SE

M I N A R S I N

P

E R I N A T O L O G Y

39 (2015) 147–158

Available online at www.sciencedirect.com

www.elsevier.com/locate/semperi

The predictive validity of neonatal MRI for neurodevelopmental outcome in very preterm children Peter J. Anderson, PhDa,b,n, Jeanie L.Y. Cheong, MDa,c,d, and Deanne K. Thompson, PhDa,b a

Clinical Sciences, Murdoch Childrens Research Institute, Melbourne, Australia Department of Paediatrics, The University of Melbourne, Melbourne, Australia c Neonatal Services, Royal Women's Hospital, Melbourne, Australia d Department of Obstetrics & Gynaecology, University of Melbourne, Melbourne, Australia b

article info

abstra ct

Keywords:

Very preterm children are at a high risk for neurodevelopmental impairments, but there is

very preterm

variability in the pattern and severity of outcome. Neonatal magnetic resonance imaging

magnetic resonance imaging

(MRI) enhances the capacity to detect brain injury and altered brain development and

diffusion weight imaging

assists in the prediction of high-risk children who warrant surveillance and early

neurodevelopmental outcome

intervention. This review describes the application of conventional and advanced MRI with very preterm neonates, specifically focusing on the relationship between neonatal MRI findings and later neurodevelopmental outcome. Research demonstrates that conventional MRI is strongly associated with neurodevelopmental outcome in childhood. Further studies are needed to examine the role of advanced MRI techniques in predicting outcome in very preterm children, but early research findings are promising. In conclusion, neonatal MRI is predictive of later neurodevelopment but is dependent on appropriately trained specialists and should be interpreted in conjunction with other clinical and social information. & 2015 Elsevier Inc. All rights reserved.

Introduction Infants born very preterm [VP; o32 weeks' gestational age (GA)] are considered “high risk” for later neurodevelopmental impairments including deficits in cognitive, language, motor, educational, behavioral, and social domains.1 In a large geographic cohort, approximately 70% of extremely preterm 8-yearolds displayed at least one significant neurodevelopmental

impairment, with 50% exhibiting multiple impairments.2 However, it needs to be stressed that outcomes in this population are variable, both in terms of the pattern and severity of impairment.1,3 There are many risk and protective factors that are likely to explain this variability in neurodevelopmental outcome, including neurological abnormalities (e.g., brain injury and delayed/aberrant brain growth), neonatal medical complications [e.g., bronchopulmonary dysplasia (BPD) and infections],

Acknowledgments: This work was funded by the National Health & Medical Research Council (Senior Research Fellowship to PJAAPP1081288, Early Career Research Fellowship to DKT—APP1012236, and Early Career Research Fellowship to JLYC—APP1053787) and the Victorian Government Operational Infrastructure Support Program. n Correspondence to: Victorian Infant Brain Studies, Clinical Sciences, Murdoch Childrens Research Institute, Flemington Rd, Parkville, VIC 3052, Australia. E-mail address: [email protected] (P.J. Anderson). http://dx.doi.org/10.1053/j.semperi.2015.01.008 0146-0005/& 2015 Elsevier Inc. All rights reserved.

148

SE

M I N A R S I N

P

E R I N A T O L O G Y

neonatal interventions (e.g., postnatal corticosteroids), genetic and epigenetic features, as well as distal and proximal social/ environmental factors [e.g., socio-economic status (SES) and parenting]. An appreciation of these risk and protective factors is critical for improving the detection of infants and families who are at greatest risk for neurodevelopmental impairment, so that appropriate surveillance and counseling can be provided. Furthermore, it is widely accepted that early intervention is beneficial,4 but a better understanding of the factors contributing to long-term outcomes has the potential to enhance the effectiveness of early interventions by making them more tailored to the needs of the individual child. This review will focus on abnormalities observed with neonatal brain magnetic resonance imaging (MRI), and their association with later neurodevelopmental outcomes in VP children. While cranial ultrasonography continues to be the primary neuroimaging tool, over the past few decades, there has been an increased application of MRI in newborns due to increased sensitivity in assessing subtle brain injury and development.5 Conventional T1-weighted and T2-weighted MRI sequences are commonly utilized for infants in clinical settings, and this review will largely focus on research exploring the relationship between clinically available qualitative assessment of neonatal MRI and neurodevelopmental outcome. Further, this review will summarize the predictive utility of advanced MRI techniques including brain volumetrics, cortical folding, diffusion weight imaging (DWI), resting-state MRI (rsMRI), and magnetic resonance spectroscopy (MRS), while recognizing that not all these techniques are readily available to practicing clinicians currently. Finally, we will discuss the practical and clinical implications regarding neonatal MRI and its application for predicting later outcomes.

Qualitative MRI White matter abnormality (WMA) White matter abnormality (WMA) is the most common pathology seen in preterm infants with conventional MRI, including cystic lesions, punctate lesions, delayed myelination, volume loss, thinning of the corpus callosum, and T2weighted diffuse excessive high signal intensity (DEHSI).6–8 Figure 1 provides examples of this spectrum of abnormalities including a small periventricular cyst (Fig. 1C), diffuse white matter signal abnormality (Fig. 1D), white matter volume loss and enlarged lateral ventricles (Fig. 1C, D, and F) and DEHSI (Fig. 1E). Using a subjective scoring system to assess MRI abnormalities at term-equivalent age (TEA) in a cohort of 100 very-low-birth-weight (VLBW) infants, Inder et al.6 reported a low rate of cystic lesions (4%) but high rates of punctate lesions (64%), white matter volume loss (45%), ventricular dilatation (60%), and delayed myelination/thinning of the corpus callosum (69%). Approximately half of this cohort was classified as exhibiting mild WMA and 20% with moderate-to-severe WMA.6 Equivalent rates of pathology were reported for a Stockholm cohort of extremely preterm (EP) infants, which adopted a similar system for assessing WMA.9 The nature of WMA is likely to vary according to the

39 (2015) 147–158

timing of the brain scan and method for assessing pathology.7,10 For example, a higher rate of moderate-to-severe WMA and ventriculomegaly (37%) but a lower rate of mild WMA (23%) were reported in a sample of preterm infants born o34 weeks' GA who were scanned as soon after birth as possible using a different qualitative scoring system.10 The rate of WMA observed on neonatal MRI is justification for examining the relationship between these abnormalities and later development. In one of the first studies to examine neonatal MRI abnormalities and early neurodevelopmental outcome, Miller et al.10 demonstrated that abnormal outcome at 12–18 months [defined by delayed cognitive development on the Bayley Scales of Infant Development (BSID-II) and/or neuromotor abnormality] was significantly associated with WMA and ventriculomegaly, even after adjusting for GA at birth and postnatal infection. Lesion location and laterality were not related to early outcome. Shortly afterwards, a large Australasian study was published that examined qualitative assessment of neonatal MRI in VP infants and neurodevelopmental outcome at 24 months of age.11 Cognitive and motor development scores decreased with increasing severity of WMA, while the rate of delay and cerebral palsy increased with increasing WMA severity. Multivariable analyses revealed that moderate-to-severe WMA remained an independent predictor of early neurodevelopmental outcome, even after adjusting for a wide range of risk factors. These findings have been replicated in the Stockholm cohort.12 MRI features that most strongly related to CP were severe white matter reduction, cystic lesions, and delayed myelination. Longitudinal cohorts from Christchurch, New Zealand, and Melbourne, Australia, have contributed most regarding the long-term consequences of WMA on neonatal MRI. The Christchurch cohort had a conventional MRI at TEA, and with remarkable retention, neurodevelopmental assessments at 2, 4, and 6 years of age. The latter two follow-up assessments included measures of general intellectual ability, language development, executive functioning, and behavior,13–17 with declining functioning according to severity of WMA across all domains at 4 and 6 years of age.15 Consistent with these findings, the rate of impairment increased with increasing WMA severity at the preschool and school-age assessments. After adjustment for sex, neonatal medical risk, and family social risk, children with mild and moderate-to-severe WMA were significantly more likely than full-term controls to have a general intellectual, language, and executive functioning impairment at 6 years of age.15 Of particular interest, VP children without evidence of WMA performed similarly to a representative full-term control group and exhibited similar rates of impairment, providing further evidence that WMA may be an important marker for later development. Of interest, all elements that comprise the WMA composite score were associated with later neurocognitive outcomes, in particular punctate lesions and white matter loss. The Melbourne cohort comprised 224 VP (o30 weeks' GA or birth weight o1250 g) children who had neonatal MRI at TEA and neurodevelopmental assessments at 2, 5, and 7 years of age. Based on reports from the 5- and 7-year follow-ups, neonatal WMA in this VP cohort was associated with poorer neurodevelopmental outcomes.18–22 Compared with VP children without WMA, those with moderate-to-severe WMA were

S

E M I N A R S I N

P

E R I N A T O L O G Y

39 (2015) 147–158

149

Fig. 1 – Brain abnormalities seen on structural MRI. (A) T1 image of normal term control, (B) T1 image of normal preterm infant, (C) T1 image showing small periventricular cyst, (D) T1 image showing diffuse white matter signal abnormalities, (E) T2 image showing diffuse excessive high signal intensity (DEHSI) of the white matter, (F) T2 image showing enlarged interhemispheric distance, (G) T2 image showing basal ganglia signal abnormality, and (H) T2 image showing cerebellum signal abnormalities. Enlarged ventricles and reduced white matter volume can also be seen in (C), (D), and (F).

19 times more likely to have a significant motor impairment at 5 years of age, while children with mild WMA were 5.6 times more likely to have a significant motor impairment.19 These findings persisted after excluding children with CP and adjusting for neonatal and social risk factors.19 In the Melbourne cohort, neonatal WMA has also been reported to be associated with language development,22 learning capacity,21 attention,20 and processing speed20 at 7 years of age. One may expect that the association between WMA on neonatal MRI and neurodevelopmental outcome may diminish with increasing age with the cumulating influence of social and environmental factors. Alternatively, it is possible that children with WMA will fall further behind their peers due to the compounding effect of developmental difficulties across multiple domains. The findings from a longitudinal cohort recruited between 1995 and 2001 from Nagano Children's Hospital, Japan, suggest that any catch-up in middle childhood is minimal. In this cohort, VP children with neonatal WMA (n ¼ 23) had a mean IQ at 9 years of age that was significantly lower than VP children without WMA (n ¼ 37) (WMA group: mean ¼ 88.8, SD ¼ 17.0; no WMA group: mean ¼ 100.8, SD ¼ 12.9) and significantly elevated rates of CP (22% vs 3%) and special assistance at school (83% vs 41%).

Diffuse excessive high signal intensity (DEHSI) DEHSI on neonatal T2-weighted sequences is a common observation in VP infants,23 reported in up to 80–90% of VP

infants.7,24,25 While the underlying pathology of DEHSI is unclear, DWI studies report that the apparent diffusion coefficient (ADC) is significantly elevated in the presence of DEHSI,24,26 leading to speculation that it is a characteristic of white matter pathology or delayed white matter development.27 This premise was consistent with the early findings by Dyet et al.7 who found that the Developmental Quotient (DQ) on the Griffiths Mental Development Scales was significantly lower in VP infants with DEHSI at TEA than VP infants without DEHSI. Since this initial report, a number of studies have been unable to replicate this significant association, and based on research to date, it would seem that DEHSI is not associated with early cognitive, language, or motor development12,24,25,28,29 or later IQ.30 DEHSI as a pathological radiological feature remains under much debate.

Cortical gray matter abnormality Cortical gray matter lesions are rare in VP infants8; however, delayed cortical folding, wide interhemispheric fissure (Fig. 1F), and enlarged extracerebral space (Fig. 1C and D) are relatively common.6,9,23 In the 1998–2000 Christchurch cohort, 51% of VP infants had mild enlargement of the extracerebral space while an additional 17% had moderate-to-severe enlargement.6 Gyral maturation was rated to be delayed by 2–4 weeks in 49% of infants and by more than 4 weeks in 14%. Applying a similar scoring procedure, the Stockholm cohort

150

SE

M I N A R S I N

P

E R I N A T O L O G Y

of EP infants had lower rates of enlarged extracerebral space (33%) and delayed cortical folding (35%).9 A combined Australasian cohort included 82 VP infants with cortical gray matter abnormality and 85 infants without these abnormalities on neonatal MRI.11 Children with cortical gray matter abnormalities had lower cognitive and motor development scores at 24 months of age than those without and an elevated rate of CP (16% vs 5%). While cortical gray matter abnormality was a significant predictor of severe cognitive and motor delay and CP, this attenuated after adjusting for traditional clinical risk factors. In terms of long-term neurodevelopment, cortical gray matter abnormalities on neonatal MRI were not strongly associated with memory, learning, or attention outcomes at 7 years of age in the Melbourne cohort, but there was a trend to be associated with slower processing speed.20,21 Iwata et al.30 also failed to find a significant association between cortical gray matter abnormalities and cognitive functioning at 9 years of age. In contrast, an association with working memory performance at 6 years of age was reported in the Christchurch cohort.13 Some of the subjectivity of the qualitative scoring systems can be reduced by applying 1-dimensional biometric methods to assess distance, diameter, and area of specific structures,31 providing a simple and reliable technique for assessing brain growth. When scanned at TEA interhemispheric distance was significantly greater in the Melbourne cohort VP infants in comparison to full-term infants, while bifrontal and biparietal diameters were significantly reduced.31 When related to neurodevelopment at 24 months of age, bifrontal diameter and biparietal diameter at TEA were significantly associated with cognitive and motor development.32 Kidokoro et al.33 recently examined brain growth, using biometric techniques, and neurodevelopment at 24 months of age in a combined cohort from Christchurch, Melbourne, and St Louis. VP children who had small biparietal diameter or increased interhemispheric distance at TEA had poorer cognitive development than children classified as having appropriate growth. The children who had both small biparietal diameter and increased interhemispheric distance had the poorest cognitive and motor development, even in the absence of high-grade injury.

Subcortical gray matter abnormality While relatively rare, basal ganglia and thalamic lesions have been reported in neonatal MRI studies of VP infants (Fig. 1G), with the caudate nucleus most vulnerable.7,23 In a cohort of 97 VP infants born between 2007 and 2010 in St Louis, lesions at TEA were identified in 5 infants (2 focal unilateral, 2 focal bilateral, and 1 extensive bilateral) and reduced subcortical gray matter area reported in 44 infants.8 Given subcortical gray matter lesions are rare and a proportion of these infants die, the outcome for infants with subcortical lesions is not well established and requires further research. However, initial findings suggest that smaller basal ganglia and thalamic size on neonatal MRI are related to later neurodevelopmental impairments such as poorer memory and learning, attention, and speed of processing.20,21

39 (2015) 147–158

Cerebellum abnormality Conventional MRI studies have also identified cerebellar lesions in VP neonates (Fig. 1H), with rates of up to 20% in some studies.7,8,34,35 The lesions are generally unilateral and frequently co-exist with IVH.7,8,36 Smaller cerebellar size at TEA is more common, with a study reporting atrophy in more than 50% of VP infants.8 Cerebellum volume increases 5-fold in the last trimester, so it is not surprising that growth of this structure is impaired in infants born VP.36 The outcome for VP children with major cerebellar lesions is often poor,7 although judging the unique contribution of cerebellar injury is difficult given supratentorial lesions are also often present. To investigate the role of cerebellar lesions, Limperopoulos et al.37 followed up a cohort of 35 preterm infants with isolated cerebellar injury at a mean age of 32 months. This group of infants had high rates of neurologic abnormalities (66%), severe motor delay (48%), expressive and receptive language delay (42% and 37% respectively), general cognitive deficits (40%), and elevated rates of autism symptoms (37%) and internalizing behavior problems (34%). In contrast to these concerning results, an analysis combining data from the Melbourne, Christchurch and St Louis cohorts found that cerebellar lesions were not strongly related to cognitive and motor development at 24 months of age, although the rates of cerebellar injury in these cohorts were significantly lower.33 Even fewer studies have examined the long-term consequences of cerebellar injury or delayed cerebellar growth on neonatal MRI, although a study reported cerebellar hemorrhage to be associated with neurologic abnormalities in the preschool period,35 while another study reported that cerebellar pathology (lesion and/or delayed growth) was associated with poorer attention and learning capacity at 7 years of age.20,21 In summary, conventional T1-weighted and T2-weighted MRI sequences detect a spectrum of injuries and delayed maturation processes in the VP infant. While few long-term follow-up studies have been reported describing the consequences of these MRI abnormalities, the evidence to date suggests that later cognitive and motor outcomes are strongly related to early brain pathology and impaired growth. The research published to date indicates that the association between neonatal MRI abnormalities and neurodevelopmental outcome persists after controlling for other clinical factors, and neonatal MRI seems to be more predictive of long-term impairments than traditional risk factors. However, the sensitivity and specificity of conventional neonatal MRI to predict long-term neurodevelopment is only moderate, and accordingly, MRI findings should be interpreted with caution and in conjunction with other clinical information.

Volumetric MRI Post-acquisition analytic methods are available for quantifying overall brain, tissue (white matter, cortical gray matter, subcortical gray matter, and cerebrospinal fluid), and regional volumes from infant MRI scans (Fig. 2). The techniques are principally research tools due the specialist infrastructure and expertise required; however, they have the potential to

S

E M I N A R S I N

P

E R I N A T O L O G Y

provide insight into how preterm birth influences brain development globally as well as regionally. Well-established methods used for older populations are not appropriate for neonates due to tissue segmentation issues arising from differences in white matter/gray matter contrast. Thus, neonate-specific tissue segmentation techniques are required to quantify brain volumes in VP infants. Using 3-dimensional MRI and a neonatal segmentation algorithm, Huppi et al.38 described the rapid rate of brain development that occurs from 28 to 40 weeks GA. In this period, total brain tissue volume increased nearly threefold from 29 weeks to 40 weeks, cortical gray matter volume increased fourfold, and myelinated white matter increased fivefold (mostly from 36 weeks). This landmark study highlighted the incredible rate of brain development that occurs when VP infants are critically ill and exposed to numerous medical interventions and sensory stimuli. Inder et al.39 reported that when scanned at TEA, VP infants demonstrated significant reductions in cortical gray matter, subcortical gray matter, and myelinated white matter volumes and a significant increase in CSF volume when compared with term infants. White matter injury was associated with reduced cortical gray matter and myelinated white matter, while extreme prematurity and severe respiratory illness were related to reduced subcortical gray matter.39 Regionalspecific vulnerabilities have also been reported in VP infants,40,41 with significant reductions reported in the orbitofrontal (31%), sensorimotor (12%), parieto-occipital (8%), and premotor (8%) regions.41 Manual segmentation of specific anatomic structures have revealed a significant reduction in the mid-sagittal area of the corpus callosum in comparison to term peers (9%)42 but only marginal

39 (2015) 147–158

151

reductions in cerebellar and hippocampal volumes in VP infants compared to term peers.43,44 The association between neonatal brain volumes and later neurodevelopmental outcome has been explored in a few studies. Inder et al.39 found that VP infants with moderate-tosevere disability at 12 months of age (based on clinical examination) had reduced cortical and subcortical gray matter volumes and increased CSF volumes at TEA than those infants with no or mild disability. Similarly, a large Finnish study (n ¼ 164) reported that children with larger total brain, cerebral, frontal lobe, basal ganglia and thalami, and cerebellar volumes on neonatal MRI were less likely to have neurodevelopmental impairment defined as CP, hearing loss, blindness, and/or severe cognitive delay. Peterson et al.40 were the first to investigate regional correlates of early neurodevelopmental outcome (cognitive and motor development at 18–20 months) in a small sample of preterm infants (n ¼ 9) and found cognitive development to be positively related to white matter volumes in premotor, sensorimotor, midtemporal, and subgenual regions, while motor development was positively associated with white matter volume in the subgenual region. Using a similar parcellation approach to Peterson et al.45, working memory performance at 24 months of age was positively associated with dorsal prefrontal, premotor, sensorimotor, and parieto-occipital volumes in the Christchurch cohort. The cerebellum, hippocampus, and corpus callosum are specific anatomic regions that have attracted interest. In the Melbourne cohort, cerebellar volume at TEA was only weakly associated with motor and cognitive development at 24 months of age,43 but these associations were stronger in a Utrecht cohort.46 Neonatal hippocampal volume has been reported to be related to cognitive

Fig. 2 – (A) Top: full-term infant with tissue segmentation into the cortical gray matter (light green), deep nuclear gray matter (medium blue), white matter (brown), brainstem (dark green), cerebellum (red), hippocampus (light blue), amygdala (cherry), and cerebrospinal fluid (white). Bottom: corresponding T2-weighted image shown in coronal, axial, and sagittal planes. (B) Cortical parcellation of a full-term infant's brain, shown in lateral (top) and medial (bottom) views. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

152

SE

M I N A R S I N

P

E R I N A T O L O G Y

development44 and working memory47 at 24 months, even after adjusting for a range of potential confounders, while the cross-sectional area of the corpus callosum has not been found to be associated with early neurodevelopment.48 Published studies evaluating the relationship between neonatal brain volumes and long-term neurodevelopmental outcome are lacking. Based on the few long-term studies reported, neonatal brain volumes appear to be only weakly related to long-term outcome, especially when the number of associations explored is taken into consideration. Lind et al.49 re-assessed their Finnish cohort who had neonatal MRI at 5 years of age using a combination of neuropsychological measures and parent questionnaire. Neonatal brain volumes were not associated with the neuropsychological measures; however, smaller total brain volumes were related to parentreported executive functioning and smaller cerebellar volumes were related to parent-reported executive functioning and motor skills. In another study, Thompson et al.50 assessed memory and learning skills at 7 years of age in the Melbourne cohort and found test performance to be significantly associated with neonatal hippocampal volume, especially verbal memory and learning capacity. In addition to examining regional volumes, a potentially more important feature to explore when investigating the impact of prematurity on brain development is structural shape alterations. While limited evidence is available to date, it is possible that certain dysmorphic features of important structures such as the hippocampus, cerebellum, and corpus callosum may assist is predicting future maturation processes and later neurodevelopmental impairments.48,51 Furthermore, directly related to tissue and regional volumes is cortical folding. Primary sulci form from 14 to 26 weeks GA, secondary sulci evolve between 30 and 35 weeks, and tertiary sulci from 36 weeks, and as such, a critical period for cortical gyration for VP infants occurs when they are critically ill and exposed to a range of potentially harmful agents and procedures.52 Numerous studies have described the marked cortical gyration that occurs in VP infants between birth and TEA, but despite this rapid maturation, VP infants remained delayed at TEA when compared with term peers.53–56 Delayed or altered cortical gyration may reflect functional consequences,55 and therefore these features on neonatal MRI may be a marker for later neurodevelopmental impairments.53 Published studies investigating the long-term

39 (2015) 147–158

consequences of altered cortical folding on neonatal MRI are lacking; however, quantifiable measures of cortical surface development shortly after birth have been found to be associated with neurobehavioral assessment scores at TEA.55 In summary, there is evidence that in the neonatal period, the brains of VP infants are smaller and less folded, with some regions more vulnerable than others, and some structures being dysmorphic. In contrast, there is only limited evidence that these features on neonatal MRI are predictive of later neurodevelopmental functioning largely due to the lack of long-term studies that have adopted appropriate advanced post-acquisition analysis techniques.

Diffusion-weighted imaging Diffusion-weighted imaging is a technique that measures the movement of water molecules within the brain. In the cerebrospinal fluid, diffusion is isotropic, whereas the presence of axons and myelin hinder the movement of water molecules, leading to anisotropic diffusion within the white matter of the brain. One of the most common ways to model water diffusion in brain images is using the diffusion tensor model, which provides measures of fractional anisotropy (FA), the directionality of water movement; mean diffusivity (MD), overall diffusion; axial diffusivity (AD), diffusion along the principal direction; and radial diffusivity (RD), diffusion perpendicular to the principal direction. FA is thought to be a measure of white matter microstructural organization and increases during early development. MD decreases with age; thus, it is assumed that higher MD corresponds to less mature white matter. AD also decreases during maturation, and high infant AD values may reflect immaturity of the fiber cytoskeleton and increased water content within the brain. A higher RD in infants may suggest delay or disruption to myelination, or less densely compacted fiber bundles. Traditionally, a region-of-interest approach was used to define white matter microstructural organization. More recently, a process known as tractography has become more common, which can then be used to provide a virtual representation of the white matter fiber tracts (Fig. 3). Another popular wholebrain white matter diffusion image analysis technique is tract-based spatial statistics (TBSS).57 Several groups are beginning to apply more advanced diffusion analyses

Fig. 3 – Full-term infant with whole-brain white matter fiber tractography, overlaid on a T2-weighted image, shown in coronal, axial, and sagittal planes.

S

E M I N A R S I N

P

E R I N A T O L O G Y

techniques to infant populations such as structural connectivity, utilizing graph theory,58 and network-based statistics.59 These techniques may play an important role in uncovering alterations to large-scale brain networks that underlie neurodevelopmental impairment in preterm populations. A large number of studies have examined white matter microstructure in VP infants using DWI in the neonatal period, demonstrating marked changes in diffusion parameters from birth to TEA.60 Widespread white matter regions have been shown to be vulnerable in preterm infants using region of interest,60–62 TBSS,63,64 tractography,42,65 and structural connectivity66,67 approaches, in particular, the posterior limb of the internal capsule, centrum semiovale, corpus callosum, corona radiata, and central and frontal white matter. Collectively, this body of research indicates alterations in major white matter tracts of VP infants in the neonatal period, potentially providing some insight into the impairments displayed in later childhood. The association between neonatal diffusion parameters and neurodevelopmental outcome has been investigated, although outcomes have been largely limited to the first years of life. Rose et al.68 found that VLBW preterm infants with abnormal neurodevelopment at 18 months of age had reduced FA in the splenium of the corpus callosum and right posterior limb of the internal capsule on their infant scans. Similarly, reduced FA in the posterior limb of the internal capsule of preterm infants has predicted abnormal neurological outcomes (including cerebral palsy) at 18–24 months of age.69 Numerous studies have reported significant associations between diffusion values in infancy and cognitive and motor development at 24 months of age. For example, Kaukola et al.70 found that MD was higher within the corona radiata in preterm infants with poor gross motor outcome at 24 months than those with good motor outcome. In a study using TBSS at TEA, better cognitive development correlated with higher FA in the corpus callosum, better fine-motor skills were related to higher FA and lower RD throughout the white matter, and better gross motor scores were associated with lower RD in the corpus callosum, fornix, and internal and external capsule.71 Other studies have also reported a significant association between early motor development and diffusion parameters of the posterior limb of the internal capsule, corpus callosum, and cerebellum.72,48,73 Serial DWI in the neonatal period enables developmental changes in diffusion to be studied and related to outcome. Drobyshevsky et al.74 scanned VP infants at 30 and 36 weeks and found lower FA at 30 weeks and a higher change in FA predicted poorer motor development. In another study in which VP infants had DWI shortly after birth and at TEA, FA was lower within the major white matter tracts in those with motor or language delay, a slower increase in FA within the major white matter tracts and superior white matter was observed in those with poorer cognitive development, and slower increase in FA within the superior white matter was observed in those with poorer language outcomes.75 With regard to the limited research that has examined longer term outcomes, a small preliminary study found that preterm infants with low neonatal FA in the posterior limb of the internal capsule had gait and motor deficits at 4 years of

39 (2015) 147–158

153

age,76 while another study reported that higher mean diffusivity in a right orbitofrontal region of interest at TEA was associated with social–emotional problems at 5 years of age.77 In school-aged children Thompson et al.78 found that white matter disorganization in the inferior occipital and cerebellar regions in infancy had lasting negative associations with executive and motor functioning at 7 years corrected age. While neonatal DWI has greatly improved our understanding of brain injury and development in VP infants, additional long-term studies are required to determine whether neonatal DWI can predict neurodevelopmental impairments in later childhood.

Resting-state MRI Functional MRI (fMRI) measures the blood oxygen leveldependent (BOLD) signal within the brain, providing an indirect measure of neural activity at a given time.79 Subjects can be given a task to activate specific functional networks of the brain; however, resting-state fMRI is a more appropriate approach for sleeping infants.80 Resting-state fMRI is a commonly used approach to measure which voxels of the brain simultaneously activate during rest. These synchronized low-frequency signal fluctuations represent a way of measuring functional connectivity, defined as “the temporal correlation of a neurophysiological index measured in different brain areas.”81 There are around eight core resting-state functional networks that are distinguished consistently.82 These core functionally connected networks include the sensorimotor network, primary visual network, extra-striate visual network, bilateral temporal/insular and anterior cingulate cortex network, frontal network, left and right parietal– frontal attention networks, and default mode network comprising medial frontal and inferior parietal cortical regions, precuneus, and the medial temporal lobe.83,84 In VP infants scanned in the neonatal period, a variable age-specific pattern of development has been described,85 with more mature networks consisting interhemispheric connections between homotopic counterparts.86 It has also been reported that preterm infants have different thalamocortical networks than full-term infants, with precursors of the default mode network detected in term controls but not preterm infants.86 Aberrant patterns of functional connectivity have been related to brain injury severity,87 although reductions in resting network complexity are also observed in VP infants without brain injury.88 To the best of our knowledge, no published studies have correlated restingstate functional connectivity with neurodevelopmental outcomes. It therefore remains to be seen whether alterations in preterm infant resting-state networks are able to explain their high rates of neurodevelopmental impairments.

Magnetic resonance spectroscopy (MRS) Magnetic resonance spectroscopy (MRS) can be used to obtain non-invasive metabolite information about the newborn brain. This is based on the fact that certain atomic nuclei, including hydrogen-1 (proton, 1H), produce radiofrequency

154

SE

M I N A R S I N

P

E R I N A T O L O G Y

signals in a strong magnetic field. For the newborn brain, the commonest MRS obtained is 1H MRS, given that it is the commonest nucleus with the highest nuclear magnetic resonance sensitivity. Important metabolites identified with the 1 H MRS include N-acetylaspartate (a neuronal marker); choline-containing compounds (Cho, which are intermediaries in phospholipid metabolism); creatine and phosphocreatine (Cr); myo-Inositol, glutamate (Glu), glutamine (Gln), and γ amino butyric acid (GABA, all neurotransmitters); and lactate (Lac, one of the terminal metabolites of glycolysis).89,90 MRS metabolites are presented either as peak-area ratios (most commonly involving NAA, Cho, Cr, and Lac) or absolute quantitation, although the latter is often not feasible due to the length of MRS acquisition time as well as specialized software required for processing.91,92 Studies have reported maturational changes in brain metabolites from preterm to term-equivalent age. Between 32 and 43 weeks, increases in NAA, Glu, and Cr and decreases in Lac and myo-Inositol have been observed, with Cr the only metabolite differentiating preterm and term infants at TEA.93,94 However, other studies with relatively small sample sizes have demonstrated differences at TEA particularly at lower NAA/Cho ratios in the deep nuclear gray matter and periventricular parietal white matter in preterm infants compared with term controls.95,96 Brain metabolite profiles at TEA differentiate preterm infants with and without WMA including higher Lac/Cr and myo-Inositol/Cr,97 lower NAA,98,99 as well as increased glutamine and a loss of the normal age-associated changes of myo-Inositol in the perinatal period.100 It has been speculated that these findings may reflect disrupted astroglial function and/or osmotic dysregulation, as well as a role of glutamate excitotoxicity in white matter lesions in preterm newborns.100 The association between MRS metabolite profiles and developmental outcomes have not been well characterized, with all studies to date only reporting associations with outcomes up to 2 years of age. There is some evidence of a relationship between NAA ratios at TEA and early developmental outcomes. For example, it has been reported that NAA/Cho ratios in the cerebellum at TEA correlate positively with cognitive development at 2 years,46 and higher NAA/ Cho ratios in the subventricular zone and cortex are positively associated with cognitive/language development at 18–22 months of age.101 In relation to motor development, higher NAA/Cho102,103 but lower Cho/Cr ratios103 in the posterior periventricular white matter have been reported to be associated with better gross motor scores at 12–18 months. Developmental maturation of brain metabolites between birth and term has also been studied using sequential MRS. A large recent study of 177 preterm newborns with MRS obtained at both 32 and 40 weeks' corrected age reported that slower increases in NAA/Cho was related to increasing severity of motor and cognitive outcomes at 18 months.75 However, it is important to note that other studies have failed to find an association between brain metabolites in the neonatal period and neurodevelopmental outcome, although these studies are limited by small sample sizes.95,104 So, while MRS remains a useful method for assessing early brain injury and factors influencing brain development, further research is needed to investigate the

39 (2015) 147–158

capacity for specific metabolites or profiles to predict later outcomes.

Practical considerations Performing MRI scans with infants, especially critically ill neonates, has some challenges. However, there is now over 20 years experience in scanning preterm infants, and the obstacles can be overcome in most tertiary centers. While a few neonatal intensive care units have a MRI scanner within their unit, this is uncommon, and generally, a MRI scan requires the neonate to be transported. For research purposes, MRI scans are often performed at discharge or TEA, when the infant is medically stable; however, critically ill infants can also be scanned. Many centers have a high success rate of scanning infants during natural sleep without sedation or anesthesia.5 For high-quality scans, an infant head coil is recommended, and MRI-compatible incubators are now available to assist with the transportation and scanning of unstable infants. Preparing infants for the MRI scan is important for minimizing movement and involves settling the infant, attaching ear protection, and wrapping the infant firmly. Once the sequences are acquired, access to a suitably qualified specialist for reading the structural scans for pathology and clinically relevant maturational alterations is required. In contrast, advanced quantitative analysis of MRI scans such as calculating brain volumes, assessing structural shape alterations, quantifying cortical folding, undertaking tractography and structural connectivity, and analyzing fMRI requires considerable infrastructure and expertise that are not available at most centers.

Clinical implications and summary As presented in this review, considerable research demonstrates that brain abnormalities observed on conventional structural MRI scans in the neonatal period are related to early neurodevelopmental impairments. There is also some evidence to indicate that these neonatal MRI abnormalities are associated with neurodevelopmental outcomes in preschoolers and school-aged children, suggesting that these abnormalities do not simply reflect developmental delay. However, to date, long-term follow-up has been restricted to only a few cohorts and up to middle childhood, and as such, more research is needed. It is worth noting that the infants in cohorts from neonatal MRI studies with long-term outcome were born over a decade ago, and it is possible that the rate of pathology is lower in contemporary cohorts. On the other hand, the quality of the MRI images has improved greatly in this time, possibly leading to an improved capacity to detect subtle forms of pathology. This review discussed qualitative and quantitative approaches to assessing brain injury and development on neonatal MRI images. Qualitative scoring systems and biometric measurements are the techniques available in clinical settings. Inter- and intra-rater reliabilities are a potential concern with qualitative scoring systems; however, the reliability estimates reported to date have been very high,6,8–10 and

S

E M I N A R S I N

P

E R I N A T O L O G Y

there have been attempts to reduce the level of subjectivity.8 The advanced quantitative analysis techniques described in this review are currently predominantly research tools; however, with increasing automated systems, they have the potential to be clinical tools in the future. Although advanced quantitative analytic techniques are providing incredibly rich information about brain maturation in the VP infant, correlation with long-term neurodevelopmental outcomes is lacking. In conclusion, VP children are at a high risk for neurodevelopmental impairments; however, there is enormous inter-individual variability with regard to the pattern and severity of difficulties, making it difficult to identify the children at greatest risk who warrant close surveillance and access to targeted early intervention. Numerous factors are related to neurodevelopment, which helps to explain the variability in outcome. Without doubt, brain injury and early alterations to brain development influence outcome in VP infants, and qualitative assessment of neonatal MRI is reported to be more predictive of early neurodevelopmental outcome than all other neonatal risk factors. Neonatal MRI is a valuable tool for prediction of outcome, with the potential for its predictive utility to improve with advancements in both image acquisition and post-image analysis. Nonetheless, to maximize its utility in the clinical context, interpretation of neonatal MRI needs to be done with knowledge of complementary clinical and social information. This will enable optimal feedback to families with respect to streamlining their developmental surveillance based on all the clinical, social, and neuroimaging information.

re fe r en ces

1. Anderson PJ. Neuropsychological outcomes of children born very preterm. Semin Fetal Neonatal Med. 2014;19(2):90–96. 2. Hutchinson EA, De Luca CR, Doyle LW, Roberts G, Anderson PJ, for the Victorian Infant Collaborative Study Group. School-age outcomes of extremely preterm or extremely low birth weight children. Pediatrics. 2013;131(4):E1053–E1061. 3. Lundequist A, Bohm B, Smedler AC. Individual neuropsychological profiles at age 51/2 years in children born preterm in relation to medical risk factors. Child Neuropsychol. 2013;19 (3):313–331. 4. Spittle A, Orton J, Anderson P, Boyd R, Doyle LW. Early developmental intervention programmes post-hospital discharge to prevent motor and cognitive impairments in preterm infants. Cochrane Database Syst Rev. 2012;12:103. 5. Smyser CD, Kidokoro H, Inder TE. Magnetic resonance imaging of the brain at term equivalent age in extremely premature neonates: to scan or not to scan? J Paediatr Child Health. 2012;48(9):794–800. 6. Inder TE, Wells SJ, Mogridge NB, Spencer C, Volpe JJ. Defining the nature of the cerebral abnormalities in the premature infant: a qualitative magnetic resonance imaging study. J Pediatr. 2003;143(2):171–179. 7. Dyet LE, Kennea N, Counsell SJ, et al. Natural history of brain lesions in extremely preterm infants studied with serial magnetic resonance imaging from birth and neurodevelopmental assessment. Pediatrics. 2006;118(2):536–548. 8. Kidokoro H, Neil JJ, Inder TE. New MR imaging assessment tool to define brain abnormalities in very preterm infants at term. Am J Neuroradiol. 2013;34(11):2208–2214.

39 (2015) 147–158

155

9. Horsch S, Hallberg B, Leifsdottir K, et al. Brain abnormalities in extremely low gestational age infants: a Swedish population based Mill study. Acta Paediatr. 2007;96(7):979–984. 10. Miller SP, Ferriero DM, Leonard C, et al. Early brain injury in premature newborns detected with magnetic resonance imaging is associated with adverse early neurodevelopmental outcome. J Pediatr. 2005;147(5):609–616. 11. 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(7):685–694. 12. Skiold B, Vollmer B, Bohm B, et al. Neonatal magnetic resonance imaging and outcome at age 30 months in extremely preterm infants. J Pediatr. 2012;160(4):559. 13. Clark CAC, Woodward LJ. Neonatal cerebral abnormalities and later verbal and visuospatial working memory abilities of children born very preterm. Dev Neuropsychol. 2010;35 (6):622–642. 14. Edgin JO, Inder TE, Anderson PJ, Hood KM, Clark CAC, Woodward LJ. Executive functioning in preschool children born very preterm: relationship with early white matter pathology. J Int Neuropsychol Soc. 2008;14(1):90–101. 15. Woodward LJ, Clark CAC, Bora S, Inder TE. Neonatal white matter abnormalities an important predictor of neurocognitive outcome for very preterm children. PLoS One. 2012; 7(12):9. 16. Woodward LJ, Clark CAC, Pritchard VE, Anderson PJ, Inder TE. Neonatal white matter abnormalities predict global executive function impairment in children born very preterm. Dev Neuropsychol. 2011;36(1):22–41. 17. Foster-Cohen SH, Friesen MD, Champion PR, Woodward LJ. High prevalence/low severity language delay in preschool children born very preterm. J Dev Behav Pediatr. 2010;31(8): 658–667. 18. Howard K, Roberts G, Lim J, et al. Biological and environmental factors as predictors of language skills in very preterm children at 5 years of age. J Dev Behav Pediatr. 2011;32(3):239–249. 19. Spittle AJ, Cheong J, Doyle LW, et al. Neonatal white matter abnormality predicts childhood motor impairment in very preterm children. Dev Med Child Neurol. 2011;53(11):1000–1006. 20. Murray AL, Scratch SE, Thompson DK, et al. Neonatal brain pathology predicts adverse attention and processing speed outcomes in very preterm and/or very low birth weight children. Neuropsychology. 2014;28(4):552–562. 21. Omizzolo C, Scratch SE, Stargatt R, et al. Neonatal brain abnormalities and memory and learning outcomes at 7 years in children born very preterm. Memory. 2014;22(6): 605–615. 22. Reidy N, Morgan A, Thompson DK, Inder TE, Doyle LW, Anderson PJ. Impaired language abilities and white matter abnormalities in children born very preterm and/or very low birth weight. J Pediatr. 2013;162(4):719–724. 23. Maalouf EF, Duggan PJ, Rutherford MA, et al. Magnetic resonance imaging of the brain in a cohort of extremely preterm infants. J Pediatr. 1999;135(3):351–357. 24. Kidokoro H, Anderson PJ, Doyle LW, Neil JJ, Inder TE. High signal intensity on t2-weighted MR imaging at term-equivalent age in preterm infants does not predict 2-year neurodevelopmental outcomes. Am J Neuroradiol. 2011;32(11): 2005–2010. 25. de Bruine FT, van den Berg-Huysmans AA, Leijser LM, et al. Clinical implications of MR imaging findings in the white matter in very preterm infants: a 2-year follow-up study. Radiology. 2011;261(3):899–906. 26. Counsell SJ, Allsop JM, Harrison MC, et al. Diffusion-weighted imaging of the brain in preterm infants with focal and diffuse white matter abnormality. Pediatrics. 2003;112(1):1–7.

156

SE

M I N A R S I N

P

E R I N A T O L O G Y

27. Kwon SH, Vasung L, Ment LR, Huppi PS. The role of neuroimaging in predicting neurodevelopmental outcomes of preterm neonates. Clin Perinatol. 2014;41(1):257–283. 28. Hart A, Whitby E, Wilkinson S, Alladi S, Paley M, Smith M. Neuro-developmental outcome at 18 months in premature infants with diffuse excessive high signal intensity on MR imaging of the brain. Pediatr Radiol. 2011;41(10):1284–1292. 29. Jeon TY, Kim JH, Yoo SY, et al. Neurodevelopmental outcomes in preterm infants: comparison of infants with and without diffuse excessive high signal intensity on MR images at near-term-equivalent age. Radiology. 2012;263(2): 518–526. 30. Iwata S, Nakamura T, Hizume E, et al. Qualitative brain MRI at term and cognitive outcomes at 9 years after very preterm birth. Pediatrics. 2012;129(5):E1138–E1147. 31. Tich SNT, Anderson PJ, Shimony JS, Hunt RW, Doyle LW, Inder TE. A novel quantitative simple brain metric using mr imaging for preterm infants. Am J Neuroradiol. 2009;30(1): 125–131. 32. Tich SNT, Anderson PJ, Hunt RW, Lee KJ, Doyle LW, Inder TE. Neurodevelopmental and perinatal correlates of simple brain metrics in very preterm infants. Arch Pediatr Adolesc Med. 2011;165(3):216–222. 33. Kidokoro H, Anderson PJ, Doyle LW, Woodward LJ, Neil JJ, Inder TE. Brain injury and altered brain growth in preterm infants: predictors and prognosis. Pediatrics. 2014;134(2): E444–E453. 34. Steggerda SJ, Leijser LM, Wiggers-de Bruine FT, van der Grond J, Walther FJ, van Wezel-Meijler G. Cerebellar injury in preterm infants: incidence and findings on US and MR images. Radiology. 2009;252(1):190–199. 35. Tam EWY, Rosenbluth G, Rogers EE, et al. Cerebellar hemorrhage on magnetic resonance imaging in preterm newborns associated with abnormal neurologic outcome. J Pediatr. 2011;158(2):245–250. 36. Volpe JJ. Cerebellum of the premature infant: rapidly developing, vulnerable, clinically important. J Child Neurol. 2009; 24(9):1085–1104. 37. Limperopoulos C, Bassan H, Gauvreau K, et al. Does cerebellar injury in premature infants contribute to the high prevalence of long-term cognitive, learning, and behavioral disability in survivors? Pediatrics. 2007;120(3):584–593. 38. Huppi PS, Warfield S, Kikinis R, et al. Quantitative magnetic resonance imaging of brain development in premature and mature newborns. Ann Neurol. 1998;43(2):224–235. 39. Inder TE, Warfield SK, Wang H, Huppi PS, Volpe JJ. Abnormal cerebral structure is present at term in premature infants. Pediatrics. 2005;115(2):286–294. 40. Peterson BS, Anderson AW, Ehrenkranz R, et al. Regional brain volumes and their later neurodevelopmental correlates in term and preterm infants. Pediatrics. 2003;111(5): 939–948. 41. Thompson DK, Warfield SK, Carlin JB, et al. Perinatal risk factors altering regional brain structure in the preterm infant. Brain. 2007;130(Part 3):667–677. 42. Thompson DK, Inder TE, Faggian N, et al. Characterization of the corpus callosum in very preterm and full-term infants utilizing MRI. Neuroimage. 2011;55(2):479–490. 43. Shah DK, Anderson PJ, Carlin JB, et al. Reduction in cerebeller volumes in preterm infants: Relationships to white matter injury and neurodevelopment at two years of age. Pediatr Res. 2006;60(1):97–102. 44. Thompson DK, Wood SJ, Doyle LW, et al. Neonate hippocampal volumes: prematurity, perinatal predictors, and 2-year outcome. Ann Neurol. 2008;63(5):642–651. 45. Woodward LJ, Edgin JO, Thompson D, Inder TE. Object working memory deficits predicted by early brain injury and development in the preterm infant. Brain. 2005;128(Part 11):2578–2587.

39 (2015) 147–158

46. Van Kooij BJM, Benders M, Anbeek P, Van Haastert IC, De Vries LS, Groenendaal F. Cerebellar volume and proton magnetic resonance spectroscopy at term, and neurodevelopment at 2 years of age in preterm infants. Dev Med Child Neurol. 2012;54(3):260–266. 47. Beauchamp MH, Thompson DK, Howard K, et al. Preterm infant hippocampal volumes correlate with later working memory deficits. Brain. 2008;131(Part 11):2986–2994. 48. Thompson DK, Inder TE, Faggian N, et al. Corpus callosum alterations in very preterm infants: perinatal correlates and 2 year neurodevelopmental outcomes. Neuroimage. 2012;59 (4):3571–3581. 49. Lind A, Haataja L, Rautava L, et al. Relations between brain volumes, neuropsychological assessment and parental questionnaire in prematurely born children. Eur Child Adolesc Psychiatry. 2010;19(5):407–417. 50. Thompson DK, Adamson C, Roberts G, et al. Hippocampal shape variations at term equivalent age in very preterm infants compared with term controls: perinatal predictors and functional significance at age 7. Neuroimage. 2013;70:278–287. 51. Thompson DK, Omizzolo C, Adamson C, et al. Longitudinal growth and morphology of the hippocampus through childhood: impact of prematurity and implications for memory and learning. Hum Brain Mapp. 2014;35(8):4129–4139. 52. Ortinau C, Neil J. The neuroanatomy of prematurity: normal brain development and the impact of preterm birth. Clin Anat. 2015;28(2):168–183. 53. Ajayi-Obe M, Saeed N, Cowan FM, Rutherford MA, Edwards AD. Reduced development of cerebral cortex in extremely preterm infants. Lancet. 2000;356(9236):1162–1163. 54. Battin MR, Maalouf EF, Counsell SJ, et al. Magnetic resonance imaging of the brain in very preterm infants: visualization of the germinal matrix, early myelination, and cortical folding. Pediatrics. 1998;101(6):957–962. 55. Dubois J, Benders M, Borradori-Tolsa C, et al. Primary cortical folding in the human newborn: an early marker of later functional development. Brain. 2008;131(Part 8):2028–2041. 56. Dubois J, Benders M, Cachia A, et al. Mapping the early cortical folding process in the preterm newborn brain. Cereb Cortex. 2008;18(6):1444–1454. 57. Smith SM, Jenkinson M, Johansen-Berg H, et al. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage. 2006;31(4):1487–1505. 58. Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci. 2009;10(3):186–198. 59. Zalesky A, Fornito A, Bullmore ET. Network-based statistic: identifying differences in brain networks. Neuroimage. 2010;53(4):1197–1207. 60. Huppi PS, Maier SE, Peled S, et al. Microstructural development of human newborn cerebral white matter assessed in vivo by diffusion tensor magnetic resonance imaging. Pediatr Res. 1998;44(4):584–590. 61. Ling X, Tang W, Liu G, et al. Assessment of brain maturation in the preterm infants using diffusion tensor imaging (DTI) and enhanced T2 star weighted angiography (ESWAN). Eur J Radiol. 2013;82(9):e476–e483. 62. Pogribna U, Yu X, Burson K, et al. Perinatal clinical antecedents of white matter microstructural abnormalities on diffusion tensor imaging in extremely preterm infants. PLoS One. 2013;8(8):e72974. 63. Alexandrou G, Skiold B, Karlen J, et al. Early hyperglycemia is a risk factor for death and white matter reduction in preterm infants. Pediatrics. 2010;125(3):e584–e591. 64. Anjari M, Srinivasan L, Allsop JM, et al. Diffusion tensor imaging with tract-based spatial statistics reveals local white matter abnormalities in preterm infants. Neuroimage. 2007;35(3):1021–1027.

S

E M I N A R S I N

P

E R I N A T O L O G Y

65. Kaur S, Powell S, He L, Pierson CR, Parikh NA. Reliability and repeatability of quantitative tractography methods for mapping structural white matter connectivity in preterm and term infants at term-equivalent age. PLoS One. 2014;9(1): e85807. 66. Ball G, Boardman JP, Aljabar P, et al. The influence of preterm birth on the developing thalamocortical connectome. Cortex. 2013;49(6):1711–1721. 67. Pannek K, Hatzigeorgiou X, Colditz PB, Rose S. Assessment of structural connectivity in the preterm brain at term equivalent age using diffusion MRI and T2 relaxometry: a network-based analysis. PLoS One. 2013;8(8):e68593. 68. 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(7): 526–535. 69. Arzoumanian Y, Mirmiran M, Barnes PD, et al. Diffusion tensor brain imaging findings at term-equivalent age may predict neurologic abnormalities in low birth weight preterm infants. Am J Neuroradiol. 2003;24(8):1646–1653. 70. Kaukola T, Perhomaa M, Vainionpaa L, et al. Apparent diffusion coefficient on magnetic resonance imaging in pons and in corona radiata and relation with the neurophysiologic measurement and the outcome in very preterm infants. Neonatology. 2010;97(1):15–21. 71. van Kooij BJ, de Vries LS, Ball G, et al. Neonatal tract-based spatial statistics findings and outcome in preterm infants. Am J Neuroradiol. 2012;33(1):188–194. 72. De Bruine FT, Van Wezel-Meijler G, Leijser LM, et al. Tractography of white-matter tracts in very preterm infants: a 2-year follow-up study. Dev Med Child Neurol. 2013;55(5): 427–433. 73. Brouwer MJ, van Kooij BJ, van Haastert IC, et al. Sequential cranial ultrasound and cerebellar diffusion weighted imaging contribute to the early prognosis of neurodevelopmental outcome in preterm infants. PLoS One. 2014;9(10):e109556. 74. Drobyshevsky A, Bregman J, Storey P, et al. Serial diffusion tensor imaging detects white matter changes that correlate with motor outcome in premature infants. Dev Neurosci. 2007;29(4–5):289–301. 75. Chau V, Synnes A, Grunau RE, Poskitt KJ, Brant R, Miller SP. Abnormal brain maturation in preterm neonates associated with adverse developmental outcomes. Neurology. 2013;81 (24):2082–2089. 76. Rose J, Mirmiran M, Butler EE, et al. Neonatal microstructural development of the internal capsule on diffusion tensor imaging correlates with severity of gait and motor deficits. Dev Med Child Neurol. 2007;49(10):745–750. 77. Rogers CE, Anderson PJ, Thompson DK, et al. Regional cerebral development at term relates to school-age social– emotional development in very preterm children. J Am Acad Child Adolesc Psychiatry. 2012;51(2):181–191. 78. Thompson DK, Lee KJ, Egan GF, et al. Regional white matter microstructure in very preterm infants: predictors and 7 year outcomes. Cortex. 2014;52:60–74. 79. Ogawa S, Menon RS, Tank DW, et al. Functional brain mapping by blood oxygenation level-dependent contrast magnetic resonance imaging. A comparison of signal characteristics with a biophysical model. Biophys J. 1993;64 (3):803–812. 80. Fransson P, Skiold B, Horsch S, et al. Resting-state networks in the infant brain. Proc Natl Acad Sci U S A. 2007;104 (39):15531–15536. 81. Friston KJ, Frith CD, Liddle PF, Frackowiak RS. Functional connectivity: the principal-component analysis of large (PET) data sets. J Cereb Blood Flow Metab: Off J Int Soc Cereb Blood Flow Metab. 1993;13(1):5–14.

39 (2015) 147–158

157

82. van den Heuvel MP, Hulshoff Pol HE. Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur Neuropsychopharmacol. 2010;20(8): 519–534. 83. Damoiseaux JS, Rombouts SA, Barkhof F, et al. Consistent resting-state networks across healthy subjects. Proc Natl Acad Sci U S A. 2006;103(37):13848–13853. 84. van den Heuvel M, Mandl R, Hulshoff Pol H. Normalized cut group clustering of resting-state FMRI data. PLoS One. 2008;3 (4):e2001. 85. Doria V, Beckmann CF, Arichi T, et al. Emergence of resting state networks in the preterm human brain. Proc Natl Acad Sci U S A. 2010;107(46):20015–20020. 86. Smyser CD, Inder TE, Shimony JS, et al. Longitudinal analysis of neural network development in preterm infants. Cereb Cortex. 2010;20(12):2852–2862. 87. Smyser CD, Snyder AZ, Shimony JS, Blazey TM, Inder TE, Neil JJ. Effects of white matter injury on resting state fMRI measures in prematurely born infants. PLoS One. 2013;8(7): e68098. 88. Smyser CD, Snyder AZ, Shimony JS, Mitra A, Inder TE, Neil JJ. Resting-state network complexity and magnitude are reduced in prematurely born infants. Cereb Cortex. 2014; 10.1093/cercor/bhu251. 89. Birken DL, Oldendorf WH. N-acetyl-l-aspartic acid—a literature-review of a compound prominent in h-1-nmr spectroscopic studies of brain. Neurosci Biobehav Rev. 1989;13(1):23–31. 90. Robertson NJ, IJ C. Magnetic resonance spectroscopy of the neonatal brain. In: Rutherford M, ed, MRI of the neonatal brain. London: Saunders; 2001. 91. Cheong JLY, Cady EB, Penrice J, Wyatt JS, Cox IJ, Robertson NJ. Proton MR spectroscopy in neonates with perinatal cerebral hypoxic-ischemic injury: metabolite peak-area ratios, relaxation times, and absolute concentrations. Am J Neuroradiol. 2006;27(7):1546–1554. 92. Provencher SW. Automatic quantitation of localized in vivo H-1 spectra with LC Model. NMR Biomed. 2001;14 (4):260–264. 93. Kreis R, Hofmann L, Kuhlmann B, Boesch C, Bossi E, Huppi PS. Brain metabolite composition during early human brain development as measured by quantitative in vivo H-1 magnetic resonance spectroscopy. Magn Reson Med. 2002;48 (6):949–958. 94. Xu DA, Bonifacio SL, Charlton NN, et al. MR spectroscopy of normative premature newborns. J Magn Reson Imaging. 2011;33(2):306–311. 95. Gadin E, Lobo M, Paul DA, et al. Volumetric MRI and MRS and early motor development of infants born preterm. Pediatr Phys Ther. 2012;24(1):38–44. 96. Vigneron DB, Barkovich AJ, Noworolski SM, et al. Threedimensional proton MR spectroscopic imaging of premature and term neonates. Am J Neuroradiol. 2001;22(7):1424–1433. 97. Robertson NJ, Kuint J, Counsell SJ, et al. Characterization of cerebral white matter damage in preterm infants using H-1 and P-31 magnetic resonance spectroscopy. J Cereb Blood Flow Metab. 2000;20(10):1446–1456. 98. Card D, Nossin-Manor R, Moore AM, Raybaud C, Sled JG, Taylor MJ. Brain metabolite concentrations are associated with illness severity scores and white matter abnormalities in very preterm infants. Pediatr Res. 2013;74(1):75–81. 99. Wisnowski JL, Schmithorst VJ, Rosser T, et al. Magnetic resonance spectroscopy markers of axons and astrogliosis in relation to specific features of white matter injury in preterm infants. Neuroradiology. 2014;56(9):771–779. 100. Wisnowski JL, Bluml S, Paquette L, et al. Altered glutamatergic metabolism associated with punctate white matter lesions in preterm infants. PLoS One. 2013;8(2):8.

158

SE

M I N A R S I N

P

E R I N A T O L O G Y

101. Bapat R, Narayana PA, Zhou YX, Parikh NA. Magnetic resonance spectroscopy at term-equivalent age in extremely preterm infants: association with cognitive and language development. Pediatr Neurol. 2014;51(1):53–59. 102. Hart AR, Smith MF, Whitby EH, et al. Diffusion-weighted imaging and magnetic resonance proton spectroscopy following preterm birth. Clin Radiol. 2014;69(8): 870–879.

39 (2015) 147–158

103. Kendall GS, Melbourne A, Johnson S, et al. White matter NAA/Cho and Cho/Cr Ratios at MR spectroscopy are predictive of motor outcome in preterm infants. Radiology. 2014;271 (1):230–238. 104. Augustine EM, Spielman DM, Barnes PD, et al. Can magnetic resonance spectroscopy predict neurodevelopmental outcome in very low birth weight preterm infants? J Perinatol. 2008;28(9):611–618.