapping: an encyclopedic reference. Elsevier Inc https://doi.org/10.1016/B978-0-12-397025-1.00360-2 · DOI
  17. Evangelou IE, Serag A, Bouyssi-Kobar M, Plessis AJ du, Limperopoulos C, 2014 Optimized methodology for neonatal diffusion tensor imaging processing and study-specific template construction. Conf. Proc. … Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. IEEE Eng. Med. Biol. Soc. Annu. Conf 2014, 2372– 2375. https://doi.org/10.1109/EMBC.2014.6944098 25570466 · DOI
  18. Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R, 2012 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn. Reson. Imaging 30, 1323– 1341. https://doi.org/10.1016/j.mri.2012.05.001 22770690 PMC3466397 · DOI
  19. Fogtmann M, Seshamani S, Kroenke C, Cheng X, Chapman T, Wilm J, Rousseau F, Studholme C, 2014 A unified approach to diffusion direction sensitive slice registration and 3-D DTI reconstruction from moving fetal brain anatomy. IEEE Trans. Med. Imaging 33, 272– 289. https://doi.org/10.1109/TMI.2013.2284014 24108711 PMC4271809 · DOI
  20. Garcia-Lazaro HG, Becerra-Laparra I, Cortez-Conradis D, Roldan-Valadez E, 2016 Global fractional anisotropy and mean diffusivity together with segmented brain volumes assemble a predictive discriminant model for young and elderly healthy brains: A pilot study at 3T. Funct. Neurol 31, 39– 46. https://doi.org/10.11138/FNeur/2016.31.1.039 27027893 PMC4819817 · DOI
  21. Geng X, Gouttard S, Sharma A, Gu H, Styner M, Lin W, Gerig G, Gilmore JH, 2012 Quantitative tract-based white matter development from birth to age 2years. Neuroimage 61, 542– 557. https://doi.org/10.1016/j.neuroimage.2012.03.057 22510254 PMC3358435 · DOI · Paper page
  22. Gholipour A, Estroff JA, Barnewolt CE, Robertson RL, Grant PE, Gagoski B, Warfield SK, Afacan O, Connolly SA, Neil JJ, Wolfberg A, Mulkern RV, 2014 Fetal MRI: A technical update with educational aspirations. Concepts Magn. Reson. Part A Bridg. Educ. Res 43, 237– 266. https://doi.org/10.1002/cmr.a.21321 26225129 PMC4515352 · DOI
  23. Gholipour A, Rollins CK, Velasco-Annis C, Ouaalam A, Akhondi-Asl A, Afacan O, Ortinau CM, Clancy S, Limperopoulos C, Yang E, Estroff JA, Warfield SK, 2017 A normative spatiotemporal MRI atlas of the fetal brain for automatic segmentation and analysis of early brain growth. Sci. Rep 7, 476 https://doi.org/10.1038/s41598-017-00525-w 28352082 PMC5428658 · DOI
  24. Hermoye L, Saint-martin C, Cosnard G, Lee S, Kim J, Nassogne M, Menten R, Clapuyt P, Donohue PK, Hua K, Wakana S, Jiang H, Zijl PCM Van, Mori S, 2006 Pediatric diffusion tensor imaging: Normal database and observation of the white matter maturation in early childhood 29, 493– 504. https://doi.org/10.1016/j.neuroimage.2005.08.017 16194615 · DOI
  25. Huang H, James Barkovich A, Vasung L, Lepage C, Radoš M, Pletikos M, Goldman JS, Richiardi J, Raguž M, Fischi-Gómez E, Karama S, Huppi PS, Evans AC, Kostovic I, 2016 Quantitative and Qualitative Analysis of Transient Fetal Compartments during Prenatal Human Brain Development. Artic. Front. Neuroanat 10 https://doi.org/10.3389/fnana.2016.00011 PMC4764715 26941612 · DOI
  26. Huang H, Vasung L, 2014 Gaining insight of fetal brain development with diffusion MRI and histology. Int. J. Dev. Neurosci 32, 11– 22. https://doi.org/10.1016/j.ijdevneu.2013.06.005 23796901 PMC3825830 · DOI
  27. Huang H, Xue R, Zhang J, Ren T, Richards LJ, Yarowsky P, Miller MI, Mori S, 2009 Anatomical Characterization of Human Fetal Brain Development with Diffusion Tensor Magnetic Resonance Imaging. J. Neurosci 29, 4263– 4273. https://doi.org/10.1523/JNEUROSCI.2769-08.2009 19339620 PMC2721010 · DOI · Paper page
  28. Huang H, Zhang J, Wakana S, Zhang W, Ren T, Richards LJ, Yarowsky P, Donohue P, Graham E, van Zijl PCM, Mori S, 2006 White and gray matter development in human fetal, newborn and pediatric brains. Neuroimage https://doi.org/10.1016/j.neuroimage.2006.06.009 16905335 · DOI
  29. Jakab A, Kasprian G, Schwartz E, Gruber GM, Mitter C, Prayer D, Schöpf V, Langs G, 2015 Disrupted developmental organization of the structural connectome in fetuses with corpus callosum agenesis. Neuroimage 111, 277– 288. https://doi.org/10.1016/j.neuroimage.2015.02.038 25725467 · DOI
  30. Jiang S, Xue H, Counsell S, Anjari M, Allsop J, Rutherford M, Rueckert D, Hajnal JV, 2009 Diffusion tensor imaging (DTI) of the brain in moving subjects: Application to in-utero fetal and ex-utero studies. Magn. Reson. Med 62, 645– 655. https://doi.org/10.1002/mrm.22032 19526505 · DOI
  31. Kainz B, Steinberger M, Wein W, Kuklisova-murgasova M, Malamateniou C, Keraudren K, Torsney-weir T, Rutherford M, Aljabar P, Hajnal JV, Rueckert D, 2015 Fast Volume Reconstruction from Motion Corrupted Stacks of 2D Slices. IEEE Trans. Med. Imaging 34, 1901– 1913. 25807565 PMC7115883
  32. Kasprian G, Brugger PC, Schöpf V, Mitter C, Weber M, Hainfellner JA, Prayer D, 2013 Assessing prenatal white matter connectivity in commissural agenesis. Brain 136, 168– 179. https://doi.org/10.1093/brain/aws332 23365096 · DOI
  33. Kasprian G, Brugger PC, Weber M, Krssák M, Krampl E, Herold C, Prayer D, 2008 In utero tractography of fetal white matter development. Neuroimage 43, 213– 224. https://doi.org/10.1016/j.neuroimage.2008.07.026 18694838 · DOI
  34. Kersbergen KJ, Leemans A, Groenendaal F, van der Aa NE, Viergever MA, de Vries LS, Benders MJNL, 2014 Microstructural brain development between 30 and 40 weeks corrected age in a longitudinal cohort of extremely preterm infants. Neuroimage 103, 214– 224. https://doi.org/10.1016/j.neuroimage.2014.09.039 25261000 · DOI
  35. Khan S, Rollins CK, Ortinau CM, Afacan O, Warfield SK, Gholipour A, 2018 Tract-Specific Group Analysis in Fetal Cohorts using in utero Diffusion Tensor Imaging, in: Medical Image Computing and Computer Assisted Intervention (MICCAI) PMC7456441 32869014
  36. Kim D-H, Chung S, Vigneron DB, Barkovich a J, Glenn O. a, 2008 Diffusion-weighted imaging of the fetal brain in vivo. Magn. Reson. Med 59, 216– 20. https://doi.org/10.1002/mrm.21459 18050314 · DOI
  37. Koay CG, Chang LC, Carew JD, Pierpaoli C, Basser PJ, 2006 A unifying theoretical and algorithmic framework for least squares methods of estimation in diffusion tensor imaging. J. Magn. Reson 182, 115– 125. https://doi.org/10.1016/j.jmr.2006.06.020 16828568 · DOI
  38. Kolasinski J, Takahashi E, Stevens AA, Benner T, Fischl B, Zöllei L., Grant PE, 2013 Radial and tangential neuronal migration pathways in the human fetal brain: Anatomically distinct patterns of diffusion MRI coherence. Neuroimage https://doi.org/10.1016/j.neuroimage.2013.04.125 PMC4111232 23672769 · DOI
  39. Kostović I, Jovanov-Milošević N, 2006 The development of cerebral connections during the first 20–45 weeks’ gestation. Semin. Fetal Neonatal Med 11, 415– 422. https://doi.org/10.1016/j.siny.2006.07.001 16962836 · DOI
  40. Kostović I, Jovanov-Milošević N, Radoš M, Sedmak G, Benjak V, Kostović-Srzentić M, Vasung L, Čuljat M, Radoš M, Hüppi P, Judaš M, 2014 Perinatal and early postnatal reorganization of the subplate and related cellular compartments in the human cerebral wall as revealed by histological and MRI approaches. Brain Struct. Funct 219, 231– 253. https://doi.org/10.1007/s00429-012-0496-0 23250390 · DOI
  41. Kostovic I, Vasung L, 2009 Insights From In Vitro Fetal Magnetic Resonance Imaging of Cerebral Development. Semin. Perinatol https://doi.org/10.1053/j.semperi.2009.04.003 19631083 · DOI
  42. Kunz N, Zhang H, Vasung L, O’Brien KR, Assaf Y, Lazeyras F, Alexander DC, Hüppi PS, 2014 Assessing white matter microstructure of the newborn with multi-shell diffusion MRI and biophysical compartment models. Neuroimage 96, 288– 299. https://doi.org/10.1016/j.neuroimage.2014.03.057 24680870 · DOI
  43. LaMantia AS, Rakic P, 1990 Axon overproduction and elimination in the corpus callosum of the developing rhesus monkey. J. Neurosci 10, 2156– 2175. https://doi.org/10.1002/cne.903400304 2376772 PMC6570389 · DOI
  44. Li K, Lu C, Huang Y, Yuan L, Zeng D, Wu K, 2014 Alteration of fractional anisotropy and mean diffusivity in glaucoma: Novel results of a meta-analysis of diffusion tensor imaging studies. PLoS One 9. https://doi.org/10.1371/journal.pone.0097445 PMC4020845 24828063 · DOI
  45. Liu C, Tian X, Liu H, Mo Y, Bai F, Zhao X, Ma Y, Wang J, 2015 Rhesus monkey brain development during late infancy and the effect of phencyclidine: A longitudinal MRI and DTI study. Neuroimage 107, 65– 75. https://doi.org/10.1016/j.neuroimage.2014.11.056 25485715 · DOI
  46. Maas LC, Mukherjee P, Carballido-Gamio J, Veeraraghavan S, Miller SP, Partridge SC, Henry RG, Barkovich AJ, Vigneron DB, 2004 Early laminar organization of the human cerebrum demonstrated with diffusion tensor imaging in extremely premature infants. Neuroimage 22, 1134– 1140. https://doi.org/10.1016/j.neuroimage.2004.02.035 15219585 · DOI
  47. Marami B, Mohseni Salehi SS, Afacan O, Scherrer B, Rollins CK, Yang E, Estroff JA, Warfield SK, Gholipour A, 2017 Temporal slice registration and robust diffusion-tensor reconstruction for improved fetal brain structural connectivity analysis. Neuroimage 156, 475– 488. https://doi.org/10.1016/j.neuroimage.2017.04.033 28433624 PMC5548611 · DOI
  48. McKinstry RC, Mathur A, Miller JH, Ozcan A, Snyder AZ, Schefft GL, Almli CR, Shiran SI, Conturo TE, Neil JJ, 2002 Radial organization of developing preterm human cerebral cortex revealed by non-invasive water diffusion anisotropy MRI. Cereb. Cortex 12, 1237– 1243. https://doi.org/10.1093/cercor/12.12.1237 12427675 · DOI
  49. Mitter C, Prayer D, Brugger PC, Weber M, Kasprian G, 2015b In Vivo Tractography of Fetal Association Fibers 1– 18. https://doi.org/10.1371/journal.pone.0119536 PMC4350986 25742520 · DOI
  50. Mrzljak L, Uylings HB, Kostovic I, van Eden CG, 1992 Prenatal development of neurons in the human prefrontal cortex. II. A quantitative Golgi study. J Comp Neurol 316, 485– 496. https://doi.org/10.1002/cne.903160408 1577996 · DOI
  51. Nossin-Manor R, Card D, Raybaud C, Taylor MJ, Sled JG, 2015 Cerebral maturation in the early preterm period-A magnetization transfer and diffusion tensor imaging study using voxel-based analysis. Neuroimage 112, 30– 42. https://doi.org/10.1016/j.neuroimage.2015.02.051 25731990 · DOI
  52. Oishi K, Faria AV, Yoshida S, Chang L, Mori S, 2013 Quantitative evaluation of brain development using anatomical MRI and diffusion tensor imaging. Int. J. Dev. Neurosci 31, 512– 524. https://doi.org/10.1016/j.ijdevneu.2013.06.004 23796902 PMC3830705 · DOI
  53. Oishi K, Mori S, Donohue PK, Ernst T, Anderson L, Buchthal S, Faria A, Jiang H, Li X, Miller MI, van Zijl PCM, Chang L, 2011 Multi-contrast human neonatal brain atlas: Application to normal neonate development analysis. Neuroimage 56, 8– 20. https://doi.org/10.1016/j.neuroimage.2011.01.051 21276861 PMC3066278 · DOI
  54. Ou Y, Zöllei L, Retzepi K, Castro V, Bates SV, Pieper S, Andriole KP, Murphy SN, Gollub RL, Grant PE, 2017 Using clinically acquired MRI to construct age-specific ADC atlases: Quantifying spatiotemporal ADC changes from birth to 6-year old. Hum. Brain Mapp 38, 3052– 3068. https://doi.org/10.1002/hbm.23573 28371107 PMC5426959 · DOI
  55. Oubel E, Koob M, Studholme C, Dietemann JL, Rousseau F, 2012 Reconstruction of scattered data in fetal diffusion MRI. Med. Image Anal 16, 28– 37. https://doi.org/10.1016/j.media.2011.04.004 21636311 PMC3682117 · DOI
  56. Petrides M, Pandya DN, 2012 The Frontal Cortex, in: The Human Nervous System pp. 988– 1011. https://doi.org/10.1016/B978-0-12-374236-0.10026-4 · DOI
  57. Qiu A, Mori S, Miller MI, 2015 Diffusion Tensor Imaging for Understanding Brain Development in Early Life https://doi.org/10.1146/annurev-psych-010814-015340 PMC4474038 25559117 · DOI · Paper page
  58. Righini A, Bianchini E, Parazzini C, Gementi P, Ramenghi L, Baldoli C, Nicolini U, Mosca F, Triulzi F, 2003 Apparent diffusion coefficient determination in normal fetal brain: a prenatal MR imaging study. Am. J. Neuroradiol 24, 799– 804. 12748074 PMC7975808
  59. Sadeghi N, Prastawa M, Fletcher PT, Wolff J, Gilmore JH, Gerig G, 2013 Regional characterization of longitudinal DT-MRI to study white matter maturation of the early developing brain. Neuroimage 68, 236– 247. https://doi.org/10.1016/j.neuroimage.2012.11.040 23235270 PMC3693970 · DOI
  60. Salehi SSM, Erdogmus D, Gholipour A, 2017 Auto-context Convolutional Neural Network for Geometry-Independent Brain Extraction in Magnetic Resonance Imaging 1– 9. PMC5715475 28678704
  61. Sanchez CE, Richards JE, Almli CR, 2012 Neurodevelopmental MRI brain templates for children from 2 weeks to 4 years of age. Dev. Psychobiol 54, 77– 91. https://doi.org/10.1002/dev.20579 PMC3184192 21688258 · DOI
  62. Schmechel DE, Rakic P, 1979 A golgi study of radial glial cells in developing monkey telencephalon: Morphogenesis and transformation into astrocytes. Anat. Embryol. (Berl) 156, 115– 152. https://doi.org/10.1007/BF00300010 111580 · DOI
  63. Serag A, Aljabar P, Ball G, Counsell SJ, Boardman JP, Rutherford MA, Edwards AD, Hajnal JV, Rueckert D, 2012 Construction of a consistent high-definition spatio-temporal atlas of the developing brain using adaptive kernel regression. Neuroimage 59, 2255– 2265. https://doi.org/10.1016/j.neuroimage.2011.09.062 21985910 · DOI
  64. Smith SM, Kindlmann G, Jbabdi S, 2013 Cross-Subject Comparison of Local Diffusion MRI Parameters, Second Edi. ed, Diffusion MRI: From Quantitative Measurement to In vivo Neuroanatomy: Second Edition. Elsevier https://doi.org/10.1016/B978-0-12-396460-1.00010-X · DOI · Paper page
  65. Takahashi E, Folkerth RD, Galaburda AM, Grant PE, 2012 Emerging cerebral connectivity in the human fetal Brain: An MR tractography study. Cereb. Cortex 22, 455– 464. https://doi.org/10.1093/cercor/bhr126 21670100 PMC3256410 · DOI
  66. Taquet M, Scherrer B, Commowick O, Peters JM, Sahin M, Macq B, Warfield SK, 2014 A mathematical framework for the registration and analysis of multi-fascicle models for population studies of the brain microstructure. IEEE Trans. Med. Imaging 33, 504– 517. https://doi.org/10.1109/TMI.2013.2289381 24235301 PMC3984609 · DOI
  67. Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, Gee JC, 2010 N4ITK: Improved N3 bias correction. IEEE Trans. Med. Imaging 29, 1310– 1320. https://doi.org/10.1109/TMI.2010.2046908 20378467 PMC3071855 · DOI
  68. Vasung L, Huang H, Jovanov-Milošević N, Pletikos M, Mori S, Kostović I, 2010 Development of axonal pathways in the human fetal fronto-limbic brain: Histochemical characterization and diffusion tensor imaging. J. Anat 217, 400– 417. https://doi.org/10.1111/j.1469-7580.2010.01260.x 20609031 PMC2992416 · DOI · Paper page
  69. Vasung L, Lepage C, Radoš M, Pletikos M, Goldman JS, Richiardi J, Raguž M, Fischi-Gómez E, Karama S, Huppi PS, Evans AC, Kostovic I, 2016 Quantitative and Qualitative Analysis of Transient Fetal Compartments during Prenatal Human Brain Development. Artic. Front. Neuroanat 10 https://doi.org/10.3389/fnana.2016.00011 PMC4764715 26941612 · DOI
  70. Vasung L, Raguz M, Kostovic I, Takahashi E, 2017 Spatiotemporal Relationship of Brain Pathways during Human Fetal Development Using High-Angular Resolution Diffusion MR Imaging and Histology. Front. Neurosci 11 https://doi.org/10.3389/fnins.2017.00348 PMC5504538 28744187 · DOI
  71. Veraart J, Sijbers J, Sunaert S, Leemans A, Jeurissen B, 2013 Weighted linear least squares estimation of diffusion MRI parameters: strengths, limitations, and pitfalls. Neuroimage 81, 335– 346. https://doi.org/10.1016/j.neuroimage.2013.05.028 23684865 · DOI
  72. Wang Y, Gupta A, Liu Z, Zhang H, Escolar ML, Gilmore JH, Gouttard S, Fillard P, Maltbie E, Gerig G, Styner M, 2011 DTI registration in atlas based fiber analysis of infantile Krabbe disease. Neuroimage 55, 1577– 1586. https://doi.org/10.1016/j.neuroimage.2011.01.038 21256236 PMC3062693 · DOI
  73. Xu G, Takahashi E, Folkerth RD, Haynes RL, Volpe JJ, Grant PE, Kinney HC, 2014 Radial Coherence of Diffusion Tractography in the Cerebral White Matter of the Human Fetus: Neuroanatomic Insights. Cereb. Cortex 24, 579– 592. https://doi.org/10.1093/cercor/bhs330 23131806 PMC3920761 · DOI
  74. Zanin E, Ranjeva J-P, Confort-Gouny S, Guye M, Denis D, Cozzone PJ, Girard N, 2011 White matter maturation of normal human fetal brain. An in vivo diffusion tensor tractography study. Brain Behav 1, 95– 108. https://doi.org/10.1002/brb3.17 22399089 PMC3236541 · DOI
  75. Zhang H, Avants BB, Yushkevich PA, Woo JH, Wang S, McCluskey LF, Elman LB, Melhem ER, Gee JC, 2007 High-dimensional spatial normalization of diffusion tensor images improves the detection of white matter differences: An example study using amyotrophic lateral sclerosis. IEEE Trans. Med. Imaging 26, 1585– 1597. https://doi.org/10.1109/TMI.2007.906784 18041273 · DOI
  76. Zhang H, Yushkevich PA, Alexander DC, Gee JC, 2006 Deformable registration of diffusion tensor MR images with explicit orientation optimization. Med. Image Anal 10, 764– 785. https://doi.org/10.1016/j.media.2006.06.004 16899392 · DOI

Cited by

Sorted by citations

Authors
Khan, S., Vasung, L., Marami, B., Rollins, C. K., Afacan, O., Ortinau, C. M., Yang, E., Warfield, S. K., & Gholipour, A.
Affiliations
  • Boston Children's Hospital (US)
  • Washington University in St. Louis (US)
Corresponding author
Khan, Shadab
ORCID
Venue
NeuroImage
Publisher
Elsevier BV
Published
2019-01
Volume
185
Pages
593-608
ISSN
1053-8119 (ISSN-L)
DOI
10.1016/j.neuroimage.2018.08.030
PMC
PMC6289660
OpenAlex
W2889410733
Cited by
115
References
8
Citations by year
2026: 6 · 2025: 18 · 2024: 12 · 2023: 17 · 2022: 19 · 2021: 18 · 2020: 11 · 2019: 13 · 2018: 1
FWCI
1.68
Type
journal article
Language
en
Open access
Licence
Locations
  • NeuroImage (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • McKnight Foundation
  • Fetal Health Foundation
  • Brain and Behavior Research Foundation
  • National Institutes of Health (R01 EB018988, R01 EB013248, R01 EB019483, K12 NS079414)
  • Swiss National Science Foundation (P300PB_167804)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
  • Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Fetal and Pediatric Neurological Disorders
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
Keywords
fetal brain development, spatiotemporal atlas, fetal brain maturation, gestational age, diffeomorphic registration, fractional anisotropy, neurodevelopmental disorders
MeSH terms
Algorithms, Atlases as Topic, Brain, Female, Fetus, Humans, Image Interpretation, Computer-Assisted, Male, Fetal Development, Neurogenesis, Diffusion Tensor Imaging

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