IMPORTANCECerebral palsy describes the most common physical disability in childhood and occurs in 1 in 500 live births. Historically, the diagnosis has been made between age 12 and 24 months but now can be made before 6 months' corrected age.OBJECTIVES To systematically review best available evidence for early, accurate diagnosis of cerebral palsy and to summarize best available evidence about cerebral palsy-specific early intervention that should follow early diagnosis to optimize neuroplasticity and function.

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Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages. This paper presents a method for the automatic segmentation of MR brain images into a number of tissue classes using a convolutional neural network. To ensure that the method obtains accurate segmentation details as well as spatial consistency, the network uses multiple patch sizes and multiple convolution kernel sizes to acquire multi-scale information about each voxel. The method is not dependent on explicit features, but learns to recognise the information that is important for the classification based on training data. The method requires a single anatomical MR image only. The segmentation method is applied to five different data sets: coronal T-weighted images of preterm infants acquired at 30 weeks postmenstrual age (PMA) and 40 weeks PMA, axial T-weighted images of preterm infants acquired at 40 weeks PMA, axial T-weighted images of ageing adults acquired at an average age of 70 years, and T-weighted images of young adults acquired at an average age of 23 years. The method obtained the following average Dice coefficients over all segmented tissue classes for each data set, respectively: 0.87, 0.82, 0.84, 0.86, and 0.91. The results demonstrate that the method obtains accurate segmentations in all five sets, and hence demonstrates its robustness to differences in age and acquisition protocol.

Early aEEG patterns can be used to predict outcome for infants treated with normothermia but not hypothermia. Infants with good outcome had normalized background pattern by 24 hours when treated with normothermia and by 48 hours when treated with hypothermia.

Aim-To assess the prognostic value of amplitude integrated EEG (aEEG) 3 and 6 hours after birth. Methods-Seventy three term, asphyxiated infants were studied (from two diVerent centres), using the Cerebral Function Monitor (CFM Lectromed). The diVerent aEEG tracings were compared using pattern recognition (flat tracing mainly isoelectric (FT); continuous extremely low voltage (CLV); burstsuppression (BS); discontinuous normal voltage (DNV); continuous normal voltage (CNV)) with subsequent outcome. Results-Sixty eight infants were followed up for more than 12 months (range 12 months to 6 years).Twenty one out of 68 infants (31%) showed a change in pattern from 3 to 6 hours, but this was only significant in five cases (24%). In three this changed from BS to CNV with a normal outcome. One infant showed a change in pattern from CNV to FT and had a major handicap at follow up. Another infant showed a change in pattern from DNV to BS, and developed a major handicap at follow up. The other 16 infants did not have any significant changes in pattern: 11 infants had CLV, BS, or FT at 3 and 6 hours and died (n = 9) in the neonatal period or developed a major handicap (n = 2). Five infants had a CNV or DNV pattern at 3 and 6 hours, with a normal outcome. The sensitivity and specificity of BS, together with FT and CLV, for poor outcome at 3 hours was 0.85 and 0.77, respectively; at 6 hours 0.91 and 0.86, respectively. The positive predictive value (PPV) was 78% and the negative predictive value (NPV) 84% 3 hours after birth. At 6 hours the PPV was 86% and the NPV was 91%. Conclusion-aEEG could be very useful for selecting those infants who might benefit from intervention after birth asphyxia.

The human connectome is the result of an elaborate developmental trajectory. Acquiring diffusion-weighted imaging and resting-state fMRI, we studied connectome formation during the preterm phase of macroscopic connectome genesis. In total, 27 neonates were scanned at week 30 and/or week 40 gestational age (GA). Examining the architecture of the neonatal anatomical brain network revealed a clear presence of a small-world modular organization before term birth. Analysis of neonatal functional connectivity (FC) showed the early formation of resting-state networks, suggesting that functional networks are present in the preterm brain, albeit being in an immature state. Moreover, structural and FC patterns of the neonatal brain network showed strong overlap with connectome architecture of the adult brain (85 and 81%, respectively). Analysis of brain development between week 30 and week 40 GA revealed clear developmental effects in neonatal connectome architecture, including a significant increase in white matter microstructure (P < 0.01), small-world topology (P < 0.01) and interhemispheric FC (P < 0.01). Computational analysis further showed that developmental changes involved an increase in integration capacity of the connectivity network as a whole. Taken together, we conclude that hallmark organizational structures of the human connectome are present before term birth and subject to early development.

HPeVs should be added to the list of neurotropic viruses that may cause severe central nervous system infection in the neonatal period. White matter injury can be visualized with cranial ultrasonography, but more detailed information is obtained with MRI and especially diffusion-weighted imaging. Because clinical presentation of HPeV encephalitis is similar to that of enterovirus, real-time polymerase chain reaction for both viruses should be performed in atypical presentation of neonatal seizures.

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