Elsevier

NeuroImage

Volume 103, December 2014, Pages 214-224
NeuroImage

Microstructural brain development between 30 and 40 weeks corrected age in a longitudinal cohort of extremely preterm infants

https://doi.org/10.1016/j.neuroimage.2014.09.039Get rights and content

Highlights

  • Longitudinal diffusivity reference values are presented from preterm up to term age.

  • FA changed significantly in 84, MD in 112, AD in 104 and RD in 114 out of 122 brain regions.

  • Maturation showed a central–peripheral and occipital–frontal directed gradient.

  • Cortical brain regions showed a decrease in FA in the temporo-occipital part of the brain.

Abstract

Diffusion tensor imaging (DTI) is frequently used to assess brain development in preterm infants. This study investigates maturational changes in diffusivity measures in 122 regions of the brain between 30 and 40 weeks postmenstrual age (PMA) using the neonatal atlas of Oishi and colleagues (Oishi et al., 2011). Forty infants without cerebral injury and with normal neurodevelopmental outcome were selected from a cohort of preterm infants (gestational age < 28 weeks), scanned longitudinally at 30 and 40 weeks PMA. Fractional anisotropy (FA) changed significantly in 84 brain regions, with the largest increase in the central brain regions; by contrast, the cortical brain regions showed a decrease in FA. Mean, radial and axial diffusivity all showed a clear decrease in the majority of brain regions. This study provides longitudinal reference diffusivity values in a cohort of extremely preterm infants, showing a central to peripheral and posterior to anterior directed gradient, in line with our current understanding of brain maturation, and adding to this knowledge. This study further elucidates brain maturation in preterm infants during the last 10 weeks prior to term equivalent age. The presented values can be used as a reference for assessing brain development in other cohorts, when investigating the effects of brain injury in this vulnerable period, and to evaluate the effect of future neuroprotective strategies.

Introduction

During the second half of gestation, the human brain undergoes major growth and development. In recent years, MRI, and more recently diffusion tensor imaging (DTI), has become an important tool to evaluate microstructural changes in vivo in preterm infants (Tournier et al., 2011).

DTI has shown significant changes in white matter tracts in the second trimester in post-mortem fetuses and large changes in fractional anisotropy (FA) and mean diffusivity (MD) values between 29 weeks and term age (Aeby et al., 2012, Berman et al., 2005, Huang et al., 2009, Huppi et al., 1998), which suggests an ongoing maturation of the brain. This maturational process continues after term equivalent age, with an ongoing organization of axons and myelination of tracts until childhood and adulthood (Lebel et al., 2008, Miller et al., 2003a).

In particular the FA measure has often been used to quantify brain maturation. It has been extensively shown that FA values in the posterior limb of the internal capsule (PLIC) are positively related to increasing gestational age (Bonifacio et al., 2010, Huppi et al., 1998, Neil et al., 1998). This FA increase with a simultaneous MD decrease is thought to be due to a combination of factors related to brain development, including a decrease in water content of the brain, greater cohesiveness and compactness of the fiber tracts, reduced extra-axonal space and an increase in myelination, restricting water diffusion perpendicular to the axons (Beaulieu, 2002).

Several methods have been applied to quantify diffusivity measures, of which the manual drawing of regions of interest (ROI), tractography and tract based spatial statistics (TBSS) are most commonly used (for a review, see (Deprez et al., 2013)). To avoid the subjective nature of manual ROI-based measurements and to provide the opportunity to study the entire brain within a manageable time frame, an automated, atlas based approach can be used. Because of the rapid development of the neonatal brain, adult brain atlases are not suitable for implementation in this population. Recently, Oishi and colleagues published a neonatal brain atlas for DTI, where they identified 122 brain regions according to the Talairach atlas (Oishi et al., 2011, Talairach and Tournoux, 1988).

The aim of our study was to gain more information about maturational changes of the preterm brain, by computing the main diffusivity measures (i.e., FA, MD, and also the radial (RD) and axial (AD) diffusivities) at 30 and 40 weeks and the change in these measures during this period in a unique group of longitudinally scanned, preterm born infants without brain injury and with a normal neurodevelopmental outcome at 15 months.

Section snippets

Clinical data

From a cohort of extremely preterm infants with a gestational age below 28 weeks, consecutively admitted to the neonatal intensive care unit of the Wilhelmina Children's Hospital between June 2008 and June 2011, all 59 infants with serial MR imaging and available outcome data at 15 months were selected for initial inclusion. According to the clinical protocol, these infants are scanned around 30 weeks gestational age, if clinically stable, and again at term equivalent age with DTI as part of the

Results

Clinical characteristics of the infants are shown in Table 1.

For each brain region, the average FA value of the 40 infants is shown at 30 and 40 weeks in Figs. 1 A and B, respectively. Fig. 2, Fig. 3, Fig. 4 show the equivalent maps for the MD, AD and RD, respectively.

Fig. 5 depicts the change in the average FA values across the 40 infants between the two scans. In 70 brain regions, a significant FA increase and in 14 brain regions, a significant FA decrease was seen between the first and the

Discussion

In this study, the longitudinal change of FA, MD, AD and RD in the preterm brain over the period of 30 to 40 weeks postmenstrual age has been analyzed for infants with a normal neurodevelopmental outcome at 15 months corrected age. FA changed significantly in 84 out of 122 atlas-based brain regions, with the largest increase in the central brain regions, up to 76% in a period of 10 weeks. In contrast, cortical brain regions showed a small decrease in FA. For the other diffusivity measures a

References (64)

  • C. Lebel et al.

    Microstructural maturation of the human brain from childhood to adulthood

    NeuroImage

    (2008)
  • R. Nossin-Manor et al.

    Quantitative MRI in the very preterm brain: assessing tissue organization and myelination using magnetization transfer, diffusion tensor and T(1) imaging

    NeuroImage

    (2013)
  • K. Oishi et al.

    Multi-contrast human neonatal brain atlas: application to normal neonate development analysis

    NeuroImage

    (2011)
  • L.A. Papile et al.

    Incidence and evolution of subependymal and intraventricular hemorrhage: a study of infants with birth weights less than 1500 gm

    J. Pediatr.

    (1978)
  • S.C. Partridge et al.

    Diffusion tensor imaging: serial quantitation of white matter tract maturity in premature newborns

    NeuroImage

    (2004)
  • D. Raffelt et al.

    Apparent fibre density: a novel measure for the analysis of diffusion-weighted magnetic resonance images

    NeuroImage

    (2012)
  • J. Rose et al.

    Brain microstructural development at near-term age in very-low-birth-weight preterm infants: an atlas-based diffusion imaging study

    NeuroImage

    (2014)
  • R.L. Sidman et al.

    Neuronal migration, with special reference to developing human brain: a review

    Brain Res.

    (1973)
  • W. van Hecke et al.

    On the construction of an inter-subject diffusion tensor magnetic resonance atlas of the healthy human brain

    NeuroImage

    (2008)
  • W. van Hecke et al.

    The effect of template selection on diffusion tensor voxel-based analysis results

    NeuroImage

    (2011)
  • J. Veraart et al.

    Weighted linear least squares estimation of diffusion MRI parameters: strengths, limitations, and pitfalls

    NeuroImage

    (2013)
  • J.J. Volpe

    Neurology of the Newborn. Chapter 2: Neuronal Proliferation, Migration, Organization, and Myelination

    (2008)
  • S.B. Vos et al.

    Partial volume effect as a hidden covariate in DTI analyses

    NeuroImage

    (2011)
  • S.B. Vos et al.

    The influence of complex white matter architecture on the mean diffusivity in diffusion tensor MRI of the human brain

    NeuroImage

    (2012)
  • A. Aeby et al.

    Maturation of thalamic radiations between 34 and 41 weeks' gestation: a combined voxel-based study and probabilistic tractography with diffusion tensor imaging

    AJNR Am. J. Neuroradiol.

    (2009)
  • S.A. Back et al.

    Arrested oligodendrocyte lineage progression during human cerebral white matter development: dissociation between the timing of progenitor differentiation and myelinogenesis

    J. Neuropathol. Exp. Neurol.

    (2002)
  • G. Ball et al.

    Development of cortical microstructure in the preterm human brain

    Proc. Natl. Acad. Sci. U. S. A.

    (2013)
  • C. Beaulieu

    The basis of anisotropic water diffusion in the nervous system — a technical review

    NMR Biomed.

    (2002)
  • S.L. Bonifacio et al.

    Extreme premature birth is not associated with impaired development of brain microstructure

    J. Pediatr.

    (2010)
  • J.P. Bourgeois et al.

    Synaptogenesis in visual cortex of normal and preterm monkeys: evidence for intrinsic regulation of synaptic overproduction

    Proc. Natl. Acad. Sci. U. S. A.

    (1989)
  • T. Bui et al.

    Microstructural development of human brain assessed in utero by diffusion tensor imaging

    Pediatr. Radiol.

    (2006)
  • J.L. Cheong et al.

    Abnormal white matter signal on MR imaging is related to abnormal tissue microstructure

    AJNR Am. J. Neuroradiol.

    (2009)
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    This study includes infants participating in the Neobrain study (LSHM-CT-2006-036534), and infants from a study funded by the Wilhelmina Research Fund (OZF2008-19). The research of A.L. is supported by VIDI Grant 639.072.411 from the Netherlands Organisation for Scientific Research (NWO).

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