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Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight 1 . Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data ( http://www.brainchart.io/ ). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories 2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones 3 , showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes., (© 2022. The Author(s).)

The human adult structural connectome has a rich nodal hierarchy, with highly diverse connectivity patterns aligned to the diverse range of functional specializations in the brain. The emergence of this hierarchical complexity in human development is unknown. Here, we substantiate the hierarchical tiers and hierarchical complexity of brain networks in the newborn period, assess correspondences with hierarchical complexity in adulthood, and investigate the effect of preterm birth, a leading cause of atypical brain development and later neurocognitive impairment, on hierarchical complexity. We report that neonatal and adult structural connectomes are both composed of distinct hierarchical tiers and that hierarchical complexity is greater in term born neonates than in preterms. This is due to diversity of connectivity patterns of regions within the intermediate tiers, which consist of regions that underlie sensorimotor processing and its integration with cognitive information. For neonates and adults, the highest tier (hub regions) is ordered, rather than complex, with more homogeneous connectivity patterns in structural hubs. This suggests that the brain develops first a more rigid structure in hub regions allowing for the development of greater and more diverse functional specialization in lower level regions, while connectivity underpinning this diversity is dysmature in infants born preterm., (© The Author(s) 2020. Published by Oxford University Press.)

Birth weight, an indicator of fetal growth, is associated with cognitive outcomes in early life (which are predictive of cognitive ability in later life) and risk of metabolic and cardiovascular disease across the life course. Brain health in older age, indexed by MRI features, is associated with cognitive performance, but little is known about how variation in normal birth weight impacts on brain structure in later life. In a community dwelling cohort of participants in their early seventies we tested the hypothesis that birth weight is associated with the following MRI features: total brain (TB), grey matter (GM) and normal appearing white matter (NAWM) volumes; whiter matter hyperintensity (WMH) volume; a general factor of fractional anisotropy (gFA) and peak width skeletonised mean diffusivity (PSMD) across the white matter skeleton. We also investigated the associations of birth weight with cortical surface area, volume and thickness. Birth weight was positively associated with TB, GM and NAWM volumes in later life (β ≥ 0.194), and with regional cortical surface area but not gFA, PSMD, WMH volume, or cortical volume or thickness. These positive relationships appear to be explained by larger intracranial volume, rather than by age-related tissue atrophy, and are independent of body height and weight in adulthood. This suggests that larger birth weight is linked to more brain tissue reserve in older life, rather than age-related brain structural features, such as tissue atrophy or WMH volume., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Preterm birth (PTB) is a leading cause of neurodevelopmental and neurocognitive impairment in childhood and is closely associated with psychiatric disease. The biological and environmental factors that confer risk and resilience for healthy brain development and long-term outcome after PTB are uncertain, which presents challenges for risk stratification and for the discovery and evaluation of neuroprotective strategies. Neonatal magnetic resonance imaging reveals a signature of PTB that includes dysconnectivity of neural networks and atypical development of cortical and deep grey matter structures. Here we provide a brief review of perinatal factors that are associated with the MRI signature of PTB. We consider maternal and foetal factors including chorioamnionitis, foetal growth restriction, socioeconomic deprivation and prenatal alcohol, drug and stress exposures; and neonatal factors including co-morbidities of PTB, nutrition, pain and medication during neonatal intensive care and variation conferred by the genome/epigenome. Association studies offer the first insights into pathways to adversity and resilience after PTB. Future challenges are to analyse quantitative brain MRI data with collateral biological and environmental data in study designs that support causal inference, and ultimately to use the output of such analyses to stratify infants for clinical trials of therapies designed to improve outcome., (© 2019 The Authors. Neuropathology and Applied Neurobiology published by John Wiley & Sons Ltd on behalf of British Neuropathological Society.)

Multi-contrast MRI captures information about brain macro- and micro-structure which can be combined in an integrated model to obtain a detailed "fingerprint" of the anatomical properties of an individual's brain. Inter-regional similarities between features derived from structural and diffusion MRI, including regional volumes, diffusion tensor metrics, neurite orientation dispersion and density imaging measures, can be modelled as morphometric similarity networks (MSNs). Here, individual MSNs were derived from 105 neonates (59 preterm and 46 term) who were scanned between 38 and 45 weeks postmenstrual age (PMA). Inter-regional similarities were used as predictors in a regression model of age at the time of scanning and in a classification model to discriminate between preterm and term infant brains. When tested on unseen data, the regression model predicted PMA at scan with a mean absolute error of 0.70  ±  0.56 weeks, and the classification model achieved 92% accuracy. We conclude that MSNs predict chronological brain age accurately; and they provide a data-driven approach to identify networks that characterise typical maturation and those that contribute most to neuroanatomic variation associated with preterm birth., Competing Interests: Declaration of Competing Interest Authors declare no conflict of interests., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Preterm infants are at increased risk of alterations in brain structure and connectivity, and subsequent neurocognitive impairment. Breast milk may be more advantageous than formula feed for promoting brain development in infants born at term, but uncertainties remain about its effect on preterm brain development and the optimal nutritional regimen for preterm infants. We test the hypothesis that breast milk exposure is associated with improved markers of brain development and connectivity in preterm infants at term equivalent age. We collected information about neonatal breast milk exposure and brain MRI at term equivalent age from 47 preterm infants (mean postmenstrual age [PMA] 29.43 weeks, range 23.28-33.0). Network-Based Statistics (NBS), Tract-based Spatial Statistics (TBSS) and volumetric analysis were used to investigate the effect of breast milk exposure on white matter water diffusion parameters, tissue volumes, and the structural connectome. Twenty-seven infants received exclusive breast milk feeds for ≥75% of days of in-patient care and this was associated with higher connectivity in the fractional anisotropy (FA)-weighted connectome compared with the group who had < 75% of days receiving exclusive breast milk feeds (NBS, p = 0.04). Within the TBSS white matter skeleton, the group that received ≥75% exclusive breast milk days exhibited higher FA within the corpus callosum, cingulum cingulate gyri, centrum semiovale, corticospinal tracts, arcuate fasciculi and posterior limbs of the internal capsule compared with the low exposure group after adjustment for PMA at birth, PMA at image acquisition, bronchopulmonary dysplasia, and chorioamnionitis (p < 0.05). The effect on structural connectivity and tract water diffusion parameters was greater with ≥90% exposure, suggesting a dose effect. There were no significant groupwise differences in brain volumes. Breast milk feeding in the weeks after preterm birth is associated with improved structural connectivity of developing networks and greater FA in major white matter fasciculi., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

Methadone is used for medication-assisted treatment of heroin addiction during pregnancy. The neurodevelopmental outcome of children with prenatal methadone exposure can be sub-optimal. We tested the hypothesis that brain development is altered among newborn infants whose mothers were prescribed methadone. 20 methadone-exposed neonates born after 37 weeks' postmenstrual age (PMA) and 20 non-exposed controls underwent diffusion MRI at mean PMA of 39 + 2 and 41 + 1  weeks, respectively. An age-optimized Tract-based Spatial Statistics (TBSS) pipeline was used to perform voxel-wise statistical comparison of fractional anisotropy (FA) data between exposed and non-exposed neonates. Methadone-exposed neonates had decreased FA within the centrum semiovale, inferior longitudinal fasciculi (ILF) and the internal and external capsules after adjustment for GA at MRI (p < 0.05, TFCE corrected). Median FA across the white matter skeleton was 12% lower among methadone-exposed infants. Mean head circumference (HC) z-scores were lower in the methadone-exposed group (- 0.52 (0.99) vs 1.15 (0.84), p < 0.001); after adjustment for HC z-scores, differences in FA remained in the anterior and posterior limbs of the internal capsule and the ILF. Polydrug use among cases was common. Prenatal methadone exposure is associated with microstructural alteration in major white matter tracts, which is present at birth and is independent of head growth. Although the findings cannot be attributed to methadone per se , the data indicate that further research to determine optimal management of opioid use disorder during pregnancy is required. Future studies should evaluate childhood outcomes including infant brain development and long-term neurocognitive function.

Background and Purpose: Offspring exposed to maternal diabetes are at increased risk of neurocognitive impairment, but its origins are unknown. With MR imaging, we investigated the feasibility of comprehensive assessment of brain metabolism ( 1 H-MRS), microstructure (DWI), and macrostructure (structural MRI) in third-trimester fetuses in women with diabetes and determined normal ranges for the MR imaging parameters measured., Materials and Methods: Women with singleton pregnancies with diabetes ( n = 26) and healthy controls ( n = 26) were recruited prospectively for MR imaging studies between 34 and 38 weeks' gestation., Results: Data suitable for postprocessing were obtained from 79%, 71%, and 46% of women for 1 H-MRS, DWI, and structural MRI, respectively. There was no difference in the NAA/Cho and NAA/Cr ratios (mean [SD]) in the fetal brain in women with diabetes compared with controls (1.74 [0.79] versus 1.79 [0.64], P = .81; and 0.78 [0.28] versus 0.94 [0.36], P = .12, respectively), but the Cho/Cr ratio was marginally lower (0.46 [0.11] versus 0.53 [0.10], P = .04). There was no difference in mean [SD] anterior white, posterior white, and deep gray matter ADC between patients and controls (1.16 [0.12] versus 1.16 [0.08], P = .96; 1.54 [0.16] versus 1.59 [0.20], P = .56; and 1.49 [0.23] versus 1.52 [0.23], P = .89, respectively) or volume of the cerebrum (243.0 mL [22.7 mL] versus 253.8 mL [31.6 mL], P = .38)., Conclusions: Acquiring multimodal MR imaging of the fetal brain at 3T from pregnant women with diabetes is feasible. Further study of fetal brain metabolism in maternal diabetes is warranted., (© 2017 by American Journal of Neuroradiology.)

Fetal brain magnetic resonance imaging (MRI) is a rapidly emerging diagnostic imaging tool. However, automated fetal brain localization is one of the biggest obstacles in expediting and fully automating large-scale fetal MRI processing. We propose a method for automatic localization of fetal brain in 3 T MRI when the images are acquired as a stack of 2D slices that are misaligned due to fetal motion. First, the Histogram of Oriented Gradients (HOG) feature descriptor is extended from 2D to 3D images. Then, a sliding window is used to assign a score to all possible windows in an image, depending on the likelihood of it containing a brain, and the window with the highest score is selected. In our evaluation experiments using a leave-one-out cross-validation strategy, we achieved 96% of complete brain localization using a database of 104 MRI scans at gestational ages between 34 and 38 weeks. We carried out comparisons against template matching and random forest based regression methods and the proposed method showed superior performance. We also showed the application of the proposed method in the optimization of fetal motion correction and how it is essential for the reconstruction process. The method is robust and does not rely on any prior knowledge of fetal brain development., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interests.

The Brain Images of Normal Subjects (BRAINS) Imagebank (http://www.brainsimagebank.ac.uk) is an integrated repository project hosted by the University of Edinburgh and sponsored by the Scottish Imaging Network: A Platform for Scientific Excellence (SINAPSE) collaborators. BRAINS provide sharing and archiving of detailed normal human brain imaging and relevant phenotypic data already collected in studies of healthy volunteers across the life-course. It particularly focusses on the extremes of age (currently older age, and in future perinatal) where variability is largest, and which are under-represented in existing databanks. BRAINS is a living imagebank where new data will be added when available. Currently BRAINS contains data from 808 healthy volunteers, from 15 to 81years of age, from 7 projects in 3 centres. Additional completed and ongoing studies of normal individuals from 1st to 10th decades are in preparation and will be included as they become available. BRAINS holds several MRI structural sequences, including T1, T2, T2* and fluid attenuated inversion recovery (FLAIR), available in DICOM (http://dicom.nema.org/); in future Diffusion Tensor Imaging (DTI) will be added where available. Images are linked to a wide range of 'textual data', such as age, medical history, physiological measures (e.g. blood pressure), medication use, cognitive ability, and perinatal information for pre/post-natal subjects. The imagebank can be searched to include or exclude ranges of these variables to create better estimates of 'what is normal' at different ages., (Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.)

Background and Hypothesis: Prolonged electroencephalographic (EEG) discontinuity has been associated with poor neurodevelopmental outcomes after perinatal asphyxia but its predictive value in the era of therapeutic hypothermia (TH) is unknown. In infants undergoing TH for hypoxic-ischaemic encephalopathy (HIE) prolonged EEG discontinuity is associated with cerebral tissue injury on MRI and adverse neurodevelopmental outcome., Method: Retrospective study of term neonates from three UK centres who received TH for perinatal asphyxia, had continuous two channel amplitude-integrated EEG with EEG for a minimum of 48 h, brain MRI within 6 weeks of birth and neurodevelopmental outcome data at a median age of 24 months. Mean discontinuity was calculated using a novel automated algorithm designed for analysis of the raw EEG signal., Results: Of 49 eligible infants, 17 (35%) had MR images predictive of death or severe neurodisability (unfavourable outcome) and 29 (59%) infants had electrographic seizures. In multivariable logistic regression, mean discontinuity at 24 h and 48 h (both p=0.01), and high seizure burden (p=0.05) were associated with severe cerebral tissue injury on MRI. A mean discontinuity >30 s/min-long epoch, had a specificity and positive predictive value of 100%, sensitivity of 71% and a negative predictive value of 88% for unfavourable neurodevelopmental outcome at a 10 µV threshold., Conclusions: In addition to seizure burden, excessive EEG discontinuity is associated with increased cerebral tissue injury on MRI and is predictive of abnormal neurodevelopmental outcome in infants treated with TH. The high positive predictive value of EEG discontinuity at 24 h may be valuable in selecting newborns with HIE for adjunctive treatments., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.)

Preterm infants are susceptible to inflammation-induced white matter injury but the exposures that lead to this are uncertain. Histologic chorioamnionitis (HCA) reflects intrauterine inflammation, can trigger a fetal inflammatory response, and is closely associated with premature birth. In a cohort of 90 preterm infants with detailed placental histology and neonatal brain magnetic resonance imaging (MRI) data at term equivalent age, we used Tract-based Spatial Statistics (TBSS) to perform voxel-wise statistical comparison of fractional anisotropy (FA) data and computational morphometry analysis to compute the volumes of whole brain, tissue compartments and cerebrospinal fluid, to test the hypothesis that HCA is an independent antenatal risk factor for preterm brain injury. Twenty-six (29%) infants had HCA and this was associated with decreased FA in the genu, cingulum cingulate gyri, centrum semiovale, inferior longitudinal fasciculi, limbs of the internal capsule, external capsule and cerebellum (p < 0.05, corrected), independent of degree of prematurity, bronchopulmonary dysplasia and postnatal sepsis. This suggests that diffuse white matter injury begins in utero for a significant proportion of preterm infants, which focuses attention on the development of methods for detecting fetuses and placentas at risk as a means of reducing preterm brain injury., Competing Interests: All co-authors have reviewed and approved the contents of the manuscript. The authors report no real or potential conflicts of interest concerning this work.

Accurate whole-brain segmentation, or brain extraction, of magnetic resonance imaging (MRI) is a critical first step in most neuroimage analysis pipelines. The majority of brain extraction algorithms have been developed and evaluated for adult data and their validity for neonatal brain extraction, which presents age-specific challenges for this task, has not been established. We developed a novel method for brain extraction of multi-modal neonatal brain MR images, named ALFA (Accurate Learning with Few Atlases). The method uses a new sparsity-based atlas selection strategy that requires a very limited number of atlases 'uniformly' distributed in the low-dimensional data space, combined with a machine learning based label fusion technique. The performance of the method for brain extraction from multi-modal data of 50 newborns is evaluated and compared with results obtained using eleven publicly available brain extraction methods. ALFA outperformed the eleven compared methods providing robust and accurate brain extraction results across different modalities. As ALFA can learn from partially labelled datasets, it can be used to segment large-scale datasets efficiently. ALFA could also be applied to other imaging modalities and other stages across the life course.

DNA methylation (DNAm) plays a determining role in neural cell fate and provides a molecular link between early-life stress and neuropsychiatric disease. Preterm birth is a profound environmental stressor that is closely associated with alterations in connectivity of neural systems and long-term neuropsychiatric impairment. The aims of this study were to examine the relationship between preterm birth and DNAm, and to investigate factors that contribute to variance in DNAm. DNA was collected from preterm infants (birth<33 weeks gestation) and healthy controls (birth>37 weeks), and a genome-wide analysis of DNAm was performed; diffusion magnetic resonance imaging (dMRI) data were acquired from the preterm group. The major fasciculi were segmented, and fractional anisotropy, mean diffusivity and tract shape were calculated. Principal components (PC) analysis was used to investigate the contribution of MRI features and clinical variables to variance in DNAm. Differential methylation was found within 25 gene bodies and 58 promoters of protein-coding genes in preterm infants compared with controls; 10 of these have neural functions. Differences detected in the array were validated with pyrosequencing. Ninety-five percent of the variance in DNAm in preterm infants was explained by 23 PCs; corticospinal tract shape associated with 6th PC, and gender and early nutritional exposure associated with the 7th PC. Preterm birth is associated with alterations in the methylome at sites that influence neural development and function. Differential methylation analysis has identified several promising candidate genes for understanding the genetic/epigenetic basis of preterm brain injury.

Early neuroimaging may provide a surrogate marker for brain development and outcome after preterm birth. Tract-Based Spatial Statistics (TBSS) is an advanced Diffusion Tensor Image (DTI) analysis technique that is sensitive to the effects of prematurity and may provide a quantitative marker for neuroprotection following perinatal brain injury or preterm birth. Here, we test the sensitivity of TBSS to detect diffuse microstructural differences in the developing white matter of preterm infants at term-equivalent age by modelling a 'treatment' effect as a global increase in fractional anisotropy (FA). As proof of concept we compare these simulations to a real effect of increasing age at scan. 3-Tesla, 15-direction diffusion tensor imaging (DTI) was acquired from 90 preterm infants at term-equivalent age. Datasets were randomly assigned to 'treated' or 'untreated' groups of increasing size and voxel-wise increases in FA were used to simulate global treatment effects of increasing magnitude in all 'treated' maps. 'Treated' and 'untreated' FA maps were compared using TBSS. Predictions from simulated data were then compared to exemplar TBSS group comparisons based on increasing postmenstrual age at scan. TBSS proved sensitive to global differences in FA within a clinically relevant range, even in relatively small group sizes, and simulated data were shown to predict well a true biological effect of increasing age on white matter development. These data confirm that TBSS is a sensitive tool for detecting global group-wise differences in FA in this population.

Preterm birth is a leading cause of cognitive impairment in childhood and is associated with cerebral gray and white matter abnormalities. Using multimodal image analysis, we tested the hypothesis that altered thalamic development is an important component of preterm brain injury and is associated with other macro- and microstructural alterations. T(1)- and T(2)-weighted magnetic resonance images and 15-direction diffusion tensor images were acquired from 71 preterm infants at term-equivalent age. Deformation-based morphometry, Tract-Based Spatial Statistics, and tissue segmentation were combined for a nonsubjective whole-brain survey of the effect of prematurity on regional tissue volume and microstructure. Increasing prematurity was related to volume reduction in the thalamus, hippocampus, orbitofrontal lobe, posterior cingulate cortex, and centrum semiovale. After controlling for prematurity, reduced thalamic volume predicted: lower cortical volume; decreased volume in frontal and temporal lobes, including hippocampus, and to a lesser extent, parietal and occipital lobes; and reduced fractional anisotropy in the corticospinal tracts and corpus callosum. In the thalamus, reduced volume was associated with increased diffusivity. This demonstrates a significant effect of prematurity on thalamic development that is related to abnormalities in allied brain structures. This suggests that preterm delivery disrupts specific aspects of cerebral development, such as the thalamocortical system.

Medical imaging has shown that, during early development, the brain undergoes more changes in size, shape and appearance than at any other time in life. A better understanding of brain development requires a spatio-temporal atlas that characterizes the dynamic changes during this period. In this paper we present an approach for constructing a 4D atlas of the developing brain, between 28 and 44 weeks post-menstrual age at time of scan, using T1 and T2 weighted MR images from 204 premature neonates. The method used for the creation of the average 4D atlas utilizes non-rigid registration between all pairs of images to eliminate bias in the atlas toward any of the original images. In addition, kernel regression is used to produce age-dependent anatomical templates. A novelty in our approach is the use of a time-varying kernel width, to overcome the variations in the distribution of subjects at different ages. This leads to an atlas that retains a consistent level of detail at every time-point. Comparisons between the resulting atlas and atlases constructed using affine and non-rigid registration are presented. The resulting 4D atlas has greater anatomic definition than currently available 4D atlases created using various affine and non-rigid registration approaches, an important factor in improving registrations between the atlas and individual subjects. Also, the resulting 4D atlas can serve as a good representative of the population of interest as it reflects both global and local changes. The atlas is publicly available at www.brain-development.org., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Probabilistic atlases are widely used in the neuroscience community as a tool for providing a standard space for comparison of subjects and as tissue priors used to enhance the intensity-based classification of brain MRI. Most efforts so far have focused on static brain atlases either for adult or pediatric cohorts. In contrast to the adult brain the rapid growth of the neonatal brain requires an age-specific spatial probabilistic atlas to provide suitable anatomical and structural information. In this paper we describe a 4D probabilistic atlas that allows dynamic generation of prior tissue probability maps for any chosen stage of neonatal brain development between 29 and 44 gestational weeks. The atlas is created from the segmentations of 142 neonatal subjects at different ages using a kernel-based regression method and provides prior tissue probability maps for six structures - cortex, white matter, subcortical grey matter, brainstem, cerebellum and cerebro-spinal fluid. The atlas is publicly available at www.brain-development.org., (Copyright © 2010 Elsevier Inc. All rights reserved.)

Preterm birth is associated with altered white matter microstructure, defined by metrics derived from diffusion tensor imaging (DTI). Tract-based spatial statistics (TBSS) is a useful tool for investigating developing white matter using DTI, but standard TBSS protocols have limitations for neonatal studies. We describe an optimised TBSS protocol for neonatal DTI data, in which registration errors are reduced. As chronic lung disease (CLD) is an independent risk factor for abnormal white matter development, we investigate the effect of this condition on white matter anisotropy and diffusivity using the optimised protocol in a proof of principle experiment. DTI data were acquired from 93 preterm infants (48 male) with a median gestational age at birth of 28(+5) (23(+4)-35(+2))weeks at a median postmenstrual age at scan of 41(+4) (38(+1)-46(+6))weeks. Nineteen infants developed CLD, defined as requiring supplemental oxygen at 36weeks postmenstrual age. TBSS was modified to include an initial low degrees-of-freedom linear registration step and a second registration to a population-average FA map. The additional registration steps reduced global misalignment between neonatal fractional anisotropy (FA) maps. Infants with CLD had significantly increased radial diffusivity (RD) and significantly reduced FA within the centrum semiovale, corpus callosum and inferior longitudinal fasciculus (p<0.05) compared to their peers, controlling for degree of prematurity and age at scan. The optimised TBSS protocol improved reliability for neonatal DTI analysis. These data suggest that potentially modifiable respiratory morbidity is associated with widespread altered white matter microstructure in preterm infants at term-equivalent age., (Copyright 2010 Elsevier Inc. All rights reserved.)

Diffuse white matter injury is common in preterm infants and is a candidate substrate for later cognitive impairment. This injury pattern is associated with morphological changes in deep grey nuclei, the localization of which is uncertain. We test the hypotheses that diffuse white matter injury is associated with discrete focal tissue loss, and that this image phenotype is associated with impairment at 2years. We acquired magnetic resonance images from 80 preterm infants at term equivalent (mean gestational age 29(+6)weeks) and 20 control infants (mean GA 39(+2)weeks). Diffuse white matter injury was defined by abnormal apparent diffusion coefficient values in one or more white matter region (frontal, central or posterior white matter at the level of the centrum semiovale), and morphological difference between groups was calculated from 3D images using deformation based morphometry. Neurodevelopmental assessments were obtained from preterm infants at a mean chronological age of 27.5months, and from controls at a mean age of 31.1months. We identified a common image phenotype in 66 of 80 preterm infants at term equivalent comprising: diffuse white matter injury; and tissue volume reduction in the dorsomedial nucleus of the thalamus, the globus pallidus, periventricular white matter, the corona radiata and within the central region of the centrum semiovale (t=4.42 p<0.001 false discovery rate corrected). The abnormal image phenotype is associated with reduced median developmental quotient (DQ) at 2years (DQ=92) compared with control infants (DQ=112), p<0.001. These findings indicate that specific neural systems are susceptible to maldevelopment after preterm birth, and suggest that neonatal image phenotype may serve as a useful biomarker for studying mechanisms of injury and the effect of putative therapeutic interventions., (Copyright (c) 2010 Elsevier Inc. All rights reserved.)

MR image data can provide many features or measures although any single measure is unlikely to comprehensively characterize the underlying morphology. We present a framework in which multiple measures are used in manifold learning steps to generate coordinate embeddings which are then combined to give an improved single representation of the population. An application to neonatal brain MRI data shows that the use of shape and appearance measures in particular leads to biologically plausible and consistent representations correlating well with clinical data. Orthogonality among the correlations suggests the embedding components relate to comparatively independent morphological features. The rapid changes that occur in brain shape and in MR image appearance during neonatal brain development justify the use of shape measures (obtained from a deformation metric) and appearance measures (obtained from image similarity). The benefit of combining separate embeddings is demonstrated by improved correlations with clinical data and we illustrate the potential of the proposed framework in characterizing trajectories of brain development.

Survivors of preterm birth have a high incidence of neurodevelopmental impairment which is not explained by currently understood brain abnormalities. The aim of this study was to test the hypothesis that the neurodevelopmental abilities of 2-year-old children who were born preterm and who had no evidence of focal abnormality on conventional MR imaging were consistently linearly related to specific local changes in white matter microstructure. We studied 33 children, born at a median (range) gestational age of 28(+5) (24(+4)-32(+1)) weeks. The children were recruited as infants from the Neonatal Intensive Care Unit at Queen Charlotte's and Hammersmith Hospital in the early neonatal period and imaged at a median corrected age of 25.5 (24-27) months. The children underwent diffusion tensor imaging to measure fractional anisotropy (FA) as a measure of tissue microstructure, and neurodevelopmental assessment using the Griffiths Mental Development Scales [giving an overall developmental quotient (DQ) and sub-quotients scores for motor, personal-social, hearing-language, eye-hand coordination and performance scales] at 2 years corrected age. Tract-based spatial statistics with linear regression analysis of voxel-wise cross-subject statistics were used to assess the relationship between FA and DQ/sub-quotient scores and results confirmed by reduced major axis regression of regions with significant correlations. We found that DQ was linearly related to FA values in parts of the corpus callosum; performance sub-scores to FA values in the corpus callosum and right cingulum; and eye-hand coordination sub-scores to FA values in the cingulum, fornix, anterior commissure, corpus callosum and right uncinate fasciculus. This study shows that specific neurodevelopmental impairments in infants born preterm are precisely related to microstructural abnormalities in particular regions of cerebral white matter which are consistent between individuals. FA may aid prognostication and provide a biomarker for therapeutic or mechanistic studies of preterm brain injury.

Background and Purpose: Diffusion-weighted MR imaging studies of the adult brain have shown that contrast between lesions and normal tissue is increased at high b-values. We designed a prospective study to test the hypothesis that diffusion tensor imaging (DTI) obtained at high b-values increases image contrast and lesion conspicuity in the neonatal brain., Materials and Methods: We studied 17 neonates, median (range) age of 10 (2-96) days, who were undergoing MR imaging for clinical indications. DTI was performed on a Philips 3T Intera system with b-values of 350, 700, 1500, and 3000 s/mm(2). Image contrast and lesion conspicuity at each b-value were visually assessed. In addition, regions of interest were positioned in the central white matter at the level of the centrum semiovale, frontal and occipital white matter, splenium of the corpus callosum, posterior limb of the internal capsule, and the thalamus. Apparent diffusion coefficient (ADC) and fractional anisotropy (FA) values for these regions were calculated., Results: Isotropic diffusion image contrast and lesion-to-normal-tissue contrast increased with increasing b-value. ADC values decreased with increasing b-value in all regions studied; however, there was no change in FA with increasing b-value., Conclusions: Diffusion image contrast increased at high b-values may be useful in identifying lesions in the neonatal brain.

Background: Symptomatic neonatal hypoglycemia may be associated with later neurodevelopmental impairment. Brain injury patterns identified on early MRI scans and their relationships to the nature of the hypoglycemic insult and neurodevelopmental outcomes are poorly defined., Methods: We studied 35 term infants with early brain MRI scans after symptomatic neonatal hypoglycemia (median glucose level: 1 mmol/L) without evidence of hypoxic-ischemic encephalopathy. Perinatal data were compared with equivalent data from 229 term, neurologically normal infants (control subjects), to identify risk factors for hypoglycemia. Neurodevelopmental outcomes were assessed at a minimum of 18 months., Results: White matter abnormalities occurred in 94% of infants with hypoglycemia, being severe in 43%, with a predominantly posterior pattern in 29% of cases. Cortical abnormalities occurred in 51% of infants; 30% had white matter hemorrhage, 40% basal ganglia/thalamic lesions, and 11% an abnormal posterior limb of the internal capsule. Three infants had middle cerebral artery territory infarctions. Twenty-three infants (65%) demonstrated impairments at 18 months, which were related to the severity of white matter injury and involvement of the posterior limb of the internal capsule. Fourteen infants demonstrated growth restriction, 1 had macrosomia, and 2 had mothers with diabetes mellitus. Pregnancy-induced hypertension, a family history of seizures, emergency cesarean section, and the need for resuscitation were more common among case subjects than control subjects., Conclusions: Patterns of injury associated with symptomatic neonatal hypoglycemia were more varied than described previously. White matter injury was not confined to the posterior regions; hemorrhage, middle cerebral artery infarction, and basal ganglia/thalamic abnormalities were seen, and cortical involvement was common. Early MRI findings were more instructive than the severity or duration of hypoglycemia for predicting neurodevelopmental outcomes.

Three-dimensional atlases and databases of the brain at different ages facilitate the description of neuroanatomy and the monitoring of cerebral growth and development. Brain segmentation is challenging in young children due to structural differences compared to adults. We have developed a method, based on established algorithms, for automatic segmentation of young children's brains into 83 regions of interest (ROIs), and applied this to an exemplar group of 33 2-year-old subjects who had been born prematurely. The algorithm uses prior information from 30 normal adult brain magnetic resonance (MR) images, which had been manually segmented to create 30 atlases, each labeling 83 anatomical structures. Each of these adult atlases was registered to each 2-year-old target MR image using non-rigid registration based on free-form deformations. Label propagation from each adult atlas yielded a segmentation of each 2-year-old brain into 83 ROIs. The final segmentation was obtained by combination of the 30 propagated adult atlases using decision fusion, improving accuracy over individual propagations. We validated this algorithm by comparing the automatic approach with three representative manually segmented volumetric regions (the subcortical caudate nucleus, the neocortical pre-central gyrus and the archicortical hippocampus) using similarity indices (SI), a measure of spatial overlap (intersection over average). SI results for automatic versus manual segmentations for these three structures were 0.90+/-0.01, 0.90+/-0.01 and 0.88+/-0.03 respectively. This registration approach allows the rapid construction of automatically labelled age-specific brain atlases for children at the age of 2 years.

We present methods for the quantitative analysis of brain growth based on the registration of longitudinal MR image data with the use of Jacobian determinant maps to characterise neuroanatomical changes. The individual anatomies, growth maps and tissue classes are also spatially normalised in an 'average space' and aggregated to provide atlases for the population at each timepoint. The average space representation is obtained using the average intersubject transformation within each timepoint. In an exemplar study, this approach is used to assess brain development in 25 infants between 1 and 2 years, and we show consistency in growth estimates between registration and segmentation approaches.

Objective: The aim of this study was to develop a simple reproducible method for the measurement of apparent diffusion coefficient values in the white matter of preterm infants using diffusion-weighted imaging to test the hypothesis that elevated mean apparent diffusion coefficient values are associated with lower developmental quotient scores at 2 years' corrected age., Methods: We obtained diffusion-weighted imaging in 38 preterm infants at term-equivalent age who had no evidence of overt cerebral pathology on conventional MRI. Mean apparent diffusion coefficient values at the level of the centrum semiovale were determined. The children were assessed using a standardized neurologic examination, and the Griffiths Mental Development Scales were administered to obtain a developmental quotient at 2 years' corrected age. The relationship between mean apparent diffusion coefficient values and developmental quotient was examined. Clinical data relating to postnatal sepsis, antenatal steroid exposure, supplemental oxygen, gender, patent ductus arteriosus, and inotrope requirement were collected, and the mean apparent diffusion coefficient values for each group were compared., Results: The mean (+/-SD) apparent diffusion coefficient value in the white matter was 1.385 +/- 0.07 x 10(-3) mm2/second, and the mean developmental quotient was 108.9 +/- 11.5. None of the children had a significant neurologic problem. There was a significant negative correlation between mean apparent diffusion coefficient and developmental quotient., Conclusion: These findings suggest that higher white matter apparent diffusion coefficient values at term-equivalent age in preterm infants without overt lesions are associated with poorer developmental performance in later childhood. Consequently, apparent diffusion coefficient values at term may be of prognostic value for neurodevelopmental outcome in infants who are born preterm and who have no other imaging indicators of abnormality.

Objective: Preterm infants have reduced cerebral tissue volumes in adolescence. This study addresses the question: Is reduced global brain growth in the neonatal period inevitable after premature birth, or is it associated with specific medical risk factors?, Methods: Eighty-nine preterm infants at term equivalent age without focal parenchymal brain lesions were studied with 20 full-term control infants. Using a deformation-based morphometric approach, we transformed images to a reference anatomic space, and we used the transformations to calculate whole-brain volume and ventricular volume for each subject. Patterns of volume difference were correlated with clinical data., Results: Cerebral volume is not reduced compared with term born control infants (p = 0.765). Supplemental oxygen requirement at 28 postnatal days is associated with lower cerebral tissue volume at term (p < 0.001), but there were no significant differences in cerebral volumes attributable to perinatal sepsis (p = 0.515) and quantitatively defined diffuse white matter injury (p = 0.183). As expected, the ventricular system is significantly larger in preterm infants at term equivalent age compared with term control infants (p < 0.001)., Interpretation: Cerebral volume is not reduced during intensive care for the majority of preterm infants, but prolonged supplemental oxygen dependence is a risk factor for early attenuation of global brain growth. The reduced cerebral tissue volume seen in adolescents born preterm does not appear to be an inevitable association of prematurity, but rather caused by either specific disease during intensive care or factors operating beyond the neonatal period.

The creation of average anatomical atlases has been a growing area of research in recent years. It is of increased value to construct representations of, not only intensity atlases, but also their segmentation into required tissues or structures. This paper presents novel groupwise combined segmentation and registration approaches, which aim to simultaneously improve both the alignment of intensity images to their average shape, as well as the segmentations of structures in the average space. An iterative EM framework is used to build average 3D MR atlases of populations for which prior atlases do not currently exist: preterm infants at one- and two-years old. These have been used to quantify the growth of tissues occurring between these ages.

Preterm birth is a leading risk factor for neurodevelopmental and cognitive impairment in childhood and adolescence. The most common known cerebral abnormality among preterm infants at term equivalent age is a diffuse white matter abnormality seen on magnetic resonance (MR) images. It occurs with a similar prevalence to subsequent impairment, but its effect on developing neural systems is unknown. MR images were obtained at term equivalent age from 62 infants born at 24-33 completed weeks gestation and 12 term born controls. Tissue damage was quantified using diffusion-weighted imaging, and deformation-based morphometry was used to make a non-subjective survey of the whole brain to identify significant cerebral morphological alterations associated with preterm birth and with diffuse white matter injury. Preterm infants at term equivalent age had reduced thalamic and lentiform volumes without evidence of acute injury in these regions (t = 5.81, P < 0.05), and these alterations were more marked with increasing prematurity (t = 7.13, P < 0.05 for infants born at less than 28 weeks) and in infants with diffuse white matter injury (t = 6.43, P < 0.05). The identification of deep grey matter growth failure in association with diffuse white matter injury suggests that white matter injury is not an isolated phenomenon, but rather, it is associated with the maldevelopment of remote structures. This could be mediated by a disturbance to corticothalamic connectivity during a critical period in cerebral development. Deformation-based morphometry is a powerful tool for modelling the developing brain in health and disease, and can be used to test putative aetiological factors for injury.

Objective: Diffuse excessive high signal intensity (DEHSI) is observed in the majority of preterm infants at term-equivalent age on conventional MRI, and diffusion-weighted imaging has shown that apparent diffusion coefficient values are elevated in the white matter (WM) in DEHSI. Our aim was to obtain diffusion tensor imaging on preterm infants at term-equivalent age and term control infants to test the hypothesis that radial diffusivity was significantly different in the WM in preterm infants with DEHSI compared with both preterm infants with normal-appearing WM on conventional MRI and term control infants., Methods: Diffusion tensor imaging was obtained on 38 preterm infants at term-equivalent age and 8 term control infants. Values for axial (lambda1) and radial [(lambda2 + lambda3)/2] diffusivity were calculated in regions of interest positioned in the central WM at the level of the centrum semiovale, frontal WM, posterior periventricular WM, occipital WM, anterior and posterior portions of the posterior limb of the internal capsule, and the genu and splenium of the corpus callosum., Results: Radial diffusivity was elevated significantly in the posterior portion of the posterior limb of the internal capsule and the splenium of the corpus callosum, and both axial and radial diffusivity were elevated significantly in the WM at the level of the centrum semiovale, the frontal WM, the periventricular WM, and the occipital WM in preterm infants with DEHSI compared with preterm infants with normal-appearing WM and term control infants. There was no significant difference between term control infants and preterm infants with normal-appearing WM in any region studied., Conclusions: These findings suggest that DEHSI represents an oligodendrocyte and/or axonal abnormality that is widespread throughout the cerebral WM.

Preterm birth is associated with a high prevalence of neuropsychiatric impairment in childhood and adolescence, but the neural correlates underlying these disorders are not fully understood. Quantitative magnetic resonance imaging techniques have been used to investigate subtle differences in cerebral growth and development among children and adolescents born preterm or with very low birth weight. Diffusion tensor imaging and computer-assisted morphometric techniques (including voxel-based morphometry and deformation-based morphometry) have identified abnormalities in tissue microstructure and cerebral morphology among survivors of preterm birth at different ages, and some of these alterations have specific functional correlates. This chapter reviews the literature reporting differential brain development following preterm birth, with emphasis on the morphological changes that correlate with neuropsychiatric impairment.

Objective: Motor impairment after neonatal and childhood-onset ischemic stroke (IS) is common, although the prevalence and type of hemiparesis differs between the 2 age groups. Lesion topography is an important predictor of hemiparesis after neonatal IS, but it is not known if the same topographic predictors of adverse motor outcome apply to childhood-onset IS. We used a consistent approach to define lesion topography and evaluate motor outcome in both age groups to (1) investigate whether early topographic predictors of hemiparesis after unilateral middle cerebral artery-territory stroke are the same in neonates and older children and (2) compare the prevalence of dystonia and loss of independent finger movements between the 2 age groups., Design: Twenty-eight patients with neonatal-onset IS (Hammersmith Hospital, London, United Kingdom) were studied together with 43 patients with childhood-onset IS (Great Ormond Street Hospital, London, United Kingdom). All patients had exclusive unilateral middle cerebral artery-territory IS. Lesion topography was studied by using the first magnetic resonance image acquired after the onset of symptoms and was coded for involvement of cerebral cortex (CC), posterior limb of the internal capsule (PLIC), basal ganglia (BG), and white matter. The primary outcome was hemiparesis, and secondary outcomes were dystonia and loss of age-appropriate independent finger movements., Results: Hemiparesis was more common after childhood-onset IS (56%) than neonatal-onset IS (24%). In neonatal-onset IS, concomitant involvement of BG, CC, and PLIC predicts the development of hemiparesis (odds ratio: 99; 95% confidence interval: 5.2-1883.8), and no child with 1 or 2 of these structures involved developed hemiparesis. In contrast, in childhood-onset IS, concomitant BG, CC, and PLIC lesions tended to be associated with hemiparesis (9 of 11), but this adverse outcome was seen also among patients with 1- or 2-site involvement. However, hemiparesis was less likely if the infarction involved BG only (odds ratio: 0.162; 95% confidence interval: 0.036-0.729). Dystonia was present in 15 of 24 in the childhood-onset group with hemiparesis but was not seen after neonatal-onset IS. In both age groups upper-limb impairment was more severe than lower-limb impairment, with frequent loss of independent hand function among hemiparetic patients., Conclusions: In neonatal and childhood-onset IS, early magnetic resonance imaging provides useful prognostic information about subsequent motor outcome. There are differences in the functional response of the neuromotor system to injury between the 2 age groups that cannot be attributed to methodological differences alone.