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The evolution of non-hemorrhagic white matter injury (WMI) based on sequential magnetic resonance imaging (MRI) has not been well studied. Our aim was to describe sequential MRI findings in preterm infants with non-hemorrhagic WMI and to develop an MRI classification system for preterm WMI based on these findings. Eighty-two preterm infants (gestation ≤35 weeks) were retrospectively included. WMI was diagnosed and classified based on sequential cranial ultrasound (cUS) and confirmed on MRI. 138 MRIs were obtained at three time-points: early (<2 weeks; n = 32), mid (2-6 weeks; n = 30) and term equivalent age (TEA; n = 76). 63 infants (77%) had 2 MRIs during the neonatal period. WMI was non-cystic in 35 and cystic in 47 infants. In infants with cystic-WMI early MRI showed extensive restricted diffusion abnormalities, cysts were already present in 3 infants; mid MRI showed focal or extensive cysts, without acute diffusion changes. A significant reduction in the size and/or extent of the cysts was observed in 32% of the infants between early/mid and TEA MRI. In 4/9 infants previously seen focal cysts were no longer identified at TEA. All infants with cystic WMI showed ≥2 additional findings at TEA: significant reduction in WM volume, mild-moderate irregular ventriculomegaly, several areas of increased signal intensity on T1-weighted-images, abnormal myelination of the PLIC, small thalami. In infants with extensive WM cysts at 2-6 weeks, cysts may be reduced in number or may even no longer be seen at TEA. A single MRI at TEA, without taking sequential cUS data and pre-TEA MRI findings into account, may underestimate the extent of WMI; based on these results we propose a new MRI classification for preterm non-hemorrhagic WMI.

The cerebral cortex develops rapidly in the last trimester of pregnancy. In preterm infants, brain development is very vulnerable because of their often complicated extra-uterine conditions. The aim of this study was to quantitatively describe cortical development in a cohort of 85 preterm infants with and without brain injury imaged at 30 and 40 weeks postmenstrual age (PMA). In the acquired T2-weighted MR images, unmyelinated white matter (UWM), cortical grey matter (CoGM), and cerebrospinal fluid in the extracerebral space (CSF) were automatically segmented. Based on these segmentations, cortical descriptors evaluating volume, surface area, thickness, gyrification index, and global mean curvature were computed at both time points, for the whole brain, as well as for the frontal, temporal, parietal, and occipital lobes separately. Additionally, visual scoring of brain abnormality was performed using a conventional scoring system at 40 weeks PMA. The evaluated descriptors showed larger change in the occipital lobes than in the other lobes. Moreover, the cortical descriptors showed an association with the abnormality scores: gyrification index and global mean curvature decreased, whereas, interestingly, median cortical thickness increased with increasing abnormality score. This was more pronounced at 40 weeks PMA than at 30 weeks PMA, suggesting that the period between 30 and 40 weeks PMA might provide a window of opportunity for intervention to prevent delay in cortical development.

With the increased use of MRI in preterm infants, punctate white matter lesions (PWML) are more often recognized. The aim of this study was to describe the incidence and characteristics of these lesions as well as short-term outcome in a cohort of serially scanned preterm infants, using both conventional imaging, diffusion (DWI) and susceptibility (SWI) weighted imaging. 112 preterm infants with 2 MRIs in the neonatal period, with evidence of punctate white matter lesions, were included. Appearance, lesion load, location, and abnormalities on DWI and SWI were scored and outcome data were collected. Different patterns of punctate white matter lesions did appear: a linear appearance associated with signal loss on SWI, and a cluster appearance associated with restricted diffusion on DWI on the first MRI. Cluster and mixed lesions on the first scan changed in appearance in over 50% on the second scan, whereas linear lesions generally kept their appearance. Lesions were only visible on the early scan in 33%, and were only seen at term equivalent age in 20%. Nine infants developed cerebral palsy, due to additional overt white matter lesions in six. Two patterns of punctate white matter lesions were identified: one with loss of signal on SWI in a linear appearance, and the other with DWI lesions with restricted diffusion in a cluster appearance. These different patterns are suggestive of a difference in underlying pathophysiology. To reliably classify PWML in the preterm infant in either pattern, an early MRI with DWI and SWI sequences is required.

Volumetric measurements of neonatal brain tissues may be used as a biomarker for later neurodevelopmental outcome. We propose an automatic method for probabilistic brain segmentation in neonatal MRIs. In an IRB-approved study axial T1- and T2-weighted MR images were acquired at term-equivalent age for a preterm cohort of 108 neonates. A method for automatic probabilistic segmentation of the images into eight cerebral tissue classes was developed: cortical and central grey matter, unmyelinated and myelinated white matter, cerebrospinal fluid in the ventricles and in the extra cerebral space, brainstem and cerebellum. Segmentation is based on supervised pixel classification using intensity values and spatial positions of the image voxels. The method was trained and evaluated using leave-one-out experiments on seven images, for which an expert had set a reference standard manually. Subsequently, the method was applied to the remaining 101 scans, and the resulting segmentations were evaluated visually by three experts. Finally, volumes of the eight segmented tissue classes were determined for each patient. The Dice similarity coefficients of the segmented tissue classes, except myelinated white matter, ranged from 0.75 to 0.92. Myelinated white matter was difficult to segment and the achieved Dice coefficient was 0.47. Visual analysis of the results demonstrated accurate segmentations of the eight tissue classes. The probabilistic segmentation method produced volumes that compared favorably with the reference standard. The proposed method provides accurate segmentation of neonatal brain MR images into all given tissue classes, except myelinated white matter. This is the one of the first methods that distinguishes cerebrospinal fluid in the ventricles from cerebrospinal fluid in the extracerebral space. This method might be helpful in predicting neurodevelopmental outcome and useful for evaluating neuroprotective clinical trials in neonates.

Our brain is a complex network of structurally and functionally interconnected regions, shaped to efficiently process and integrate information. The development from a brain equipped with basic functionalities to an efficient network facilitating complex behavior starts during gestation and continues into adulthood. Resting-state functional MRI (rs-fMRI) enables the examination of developmental aspects of functional connectivity (FC) and functional brain networks. This review will discuss changes observed in the developing brain on the level of network FC from a gestational age of 20 weeks onwards. We discuss findings of resting-state fMRI studies showing that functional network development starts during gestation, creating a foundation for each of the resting-state networks (RSNs) to be established. Visual and sensorimotor areas are reported to develop first, with other networks, at different rates, increasing both in network connectivity and size over time. Reaching childhood, marked fine-tuning and specialization takes place in the regions necessary for higher-order cognitive functions.

First, to apply a recently extended scoring system for preterm brain injury at term-equivalent age (TEA-)MRI in a regional extremely preterm cohort; second, to identify independent perinatal factors associated with this score; and third, to assess the prognostic value of this TEA-MRI score with respect to early neurodevelopmental outcome. 239 extremely preterm infants (median gestational age [range] in weeks: 26.6 [24.3-27.9]), admitted to the Wilhelmina Children's Hospital between 2006 and 2012 were included. Brain abnormalities in white matter, cortical and deep grey matter and cerebellum and brain growth were scored on T1- and T2-weighted TEA-MRI using the Kidokoro scoring system. Neurodevelopmental outcome was assessed at two years corrected age using the Bayley Scales of Infant and Toddler Development, third edition. The association between TEA-MRI and perinatal factors as well as neurodevelopmental outcome was evaluated using multivariable regression analysis. The distribution of brain abnormalities and brain metrics in the Utrecht cohort differed from the original St. Louis cohort (p < .05). Mechanical ventilation >7 days (β [95% confidence interval, CI]: 1.3 [.5; 2.0]) and parenteral nutrition >21 days (2.2 [1.2; 3.2]) were independently associated with higher global brain abnormality scores (p < .001). Global brain abnormality scores were inversely associated with cognitive (β in composite scores [95% CI]: -.7 [-1.2; -.2], p = .004), fine motor (β in scaled scores [95% CI]: -.1 [-.3; -.0], p = .007) and gross motor outcome (β in scaled scores [95% CI]: -.2 [-.3; -.1], p < .001) at two years corrected age, although the explained variances were low (R2 ≤.219). Patterns of brain injury differed between cohorts. Prolonged mechanical ventilation and parenteral nutrition were identified as independent perinatal risk factors. The prognostic value of the TEA-MRI score was rather limited in this well-performing cohort.

BackgroundThis study aimed to investigate the effect of nutrition and growth during the first 4 weeks after birth on cerebral volumes and white matter maturation at term equivalent age (TEA) and on neurodevelopmental outcome at 2 years' corrected age (CA), in preterm infants.MethodsOne hundred thirty-one infants born at a gestational age (GA) <31 weeks with magnetic resonance imaging (MRI) at TEA were studied. Cortical gray matter (CGM) volumes, basal ganglia and thalami (BGT) volumes, cerebellar volumes, and total brain volume (TBV) were computed. Fractional anisotropy (FA) in the posterior limb of internal capsule (PLIC) was obtained. Cognitive and motor scores were assessed at 2 years' CA.ResultsCumulative fat and enteral intakes were positively related to larger cerebellar and BGT volumes. Weight gain was associated with larger cerebellar, BGT, and CGM volume. Cumulative fat and caloric intake, and enteral intakes were positively associated with FA in the PLIC. Cumulative protein intake was positively associated with higher cognitive and motor scores (all P<0.05).ConclusionOur study demonstrated a positive association between nutrition, weight gain, and brain volumes. Moreover, we found a positive relationship between nutrition, white matter maturation at TEA, and neurodevelopment in infancy. These findings emphasize the importance of growth and nutrition with a balanced protein, fat, and caloric content for brain development.

Increased levels of end-tidal carbon monoxide (ETCOc) in preterm infants during the first day of life are associated with oxidative stress, inflammatory processes and adverse neurodevelopmental outcome at 2 years of age. Therefore, we hypothesized that early ETCOc levels may also be associated with impaired growth of unmyelinated cerebral white matter. From a cohort of 156 extremely and very preterm infants in which ETCOc was determined within 24 h after birth, in 36 infants 3D-MRI was performed at term-equivalent age to assess cerebral tissue volumes of important brain regions. Linear regression analysis between cerebral ventricular volume, unmyelinated white matter/total brain volume-, and cortical grey matter/total brain volume-ratio and ETCOc showed a positive, negative and positive correlation, respectively. Multivariable analyses showed that solely ETCOc was positively related to cerebral ventricular volume and cortical grey matter/total brain volume ratio, and that solely ETCOc was inversely related to the unmyelinated white matter/total brain volume ratio, suggesting that increased levels of ETCOc, associated with oxidative stress and inflammation, were related with impaired growth of unmyelinated white matter. Increased values of ETCOc, measured within the first 24 hours of life may be indicative of oxidative stress and inflammation in the immediate perinatal period, resulting in impaired growth of the vulnerable unmyelinated white matter of the preterm brain.

Preterm infants who develop neurodevelopmental impairment do not always have recognized abnormalities on cerebral ultrasound, a modality routinely used to assess prognosis. In a high proportion of infants, MRI detects punctate white matter lesions that are not seen on ultrasonography. To determine the relation of punctate lesions to brain development and early neurodevelopmental outcome we used multimodal brain MRI to study a large cohort of preterm infants. Punctate lesions without other focal cerebral or cerebellar lesions were detected at term equivalent age in 123 (24.3%) (59 male) of the 506 infants, predominantly in the centrum semiovale and corona radiata. Infants with lesions had higher gestational age, birth weight, and less chronic lung disease. Punctate lesions showed a dose dependent relation to abnormalities in white matter microstructure, assessed with tract-based spatial statistics, and reduced thalamic volume (p < 0.0001), and predicted unfavourable motor outcome at a median (range) corrected age of 20.2 (18.4–26.3) months with sensitivity (95% confidence intervals) 71 (43–88) and specificity 72 (69–77). Punctate white matter lesions without associated cerebral lesions are common in preterm infants currently not regarded as at highest risk for cerebral injury, and are associated with widespread neuroanatomical abnormalities and adverse early neurodevelopmental outcome.

Preterm birth is often associated with impaired brain development. The state and expected progression of preterm brain development can be evaluated using quantitative assessment of MR images. Such measurements require accurate segmentation of different tissue types in those images. This paper presents an algorithm for the automatic segmentation of unmyelinated white matter (WM), cortical grey matter (GM), and cerebrospinal fluid in the extracerebral space (CSF). The algorithm uses supervised voxel classification in three subsequent stages. In the first stage, voxels that can easily be assigned to one of the three tissue types are labelled. In the second stage, dedicated analysis of the remaining voxels is performed. The first and the second stages both use two-class classification for each tissue type separately. Possible inconsistencies that could result from these tissue-specific segmentation stages are resolved in the third stage, which performs multi-class classification. A set of T1- and T2-weighted images was analysed, but the optimised system performs automatic segmentation using a T2-weighted image only. We have investigated the performance of the algorithm when using training data randomly selected from completely annotated images as well as when using training data from only partially annotated images. The method was evaluated on images of preterm infants acquired at 30 and 40weeks postmenstrual age (PMA). When the method was trained using random selection from the completely annotated images, the average Dice coefficients were 0.95 for WM, 0.81 for GM, and 0.89 for CSF on an independent set of images acquired at 30weeks PMA. When the method was trained using only the partially annotated images, the average Dice coefficients were 0.95 for WM, 0.78 for GM and 0.87 for CSF for the images acquired at 30weeks PMA, and 0.92 for WM, 0.80 for GM and 0.85 for CSF for the images acquired at 40weeks PMA. Even though the segmentations obtained using training data from the partially annotated images resulted in slightly lower Dice coefficients, the performance in all experiments was close to that of a second human expert (0.93 for WM, 0.79 for GM and 0.86 for CSF for the images acquired at 30weeks, and 0.94 for WM, 0.76 for GM and 0.87 for CSF for the images acquired at 40weeks). These results show that the presented method is robust to age and acquisition protocol and that it performs accurate segmentation of WM, GM, and CSF when the training data is extracted from complete annotations as well as when the training data is extracted from partial annotations only. This extends the applicability of the method by reducing the time and effort necessary to create training data in a population with different characteristics. •An automatic segmentation method for WM, GM and CSF in neonatal MRI is presented.•The method is based on sequential supervised voxel classification.•The method can perform segmentation based on a T2-weighted image only.•The method is robust to age and acquisition protocol.•The method does not require fully annotated training images.