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Measuring the distribution of brain tissue types (tissue classification) in neonates is necessary for studying typical and atypical brain development, such as that associated with preterm birth, and may provide biomarkers for neurodevelopmental outcomes. Compared with magnetic resonance images of adults, neonatal images present specific challenges that require the development of specialized, population-specific methods. This paper introduces MANTiS (Morphologically Adaptive Neonatal Tissue Segmentation), which extends the unified segmentation approach to tissue classification implemented in Statistical Parametric Mapping (SPM) software to neonates. MANTiS utilizes a combination of unified segmentation, template adaptation via morphological segmentation tools and topological filtering, to segment the neonatal brain into eight tissue classes: cortical gray matter, white matter, deep nuclear gray matter, cerebellum, brainstem, cerebrospinal fluid (CSF), hippocampus and amygdala. We evaluated the performance of MANTiS using two independent datasets. The first dataset, provided by the NeoBrainS12 challenge, consisted of coronal T 2-weighted images of preterm infants (born ≤30 weeks' gestation) acquired at 30 weeks' corrected gestational age (n = 5), coronal T 2-weighted images of preterm infants acquired at 40 weeks' corrected gestational age (n = 5) and axial T 2-weighted images of preterm infants acquired at 40 weeks' corrected gestational age (n = 5). The second dataset, provided by the Washington University NeuroDevelopmental Research (WUNDeR) group, consisted of T 2-weighted images of preterm infants (born <30 weeks' gestation) acquired shortly after birth (n = 12), preterm infants acquired at term-equivalent age (n = 12), and healthy term-born infants (born ≥38 weeks' gestation) acquired within the first 9 days of life (n = 12). For the NeoBrainS12 dataset, mean Dice scores comparing MANTiS with manual segmentations were all above 0.7, except for the cortical gray

BACKGROUND: Uncertainty remains about the rate of specific psychiatric disorders and associated predictive factors for very preterm (VPT) children. The aims of this study were to document rates of psychiatric disorders in VPT children aged 7 years compared with term born children, and to examine potential predictive factors for psychiatric diagnoses in VPT children. METHODS: Participants were 177 VPT and 65 term born children. Perinatal medical data were collected, which included brain abnormalities detected using magnetic resonance imaging. The Infant-Toddler Social-Emotional Assessment (ITSEA) and Strengths and Difficulties Questionnaire (SDQ) were administered at 2 and 5 years respectively. At 7 years of age, the Developmental and Well-being Assessment (DAWBA) was used to indicate psychiatric diagnoses. RESULTS: Compared with term born children, VPT children had three times the odds of meeting criteria for any psychiatric diagnosis at age 7 years (odds ratio 3.03; 95% confidence interval 1.23, 7.47, p = .02). The most common diagnoses were anxiety disorders (11% VPT, 8% term), attention-deficit/hyperactivity disorder (10% VPT, 3% term) and autism spectrum disorder (4.5% VPT, 0% term). For VPT children, those with severe global brain abnormalities (p = .02), those who displayed social-emotional problems at age 5 (p = .000) and those with higher social risk at age 7 (p = .001) were more likely to meet criteria for a psychiatric illness at age 7. CONCLUSIONS: Compared with term born children, VPT children have higher rates of psychiatric diagnoses at early school age, predicted by neonatal brain abnormalities, prior social-emotional problems and social factors.