Decisions regarding whether to administer intensive care to extremely premature infants are often based on gestational age alone. However, other factors also affect the prognosis for these patients. We prospectively studied a cohort of 4446 infants born at 22 to 25 weeks' gestation (determined on the basis of the best obstetrical estimate) in the Neonatal Research Network of the National Institute of Child Health and Human Development to relate risk factors assessable at or before birth to the likelihood of survival, survival without profound neurodevelopmental impairment, and survival without neurodevelopmental impairment at a corrected age of 18 to 22 months. Among study infants, 3702 (83%) received intensive care in the form of mechanical ventilation. Among the 4192 study infants (94%) for whom outcomes were determined at 18 to 22 months, 49% died, 61% died or had profound impairment, and 73% died or had impairment. In multivariable analyses of infants who received intensive care, exposure to antenatal corticosteroids, female sex, singleton birth, and higher birth weight (per each 100-g increment) were each associated with reductions in the risk of death and the risk of death or profound or any neurodevelopmental impairment; these reductions were similar to those associated with a 1-week increase in gestational age. At the same estimated likelihood of a favorable outcome, girls were less likely than boys to receive intensive care. The outcomes for infants who underwent ventilation were better predicted with the use of the above factors than with use of gestational age alone. The likelihood of a favorable outcome with intensive care can be better estimated by consideration of four factors in addition to gestational age: sex, exposure or nonexposure to antenatal corticosteroids, whether single or multiple birth, and birth weight. (ClinicalTrials.gov numbers, NCT00063063 and NCT00009633.)

Quiz Ref ID Hypoxic-ischemic encephalopathy represents a subset of neonatal encephalopathy, occurring in approximately 1.5 per 1000 live births.¹ It is an important etiology of neonatal mortality and serious or devastating lifelong cerebral palsy, neurosensory deficits, and cognitive impairments.² Therapeutic hypothermia initiated within 6 hours after birth for moderate or severe hypoxic-ischemic encephalopathy reduced the composite outcome of death or disability at 18 months in multiple randomized clinical trials^3-8 and improved outcomes at 6 to 7 years.^9,10 The American Academy of Pediatrics published a framework to ensure appropriate use of hypothermia.¹¹

Early diagnosis remains a significant challenge for many neurological disorders, especially for rare disorders where studying large cohorts is not possible. A novel solution that investigators have undertaken is combining advanced machine learning algorithms with resting-state functional Magnetic Resonance Imaging to unveil hidden pathological brain connectome patterns to uncover diagnostic and prognostic biomarkers. Recently, state-of-the-art deep learning techniques are outperforming traditional machine learning methods and are hailed as a milestone for artificial intelligence. However, whole brain classification that combines brain connectome with deep learning has been hindered by insufficient training samples. Inspired by the transfer learning strategy employed in computer vision, we exploited previously collected resting-state functional MRI data for healthy subjects from existing databases and transferred this knowledge for new disease classification tasks. We developed a deep transfer learning neural network (DTL-NN) framework for enhancing the classification of whole brain functional connectivity patterns. Briefly, we trained a stacked sparse autoencoder (SSAE) prototype to learn healthy functional connectivity patterns in an offline learning environment. Then, the SSAE prototype was transferred to a DTL-NN model for a new classification task. To test the validity of our framework, we collected resting-state functional MRI data from the Autism Brain Imaging Data Exchange (ABIDE) repository. Using autism spectrum disorder (ASD) classification as a target task, we compared the performance of our DTL-NN approach with a traditional deep neural network and support vector machine models across four ABIDE data sites that enrolled at least 60 subjects. As compared to traditional models, our DTL-NN approach achieved an improved performance in accuracy, sensitivity, specificity and area under receiver operating characteristic curve. These findings suggest that DTL-NN approaches could enhance disease classification for neurological conditions, where accumulating large neuroimaging datasets has been challenging.

Measurements of human brain function in children are of increasing interest in cognitive neuroscience. Many techniques for brain mapping used in children, including functional near-infrared spectroscopy (fNIRS), electroencephalography (EEG), magnetoencephalography (MEG) and transcranial magnetic stimulation (TMS), use probes placed on or near the scalp. The distance between the scalp and the brain is a key variable for these techniques because optical, electrical and magnetic signals are attenuated by distance. However, little is known about how scalp-brain distance differs between different cortical regions in children or how it changes with development. We investigated scalp-brain distance in 71 children, from newborn to age 12 years, using structural T1-weighted MRI scans of the whole head. Three-dimensional reconstructions were created from the scalp surface to allow for accurate calculation of brain-scalp distance. Nine brain landmarks in different cortical regions were manually selected in each subject based on the published fNIRS literature. Significant effects were found for age, cortical region and hemisphere. Brain-scalp distances were lowest in young children, and increased with age to up to double the newborn distance. There were also dramatic differences between brain regions, with up to 50% differences between landmarks. In frontal and temporal regions, scalp-brain distances were significantly greater in the right hemisphere than in the left hemisphere. The largest contributors to developmental changes in brain-scalp distance were increases in the corticospinal fluid (CSF) and inner table of the cranium. These results have important implications for functional imaging studies of children: age and brain-region related differences in fNIRS signals could be due to the confounding factor of brain-scalp distance and not true differences in brain activity.

OBJECTIVE. Our goal was to relate postnatal dexamethasone therapy in extremely low birth weight infants (birth weight of ≤1000 g) to their total and regional brain volumes, as measured by volumetric MRI performed at term-equivalent age.METHODS. Among 53 extremely low birth weight infants discharged between June 1 and December 31, 2003, 41 had high-quality MRI studies; 30 of those infants had not received postnatal steroid treatment and 11 had received dexamethasone, all after postnatal age of 28 days, for a mean duration of 6.8 days and a mean cumulative dose of 2.8 mg/kg. Anatomic brain MRI scans obtained at 39.5 weeks (mean) postmenstrual age were segmented by using semiautomated and manual, pretested, scoring algorithms to generate three-dimensional cerebral component volumes. Volumes were adjusted according to postmenstrual age at MRI.RESULTS. After controlling for postmenstrual age at MRI, we observed a 10.2% smaller total cerebral tissue volume in the dexamethasone-treated group, compared with the untreated group. Cortical tissue volume was 8.7% smaller in the treated infants, compared with untreated infants. Regional volume analysis revealed a 20.6% smaller cerebellum and a 19.9% reduction in subcortical gray matter in the dexamethasone-treated infants, compared with untreated infants. In a series of regression analyses, the reductions in total cerebral tissue, subcortical gray matter, and cerebellar volumes associated with dexamethasone administration remained significant after controlling not only for postmenstrual age but also for bronchopulmonary dysplasia and birth weight.CONCLUSIONS. We identified smaller total and regional cerebral tissue volumes in extremely low birth weight infants treated with relatively conservative regimens of dexamethasone. These volume deficits may be the structural antecedents of neuromotor and cognitive abnormalities reported after postnatal dexamethasone treatment.