Neuropediatrics 2014; 45(04): 217-225
DOI: 10.1055/s-0033-1363299
Original Article
Georg Thieme Verlag KG Stuttgart · New York

Temporal Resolvability Analysis of Macroscopic Morphological Development in Neonatal Cerebral Magnetic Resonance Images

Maryam Momeni
1   Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
2   GRAMFC, Inserm U1105, Faculty of Medicine, University of Picardie Jules Verne, Amiens, France
,
Hamid Abrishami Moghaddam
1   Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
2   GRAMFC, Inserm U1105, Faculty of Medicine, University of Picardie Jules Verne, Amiens, France
,
Reinhard Grebe
2   GRAMFC, Inserm U1105, Faculty of Medicine, University of Picardie Jules Verne, Amiens, France
,
Catherine Gondry-Jouet
3   GRAMFC, Neuroradiology Unit, Amiens University Hospital, Amiens, France
,
Fabrice Wallois
1   Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
4   GRAMFC, Inserm U1105, EFSN Pédiatrique, Amiens University Hospital, Amiens, France
› Author Affiliations
Further Information

Publication History

13 July 2013

17 October 2013

Publication Date:
13 December 2013 (online)

Abstract

Objectives Reliable gradation of neonatal brain development is important for clinical investigation of neurological disorders. A prerequisite for such quantification of development is knowledge about temporal resolvability.

Methods We hypothesized 2-week interval as the temporal resolvability of age-related templates to study macroscopic morphological brain development in the early weeks after birth. Therefore, we constructed two templates for the gestational age (GA) ranges of 39 to 40 and 41 to 42 weeks using T1-weighted magnetic resonance (MR) images. Then, we compared the spatial variation of anatomical landmarks and the average and the maximal length of spatial deformation in 30 subjects normalized to the two templates along x, y, and z directions.

Results Multivariate analysis of variance (MANOVA) revealed significant difference between spatial variations of the above macroscopic features in the two age ranges. Furthermore, quantitative analysis of feature scattering yielded the same result even in features for which the null hypothesis was not rejected by MANOVA. Moreover, the same procedure was reiterated on two sets of subjects with the closer age range of 1 week (40 and 41 week's GA) and no significant difference could be detected.

Conclusions The results strengthen the hypothesis that 2-week is the temporal resolvability of age-related templates for macroscopic morphological studies of the developing brain in the early weeks after birth.

 
  • References

  • 1 Hüppi PS, Schuknecht B, Boesch C , et al. Structural and neurobehavioral delay in postnatal brain development of preterm infants. Pediatr Res 1996; 39 (5) 895-901
  • 2 Hüppi PS, Warfield S, Kikinis R , et al. Quantitative magnetic resonance imaging of brain development in premature and mature newborns. Ann Neurol 1998; 43 (2) 224-235
  • 3 Golland P, Grimson WE, Shenton ME, Kikinis R. Detection and analysis of statistical differences in anatomical shape. Med Image Anal 2005; 9 (1) 69-86
  • 4 Aljabar P, Bhatia KK, Murgasova M , et al. Assessment of brain growth in early childhood using deformation-based morphometry. Neuroimage 2008; 39 (1) 348-358
  • 5 Durston S, Hulshoff Pol HE, Casey BJ, Giedd JN, Buitelaar JK, van Engeland H. Anatomical MRI of the developing human brain: what have we learned?. J Am Acad Child Adolesc Psychiatry 2001; 40 (9) 1012-1020
  • 6 Mazziotta JC, Toga AW, Evans AW, Fox P, Lancaster J ; The International Consortium for Brain Mapping (ICBM). A probabilistic atlas of the human brain: theory and rationale for its development. Neuroimage 1995; 2 (2) 89-101
  • 7 Gaillard WD, Grandin CB, Xu B. Developmental aspects of pediatric fMRI: considerations for image acquisition, analysis, and interpretation. Neuroimage 2001; 13 (2) 239-249
  • 8 Muzik O, Chugani DC, Juhász C, Shen C, Chugani HT. Statistical parametric mapping: assessment of application in children. Neuroimage 2000; 12 (5) 538-549
  • 9 Burgund ED, Kang HC, Kelly JE , et al. The feasibility of a common stereotactic space for children and adults in fMRI studies of development. Neuroimage 2002; 17 (1) 184-200
  • 10 Wilke M, Schmithorst VJ, Holland SK. Assessment of spatial normalization of whole-brain magnetic resonance images in children. Hum Brain Mapp 2002; 17 (1) 48-60
  • 11 Wilke M, Schmithorst VJ, Holland SK. Normative pediatric brain data for spatial normalization and segmentation differs from standard adult data. Magn Reson Med 2003; 50 (4) 749-757
  • 12 Altaye M, Holland SK, Wilke M, Gaser C. Infant brain probability templates for MRI segmentation and normalization. Neuroimage 2008; 43 (4) 721-730
  • 13 Dehaene-Lambertz G, Dehaene S, Hertz-Pannier L. Functional neuroimaging of speech perception in infants. Science 2002; 298 (5600) 2013-2015
  • 14 Prastawa M, Gilmore JH, Lin W, Gerig G. Automatic segmentation of MR images of the developing newborn brain. Med Image Anal 2005; 9 (5) 457-466
  • 15 Kazemi K, Moghaddam HA, Grebe R, Gondry-Jouet C, Wallois F. A neonatal atlas template for spatial normalization of whole-brain magnetic resonance images of newborns: preliminary results. Neuroimage 2007; 37 (2) 463-473
  • 16 Kazemi K, Ghadimi S, Abrishami-Moghaddam H, Grebe R, Gondry-Jouet C, Wallois F. Neonatal probabilistic models for brain, CSF and skull using T1-MRI data: preliminary results. Conf Proc IEEE Eng Med Biol Soc 2008; 2008: 3892-3895
  • 17 Kazemi K, Moghaddam HA, Grebe R, Gondry-Jouet C, Wallois F. Design and construction of a brain phantom to simulate neonatal MR images. Comput Med Imaging Graph 2011; 35 (3) 237-250
  • 18 Shi F, Yap PT, Fan Y, Gilmore JH, Lin W, Shen D. Construction of multi-region-multi-reference atlases for neonatal brain MRI segmentation. Neuroimage 2010; 51 (2) 684-693
  • 19 Shi F, Yap PT, Wu G , et al. Infant brain atlases from neonates to 1- and 2-year-olds. PLoS ONE 2011; 6 (4) e18746
  • 20 Yoon U, Fonov VS, Perusse D, Evans AC ; Brain Development Cooperative Group. The effect of template choice on morphometric analysis of pediatric brain data. Neuroimage 2009; 45 (3) 769-777
  • 21 Kuklisova-Murgasova M, Aljabar P, Srinivasan L , et al. A dynamic 4D probabilistic atlas of the developing brain. Neuroimage 2011; 54 (4) 2750-2763
  • 22 Kuczmarski RJ, Ogden CL, Guo SS , et al. 2000 CDC Growth Charts for the United States: methods and development. Vital Health Stat 11 2002; 11 (246) 1-190
  • 23 Pienaar R, Fischl B, Caviness V, Makris N, Grant PE. A methodology for analyzing curvature in the developing brain from preterm to adult. Int J Imaging Syst Technol 2008; 18 (1) 42-68
  • 24 Ashburner J, Hutton C, Frackowiak R, Johnsrude I, Price C, Friston K. Identifying global anatomical differences: deformation-based morphometry. Hum Brain Mapp 1998; 6 (5-6) 348-357
  • 25 Ashburner J, Friston KJ. Nonlinear spatial normalization using basis functions. Hum Brain Mapp 1999; 7 (4) 254-266
  • 26 Garel C. Le Développement Du Cerveau Foetal: Atlas Irm et Biométrie. Sauramps Medical: Montpellier; 2000. :152p
  • 27 Dubois J, Hertz-Pannier L, Cachia A, Mangin JF, Le Bihan D, Dehaene-Lambertz G. Structural asymmetries in the infant language and sensori-motor networks. Cereb Cortex 2009; 19 (2) 414-423
  • 28 Leroy F, Glasel H, Dubois J , et al. Early maturation of the linguistic dorsal pathway in human infants. J Neurosci 2011; 31 (4) 1500-1506