Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2016 Jan 3.
Published in final edited form as: Annu Rev Psychol. 2015 Jan 3;66:853–876. doi: 10.1146/annurev-psych-010814-015340

Diffusion Tensor Imaging for Understanding Brain Development in Early Life

Anqi Qiu 1,2, Susumu Mori 3,4, Michael I Miller 5,6
PMCID: PMC4474038  NIHMSID: NIHMS679370  PMID: 25559117

Abstract

The human brain rapidly develops during the final weeks of gestation and in the first two years following birth. Diffusion tensor imaging (DTI) is a unique in vivo imaging technique that allows three-dimensional visualization of the white matter anatomy in the brain. It has been considered to be a valuable tool for studying brain development in early life. In this review, we first introduce the DTI technique. We then review DTI findings on white matter development at the fetal stage and in infancy as well as DTI applications for understanding neurocognitive development and brain abnormalities in preterm infants. Finally, we discuss limitations of DTI and potential valuable imaging techniques for studying white matter myelination.

Keywords: magnetic resonance imaging, diffusion tensor imaging, brain development, white matter, myelination, infancy

INTRODUCTION

Brain development is extremely rapid and dynamic during the fetal stage and in the first two years following birth. Brain development underpins cognitive and motor development in early childhood as well as the potential pathogenesis of neurodevelopmental disorders, such as autism, attention-deficit/hyperactivity disorder, and schizophrenia.

Magnetic resonance imaging (MRI) has significantly advanced our understanding of brain development and its correlates with cognitive development in early childhood. Using structural T1-weighted and T2-weighted MRI techniques, Knickmeyer et al. (2008) identified that total brain volume increased by 101% in the first year and by 15% in the second year. This robust growth of the human brain in the first two years of life was mainly driven by the gray matter growth, including the cortical and subcortical growth. The volume of the cortical hemispheres increased by 88% in the first year and by 15% in the second year. The volume of the subcortical region increased by 130% in the first year and by 14% in the second year (Knickmeyer et al. 2008). In contrast, the volume of hemispherical white matter increased by only 11% in the first year and by 19% in the second year (Knickmeyer et al. 2008).

Although white matter grows more slowly than does gray matter in terms of size, the early development of white matter, particularly white matter myelination, is a complex and long-lasting process. Myelination, the elaboration of myelin surrounding neuronal axons, starts late in embryonic development and continues through postnatal life. Due to the presence of myelin, electrical signals can move along nerve axons at high speed. The development of the myelin sheath enables rapid synchronized communication across the neural systems responsible for higher-order cognitive functioning.

In recent years, diffusion tensor imaging (DTI) has emerged as a method to noninvasively measure white matter microstructure in vivo throughout the life span, beyond structural size assessed using T1- and T2-weighted MRI. It has been widely used to investigate changes in white matter at different stages of brain development and their relationship to cognitive ability in early life. In this review, we first introduce diffusion MRI and DTI and summarize recent DTI findings on white matter development from the fetal stage to early childhood. We then review the applications of DTI to neurocognitive development in early-life and preterm research. Finally, we discuss the limitations of DTI and other potential imaging techniques for studying white matter development.

DIFFUSION MAGNETIC RESONANCE IMAGING AND DIFFUSION TENSOR IMAGING

MRI can be used to observe signals from various nuclei. However, MRI primarily measures the signal from protons of water molecules because more than 90% of protons in the body are located in water molecules. Interestingly, the water in the brain tissue diffuses in a restricted environment. The water diffusion reflects interactions with many obstacles, such as membranes and axonal fibers. Water molecule diffusion patterns can therefore reveal microscopic details about the brain tissue architecture, such as axonal orientation. Hence, water diffusion patterns have been considered useful for reflecting the underlying axonal organization of the brain.

Diffusion MRI is a technique that came into existence in the mid-1980s (Le Bihan & Breton 1985). It allows the in vivo and noninvasive imaging of the diffusion process of the water in the brain tissue. Diffusion MRI was designed to sensitize the MRI signal intensity to the amount of water diffusion. Pulsed magnetic field gradient, which introduces a linearly varied magnetic field in the three-dimensional space, is the technique used for this purpose. As precession of the proton is proportional to the magnet strength, the gradient pulse begins to precess at different rates, resulting in dispersion of the phase and signal loss across the spatial domain. Subsequently, another gradient pulse is applied in the same magnitude but with opposite direction to refocus or rephase the spins. However, the spins cannot be fully recovered during the refocusing process because water protons have moved during the time interval between the pulses. Hence, the signal measured by MRI is reduced. From the pulsed magnetic field gradient technique, the reduction in signal due to the application of the pulse gradient related to the amount of water diffusion can be derived at each location of the spatial domain. Alternatively, the signal loss is related to the water motion in this pulse gradient direction.

If ink is dropped into a cup of water, water freely diffuses, and the shape of diffusion becomes a sphere. This process, in which water diffuses in all directions by the same amount, is termed isotropic diffusion. In this case, only one measure—the diffusion constant—is needed to describe the diffusion. The diffusion constant is related to the diameter of the sphere. However, if the water motion is restricted in a tube, the diffusion process becomes more complicated, and the shape of water diffusion becomes ellipsoid. This type of diffusion, which often occurs in biological tissues, is termed anisotropic diffusion. The water tends to diffuse along a preferential axis or axes, such as axonal tracts in nervous tissues. Anisotropic diffusion cannot be characterized using a single diffusion constant but instead requires diffusion constants to be assessed at multiple diffusion directions.

Diffusion-weighted imaging (DWI) is a unique in vivo imaging technique that allows visualization of the water diffusion constant in different directions. DTI characterizes the ellipsoidal shape of the water diffusion profile in brain tissue using a symmetric positive definite tensor field derived from DWI. This tensor field measures the extent of diffusion in all directions in the three-dimensional space and hence maps the anisotropy of water diffusion in the brain, thus reflecting the organization and architecture of white matter fibers. Anisotropy is high because of the cylindrical geometry of the neuronal fibers in the white matter. In contrast, the diffusion anisotropy is very low in gray matter because the cellular geometry is more globular. Several metrics, such as fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD), and mean diffusivity (MD), derived from this tensor model are often used to reflect the microstructure of the brain anatomy. FA expresses the degree to which water diffusion is restricted in one direction relative to other directions. It ranges from zero to one, with zero being completely isotropic diffusion, and one being diffusion constrained in a single direction. The increase in FA with age in early life is explained by the ensheathment of oligodendrocytes around axons (Dubois et al. 2008, Hüppi & Dubois 2006). AD and RD reflect the rate of microscopic water motion parallel and perpendicular, respectively, to the direction of axonal fibers in a regional tissue; they are often used to evaluate water content (Provenzale et al. 2007). MD corresponds to the directionally averaged magnitude of water diffusion. The decrease in AD, RD, and MD are related to the premyelination stages in early life (Dubois et al. 2008, Hüppi & Dubois 2006).

Over the past several decades, many investigators have used DTI to examine white matter development during the fetal stage and infancy. Dubois et al. (2008) hypothesized three stages of white matter development that can be characterized using FA, AD, RD, and MD. These three stages sequentially involve fiber organization, membrane proliferation, and fiber myelination (as illustrated in Figure 1). At the first stage, axonal fibers appear to be more directionally organized. Dubois et al. (2008) hypothesized that progressive fiber organization may be reflected by an increase in anisotropy, which is due to an increase in AD and a decrease in RD. MD may remain unchanged. Water anisotropic diffusion exists in white matter in late intrauterine and premature infants (Kostovic & Jovanov-Milosevic 2006). The increased anisotropic diffusion process is linked to the developmental expansion of immature oligodendrocytes during the premyelination period (Drobyshevsky et al. 2005). Progressive fiber organization may be responsible for the anisotropy increase observed in unmyelinated white matter tracts of rats and rabbits (Drobyshevsky et al. 2005). In the human brain, high anisotropy is observed in poorly myelinated axons of premature newborns, such as the corpus callosum (Partridge et al. 2004). However, the change in the water diffusion profile due to the patterns of axonal fibers occurs at infancy and is not further observed between 5 and 17 weeks after birth (Dubois et al. 2006).

Figure 1.

Figure 1

Open in a new tab

The hypothesized relationship between maturational processes and diffusion indices in white matter. Abbreviations: FA, fractional anisotropy; >D<, mean diffusivity; λ//, axial diffusivity; λ⊥, radial diffusivity. Adapted with permission from Dubois et al. (2008).

The second stage of white matter development is related to the proliferation of glial cell and oligodendrocyte lineage precursors and cytoskeleton. Dubois et al. (2008) hypothesized that this may be linked to a decrease in water content of the brain and an increase in membrane density, leading to a reduction in water diffusivity (AD, RD, MD) as illustrated in the middle section of Figure 1. However, white matter development during this stage is relatively isotropic and hence has little influence on anisotropy.

The third stage is the last phase of axonal myelination, which corresponds to the ensheathment of oligodendroglial processes around the axons (Wozniak & Lim 2006). The maturation of white matter at this stage can be reflected by an increase of FA (as illustrated in the right section of Figure 1). Moreover, this stage is accompanied by a decrease in both membrane permeability and extracellular distance between membranes in the orthogonal direction to the fibers (Beaulieu 2002), which leads to a decrease in RD and MD.

WHITE MATTER DEVELOPMENT IN EARLY LIFE

The human brain develops rapidly during the final weeks of gestation and in the first two years following birth. The maturation of the white matter is asynchronous; it occurs from the second trimester of pregnancy to the end of adolescence and peaks during the first year of life. Postmortem studies suggest that the global pattern of the myelination progression sequence follows a caudorostral gradient and progresses from the center to the periphery (Brody et al. 1987, Dubois et al. 2014, Gilles et al. 1983, Kinney et al. 1988). White matter myelination occurs earlier and more quickly (a) in proximal pathways than in distal ones; (b) in sensory pathways (somatosensory, vision, audition) than in motor ones; (c) in central regions than in polar ones; (d ) in the occipital pole than in the posterior parietal white matter and in the temporal and frontal poles; and (e) in projection fibers than in association fibers. Guillery (2005) suggested that this asynchrony in the maturation sequence may be relevant to the hierarchy of connections between cortical areas: The early maturation of receptive sensory areas (responsible for low-level processing) would enable a stabilization of the information used by integrative areas (involved in high-level processing) that develop later.

Fetal Brain Development

Postmortem high-resolution (100 to 500 μm) DTI studies identified multiple neural structures during the fetal period (Huang et al. 2006, Kolasinski et al. 2013). At 19 to 20 postconceptual weeks, the Sylvian fissure and temporal lobe are visible. The cerebellum is not well developed, and a large portion of the cerebral hemisphere is occupied by the ventricle. The basal ganglia and hippocampus can be clearly identified at this early stage. Among major projection fibers, the core regions of the internal capsule and the cerebral peduncle are well developed (see Figure 2a,b). The proportion of the anterior and posterior limb of the internal capsule indicates that the anterior region is more developed. However, the corona radiatae are not clearly visible in the fetal brain. Among limbic fibers, the two most dominant tracts, the cingulum and fornix, are already present at 19 gestational weeks (see Figure 2a,b). For the fetal brain at 20 gestational weeks, the formation of the corpus callosum is more advanced in the frontal lobe (the genu of the corpus callosum) rather than the occipital lobe (the splenium of the corpus callosum). Among association fibers, the uncinate fasciculus can be clearly identified in the fetal brain (see Figure 2c,d). The other association fiber bundles, including the inferior fronto-occipital fasciculus, inferior longitudinal fasciculus, external capsule, and superior longitudinal fasciculus, show poor development at the end of the second trimester (see Figure 2c,d). During the period from 24 to 32 weeks of gestation, the major event is the development of the corona radiata from the transformation of the tangential fetal fiber-architectonic stratification. All major segments of the cerebral white matter can be recognized: corpus callosum, corona radiata, centrum semiovale, and gyral white matter (which is not yet fully developed). Fibers continue to grow at the levels of the periventricular crossroads and of the ventricular part of the corpus callosum. By term birth, all major fiber systems are in place (see the center columns of panels a–d in Figure 2).

Figure 2.

Figure 2

Open in a new tab

Panels a and b, respectively, show axial images of 19-gestational-week fetal (left), 0-year (center), and 5-year (right) brains at the levels of the brain stem and midbrain. Panels c and d, respectively, show axial images of 20-gestational-week fetal (left), 0-year (center), and 5-year (right) brains at the levels of the corpus callosum and the superior corona radiata. Abbreviations: ac, anterior commissure; acr, anterior corona radiate; alic, anterior limb of internal capsule; cc, corpus callosum; cg, cingulum; cr, corona radiata; cst, cortical spinal tract; dscp, decussation of superior cerebellar peduncle; ec, external capsule; Fmajor, forceps major; Fminor, forceps minor; fx, fornix; gcc, genu of corpus callosum; GE, ganglionic eminence; icp, inferior cerebellar peduncle; ifo, inferior fronto-occipital peduncle; ilf, inferior longitudinal fasciculus; mcp, middle cerebellar peduncle; ml, medial lemniscus; oc, optical chiasm; on, optical nerve; or, optical radiation; ot, optical tract; plic, posterior limb of internal capsule; scc, splenium of corpus callosum; scp, superior cerebellar peduncle; scr, superior region of corona radiata; sfo, superior fronto-occipital fasciculus; ss, sagittal stratum; st, stria terminalis; unc, uncinate fasciculus. Adapted with permission from Huang et al. (2006).

Beyond major white matter tracts, high-resolution (~100 μm) ex vivo DTI and tractography can also characterize the pathways reflecting the pattern of diffusion coherence associated with the dorsopallial ventricular/subventricular zone and subpallial ganglionic eminence in the human fetal brain (Kolasinski et al. 2013, Miller et al. 2011). Radial coherence reflects pathways running across the cerebral mantle, perpendicular to the cortical surface, whereas tangential coherence reflects pathways running parallel to the ventricular or cortical surface (Kolasinski et al. 2013, Takahashi et al. 2012). During postconceptual weeks 19 to 22, the pathways running within the dorsopallial ventricular/subventricular zone initially run tangential to the ventricular/subventricular border and then turn to a radial trajectory coursing toward the cortical plate (Kolasinski et al. 2013). In contrast, the pathways running within the ganglionic eminence show a dominant directionality tangential to the cortical surface. A clear tangentio-radial transition emerges from the ganglionic eminence and yields the pathways coursing radially through the cerebral mantle to the cortical plate. These DTI findings are well supported by histological data (Parnavelas et al. 2002, Ulfig et al. 2000, Yokota et al. 2007).

Infant Brain Development

The first few years of life represent a critical period during which cerebral growth and maturation (synaptogenesis and myelination) are intense (Gilmore et al. 2007, Provenzale et al. 2007). The cranial perimeter increases at 0.5 cm per week. As illustrated in Figure 3, a basic pattern of the maturation process can be indicated as an FA increase and an MD decrease with age in a posterior-to-anterior and central-to-peripheral direction of maturation (Cascio et al. 2007; Dubois et al. 2006, 2008; Lobel et al. 2009; Paus et al. 2001; Provenzale et al. 2007).

Figure 3.

Figure 3

Open in a new tab

Axial images of children at age of 0, 3, 6, 9, 12, 24, 36, and 48 months. Rows show (top to bottom) color maps, fractional anisotropy, mean diffusivity, and T2-weighted images. Figure adapted with permission from Hermoye et al. (2006).

From in vivo DTI, MD in the more superior locations is higher and decreases more quickly with age than that in the more inferior regions in the corticofugal pathway (i.e., the superior corona radiata > the posterior limb of internal capsule > the cerebral peduncle), and MD in the more anterior regions is higher and decreases more quickly with age than that in the more posterior regions in the corona radiata (the anterior portion > the superior portion > the posterior portion). The gray matter, the limbic fibers (including the fornix, the stria terminalis, and the cingulum), and some of the association fibers (such as the uncinate fasciculus, the external capsule, and the inferior fronto-occipital fasciculus) show relatively slow decreases in MD and slow increases in FA (Oishi et al. 2011). Within projection fibers, fibers that are located distant from the brain stem, including the corona radiata, posterior thalamic radiation (Aeby et al. 2009, Dubois et al. 2006, Gilmore et al. 2007, Hermoye et al. 2006, Hüppi & Dubois 2006), and the sagittal striatum, show a higher MD at 40 weeks of postconceptual age and a faster MD decrease than those that are located close to the brain stem, such as the posterior limb of the internal capsule, the retrolenticular part of the internal capsule, and the cerebral peduncle (Oishi et al. 2011). The white matter rich in crossing fibers, such as the corona radiata and the anterior limb of the internal capsule, shows a lower FA at 40 weeks of postconceptual age and a slower FA increase than those with fewer crossing fibers, including the posterior limb of the internal capsule, the retrolenticular part of the internal capsule, the cerebral peduncle, the posterior thalamic radiation, and the sagittal striatum (Oishi et al. 2011). Within association fibers, the superiorly located structures, including the superior longitudinal fasciculus and the superior fronto-occipital fasciculus, show a higher MD at 40 weeks of postconceptual age and a faster MD decrease than inferiorly located structures, such as the external capsule, the inferior fronto-occipital fasciculus, and the uncinate (Oishi et al. 2011).

From above, the patterns seen in MD are not observed in the FA analysis, especially for the structures rich in crossing fibers, such as the corona radiata (Dubois et al. 2014, Oishi et al. 2011). This is because the white matter myelination is known to begin earlier in the commissural and projection fibers than in the association fibers and earlier in the occipital and temporal lobes than in the frontal lobe (Brody et al. 1987). In general, myelination is observed at birth in pons and cerebellar peduncles and is followed by the posterior limb of the internal capsule, splenium of the corpus callosum and optic radiation at approximately age 3 months. The genu of the corpus callosum and anterior limb of internal capsule follow at approximately age 6 months (Dubois et al. 2008, Staudt et al. 2000).

However, the origins of water diffusion anisotropy in DTI are still debated (Beaulieu 2002) because it exists in premyelinating states (Hüppi et al. 1998, Prayer et al. 2001) or in nonmyelinated nerves (Beaulieu & Allen 1994, Song et al. 2002). Hence, an increase in FA with age not only may rely on myelination but also may result in an increase in the fiber’s density and in the bundle’s volume. Owing to the expression of the myelin sheath and the increasing number of oligodendrocytes, the overall water content is thought to be reduced and the extracellular space is decreased. The FA increase may also relate to the unmyelinated oligodendrocyte ensheathment around axons as seen in a brain study on the rabbit, suggesting that developmental changes in anisotropy coincide with proliferation of immature oligodendrocytes before myelination (Drobyshevsky et al. 2005). Moreover, intra-axonal macromolecules and functional ionic channels are thought to complicate diffusion through axons, resulting in more anisotropic diffusion (Barkovich 2000, Wimberger et al. 1995).

Infant Brain Networks

Beyond the characterization of tissue property, DTI in conjunction with graphic network analysis has been recently used to investigate structural networks of the infant brain. Figure 4 shows the flow for examining the structural network using DTI. Yap et al. (2011) showed that the whole-brain structural network exhibited the small-worldness property (small-world network) in the first two years of life. An examination of a large sample of normal neonates additionally supported that each cerebral hemisphere also exhibited the small-worldness properties at the neonatal period (Ratnarajah et al. 2013). These findings suggest that even in very early postnatal life the brain favors locally dense communication and minimizes the number of long-distance connections within and across two hemispheres. This topological organization of the brain at a global level was observed in the whole-brain structural network of adults (Gong et al. 2009) and functional networks of children and adults (Supekar et al. 2009). These findings support the idea that the human brain has efficient neural architecture for maximizing the power of information processing from birth.

Figure 4.

Figure 4

Open in a new tab

The major processes involved in structural network analysis using diffusion tensor imaging (DTI). (a) Diffusion-weighted (DW) images of each subject are aligned to those of the brain atlas. (b) The parcellation of cortical and subcortical regions using the brain atlas. (c) The whole-brain tractography using DTI deterministic tractography. (d ) Nodes (red spheres) representing cortical and subcortical regions. (e) Weighted edges (black lines) obtained using the tract information. Figure adapted with permission from Ratnarajah et al. (2013).

Moreover, an increase in global efficiency for the information transfer was shown in the first two years of life (Yap et al. 2011). This is parallel to the rapid development of myelination and the role of myelination in the impulse propagation speed along the fiber. Furthermore, the infant brain is organized into a number of internally densely connected subnetworks with sparser connections relating them to work as an organic whole. Interestingly, the precuneus was identified as a provincial hub with the vast majority of links within its subnetwork.

Structural network analysis also revealed the first evidence of asymmetry of structural connectivity in the neonatal brain (Ratnarajah et al. 2013). In neonates, small-world characteristics were exhibited but did not differ between the two cerebral hemispheres, suggesting that neighboring brain regions are tightly connected to one another and that one region is only a few paths away from any other region within each hemisphere. Moreover, the neonatal brain showed greater structural efficiency in the left hemisphere than in the right, suggesting that the brain regions in the left hemisphere interconnect in better integration and segregation as compared to the right hemisphere. Furthermore, in neonates, brain regions, including the precentral gyrus, precuneus, fusiform, entorhinal cortex, and insula, which are known to be involved at later ages in motor, language, and memory functions, play crucial roles in efficient communications in the left hemisphere, whereas brain regions such as the gyrus rectus, cingulate, hippocampus, and putamen, which are involved in emotional processes, play crucial roles in efficient communications in the right hemisphere. Together, these findings suggest that even at birth, the topology of each cerebral hemisphere is organized in an efficient and compact manner. Lateralization of such organization may support specific lateralized brain functions at birth.

DIFFUSION TENSOR IMAGING APPLICATIONS IN INFANT RESEARCH

White Matter Development in Relation to Cognition in Infants

DTI has been widely used to assess brain abnormalities and their functional correlations in clinical samples. Efficient signal transmission allows for efficient information processing. The cognitive development and performance depend in part on the organizational integrity of white matter connections within the brain. Hence, research efforts have increased on the use of DTI to non-invasively characterize brain development and its functional correlates in healthy samples in early life and represent a burgeoning field with great promise in developmental cognitive neuroscience.

The white matter myelination progression sequence has been suggested to parallel language acquisition (Pujol et al. 2006, Su et al. 2008). In a cross-sectional study, Pujol et al. (2006) detailed quantitative assessment of the time course of myelination in the bilateral lateral perisylvian region between birth and age 39 months. Anterior and posterior regions of the bilateral lateral perisylvian cortex were assessed along with a nonlanguage, sensorimotor control region. The difference between the rates of myelination for language regions and for the control region was identified. The 50th percentile of myelination was achieved by 6 months of age for the control region but not until 18 months of age for both the anterior and the posterior language regions. The 90th percentile of myelination was achieved by age 8 months for the nonlanguage control region and age 35 months for the language regions. Vocabulary acquisition is accelerated after 18 months of age, once a rapid myelination phase is attained in the language regions.

In addition to language function, working memory emerges in infancy and plays a pivotal role in subsequent adaptive cognitive development. However, the neural networks important for the development of working memory during infancy are relatively unknown. Recently, Short et al. (2013) showed the first evidence that in 12-month-old infants visuospatial working memory performance correlated with DTI microstructural characteristics of white matter tracts connecting brain regions known to be involved in working memory, including the genu of the corpus callosum, anterior and superior thalamic radiations, anterior cingulum, and arcuate fasciculus. This relationship was independent of individual variations in age and developmental level (Short et al. 2013). Better working memory scores were associated with higher FA and lower RD values in these selected white matter tracts. Brain regions connected by these specific tracts are well known to support working memory in older children and adults (Courtney et al. 1997, Jonides et al. 1993, Klingberg 2006, Kwon et al. 2002, Nagy et al. 2004). Therefore, the ability to characterize subtle individual differences in infant brain development assessed using DTI associated with complex cognitive functions holds promise for improving our understanding of normative development in neurocognitive periods of developmental plasticity.

The correlates of infant memory were recently investigated with DTI of the microstructural characteristics of white matter in 6-month-old infants. Microstructure variation in posterior aspects of the ventral visual pathway was associated with the speed at which the infant is able to process and encode information about the stimulus (Colombo & Mitchell 2009). A shorter look duration is thought to reflect faster acquisition of information about the habituated stimulus, whereas a longer look duration may be the most valid and reliable aspect of habituation with regard to both individual and developmental differences (Colombo et al. 1990). Moreover, FA values in the memory circuitry, including fornix, hippocampus, parahippocampus, and inferior temporal gyrus, predicted the performance of novelty preference in infants at 6 months of age. These findings are consistent with previous hypotheses that individual differences in performance on habituation and novel preference could reflect overall brain development and myelination as well as variation specific to the visual system, hippocampus, and supporting memory circuitry (Colombo et al. 1987).

Preterm Research

Prematurity is associated with a higher risk for neurodevelopmental impairments (Eikenes et al. 2011, Moore et al. 2013, Perenyi et al. 2013, Serenius et al. 2013) that appear to be related to early brain abnormalities. The most important brain abnormalities of prematurity are cerebral white matter injury (Volpe 2003) and periventricular leukomalacia (Woodward et al. 2006), which result in disrupted white matter maturation and chronic myelination disturbances (Volpe 2008). These brain alterations are better measured with DTI, whereby increased FA and decreased MD characterize white matter maturation (Berman et al. 2005). Indeed, brain abnormalities found with DTI appear to be normal with traditional MRI, such as T1- or T2-weighted MRI (Counsell et al. 2008). Hence, DTI has been widely used to describe and quantify the white matter maturation and myelination processes in preterm or at-term newborns (Hüppi et al. 1998, Miao et al. 2014, Padilla et al. 2014). DTI enhances our understanding of the encephalopathy of prematurity, which is heavily affected by preoligodendrocyte and axonal injury and aberrant white matter development.

Converging evidence suggests that preterm infants at term-equivalent age exhibit abnormalities in FA in the corpus callosum (Anjari et al. 2007, Hasegawa et al. 2011, Padilla et al. 2014, Thompson et al. 2011). Very preterm infants (≥23 and ≤33 weeks’ gestational age) without apparent white matter lesions exhibit affected development of the posterior corpus callosum, depending on the degree of prematurity (Hasegawa et al. 2011, Thompson et al. 2011). Additionally, Anjari et al. (2007) and Thompson et al. (2011) reported lower FA values in the genu of the corpus callosum in preterm infants at term-equivalent age in comparison with term infants. We recently found that FA in the genu of the corpus callosum increases as a function of gestational age even in term infants (≥37 weeks’ gestational age) (Leutscher-Broekman et al. 2014). However, the mechanism for such prematurity influences is unclear and may include the timing of exposure to the extrauterine environment. During the second half of pregnancy, the organization and maturation of axonal pathways are highly vulnerable processes. Early exposure to the extrauterine environment could cause the maturation disruption of the corpus callosum, as late pregnancy is a critical period for the axonal growth of the corpus callosum, particularly at the genu and splenium.

The influences of prematurity on the corpus callosum perhaps can persist into late life, a hypothesis that is supported by evidence from studies on preterm children, adolescents, and adults (Eikenes et al. 2011, Feldman et al. 2012, Mullen et al. 2011, Nagy et al. 2003, Skranes et al. 2007, Vangberg et al. 2006, Yung et al. 2007). Possible explanations for the reduced anisotropy are increased membrane permeability, reduced axon density, and decreased fiber organization. These findings suggest that cerebral abnormalities seen in preterm infants may be irreversible.

The corpus callosum integrates sensory, motor, cognitive, and emotional functions from both hemispheres (Constable et al. 2008). In preterm infants, FA in both the genu and splenium of the corpus callosum was associated with the cognitive level assessed using developmental quotient (Wang et al. 2013). High MD values and short fiber lengths of the callosal splenium of preterm infants at term-equivalent age were associated with psychomotor delay at age 2 years (De Bruine et al. 2013). A positive association between FA in the corpus callosum and total IQ has been seen in children (Yung et al. 2007), adolescents (Narberhaus et al. 2007), and young adults born prematurely (Eikenes et al. 2011). These findings are consistent with evidence that the corpus callosum size is significantly correlated with gestational age, Wechsler Performance IQ, and memory performance in preterm children (Caldu et al. 2006). Abnormal corpus callosum volume has been associated with learning and behavioral difficulties, speech and language delays, and cognitive impairment as well as motor function and cerebral palsy in previous neuroimaging studies (Counsell et al. 2008, Mathew et al. 2013, Northam et al. 2012, Rademaker et al. 2004, Saksena et al. 2008). Higher MD in the splenium of the corpus callosum at birth was associated with poor outcome at 18 months as classified by the Mental Developmental Index of the Bayley Scales of Infant and Toddler Development (Takenouchi et al. 2010).

In addition to findings related to the corpus callosum, several studies also consistently identified abnormalities in the internal capsule in prematurity. Preterm infants at term age showed reduced anisotropy in the internal capsule compared to infants born at term (Dudink et al. 2007, Hüppi et al. 2001, Pogribna et al. 2013, Rose et al. 2009). It was recently shown that even in the normal range of gestation (37 to ~41 weeks), FA in the anterior limb of the internal capsule changes as a function of gestational age (Broekman et al. 2014). Anisotropy in the posterior limb of the internal capsule was found to be reduced in a group of 11-year-olds with a history of preterm birth (Nagy et al. 2003), suggesting persistence of prematurity-related white matter abnormalities into late life.

The internal capsule is visible in the second trimester of pregnancy using tractography in ex vivo DTI (Huang et al. 2006, 2009). Despite the fact that its fibers are not fully mature, the internal capsule still connects with extensions to the thalamus and basal ganglia (Knaap & Valk 1990). These tracts are involved in several neural circuits supporting basic brain functions at birth. For example, the internal capsule is a major cortico-subcortical white matter bundle that contains fibers running from the thalamus to the basal ganglia as well as connecting the thalamus to the frontal lobe, which is responsible for regulation of sensorimotor functions. Low FA values and decreased fiber lengths of the posterior limb of the internal capsule at term-equivalent age are associated with psychomotor delay and cerebral palsy at the age of 2 years (De Bruine et al. 2013). Previous studies found the internal capsule to be vulnerable to brain damage (e.g., cerebral palsy) in the perinatal and early postnatal period (Hoon et al. 2009, Shinohara et al. 1976, Yoshida et al. 2010). Reduced MD in the posterior limb of the internal capsule at birth is associated with poor neuromotor outcome at follow-up (average of 12.9 months) (Hunt et al. 2004). The reduced anisotropy in the internal capsule signifies poorer connectivity and is associated with reduced motor skills in adolescents with a history of preterm birth (Vangberg et al. 2006).

Other white matter regions have been highlighted in preterm studies; however, findings are less consistent. Nevertheless, early differences in brain maturation have great clinical implications. The integrity of axons is important in mediating neurological functions. Deficits of the complex interneuronal connections can presage cognitive impairments. Increased diffusivity in the brain stem and cerebellum is associated with intraventricular hemorrhage (IVH) in prematurely born infants, which demonstrates that IVH is associated with cerebellar hypoplasia (Tam et al. 2011). Cerebellar integrity appears to be critical for learning to predict visual sensory consequences of motor commands (Izawa et al. 2012). Reduced FA and higher RD within the entire cerebellothalamo-cerebral pathway predict lower working memory, which is a novel contribution to the understanding of cerebral-cerebellar communication (Law et al. 2011).

TECHNICAL CONCERNS REGARDING IMAGING ACQUISITION

Advanced Diffusion-Weighted Magnetic Resonance Imaging

As discussed above, DTI is valuable for studying brain white matter development in children in early life. However, a major shortcoming of DTI is that it can reveal only one dominant fiber orientation at each location, yet between one-third and two-thirds of the voxels in the human brain white matter are thought to contain multiple fiber bundles crossing each other (Behrens et al. 2007). High-angular-resolution diffusion imaging (HARDI) (Tuch et al. 2002) addresses this well-known limitation of DTI. HARDI measures diffusion along n uniformly distributed directions on the sphere and can characterize more complex fiber geometries, where the acquired number of diffusion gradient directions is greater than that for DTI. Several reconstruction techniques can be used to characterize diffusion based on the HARDI signals. One method is based on higher-order tensors (Barmpoutis et al. 2009, Ghosh et al. 2008) and leverages prior work on DTI. Another method is Q-ball imaging, which uses the Funk-Radon transform to reconstruct an orientation distribution function (ODF). The model-free ODF is the angular profile of the diffusion probability density function of water molecules and has been approximated using different sets of basis functions such as spherical harmonics (Descoteaux et al. 2007, Frank 2002, Hess et al. 2006, Ozarslan & Mareci 2003). Such methods are relatively fast to implement because the ODF is computed analytically. By quantitatively comparing fiber orientations retrieved from ODFs against histological measurements, Leergaard et al. (2010) showed that accurate fiber estimates can be obtained from HARDI data, further validating the use of HARDI in brain studies.

In contrast to DTI and HARDI, multishell diffusion-weighted imaging (mDWI) acquires data through the q-space at multiple b-values in order to more accurately reconstruct the ensemble average propagator (EAP). The EAP estimation using mDWI better characterizes more complex neural fiber geometries and non-Gaussian diffusion behavior when compared to single b-value techniques (Wu & Alexander 2007). Recently, new q-space imaging techniques, diffusion spectrum imaging (DSI) (Kuo et al. 2008, Tuch et al. 2001, Wedeen et al. 2005) and hybrid diffusion imaging (HYDI) (Wu & Alexander 2007), have been developed for estimating the EAP. HYDI is an mDWI technique that samples the diffusion signal along concentric spherical shells in the q-space. DSI and HYDI employ the fast Fourier transform to reconstruct the EAP. It has been demonstrated that DSI has the ability to resolve crossing fibers at the scale of a single MRI voxel and to identify long association tracts in the brain, including the superior longitudinal fasciculus subcomponents I, II, and III; the fronto-occipital fasciculus; the middle longitudinal fasciculus; the uncinate fasciculus; the extreme capsule; the arcuate fasciculus; the inferior longitudinal fasciculus; and the cingulum bundle (Schmahmann et al. 2007). These association tracts shown in DSI are largely consistent with those seen using autoradiographic histological tract tracing (Schmahmann et al. 2007). Wedeen et al. (2008) further demonstrated that DSI tractography accurately shows the known anatomic fiber crossings in the optic chiasm, centrum semiovale, and brain stem; fiber intersections in the cerebellar folia and the caudate nucleus; and radial fiber architecture in the cerebral cortex. However, none of these examples of fiber crossing and complex structure was identified using DTI analysis of the same data sets. These findings indicate that DSI is able to image crossing fibers in the brain (Wedeen et al. 2008); hence, DSI has the potential to cast new light on the organization of the human brain in the normal state.

Nevertheless, HARDI and mDWI have not been widely used in studying normal brain development in early life and neurodevelopmental disorders. This is mainly because of their required acquisition times, which may not be compatible with unsedated pediatric populations.

Recently, the human connectome project proposed an approach to reduce scan time by capitalizing on the simultaneous excitation of multiple brain slices and sharing diffusion preparation among all slices excited (Van Essen et al. 2012). This imaging approach is accomplished with multiple receivers and multiband excitations developed for functional MRI (Larkman et al. 2001, Moeller et al. 2010). Acquiring many slices during a single echo-planar echo train and a single contrast preparation period permits subsecond whole-brain coverage at 2- or 3-mm isotropic resolution and hence substantially reduces acquisition times for DW MRI. These advances will benefit diffusion data directly through higher data acquisition rates without serious losses in signal-to-noise ratio, and indirectly by reducing the total number of diffusion gradient pulses per whole-brain scan, which allows more time for gradient coil cooling when very high b-values are used. Accelerated imaging will enable the collection of many hundreds or even thousands of diffusion-encoded data points per voxel.

Beyond Diffusion-Weighted Magnetic Resonance Imaging

The MRI metrics derived from DTI, such as FA, AD, and RD, are informative but nonspecific. They reflect broad changes of tissue microstructure in the brain, which hence makes interpretation of the brain development in early life challenging. In the first 24 months after birth, the brain undergoes considerable changes, including synaptogenesis and myelination. However, water diffusion anisotropy exists prior to myelination (Hüppi et al. 1998, Prayer et al. 2001) or in non-myelinated nerves (Beaulieu & Allen 1994, Song et al. 2002). An increase in FA with age may rely on myelination or may result from an increase in the density of fibers and the volume of bundles. The overall water content can be reduced and the extracellular space can be decreased because of the expression of myelin sheath and the increasing number of oligodendrocytes. Moreover, intra-axonal macromolecules and functional ionic channels are thought to complicate diffusion through axons, resulting in more anisotropic diffusion (Barkovich 2000, Wimberger et al. 1995). Large FA and RD values are noted in nonmyelinated nerves from an ex vivo study (Beaulieu 2002) and in the frontal lobe regions of one-week-old infants (Dubois et al. 2008), where myelin is not yet present in the postmortem brain tissue (Provenzale et al. 2007). Thus, these DTI measures more likely characterize the local fiber architectural milieu (fiber coherence, density, size, and myelination); therefore, caution should be taken when ascribing observed changes to any specific microstructural alterations.

Recently, several MRI techniques, including magnetization transfer (MT) imaging (Vavasour et al. 2011, Wolff & Balaban 1989), T1 and T2 relaxometry (Bartzokis et al. 2010, Glasser & Van Essen 2011, Van Essen et al. 2012), and multicomponent relaxometry (Deoni et al. 2008, 2012; Kroeker & Henkelman 1986; Menon et al. 1991; Whittall et al. 1997), have been developed to provide a more specific measure of myelin content. MT is an MRI technique that examines the interaction between mobile protons in water and motionally restricted nonaqueous protons, and it has been proposed as a marker for myelin in the brain tissue. MT utilizes the exchange of magnetization between mobile and motionally restricted protons through chemical processes and diffusion to obtain information about the nonaqueous proton pool. The magnetization transfer ratio (MTR) reflects the interaction between the motionally restricted and mobile proton pools: A decrease in the immobile proton pool, an increase in the mobile pool, or both processes occurring simultaneously may cause a decrease in MTR. The MTR is significantly correlated with myelin examined in the postmortem tissue (Schmierer et al. 2004). Myelin contributes to the motionally restricted proton pool, and a loss of myelin consequently leads to a reduction of MTR. Hence, changes in the MTR in the brain tissue have been often attributed to changes in myelin content (Armstrong et al. 2004, Chen et al. 2005, Giacomini et al. 2009).

Converging evidence has suggested that the longitudinal (T1) and transverse (T2) relaxation times are sensitive to the arrival of myelin precursor proteins and the establishment of the myelin sheath. The relaxation times are also sensitive to changes in bulk water content and compartmentalization related to axonal fiber density and size, iron content, membrane permeability, and cholesterol content (MacKay et al. 2009). Sigalovsky et al. (2006) found an increased R1 signal (the inverse of T1) in the posterior medial Heschl’s gyrus, which reflects the high myelin content of primary auditory cortex. Yoshiura et al. (2000) reported that Heschl’s gyrus has a lower intensity in the T2-weighted image than does the superior or middle temporal gyri. These findings suggest that the myelin content of a cortical area covaries with the intensity of the T1- and T2-weighted (T1w and T2w) images, but in opposite directions. Glasser & Van Essen (2011) computed the ratio of T1w/T2w image intensities, termed myelin map, to eliminate the MR-related image intensity bias and enhance the contrast-to-noise ratio for myelin. The spatial gradient of the myelin map provides sharp transitions in myelin content across the cortical surface, which has excellent agreement with the gradients of published probabilistic cytoarchitectonically defined cortical areas.

A multicomponent relaxometry technique is multicomponent analysis of T1 and T2 relaxation, which quantifies the myelin-bound water signal, termed the myelin water fraction (MWF). The MWF has been suggested as a surrogate measure of myelin content and is strongly correlated with histological assessment (Laule et al. 2006). Moreover, MWF has been shown to have greater myelin specificity than diffusion anisotropy has (Madler et al. 2008). Deoni et al. (2011, 2012) have demonstrated the application of multicomponent relaxometry to the study of infant brain development. Figure 5 shows the MWF, T1, and T2 maps from the age of 3 months to 60 months. Figure 6 illustrates the MWF values in major white matter tracts and lobes. Both figures show asynchrony of white matter maturation in early life. The pattern of white matter maturation shown in MWF has demonstrated strong correlations with postmortem myelin-staining techniques (Laule et al. 2006, 2008) but not necessarily to DTI metrics of white matter, such as FA, AD, and RD (Kolind et al. 2008, Madler et al. 2008).

Figure 5.

Figure 5

Open in a new tab

The mean myelin water fraction (MWF), T1, and T2 maps from seven age groups are respectively shown from the top to bottom rows. Note that T2 was only calculated in voxels with a corresponding T1 less than 3,500 ms for ages 9 months and above. Figure adapted with permission from Deoni et al. (2012).

Figure 6.

Figure 6

Open in a new tab

Scatter plots of the mean myelin water fraction (MWF) at ages 0 days to 2,400 days. Figure adapted with permission from Deoni et al. (2012).

An emerging multicomponent relaxometry technique, multicomponent-driven equilibrium single-pulse observation of T1 and T2 (mcDESPOT), offers potential advantages over conventional multicomponent relaxometry approaches, namely decreased acquisition times, increased volumetric coverage (i.e., whole brain), and improved spatial resolution. The mcDESPOT myelin water fraction measures, denoted as myelin volume fraction (VFM), have shown consistency with MWF values but are larger than MWF. The mcDESPOT technique replicated the pattern of myelination histologically observed by Flechsig (1920) as well as the in vivo findings (Deoni et al. 2011, 2012). This work has shown a nonlinear developmental trajectory of VFM from infancy to childhood, which follows an approximate log-growth pattern. Recently, O’Muircheartaigh et al. (2014) employed the mcDESPOT technique and investigated associations of white matter myelination with early cognition with infants and toddlers. The white matter myelination underlying frontal and temporal cortices showed significant relationships to expressive and receptive language abilities. These relationships had a significant interaction with age, with VFM becoming more strongly associated with language skills with age. These data provide evidence that a changing coupling exists between developing myelin and cognitive development.

SUMMARY POINTS.

  1. Diffusion tensor imaging characterizes the ellipsoidal shape of the water diffusion profile in the brain tissue using a symmetric positive-definite tensor field derived from diffusion-weighted images.

  2. Fractional anisotropy and water diffusivity can be derived from diffusion tensor imaging and used to characterize different stages of white matter development in early life.

  3. A basic pattern of the maturation process of white matter can be indicated as a fractional anisotropy (FA) increase and a mean diffusivity (MD) decrease with age in a posterior-to-anterior and central-to-peripheral direction of maturation.

  4. The white matter myelination is known to begin earlier in the commissural and projection fibers than in the association fibers, and earlier in the occipital and temporal lobes than in the frontal lobe.

  5. The brain in early life exhibits small-world characteristics, but they do not differ between the two cerebral hemispheres, which suggests that neighboring brain regions tightly connect to each other and that within each hemisphere, one region is only a few paths away from any other region.

  6. The neonatal brain shows greater structural efficiency in the left hemisphere than in the right, which suggests that the brain regions in the left hemisphere interconnect in better integration and segregation compared with the right hemisphere.

  7. Preterm infants at term-equivalent age exhibited abnormalities in FA in multiple brain regions, but most consistently in the corpus callosum. This prematurity influence on the corpus callosum may persist into late life.

  8. The DTI measures likely characterize the local fiber architectural milieu (fiber coherence, density, size, and myelination), and therefore caution should be taken when ascribing observed alterations to any specific microstructural changes.

FUTURE ISSUES.

  1. What is the pattern of cortical myelination development in early life?

  2. What is the relationship between cortical folding development and white matter myelination?

  3. To what extent can we understand the cognitive development of infants from the point of view of white matter myelination as well as structural connectivity?

  4. How will we remedy the lack of longitudinal DTI studies that investigate the long-term impact of premature birth on white matter development and its relationship with cognition?

  5. What future imaging technique will be used for characterizing brain myelination?

ACKNOWLEDGMENTS

The authors’ studies are supported by National Medical Research Council (NMRC; NMRC/ TCR/004-NUS/2008 and NMRC/CBRG/0039/2013), the National University of Singapore Young Investigator Award (NUSYIA FY10 P07), and Singapore Ministry of Education Academic Research Fund Tier 2 (MOE2012-T2-2-130).

Glossary

T1

the longitudinal relaxation time that is required to regain longitudinal magnetization following a radio frequency pulse

T2

the transverse relaxation time that measures how long the resonating protons remain coherent or precess in phase following a 90° radio frequency pulse

Myelination

the process in which glial cells produce an insulating myelin that wraps around nerve axons

Diffusion tensor imaging (DTI)

an imaging technique that is thought to reflect fiber density, axonal diameter, and myelination in white matter

Diffusion-weighted imaging (DWI)

technique that provides image contrast that is dependent on the molecular motion of water

Projection fibers

fibers that connect the cortex with lower sensory or motor centers, such as the thalamus, brain stem, and spinal cord

Small-world network

a highly efficient and clustered graph with nodes that are not neighbors of each other but can reach from one to another by a small number of steps

Global efficiency

a measure of the overall capacity for parallel information transfer and integrated processing

Footnotes

DISCLOSURE STATEMENT

The authors are not aware of any affiliations, memberships, funding, or financial holdings that might be perceived as affecting the objectivity of this review.

LITERATURE CITED

  1. Aeby A, Liu Y, De Tiege X, Denolin V, David P, et al. Maturation of thalamic radiations between 34 and 41 weeks’ gestation: a combined voxel-based study and probabilistic tractography with diffusion tensor imaging. Am. J. Neuroradiol. 2009;30:1780–86. doi: 10.3174/ajnr.A1660. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Anjari M, Srinivasan L, Allsop JM, Hajnal JV, Rutherford MA, et al. Diffusion tensor imaging with tract-based spatial statistics reveals local white matter abnormalities in preterm infants. NeuroImage. 2007;35:1021–27. doi: 10.1016/j.neuroimage.2007.01.035. [DOI] [PubMed] [Google Scholar]
  3. Armstrong CL, Traipe E, Hunter JV, Haselgrove JC, Ledakis GE, et al. Age-related, regional, hemispheric, and medial-lateral differences in myelin integrity in vivo in the normal adult brain. Am. J. Neuroradiol. 2004;25:977–84. [PMC free article] [PubMed] [Google Scholar]
  4. Barkovich AJ. Concepts of myelin and myelination in neuroradiology. Am. J. Neuroradiol. 2000;21:1099–109. [PMC free article] [PubMed] [Google Scholar]
  5. Barmpoutis A, Hwang MS, Howland D, Forder JR, Vemuri BC. Regularized positive-definite fourth order tensor field estimation from DW-MRI. NeuroImage. 2009;45:S153–62. doi: 10.1016/j.neuroimage.2008.10.056. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Bartzokis G, Lu PH, Tingus K, Mendez MF, Richard A, et al. Lifespan trajectory of myelin integrity and maximum motor speed. Neurobiol. Aging. 2010;31:1554–62. doi: 10.1016/j.neurobiolaging.2008.08.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Beaulieu C. The basis of anisotropic water diffusion in the nervous system—a technical review. NMR Biomed. 2002;15:435–55. doi: 10.1002/nbm.782. [DOI] [PubMed] [Google Scholar]
  8. Beaulieu C, Allen PS. Determinants of anisotropic water diffusion in nerves. Magn. Reson. Med. 1994;31:394–400. doi: 10.1002/mrm.1910310408. [DOI] [PubMed] [Google Scholar]
  9. Behrens TE, Berg HJ, Jbabdi S, Rushworth MF, Woolrich MW. Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? NeuroImage. 2007;34:144–55. doi: 10.1016/j.neuroimage.2006.09.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Berman JI, Mukherjee P, Partridge SC, Miller SP, Ferriero DM, et al. Quantitative diffusion tensor MRI fiber tractography of sensorimotor white matter development in premature infants. NeuroImage. 2005;27:862–71. doi: 10.1016/j.neuroimage.2005.05.018. [DOI] [PubMed] [Google Scholar]
  11. Brody BA, Kinney HC, Kloman AS, Gilles FH. Sequence of central nervous system myelination in human infancy. I. An autopsy study of myelination. J. Neuropathol. Exp. Neurol. 1987;46:283–301. doi: 10.1097/00005072-198705000-00005. [DOI] [PubMed] [Google Scholar]
  12. Broekman B, Wang C, Li Y, Rifkin-Graboi A, Saw SM, et al. Gestational age at birth and neonatal brain microstructure in term-born infants: a birth cohort study. PLOS ONE. 2014 doi: 10.1371/journal.pone.0115229. In press. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Caldu X, Narberhaus A, Junque C, Gimenez M, Vendrell P, et al. Corpus callosum size and neuropsychologic impairment in adolescents who were born preterm. J. Child Neurol. 2006;21:406–10. doi: 10.1177/08830738060210050801. [DOI] [PubMed] [Google Scholar]
  14. Cascio CJ, Gerig G, Piven J. Diffusion tensor imaging: application to the study of the developing brain. J. Am. Acad. Child Adolesc. Psychiatry. 2007;46:213–23. doi: 10.1097/01.chi.0000246064.93200.e8. [DOI] [PubMed] [Google Scholar]
  15. Chen JT, Collins DL, Freedman MS, Atkins HL, Arnold DL. Local magnetization transfer ratio signal inhomogeneity is related to subsequent change in MTR in lesions and normal-appearing white-matter of multiple sclerosis patients. NeuroImage. 2005;25:1272–78. doi: 10.1016/j.neuroimage.2004.12.046. [DOI] [PubMed] [Google Scholar]
  16. Colombo J, Mitchell DW. Infant visual habituation. Neurobiol. Learn. Mem. 2009;92:225–34. doi: 10.1016/j.nlm.2008.06.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Colombo J, Mitchell DW, Coldren JT, Atwater JD. Discrimination learning during the first year: stimulus and positional cues. J. Exp. Psychol.: Learn. Mem. Cogn. 1990;16:98–109. doi: 10.1037//0278-7393.16.1.98. [DOI] [PubMed] [Google Scholar]
  18. Colombo J, Mitchell DW, O’Brien M, Horowitz FD. The stability of visual habituation during the first year of life. Child Dev. 1987;58:474–87. [PubMed] [Google Scholar]
  19. Constable RT, Ment LR, Vohr BR, Kesler SR, Fulbright RK, et al. Prematurely born children demonstrate white matter microstructural differences at 12 years of age, relative to term control subjects: an investigation of group and gender effects. Pediatrics. 2008;121:306–16. doi: 10.1542/peds.2007-0414. [DOI] [PubMed] [Google Scholar]
  20. Counsell SJ, Edwards AD, Chew AT, Anjari M, Dyet LE, et al. Specific relations between neurodevelopmental abilities and white matter microstructure in children born preterm. Brain. 2008;131:3201–8. doi: 10.1093/brain/awn268. [DOI] [PubMed] [Google Scholar]
  21. Courtney SM, Ungerleider LG, Keil K, Haxby JV. Transient and sustained activity in a distributed neural system for human working memory. Nature. 1997;386:608–11. doi: 10.1038/386608a0. [DOI] [PubMed] [Google Scholar]
  22. De Bruine FT, Van Wezel-Meijler G, Leijser LM, Steggerda SJ, Van Den Berg-Huysmans AA, et al. Tractography of white-matter tracts in very preterm infants: a 2-year follow-up study. Dev. Med. Child Neurol. 2013;55:427–33. doi: 10.1111/dmcn.12099. [DOI] [PubMed] [Google Scholar]
  23. Deoni SC, Dean DC, 3rd, O’Muircheartaigh J, Dirks H, Jerskey BA. Investigating white matter development in infancy and early childhood using myelin water faction and relaxation time mapping. NeuroImage. 2012;63:1038–53. doi: 10.1016/j.neuroimage.2012.07.037. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Deoni SC, Mercure E, Blasi A, Gasston D, Thomson A, et al. Mapping infant brain myelination with magnetic resonance imaging. J. Neurosci. 2011;31:784–91. doi: 10.1523/JNEUROSCI.2106-10.2011. The first quantitative study of myelination in healthy infants using a new myelin-specific MRI technique, multicomponent relaxometry. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Deoni SC, Rutt BK, Arun T, Puerpaoli C, Jones DK. Gleaning multicomponent T1 and T2 information from steady-state imaging data. Magn. Reson. Med. 2008;60:1372–87. doi: 10.1002/mrm.21704. [DOI] [PubMed] [Google Scholar]
  26. Descoteaux M, Angelino E, Fitzgibbons S, Deriche R. Regularized, fast, and robust analytical Q-ball imaging. Magn. Reson. Med. 2007;58:497–510. doi: 10.1002/mrm.21277. [DOI] [PubMed] [Google Scholar]
  27. Drobyshevsky A, Song SK, Gamkrelidze G, Wyrwicz AM, Derrick M, et al. Developmental changes in diffusion anisotropy coincide with immature oligodendrocyte progression and maturation of compound action potential. J. Neurosci. 2005;25:5988–97. doi: 10.1523/JNEUROSCI.4983-04.2005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Dubois J, Dehaene-Lambertz G, Kulikova S, Poupon C, Hüppi PS, Hertz-Pannier L. The early development of brain white matter: a review of imaging studies in fetuses, newborns and infants. Neuroscience. 2014;276C:48–71. doi: 10.1016/j.neuroscience.2013.12.044. Insightful review of MRI techniques and their use in understanding white matter myelination. [DOI] [PubMed] [Google Scholar]
  29. Dubois J, Dehaene-Lambertz G, Perrin M, Mangin JF, Cointepas Y, et al. Asynchrony of the early maturation of white matter bundles in healthy infants: quantitative landmarks revealed noninvasively by diffusion tensor imaging. Hum. Brain Mapp. 2008;29:14–27. doi: 10.1002/hbm.20363. A white matter maturation model based on the time course of mean diffusivity and anisotropy changes in early life. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Dubois J, Hertz-Pannier L, Dehaene-Lambertz G, Cointepas Y, Le Bihan D. Assessment of the early organization and maturation of infants’ cerebral white matter fiber bundles: a feasibility study using quantitative diffusion tensor imaging and tractography. NeuroImage. 2006;30:1121–32. doi: 10.1016/j.neuroimage.2005.11.022. [DOI] [PubMed] [Google Scholar]
  31. Dudink J, Lequin M, van Pul C, Buijs J, Conneman N, et al. Fractional anisotropy in white matter tracts of very-low-birth-weight infants. Pediatr. Radiol. 2007;37:1216–23. doi: 10.1007/s00247-007-0626-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Eikenes L, Lohaugen GC, Brubakk AM, Skranes J, Haberg AK. Young adults born preterm with very low birth weight demonstrate widespread white matter alterations on brain DTI. NeuroImage. 2011;54:1774–85. doi: 10.1016/j.neuroimage.2010.10.037. [DOI] [PubMed] [Google Scholar]
  33. Feldman HM, Lee ES, Yeatman JD, Yeom KW. Language and reading skills in school-aged children and adolescents born preterm are associated with white matter properties on diffusion tensor imaging. Neuropsychologia. 2012;50:3348–62. doi: 10.1016/j.neuropsychologia.2012.10.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Flechsig P. Anatomie des menschlichen Gehirns und Ruckenmarks auf myelogenetischer Grundlage. G. Thieme; Liepzig: 1920. [Google Scholar]
  35. Frank LR. Characterization of anisotropy in high angular resolution diffusion-weighted MRI. Magn. Reson. Med. 2002;47:1083–99. doi: 10.1002/mrm.10156. [DOI] [PubMed] [Google Scholar]
  36. Ghosh A, Descoteaux M, Deriche R. Riemannian framework for estimating symmetric positive definite 4th order diffusion tensors. Med. Image Comput. Comput. Assist. Interv. 2008;11:858–65. doi: 10.1007/978-3-540-85988-8_102. [DOI] [PubMed] [Google Scholar]
  37. Giacomini PS, Levesque IR, Ribeiro L, Narayanan S, Francis SJ, et al. Measuring demyelination and remyelination in acute multiple sclerosis lesion voxels. Arch. Neurol. 2009;66:375–81. doi: 10.1001/archneurol.2008.578. [DOI] [PubMed] [Google Scholar]
  38. Gilles F, Shankle W, Dooling E. Myelinated Tracts: Growth Patterns. John Wright PSG; Boston: 1983. [Google Scholar]
  39. Gilmore JH, Lin W, Corouge I, Vetsa YS, Smith JK, et al. Early postnatal development of corpus callosum and corticospinal white matter assessed with quantitative tractography. Am. J. Neuroradiol. 2007;28:1789–95. doi: 10.3174/ajnr.A0751. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Glasser MF, Van Essen DC. Mapping human cortical areas in vivo based on myelin content as revealed by T1- and T2-weighted MRI. J. Neurosci. 2011;31:11597–616. doi: 10.1523/JNEUROSCI.2180-11.2011. Pioneering research that demonstrates patterns of cortical myelination in early life using the ratio of T1-/T2-weighted image intensity. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Gong G, He Y, Concha L, Lebel C, Gross DW, et al. Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography. Cereb. Cortex. 2009;19:524–36. doi: 10.1093/cercor/bhn102. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Guillery RW. Is postnatal neocortical maturation hierarchical? Trends Neurosci. 2005;28:512–17. doi: 10.1016/j.tins.2005.08.006. [DOI] [PubMed] [Google Scholar]
  43. Hasegawa T, Yamada K, Morimoto M, Morioka S, Tozawa T, et al. Development of corpus callosum in preterm infants is affected by the prematurity: in vivo assessment of diffusion tensor imaging at term-equivalent age. Pediatr. Res. 2011;69:249–54. doi: 10.1203/PDR.0b013e3182084e54. [DOI] [PubMed] [Google Scholar]
  44. Hermoye L, Saint-Martin C, Cosnard G, Lee SK, Kim J, et al. Pediatric diffusion tensor imaging: normal database and observation of the white matter maturation in early childhood. NeuroImage. 2006;29:493–504. doi: 10.1016/j.neuroimage.2005.08.017. [DOI] [PubMed] [Google Scholar]
  45. Hess CP, Mukherjee P, Han ET, Xu D, Vigneron DB. Q-ball reconstruction of multimodal fiber orientations using the spherical harmonic basis. Magn. Reson. Med. 2006;56:104–17. doi: 10.1002/mrm.20931. [DOI] [PubMed] [Google Scholar]
  46. Hoon AH, Jr, Stashinko EE, Nagae LM, Lin DD, Keller J, et al. Sensory and motor deficits in children with cerebral palsy born preterm correlate with diffusion tensor imaging abnormalities in thalamocortical pathways. Dev. Med. Child Neurol. 2009;51:697–704. doi: 10.1111/j.1469-8749.2009.03306.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Huang H, Xue R, Zhang J, Ren T, Richards LJ, et al. Anatomical characterization of human fetal brain development with diffusion tensor magnetic resonance imaging. J. Neurosci. 2009;29:4263–73. doi: 10.1523/JNEUROSCI.2769-08.2009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Huang H, Zhang J, Wakana S, Zhang W, Ren T, et al. White and gray matter development in human fetal, newborn and pediatric brains. NeuroImage. 2006;33:27–38. doi: 10.1016/j.neuroimage.2006.06.009. An ex vivo and in vivo DTI study on the appearance of major white matter tracts in fetus, newborn, and pediatric brains. [DOI] [PubMed] [Google Scholar]
  49. Hunt RW, Neil JJ, Coleman LT, Kean MJ, Inder TE. Apparent diffusion coefficient in the posterior limb of the internal capsule predicts outcome after perinatal asphyxia. Pediatrics. 2004;114:999–1003. doi: 10.1542/peds.2003-0935-L. [DOI] [PubMed] [Google Scholar]
  50. Hüppi PS, Dubois J. Diffusion tensor imaging of brain development. Semin. Fetal Neonatal Med. 2006;11:489–97. doi: 10.1016/j.siny.2006.07.006. [DOI] [PubMed] [Google Scholar]
  51. Hüppi PS, Maier SE, Peled S, Zientara GP, Barnes PD, et al. Microstructural development of human newborn cerebral white matter assessed in vivo by diffusion tensor magnetic resonance imaging. Pediatr. Res. 1998;44:584–90. doi: 10.1203/00006450-199810000-00019. The earliest DTI study on white matter anatomy in newborns. [DOI] [PubMed] [Google Scholar]
  52. Hüppi PS, Murphy B, Maier SE, Zientara GP, Inder TE, et al. Microstructural brain development after perinatal cerebral white matter injury assessed by diffusion tensor magnetic resonance imaging. Pediatrics. 2001;107:455–60. doi: 10.1542/peds.107.3.455. [DOI] [PubMed] [Google Scholar]
  53. Izawa J, Criscimagna-Hemminger SE, Shadmehr R. Cerebellar contributions to reach adaptation and learning sensory consequences of action. J. Neurosci. 2012;32:4230–39. doi: 10.1523/JNEUROSCI.6353-11.2012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Jonides J, Smith EE, Koeppe RA, Awh E, Minoshima S, Mintun MA. Spatial working memory in humans as revealed by PET. Nature. 1993;363:623–25. doi: 10.1038/363623a0. [DOI] [PubMed] [Google Scholar]
  55. Kinney HC, Brody BA, Kloman AS, Gilles FH. Sequence of central nervous system myelination in human infancy. II. Patterns of myelination in autopsied infants. J. Neuropathol. Exp. Neurol. 1988;47:217–34. doi: 10.1097/00005072-198805000-00003. [DOI] [PubMed] [Google Scholar]
  56. Klingberg T. Development of a superior frontal-intraparietal network for visuo-spatial working memory. Neuropsychologia. 2006;44:2171–77. doi: 10.1016/j.neuropsychologia.2005.11.019. [DOI] [PubMed] [Google Scholar]
  57. Knaap MS, Valk J. MR imaging of the various stages of normal myelination during the first year of life. Neuroradiology. 1990;31:459–70. doi: 10.1007/BF00340123. [DOI] [PubMed] [Google Scholar]
  58. Knickmeyer RC, Gouttard S, Kang C, Evans D, Wilber K, et al. A structural MRI study of human brain development from birth to 2 years. J. Neurosci. 2008;28:12176–82. doi: 10.1523/JNEUROSCI.3479-08.2008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Kolasinski J, Takahashi E, Stevens AA, Benner T, Fischl B, et al. Radial and tangential neuronal migration pathways in the human fetal brain: anatomically distinct patterns of diffusion MRI coherence. NeuroImage. 2013;79:412–22. doi: 10.1016/j.neuroimage.2013.04.125. An ex vivo structural MRI and DTI study on patterns of diffusion coherence associated with subcortical neuroproliferative structures in fetus brain. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Kolind SH, Laule C, Vavasour IM, Li DK, Traboulsee AL, et al. Complementary information from multi-exponential T2 relaxation and diffusion tensor imaging reveals differences between multiple sclerosis lesions. NeuroImage. 2008;40:77–85. doi: 10.1016/j.neuroimage.2007.11.033. [DOI] [PubMed] [Google Scholar]
  61. Kostovic I, Jovanov-Milosevic N. The development of cerebral connections during the first 20–45 weeks’ gestation. Semin. Fetal Neonatal. Med. 2006;11:415–22. doi: 10.1016/j.siny.2006.07.001. [DOI] [PubMed] [Google Scholar]
  62. Kroeker RM, Henkelman RM. Analysis of biological NMR relaxation data with continuous distributions of relaxation times. J. Magn. Reson. 1986;69:218–35. [Google Scholar]
  63. Kuo LW, Chen JH, Wedeen VJ, Tseng WY. Optimization of diffusion spectrum imaging and q-ball imaging on clinical MRI system. NeuroImage. 2008;41:7–18. doi: 10.1016/j.neuroimage.2008.02.016. [DOI] [PubMed] [Google Scholar]
  64. Kwon H, Reiss AL, Menon V. Neural basis of protracted developmental changes in visuo-spatial working memory. Proc. Natl. Acad. Sci. USA. 2002;99:13336–41. doi: 10.1073/pnas.162486399. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Larkman DJ, Hajnal JV, Herlihy AH, Coutts GA, Young IR, Ehnholm G. Use of multicoil arrays for separation of signal from multiple slices simultaneously excited. J. Magn. Reson. Imaging. 2001;13:313–17. doi: 10.1002/1522-2586(200102)13:2<313::aid-jmri1045>3.0.co;2-w. [DOI] [PubMed] [Google Scholar]
  66. Laule C, Kozlowski P, Leung E, Li DK, MacKay AL, Moore GR. Myelin water imaging of multiple sclerosis at 7 T: correlations with histopathology. NeuroImage. 2008;40:1575–80. doi: 10.1016/j.neuroimage.2007.12.008. [DOI] [PubMed] [Google Scholar]
  67. Laule C, Leung E, Lis DK, Traboulsee AL, Paty DW, et al. Myelin water imaging in multiple sclerosis: quantitative correlations with histopathology. Mult. Scler. 2006;12:747–53. doi: 10.1177/1352458506070928. [DOI] [PubMed] [Google Scholar]
  68. Law N, Bouffet E, Laughlin S, Laperriere N, Briere ME, et al. Cerebello-thalamo-cerebral connections in pediatric brain tumor patients: impact on working memory. NeuroImage. 2011;56:2238–48. doi: 10.1016/j.neuroimage.2011.03.065. [DOI] [PubMed] [Google Scholar]
  69. Le Bihan D, Breton E. Imagerie de diffusion in-vivo par résonance. C. R. Acad. Sci. (Paris) 1985;301:1109–12. [Google Scholar]
  70. Leergaard TB, White NS, de Crespigny A, Bolstad I, D’Arceuil H, et al. Quantitative histological validation of diffusion MRI fiber orientation distributions in the rat brain. PLOS ONE. 2010;5:e8595. doi: 10.1371/journal.pone.0008595. [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Leutscher-Broekman B, Wang C, Rifkin-Graboi A, Chong Y-S, Kwek K, et al. Association between gestational age and neonatal brain microstructure. PLOS ONE. 2014 doi: 10.1371/journal.pone.0115229. In press. [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Lobel U, Sedlacik J, Gullmar D, Kaiser WA, Reichenbach JR, Mentzel HJ. Diffusion tensor imaging: the normal evolution of ADC, RA, FA, and eigenvalues studied in multiple anatomical regions of the brain. Neuroradiology. 2009;51:253–63. doi: 10.1007/s00234-008-0488-1. [DOI] [PubMed] [Google Scholar]
  73. MacKay AL, Vavasour IM, Rauscher A, Kolind SH, Madler B, et al. MR relaxation in multiple sclerosis. Neuroimaging Clin. N. Am. 2009;19:1–26. doi: 10.1016/j.nic.2008.09.007. [DOI] [PubMed] [Google Scholar]
  74. Madler B, Drabycz SA, Kolind SH, Whittall KP, MacKay AL. Is diffusion anisotropy an accurate monitor of myelination? Correlation of multicomponent T2 relaxation and diffusion tensor anisotropy in human brain. Magn. Reson. Imaging. 2008;26:874–88. doi: 10.1016/j.mri.2008.01.047. [DOI] [PubMed] [Google Scholar]
  75. Mathew P, Pannek K, Snow P, D’Acunto MG, Guzzetta A, et al. Maturation of corpus callosum anterior midbody is associated with neonatal motor function in eight preterm-born infants. Neural Plast. 2013;2013:359532. doi: 10.1155/2013/359532. [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Menon RS, Rusinko MS, Allen PS. Multiexponential proton relaxation in model cellular systems. Magn. Reson. Med. 1991;20:196–213. doi: 10.1002/mrm.1910200204. [DOI] [PubMed] [Google Scholar]
  77. Miao X, Qi M, Cui S, Guan Y, Jia Z, et al. Assessing sequence and relationship of regional maturation in corpus callosum and internal capsule in preterm and term newborns by diffusion-tensor imaging. Int. J. Dev. Neurosci. 2014;34C:42–47. doi: 10.1016/j.ijdevneu.2014.01.004. [DOI] [PubMed] [Google Scholar]
  78. Miller KL, Stagg CJ, Douaud G, Jbabdi S, Smith SM, et al. Diffusion imaging of whole, post-mortem human brains on a clinical MRI scanner. NeuroImage. 2011;57:167–81. doi: 10.1016/j.neuroimage.2011.03.070. [DOI] [PMC free article] [PubMed] [Google Scholar]
  79. Moeller S, Yacoub E, Olman CA, Auerbach E, Strupp J, et al. Multiband multislice GE-EPI at 7 tesla, with 16-fold acceleration using partial parallel imaging with application to high spatial and temporal whole-brain fMRI. Magn. Reson. Med. 2010;63:1144–53. doi: 10.1002/mrm.22361. [DOI] [PMC free article] [PubMed] [Google Scholar]
  80. Moore GP, Lemyre B, Barrowman N, Daboval T. Neurodevelopmental outcomes at 4 to 8 years of children born at 22 to 25 weeks’ gestational age: a meta-analysis. JAMA Pediatr. 2013;167:967–74. doi: 10.1001/jamapediatrics.2013.2395. [DOI] [PubMed] [Google Scholar]
  81. Mullen KM, Vohr BR, Katz KH, Schneider KC, Lacadie C, et al. Preterm birth results in alterations in neural connectivity at age 16 years. NeuroImage. 2011;54:2563–70. doi: 10.1016/j.neuroimage.2010.11.019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  82. Nagy Z, Westerberg H, Klingberg T. Maturation of white matter is associated with the development of cognitive functions during childhood. J. Cogn. Neurosci. 2004;16:1227–33. doi: 10.1162/0898929041920441. [DOI] [PubMed] [Google Scholar]
  83. Nagy Z, Westerberg H, Skare S, Andersson JL, Lilja A, et al. Preterm children have disturbances of white matter at 11 years of age as shown by diffusion tensor imaging. Pediatr. Res. 2003;54:672–79. doi: 10.1203/01.PDR.0000084083.71422.16. [DOI] [PubMed] [Google Scholar]
  84. Narberhaus A, Segarra D, Caldu X, Gimenez M, Junque C, et al. Gestational age at preterm birth in relation to corpus callosum and general cognitive outcome in adolescents. J. Child Neurol. 2007;22:761–65. doi: 10.1177/0883073807304006. [DOI] [PubMed] [Google Scholar]
  85. Northam GB, Liégeois F, Tournier JD, Croft LJ, Johns PN, et al. Interhemispheric temporal lobe connectivity predicts language impairment in adolescents born preterm. Brain. 2012;135:3781–98. doi: 10.1093/brain/aws276. [DOI] [PMC free article] [PubMed] [Google Scholar]
  86. O’Muircheartaigh J, Dean DC, 3rd, Ginestet CE, Walker L, Waskiewicz N, et al. White matter development and early cognition in babies and toddlers. Hum. Brain Mapp. 2014;35:4475–87. doi: 10.1002/hbm.22488. [DOI] [PMC free article] [PubMed] [Google Scholar]
  87. Oishi K, Mori S, Donohue PK, Ernst T, Anderson L, et al. Multi-contrast human neonatal brain atlas: application to normal neonate development analysis. NeuroImage. 2011;56:8–20. doi: 10.1016/j.neuroimage.2011.01.051. Neonatal brain T1- and T2-weighted MRI and DTI atlases with comprehensive anatomical parcellation. [DOI] [PMC free article] [PubMed] [Google Scholar]
  88. Ozarslan E, Mareci TH. Generalized diffusion tensor imaging and analytical relationships between diffusion tensor imaging and high angular resolution diffusion imaging. Magn. Reson. Med. 2003;50:955–65. doi: 10.1002/mrm.10596. [DOI] [PubMed] [Google Scholar]
  89. Padilla N, Junque C, Figueras F, Sanz-Cortes M, Bargallo N, et al. Differential vulnerability of gray matter and white matter to intrauterine growth restriction in preterm infants at 12 months corrected age. Brain Res. 2014;1545:1–11. doi: 10.1016/j.brainres.2013.12.007. [DOI] [PubMed] [Google Scholar]
  90. Parnavelas JG, Alifragis P, Nadarajah B. The origin and migration of cortical neurons. Prog. Brain Res. 2002;136:73–80. doi: 10.1016/s0079-6123(02)36008-4. [DOI] [PubMed] [Google Scholar]
  91. Partridge SC, Mukherjee P, Henry RG, Miller SP, Berman JI, et al. Diffusion tensor imaging: serial quantitation of white matter tract maturity in premature newborns. NeuroImage. 2004;22:1302–14. doi: 10.1016/j.neuroimage.2004.02.038. [DOI] [PubMed] [Google Scholar]
  92. Paus T, Collins DL, Evans AC, Leonard G, Pike B, Zijdenbos A. Maturation of white matter in the human brain: a review of magnetic resonance studies. Brain Res. Bull. 2001;54:255–66. doi: 10.1016/s0361-9230(00)00434-2. [DOI] [PubMed] [Google Scholar]
  93. Perenyi A, Amodio J, Katz JS, Stefanov DG. Clinical utility of corpus callosum measurements in head sonograms of preterm infants: a cohort study. BMJ Open. 2013;3:e002499. doi: 10.1136/bmjopen-2012-002499. [DOI] [PMC free article] [PubMed] [Google Scholar]
  94. Pogribna U, Yu X, Burson K, Zhou Y, Lasky RE, et al. Perinatal clinical antecedents of white matter microstructural abnormalities on diffusion tensor imaging in extremely preterm infants. PLOS ONE. 2013;8:e72974. doi: 10.1371/journal.pone.0072974. [DOI] [PMC free article] [PubMed] [Google Scholar]
  95. Prayer D, Barkovich AJ, Kirschner DA, Prayer LM, Roberts TP, et al. Visualization of nonstructural changes in early white matter development on diffusion-weighted MR images: evidence supporting premyelination anisotropy. Am. J. Neuroradiol. 2001;22:1572–76. [PMC free article] [PubMed] [Google Scholar]
  96. Provenzale JM, Liang L, DeLong D, White LE. Diffusion tensor imaging assessment of brain white matter maturation during the first postnatal year. Am. J. Roentgenol. 2007;189:476–86. doi: 10.2214/AJR.07.2132. [DOI] [PubMed] [Google Scholar]
  97. Pujol J, Soriano-Mas C, Ortiz H, Sebastian-Galles N, Losilla JM, Deus J. Myelination of language-related areas in the developing brain. Neurology. 2006;66:339–43. doi: 10.1212/01.wnl.0000201049.66073.8d. [DOI] [PubMed] [Google Scholar]
  98. Rademaker KJ, Lam JN, Van Haastert IC, Uiterwaal CS, Lieftink AF, et al. Larger corpus callosum size with better motor performance in prematurely born children. Semin. Perinatol. 2004;28:279–87. doi: 10.1053/j.semperi.2004.08.005. [DOI] [PubMed] [Google Scholar]
  99. Ratnarajah N, Rifkin-Graboi A, Fortier MV, Chong YS, Kwek K, et al. Structural connectivity asymmetry in the neonatal brain. NeuroImage. 2013;75:187–94. doi: 10.1016/j.neuroimage.2013.02.052. The first study to show asymmetry of brain structural networks in a large sample of normal neonates. [DOI] [PMC free article] [PubMed] [Google Scholar]
  100. Rose J, Butler EE, Lamont LE, Barnes PD, Atlas SW, Stevenson DK. Neonatal brain structure on MRI and diffusion tensor imaging, sex, and neurodevelopment in very-low-birthweight preterm children. Dev. Med. Child Neurol. 2009;51:526–35. doi: 10.1111/j.1469-8749.2008.03231.x. [DOI] [PubMed] [Google Scholar]
  101. Saksena S, Husain N, Das V, Pradhan M, Trivedi R, et al. Diffusion tensor imaging in the developing human cerebellum with histologic correlation. Int. J. Dev. Neurosci. 2008;26:705–11. doi: 10.1016/j.ijdevneu.2008.07.007. [DOI] [PubMed] [Google Scholar]
  102. Schmahmann JD, Pandya DN, Wang R, Dai G, D’Arceuil HE, et al. Association fibre pathways of the brain: parallel observations from diffusion spectrum imaging and autoradiography. Brain. 2007;130:630–53. doi: 10.1093/brain/awl359. [DOI] [PubMed] [Google Scholar]
  103. Schmierer K, Scaravilli F, Altmann DR, Barker GJ, Miller DH. Magnetization transfer ratio and myelin in postmortem multiple sclerosis brain. Ann. Neurol. 2004;56:407–15. doi: 10.1002/ana.20202. [DOI] [PubMed] [Google Scholar]
  104. Serenius F, Kallen K, Blennow M, Ewald U, Fellman V, et al. Neurodevelopmental outcome in extremely preterm infants at 2.5 years after active perinatal care in Sweden. JAMA. 2013;309:1810–20. doi: 10.1001/jama.2013.3786. [DOI] [PubMed] [Google Scholar]
  105. Shinohara T, Sasaki H, Morimatsu Y, Ishihara K. Thalamic lesions during the development period and their clinical correlation. Appl. Neurophysiol. 1976;39:251–56. doi: 10.1159/000102500. [DOI] [PubMed] [Google Scholar]
  106. Short SJ, Elison JT, Goldman BD, Styner M, Gu H, et al. Associations between white matter microstructure and infants’ working memory. NeuroImage. 2013;64:156–66. doi: 10.1016/j.neuroimage.2012.09.021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  107. Sigalovsky IS, Fischl B, Melcher JR. Mapping an intrinsic MR property of gray matter in auditory cortex of living humans: a possible marker for primary cortex and hemispheric differences. NeuroImage. 2006;32:1524–37. doi: 10.1016/j.neuroimage.2006.05.023. [DOI] [PMC free article] [PubMed] [Google Scholar]
  108. Skranes J, Vangberg TR, Kulseng S, Indredavik MS, Evensen KA, et al. Clinical findings and white matter abnormalities seen on diffusion tensor imaging in adolescents with very low birth weight. Brain. 2007;130:654–66. doi: 10.1093/brain/awm001. [DOI] [PubMed] [Google Scholar]
  109. Song SK, Sun SW, Ramsbottom MJ, Chang C, Russell J, Cross AH. Dysmyelination revealed through MRI as increased radial (but unchanged axial) diffusion of water. NeuroImage. 2002;17:1429–36. doi: 10.1006/nimg.2002.1267. [DOI] [PubMed] [Google Scholar]
  110. Staudt M, Krageloh-Mann I, Grodd W. Normal myelination in childhood brains using MRI—a meta analysis. Rofo. 2000;172:802–11. doi: 10.1055/s-2000-7898. [DOI] [PubMed] [Google Scholar]
  111. Su P, Kuan CC, Kaga K, Sano M, Mima K. Myelination progression in language-correlated regions in brain of normal children determined by quantitative MRI assessment. Int. J. Pediatr. Otorhinolaryngol. 2008;72:1751–63. doi: 10.1016/j.ijporl.2008.05.017. [DOI] [PubMed] [Google Scholar]
  112. Supekar K, Musen M, Menon V. Development of large-scale functional brain networks in children. PLOS Biol. 2009;7:e1000157. doi: 10.1371/journal.pbio.1000157. [DOI] [PMC free article] [PubMed] [Google Scholar]
  113. Takahashi E, Folkerth RD, Galaburda AM, Grant PE. Emerging cerebral connectivity in the human fetal brain: an MR tractography study. Cereb. Cortex. 2012;22:455–64. doi: 10.1093/cercor/bhr126. [DOI] [PMC free article] [PubMed] [Google Scholar]
  114. Takenouchi T, Heier LA, Engel M, Perlman JM. Restricted diffusion in the corpus callosum in hypoxicischemic encephalopathy. Pediatr. Neurol. 2010;43:190–96. doi: 10.1016/j.pediatrneurol.2010.04.014. [DOI] [PubMed] [Google Scholar]
  115. Tam EW, Miller SP, Studholme C, Chau V, Glidden D, et al. Differential effects of intraventricular hemorrhage and white matter injury on preterm cerebellar growth. J. Pediatr. 2011;158:366–71. doi: 10.1016/j.jpeds.2010.09.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  116. Thompson DK, Inder TE, Faggian N, Johnston L, Warfield SK, et al. Characterization of the corpus callosum in very preterm and full-term infants utilizing MRI. NeuroImage. 2011;55:479–90. doi: 10.1016/j.neuroimage.2010.12.025. [DOI] [PMC free article] [PubMed] [Google Scholar]
  117. Tuch DS, Reese TG, Wiegell MR, Makris N, Belliveau JW, Wedeen VJ. High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity. Magn. Reson. Med. 2002;48:577–82. doi: 10.1002/mrm.10268. [DOI] [PubMed] [Google Scholar]
  118. Tuch DS, Wedeen VJ, Dale AM, George JS, Belliveau JW. Conductivity tensor mapping of the human brain using diffusion tensor MRI. Proc. Natl. Acad. Sci. USA. 2001;98:11697–701. doi: 10.1073/pnas.171473898. [DOI] [PMC free article] [PubMed] [Google Scholar]
  119. Ulfig N, Neudorfer F, Bohl J. Transient structures of the human fetal brain: subplate, thalamic reticular complex, ganglionic eminence. Histol. Histopathol. 2000;15:771–90. doi: 10.14670/HH-15.771. [DOI] [PubMed] [Google Scholar]
  120. Van Essen DC, Ugurbil K, Auerbach E, Barch D, Behrens TE, et al. The Human Connectome Project: a data acquisition perspective. NeuroImage. 2012;62:2222–31. doi: 10.1016/j.neuroimage.2012.02.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  121. Vangberg TR, Skranes J, Dale AM, Martinussen M, Brubakk AM, Haraldseth O. Changes in white matter diffusion anisotropy in adolescents born prematurely. NeuroImage. 2006;32:1538–48. doi: 10.1016/j.neuroimage.2006.04.230. [DOI] [PubMed] [Google Scholar]
  122. Vavasour IM, Laule C, Li DK, Traboulsee AL, MacKay AL. Is the magnetization transfer ratio a marker for myelin in multiple sclerosis? J. Magn. Reson. Imaging. 2011;33:713–18. doi: 10.1002/jmri.22441. [DOI] [PubMed] [Google Scholar]
  123. Volpe JJ. Cerebral white matter injury of the premature infant—more common than you think. Pediatrics. 2003;112:176–80. doi: 10.1542/peds.112.1.176. [DOI] [PubMed] [Google Scholar]
  124. Volpe JJ. Neurology of the Newborn. Saunders; Philadelphia, PA: 2008. [Google Scholar]
  125. Wang S, Fan G, Xu K, Wang C. Potential of diffusion tensor MR imaging in the assessment of cognitive impairments in children with periventricular leukomalacia born preterm. Eur. J. Radiol. 2013;82:158–64. doi: 10.1016/j.ejrad.2012.06.032. [DOI] [PubMed] [Google Scholar]
  126. Wedeen VJ, Hagmann P, Tseng WY, Reese TG, Weisskoff RM. Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging. Magn. Reson. Med. 2005;54:1377–86. doi: 10.1002/mrm.20642. [DOI] [PubMed] [Google Scholar]
  127. Wedeen VJ, Wang RP, Schmahmann JD, Benner T, Tseng WY, et al. Diffusion spectrum magnetic resonance imaging (DSI) tractography of crossing fibers. NeuroImage. 2008;41:1267–77. doi: 10.1016/j.neuroimage.2008.03.036. [DOI] [PubMed] [Google Scholar]
  128. Whittall KP, MacKay AL, Graeb DA, Nugent RA, Li DK, Paty DW. In vivo measurement of T2 distributions and water contents in normal human brain. Magn. Reson. Med. 1997;7:34–43. doi: 10.1002/mrm.1910370107. [DOI] [PubMed] [Google Scholar]
  129. Wimberger DM, Roberts TP, Barkovich AJ, Prayer LM, Moseley ME, Kucharczyk J. Identification of “premyelination” by diffusion-weighted MRI. J. Comput. Assist. Tomogr. 1995;19:28–33. doi: 10.1097/00004728-199501000-00005. [DOI] [PubMed] [Google Scholar]
  130. Wolff SD, Balaban RS. Magnetization transfer contrast (MTC) and tissue water proton relaxation in vivo. Magn. Reson. Med. 1989;10:135–44. doi: 10.1002/mrm.1910100113. [DOI] [PubMed] [Google Scholar]
  131. Woodward LJ, Anderson PJ, Austin NC, Howard K, Inder TE. Neonatal MRI to predict neurodevelopmental outcomes in preterm infants. N. Engl. J. Med. 2006;355:685–94. doi: 10.1056/NEJMoa053792. [DOI] [PubMed] [Google Scholar]
  132. Wozniak JR, Lim KO. Advances in white matter imaging: a review of in vivo magnetic resonance methodologies and their applicability to the study of development and aging. Neurosci. Biobehav. Rev. 2006;30:762–74. doi: 10.1016/j.neubiorev.2006.06.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  133. Wu YC, Alexander AL. Hybrid diffusion imaging. NeuroImage. 2007;36:617–29. doi: 10.1016/j.neuroimage.2007.02.050. [DOI] [PMC free article] [PubMed] [Google Scholar]
  134. Yap PT, Fan Y, Chen Y, Gilmore JH, Lin W, Shen D. Development trends of white matter connectivity in the first years of life. PLOS ONE. 2011;6:e24678. doi: 10.1371/journal.pone.0024678. [DOI] [PMC free article] [PubMed] [Google Scholar]
  135. Yokota Y, Gashghaei HT, Han C, Watson H, Campbell KJ, Anton ES. Radial glial dependent and independent dynamics of interneuronal migration in the developing cerebral cortex. PLOS ONE. 2007;2:e794. doi: 10.1371/journal.pone.0000794. [DOI] [PMC free article] [PubMed] [Google Scholar]
  136. Yoshida S, Hayakawa K, Yamamoto A, Okano S, Kanda T, et al. Quantitative diffusion tensor tractography of the motor and sensory tract in children with cerebral palsy. Dev. Med. Child Neurol. 2010;52:935–40. doi: 10.1111/j.1469-8749.2010.03669.x. [DOI] [PubMed] [Google Scholar]
  137. Yoshiura T, Higano S, Rubio A, Shrier DA, Kwok WE, et al. Heschl and superior temporal gyri: low signal intensity of the cortex on T2-weighted MR images of the normal brain. Radiology. 2000;214:217–21. doi: 10.1148/radiology.214.1.r00ja17217. [DOI] [PubMed] [Google Scholar]
  138. Yung A, Poon G, Qiu DQ, Chu J, Lam B, et al. White matter volume and anisotropy in preterm children: a pilot study of neurocognitive correlates. Pediatr. Res. 2007;61:732–36. doi: 10.1203/pdr.0b013e31805365db. [DOI] [PubMed] [Google Scholar]

RESOURCES