Supervised contrastive learning enhances graph convolutional networks for predicting neurodevelopmental deficits in very preterm infants using brain structural connectome.

Very preterm (VPT) infants (born at less than 32 weeks gestational age) are at high risk for various adverse neurodevelopmental deficits. Unfortunately, most of these deficits cannot be accurately diagnosed until the age of 2-5 years old. Given the benefits of early interventions, accurate diagnosis and prediction soon after birth are urgently needed for VPT infants. Previous studies have applied deep learning models to learn the brain structural connectome (SC) to predict neurodevelopmental deficits in the preterm population. However, none of these models are specifically designed for graph-structured data, and thus may potentially miss certain topological information conveyed in the brain SC. In this study, we aim to develop deep learning models to learn the SC acquired at term-equivalent age for early prediction of neurodevelopmental deficits at 2 years corrected age in VPT infants. We directly treated the brain SC as a graph, and applied graph convolutional network (GCN) models to capture complex topological information of the SC. In addition, we applied the supervised contrastive learning (SCL) technique to mitigate the effects of the data scarcity problem, and enable robust training of GCN models. We hypothesize that SCL will enhance GCN models for early prediction of neurodevelopmental deficits in VPT infants using the SC. We used a regional prospective cohort of ∼280 VPT infants who underwent MRI examinations at term-equivalent age from the Cincinnati Infant Neurodevelopment Early Prediction Study (CINEPS). These VPT infants completed neurodevelopmental assessment at 2 years corrected age to evaluate cognition, language, and motor skills. Using the SCL technique, the GCN model achieved mean areas under the receiver operating characteristic curve (AUCs) in the range of 0.72∼0.75 for predicting three neurodevelopmental deficits, outperforming several competing models. Our results support our hypothesis that the SCL technique is able to enhance the GCN model in our prediction tasks.

Hypertensive Disorders of Pregnancy and Risk of Early Brain Abnormalities on Magnetic Resonance Imaging at Term among Infants Born at ≤32 Weeks' Gestational Age.

OBJECTIVE: To evaluate the proximal effects of hypertensive disorders of pregnancy (HDP) on a validated measure of brain abnormalities in infants born at ≤32 weeks' gestational age (GA) using magnetic resonance imaging at term-equivalent age. STUDY DESIGN: In a multisite prospective cohort study, 395 infants born at ≤32 weeks' GA, underwent 3T magnetic resonance imaging scan between 39 and 44 weeks' postmenstrual age. A single neuroradiologist, blinded to clinical history, evaluated the standardized Kidokoro global brain abnormality score as the primary outcome. We classified infants as HDP-exposed by maternal diagnosis of chronic hypertension, gestational hypertension, pre-eclampsia, or eclampsia. Linear regression analysis identified the independent effects of HDP on infant brain abnormalities, adjusting for histologic chorioamnionitis, maternal smoking, antenatal steroids, magnesium sulfate, and infant sex. Mediation analyses quantified the indirect effect of HDP mediated via impaired intrauterine growth and prematurity and remaining direct effects on brain abnormalities. RESULTS: A total of 170/395 infants (43%) were HDP-exposed. Adjusted multivariable analyses revealed HDP-exposed infants had 27% (95% CI 5%-53%) higher brain abnormality scores than those without HDP exposure (P = .02), primarily driven by increased white matter injury/abnormality scores (P = .01). Mediation analyses showed HDP-induced impaired intrauterine growth significantly (P = .02) contributed to brain abnormality scores (22% of the total effect). CONCLUSIONS: Maternal hypertension independently increased the risk for early brain injury and/or maturational delays in infants born at ≤32 weeks' GA with an indirect effect of 22% resulting from impaired intrauterine growth. Enhanced prevention/treatment of maternal hypertension may mitigate the risk of infant brain abnormalities and potential neurodevelopmental impairments.

Prenatal Opioid Exposure and Risk for Adverse Brain and Motor Outcomes in Infants Born Premature.

OBJECTIVE: To compare brain magnetic resonance imaging (MRI) biomarkers and neurodevelopmental test scores in infants born preterm with and without prenatal opioid exposure (POE). STUDY DESIGN: We examined 395 preterm infants (≤32 weeks gestational age) who had term-equivalent brain MRIs, composite scores from the Bayley Scales of Infant and Toddler Development-III at 2 years corrected age, and POE data. MRI parameters included total/regional brain volumes and severe punctate white matter lesions (PWMLs). We conducted bivariable analysis and multivariable logistic regression analyses. RESULTS: The mean ± SD gestational age was 29.3 ± 2.5 weeks; 35 (8.9%) had POE and 20 (5.1%) had severe PWML. Compared with unexposed infants, those with POE exhibited higher rates of severe PWML (17.1% vs 3.9%, respectively; P = .002); findings remained significant with an OR of 4.16 (95% CI, 1.26-13.68) after adjusting for confounders. On mediation analysis, the significant relationship between POE and severe PWML was not indirectly mediated through preterm birth/gestational age (OR, 0.93; 95% CI, 0.78-1.10), thus suggesting the association was largely driven by a direct adverse effect of POE on white matter. In multivariable analyses, POE was associated with a significantly lower score by -6.2 (95% CI, -11.8 to -0.6) points on the Bayley Scales of Infant and Toddler Development-III Motor subscale compared with unexposed infants. CONCLUSIONS: POE was associated with severe PWML; this outcome may be a direct effect of POE rather than being mediated by premature birth. POE was also associated with worse motor development. Continued follow-up to understand the long-term effects of POE is warranted.

Feasibility and acceptability of an online parenting intervention to address behaviour problems in moderately to extremely preterm pre-school and school-age children.

BACKGROUND: Preterm birth is associated with adverse mental health outcomes, including internalizing problems, social difficulties and inattention. Interventions are needed beyond infancy and toddlerhood to support children and their families. We examined the feasibility and acceptability of the I-InTERACT Preterm pilot study, an online parenting intervention for preterm children ages 3-8. METHOD: Families participated in a weekly intervention comprised of seven sessions with online modules followed by videoconference coaching sessions with a therapist. Following completion of the study, caregivers completed a survey to assess their satisfaction and were asked to participate in a voluntary semi-structured interview to provide feedback. We anticipated greater than a 50% participation rate (enrollment feasibility) and 75% completion rate (adherence feasibility). We also hypothesized that at least 80% of participants would be satisfied with the intervention (acceptability). RESULTS: Nineteen of 32 families (59%) enrolled in the study, suggesting adequate enrollment feasibility. Feasibility of programme completion (adherence) was lower than anticipated (59%). Regarding satisfaction, all caregivers agreed that the programme's information was relevant to them and their family. Nearly all participants (92%) indicated that they had a better understanding of the effects of preterm birth on behaviour, that they enjoyed the programme, that it met their expectations and that they recommend the programme to others. In qualitative interviews, caregivers expressed satisfaction with the content, skills they learned, and receiving direct coaching. Caregivers suggested improvements to increase intervention feasibility and skill implementation, including offering biweekly sessions and more hands-on coaching. CONCLUSION: Our largely satisfactory acceptability rates suggest the value of and need for a parenting intervention for children born preterm past the initial period of early development. Future directions include modifying the intervention in response to caregiver feedback to improve recruitment, engagement and adherence.

Association of enlarged extra-axial spaces and subdural hemorrhage in preterm infants at term-equivalent age.

To determine the incidence of enlarged extra-axial space (EES) and its association with subdural hemorrhage (SDH) in a regional cohort of preterm infants. As part of a prospective cohort study of 395 preterm infants, brain magnetic resonance imaging (MRI) was collected on each infant at term-equivalent age. Six preterm infants showed evidence of SDH. We reviewed the MRIs to identify the incidence of EES in these 6 infants and the cohort broadly. We then completed a retrospective chart review of the 6 infants to identify any concerns for non-accidental trauma (NAT) since the MRI was obtained. The incidence of SDH in the cohort was 1.6%. The incidence of EES was 48.1% including all 6 infants with SDH. The incidence of SDH in infants with EES was 3.2%. The retrospective chart review of the 6 infants did not yield any evidence of NAT. The incidence of EES and SDH in our cohort was significantly higher than similar cohorts of term infants, demonstrating an increased risk in preterm infants. The incidence of SDH in infants with EES was greater than in the total cohort, suggesting that it is a risk factor for asymptomatic SDH in preterm infants.

A novel collaborative self-supervised learning method for radiomic data.

The computer-aided disease diagnosis from radiomic data is important in many medical applications. However, developing such a technique relies on labeling radiological images, which is a time-consuming, labor-intensive, and expensive process. In this work, we present the first novel collaborative self-supervised learning method to solve the challenge of insufficient labeled radiomic data, whose characteristics are different from text and image data. To achieve this, we present two collaborative pretext tasks that explore the latent pathological or biological relationships between regions of interest and the similarity and dissimilarity of information between subjects. Our method collaboratively learns the robust latent feature representations from radiomic data in a self-supervised manner to reduce human annotation efforts, which benefits the disease diagnosis. We compared our proposed method with other state-of-the-art self-supervised learning methods on a simulation study and two independent datasets. Extensive experimental results demonstrated that our method outperforms other self-supervised learning methods on both classification and regression tasks. With further refinement, our method will have the potential advantage in automatic disease diagnosis with large-scale unlabeled data available.

Parenting Interventions Targeting Behavior for Children Born Preterm or Low Birth Weight: A Systematic Review.

OBJECTIVE: To systematically review and summarize the outcomes of parenting interventions designed to improve child and/or parenting behavior for children born preterm and/or low birth weight (LBW). METHODS: We conducted systematic searches of Embase, Scopus, PubMed, PsycInfo, and CINAHL in September 2021. We identified articles published at any time that describe the outcomes of parenting interventions targeting the child and/or parenting behavior of children born preterm/LBW and their caregivers. Two independent raters assessed the risk of bias using the Revised Cochrane Risk-of-Bias Tool. RESULTS: Eight hundred sixteen titles and abstracts were screened, followed by 71 full-text articles, resulting in 24 eligible articles reporting on nine interventions with 1,676 participants. Eligible articles had an adequate risk of bias ratings. Sample characteristics, intervention components, and intervention effects were tabulated and described narratively by the intervention type. Preventative and treatment programs demonstrated positive intervention effects on externalizing behavior, parenting stress, and parenting behaviors, with mixed effects on internalizing behavior and emotion regulation. The few studies with longitudinal follow-up found little evidence of effects beyond 6 months postintervention. CONCLUSION: Behavior problems in children born preterm/LBW may be modifiable, and interventions targeting parenting behavior are promising. However, existing interventions may not produce long-lasting changes and are not designed for children older than four. Existing treatment programs may require adaptation for the neurocognitive, medical, and family needs of children born preterm/LBW (e.g., processing speed deficits, post-traumatic stress). Interventions that account for theories of sustained change may promote long-term effectiveness and the developmental tailoring of parenting skills.

A novel Ontology-guided Attribute Partitioning ensemble learning model for early prediction of cognitive deficits using quantitative Structural MRI in very preterm infants.

Structural magnetic resonance imaging studies have shown that brain anatomical abnormalities are associated with cognitive deficits in preterm infants. Brain maturation and geometric features can be used with machine learning models for predicting later neurodevelopmental deficits. However, traditional machine learning models would suffer from a large feature-to-instance ratio (i.e., a large number of features but a small number of instances/samples). Ensemble learning is a paradigm that strategically generates and integrates a library of machine learning classifiers and has been successfully used on a wide variety of predictive modeling problems to boost model performance. Attribute (i.e., feature) bagging method is the most commonly used feature partitioning scheme, which randomly and repeatedly draws feature subsets from the entire feature set. Although attribute bagging method can effectively reduce feature dimensionality to handle the large feature-to-instance ratio, it lacks consideration of domain knowledge and latent relationship among features. In this study, we proposed a novel Ontology-guided Attribute Partitioning (OAP) method to better draw feature subsets by considering the domain-specific relationship among features. With the better-partitioned feature subsets, we developed an ensemble learning framework, which is referred to as OAP-Ensemble Learning (OAP-EL). We applied the OAP-EL to predict cognitive deficits at 2 years of age using quantitative brain maturation and geometric features obtained at term equivalent age in very preterm infants. We demonstrated that the proposed OAP-EL approach significantly outperformed the peer ensemble learning and traditional machine learning approaches.

Diffuse excessive high signal intensity in the preterm brain on advanced MRI represents widespread neuropathology.

Preterm brains commonly exhibit elevated signal intensity in the white matter on T2-weighted MRI at term-equivalent age. This signal, known as diffuse excessive high signal intensity (DEHSI) or diffuse white matter abnormality (DWMA) when quantitatively assessed, is associated with abnormal microstructure on diffusion tensor imaging. However, postmortem data are largely lacking and difficult to obtain, and the pathological significance of DEHSI remains in question. In a cohort of 202 infants born preterm at ≤32 weeks gestational age, we leveraged two newer diffusion MRI models - Constrained Spherical Deconvolution (CSD) and neurite orientation dispersion and density index (NODDI) - to better characterize the macro and microstructural properties of DWMA and inform the ongoing debate around the clinical significance of DWMA. With increasing DWMA volume, fiber density broadly decreased throughout the white matter and fiber cross-section decreased in the major sensorimotor tracts. Neurite orientation dispersion decreased in the centrum semiovale, corona radiata, and temporal lobe. These findings provide insight into DWMA's biological underpinnings and demonstrate that it is a serious pathology.

Acute histologic chorioamnionitis independently and directly increases the risk for brain abnormalities seen on magnetic resonance imaging in very preterm infants.

BACKGROUND: The independent risk for neurodevelopmental impairments attributed to chorioamnionitis in premature infants remains controversial. Delayed brain maturation or injury identified on magnetic resonance imaging at term-equivalent age can be used as a surrogate measure of neurodevelopmental impairments that is less confounded by postdelivery neonatal intensive care unit environmental factors to investigate this relationship more clearly. OBJECTIVE: This study aimed to determine whether preterm infants born with moderate to severe acute histologic chorioamnionitis would have a higher magnetic resonance imaging-determined global brain abnormality score, independent of early premature birth, when compared with preterm infants with no or mild chorioamnionitis. STUDY DESIGN: This was a prospective, multicenter cohort study involving infants born very prematurely ≤32 weeks' gestational age with acute moderate to severe histologic chorioamnionitis, graded using standard histologic criteria. Brain abnormalities were diagnosed and scored using a well-characterized, standardized scoring system captured using a high-resolution 3 Tesla magnetic resonance imaging research magnet. In secondary analyses, total brain volume and 4 magnetic resonance imaging metrics of cortical maturation (cortical surface area, sulcal depth, gyral index, and inner cortical curvature) were calculated using an automated algorithm and correlated with chorioamnionitis. The association of funisitis (any grade) with brain abnormalities was also explored. We investigated if premature birth mediated the relationship between histologic chorioamnionitis and brain abnormality score using mediation analysis. RESULTS: Of 353 very preterm infants, 297 infants had mild or no chorioamnionitis (controls), and 56 were diagnosed with moderate to severe acute histologic chorioamnionitis. The primary outcome brain abnormality score was significantly higher in histologic chorioamnionitis-exposed infants than in the controls (median, 4 vs 7; P<.001). Infants with acute histologic chorioamnionitis had significantly lower brain tissue volume (P=.03) and sulcal depth (P=.04), whereas other morphometric indices did not differ statistically. In the multiple regression analysis, we observed persistent significant relationships between moderate to severe acute histologic chorioamnionitis and brain abnormality scores (ß=2.84; 1.51-4.16; P<.001), total brain volume (P=.03), and sulcal depth (P=.02). Funisitis was also significantly associated with brain abnormality score after adjustment for clinical confounders (P=.005). Mediation analyses demonstrated that 50% of brain abnormalities was an indirect consequence of premature birth, and the remaining 50% was a direct effect of moderate to severe acute histologic chorioamnionitis when compared with preterm infants with no or mild chorioamnionitis exposure. Examining gestational age as a mediator, funisitis did not exert a significant direct effect on brain abnormalities after the significant indirect effects of preterm birth were accounted for. CONCLUSION: Acute histologic chorioamnionitis increases the risk for brain injury and delayed maturation, both directly and indirectly, by inducing premature birth.

Magnetic resonance spectroscopy brain metabolites at term and 3-year neurodevelopmental outcomes in very preterm infants.

BACKGROUND: Noninvasive advanced neuroimaging and neurochemical assessment can identify subtle abnormalities and predict neurodevelopmental impairments. Our objective was to quantify white matter metabolite levels and evaluate their relationship with neurodevelopmental outcomes at age 3 years. METHODS: Our study evaluated a longitudinal prospective cohort of very premature infants (<32 weeks gestational age) with single-voxel proton magnetic resonance spectroscopy from the centrum semiovale performed at term-equivalent age and standardized cognitive, verbal, and motor assessments at 3 years corrected age. We separately examined metabolite ratios in the left and right centrum semiovale. We also conducted an exploratory interaction analysis for high/low socioeconomic status (SES) to evaluate the relationship between metabolites and neurodevelopmental outcomes, after adjusting for confounders. RESULTS: We found significant relationships between choline/creatine levels in the left and right centrum semiovale and motor development scores. Exploratory interaction analyses revealed that, for infants with low SES, there was a negative association between choline/creatine in the left centrum semiovale and motor assessment scores at age 3 years. CONCLUSIONS: Brain metabolites from the centrum semiovale at term-equivalent age were associated with motor outcomes for very preterm infants at 3 years corrected age. This effect may be most pronounced for infants with low SES. IMPACT: Motor development at 3 years corrected age for very preterm infants is inversely associated with choline neurochemistry within the centrum semiovale on magnetic resonance spectroscopy at term-equivalent age, especially in infants with low socioeconomic status. No prior studies have studied metabolites in the centrum semiovale to predict neurodevelopmental outcomes at 3 years corrected age based on high/low socioeconomic status. For very preterm infants with lower socioeconomic status, higher choline-to-creatine ratio in central white matter is associated with worse neurodevelopmental outcomes.

A self-training deep neural network for early prediction of cognitive deficits in very preterm infants using brain functional connectome data.

BACKGROUND: Deep learning has been employed using brain functional connectome data for evaluating the risk of cognitive deficits in very preterm infants. Although promising, training these deep learning models typically requires a large amount of labeled data, and labeled medical data are often very difficult and expensive to obtain. OBJECTIVE: This study aimed to develop a self-training deep neural network (DNN) model for early prediction of cognitive deficits at 2 years of corrected age in very preterm infants (gestational age ≤32 weeks) using both labeled and unlabeled brain functional connectome data. MATERIALS AND METHODS: We collected brain functional connectome data from 343 very preterm infants at a mean (standard deviation) postmenstrual age of 42.7 (2.5) weeks, among whom 103 children had a cognitive assessment at 2 years (i.e. labeled data), and the remaining 240 children had not received 2-year assessments at the time this study was conducted (i.e. unlabeled data). To develop a self-training DNN model, we built an initial student model using labeled brain functional connectome data. Then, we applied the trained model as a teacher model to generate pseudo-labels for unlabeled brain functional connectome data. Next, we combined labeled and pseudo-labeled data to train a new student model. We iterated this procedure to obtain the best student model for the early prediction task in very preterm infants. RESULTS: In our cross-validation experiments, the proposed self-training DNN model achieved an accuracy of 71.0%, a specificity of 71.5%, a sensitivity of 70.4% and an area under the curve of 0.75, significantly outperforming transfer learning models through pre-training approaches. CONCLUSION: We report the first self-training prognostic study in very preterm infants, efficiently utilizing a small amount of labeled data with a larger share of unlabeled data to aid the model training. The proposed technique is expected to facilitate deep learning with insufficient training data.

Association between brain structural network efficiency at term-equivalent age and early development of cerebral palsy in very preterm infants.

Very preterm infants (born at less than 32 weeks gestational age) are at high risk for serious motor impairments, including cerebral palsy (CP). The brain network changes that antecede the early development of CP in infants are not well characterized, and a better understanding may suggest new strategies for risk-stratification at term, which could lead to earlier access to therapies. Graph theoretical methods applied to diffusion MRI-derived brain connectomes may help quantify the organization and information transfer capacity of the preterm brain with greater nuance than overt structural or regional microstructural changes. Our aim was to shed light on the pathophysiology of early CP development, before the occurrence of early intervention therapies and other environmental confounders, to help identify the best early biomarkers of CP risk in VPT infants. In a cohort of 395 very preterm infants, we extracted cortical morphometrics and brain volumes from structural MRI and also applied graph theoretical methods to diffusion MRI connectomes, both acquired at term-equivalent age. Metrics from graph network analysis, especially global efficiency, strength values of the major sensorimotor tracts, and local efficiency of the motor nodes and novel non-motor regions were strongly inversely related to early CP diagnosis. These measures remained significantly associated with CP after correction for common risk factors of motor development, suggesting that metrics of brain network efficiency at term may be sensitive biomarkers for early CP detection. We demonstrate for the first time that in VPT infants, early CP diagnosis is anteceded by decreased brain network segregation in numerous nodes, including motor regions commonly-associated with CP and also novel regions that may partially explain the high rate of cognitive impairments concomitant with CP diagnosis. These advanced MRI biomarkers may help identify the highest risk infants by term-equivalent age, facilitating earlier interventions that are informed by early pathophysiological changes.

Early micro- and macrostructure of sensorimotor tracts and development of cerebral palsy in high risk infants.

Infants born very preterm (VPT) are at high risk of motor impairments such as cerebral palsy (CP), and diagnosis can take 2 years. Identifying in vivo determinants of CP could facilitate presymptomatic detection and targeted intervention. Our objectives were to derive micro- and macrostructural measures of sensorimotor white matter tract integrity from diffusion MRI at term-equivalent age, and determine their association with early diagnosis of CP. We enrolled 263 VPT infants (≤32 weeks gestational age) as part of a large prospective cohort study. Diffusion and structural MRI were acquired at term. Following consensus guidelines, we defined early diagnosis of CP based on abnormal structural MRI at term and abnormal neuromotor exam at 3-4 months corrected age. Using Constrained Spherical Deconvolution, we derived a white matter fiber orientation distribution (fOD) for subjects, performed probabilistic whole-brain tractography, and segmented nine sensorimotor tracts of interest. We used the recently developed fixel-based (FB) analysis to compute fiber density (FD), fiber-bundle cross-section (FC), and combined fiber density and cross-section (FDC) for each tract. Of 223 VPT infants with high-quality diffusion MRI data, 14 (6.3%) received an early diagnosis of CP. The cohort's mean (SD) gestational age was 29.4 (2.4) weeks and postmenstrual age at MRI scan was 42.8 (1.3) weeks. FD, FC, and FDC for each sensorimotor tract were significantly associated with early CP diagnosis, with and without adjustment for confounders. Measures of sensorimotor tract integrity enhance our understanding of white matter changes that antecede and potentially contribute to the development of CP in VPT infants.

Associations Between Early Structural Magnetic Resonance Imaging, Hammersmith Infant Neurological Examination, and General Movements Assessment in Infants Born Very Preterm.

OBJECTIVE: To evaluate the prevalence and associations between structural magnetic resonance imaging (sMRI) injury/abnormality at term-equivalent age and absent fidgety General Movements Assessment (GMA) and abnormal Hammersmith Infant Neurological Examination (HINE) scores among infants born very preterm at 3-4 months of corrected age. STUDY DESIGN: This prospective cohort study enrolled 392 infants born ≤2 weeks of gestation from 5 neonatal intensive care units in the greater Cincinnati area between September 2016 and October 2019. Infants completed sMRI at term-equivalent age and GMA and HINE at 3-4 months of corrected age. All assessors were blinded. RESULTS: Of 392 infants, 375 (96%) had complete data. Of these, 44 (12%) exhibited moderate or severe brain abnormalities, 17 (4.5%) had abnormal GMA, and 77 (20.3%) had abnormal HINE. Global and regional abnormality scores on sMRI were significantly correlated with GMA (R2 range 0.05-0.17) and HINE at 3-4 months of corrected age (R2 range 0.01-0.17). These associations remained significant in multivariable analyses after adjusting for gestational age and sex. There was a significant but low correlation (R2 0.14) between GMA and HINE. CONCLUSIONS: We observed a low prevalence of moderate or severe brain abnormalities in survivors born very preterm in this geographically defined cohort. The much greater prevalence of abnormal motor examination on the HINE compared with GMA and their low correlation suggests that these tests evaluate different constructs and, thus, should be used in combination with sMRI rather than interchangeably.

Perinatal Risk and Protective Factors in the Development of Diffuse White Matter Abnormality on Term-Equivalent Age Magnetic Resonance Imaging in Infants Born Very Preterm.

OBJECTIVE: To identify perinatal clinical diseases and treatments that are associated with the development of objectively diagnosed diffuse white matter abnormality (DWMA) on structural magnetic resonance imaging (MRI) at term-equivalent age in infants born very preterm. STUDY DESIGN: A prospective cohort of 392 infants born very preterm (≤32 weeks of gestational age) was enrolled from 5 level III/IV neonatal intensive care units between September 2016 and November 2019. MRIs of the brain were collected at 39 to 45 weeks of postmenstrual age to evaluate DWMA volume. A predefined list of pertinent maternal characteristics, pregnancy/delivery data, and neonatal intensive care unit data were collected for enrolled patients to identify antecedents of objectively diagnosed DWMA. RESULTS: Of the 392 infants in the cohort, 377 (96%) had high-quality MRI data. Their mean (SD) gestational age was 29.3 (2.5) weeks. In multivariable linear regression analyses, pneumothorax (P = .027), severe bronchopulmonary dysplasia (BPD) (P = .009), severe retinopathy of prematurity (P < .001), and male sex (P = .041) were associated with increasing volume of DWMA. The following factors were associated with decreased risk of DWMA: postnatal dexamethasone therapy for severe BPD (P = .004), duration of caffeine therapy for severe BPD (P = .009), and exclusive maternal milk diet at neonatal intensive care unit discharge (P = .049). CONCLUSIONS: Severe retinopathy of prematurity and BPD exhibited the strongest adverse association with development of DWMA. We also identified treatments and nutritional factors that appear protective against the development of DWMA that also have implications for the clinical care of infants born very preterm.

Limitations of Conventional Magnetic Resonance Imaging as a Predictor of Death or Disability Following Neonatal Hypoxic-Ischemic Encephalopathy in the Late Hypothermia Trial.

OBJECTIVE: To investigate if magnetic resonance imaging (MRI) is an accurate predictor for death or moderate-severe disability at 18-22 months of age among infants with neonatal encephalopathy in a trial of cooling initiated at 6-24 hours. STUDY DESIGN: Subgroup analysis of infants ≥36 weeks of gestation with moderate-severe neonatal encephalopathy randomized at 6-24 postnatal hours to hypothermia or usual care in a multicenter trial of late hypothermia. MRI scans were performed per each center's practice and interpreted by 2 central readers using the Eunice Kennedy Shriver National Institute of Child Health and Human Development injury score (6 levels, normal to hemispheric devastation). Neurodevelopmental outcomes were assessed at 18-22 months of age. RESULTS: Of 168 enrollees, 128 had an interpretable MRI and were seen in follow-up (n = 119) or died (n = 9). MRI findings were predominantly acute injury and did not differ by cooling treatment. At 18-22 months, death or severe disability occurred in 20.3%. No infant had moderate disability. Agreement between central readers was moderate (weighted kappa 0.56, 95% CI 0.45-0.67). The adjusted odds of death or severe disability increased 3.7-fold (95% CI 1.8-7.9) for each increment of injury score. The area under the curve for severe MRI patterns to predict death or severe disability was 0.77 and the positive and negative predictive values were 36% and 100%, respectively. CONCLUSIONS: MRI injury scores were associated with neurodevelopmental outcome at 18-22 months among infants in the Late Hypothermia Trial. However, the results suggest caution when using qualitative interpretations of MRI images to provide prognostic information to families following perinatal hypoxia-ischemia. TRIAL REGISTRATION: Clinicaltrials.gov: NCT00614744.

Prenatal opioid exposure is associated with smaller brain volumes in multiple regions.

BACKGROUND: The impact of prenatal opioid exposure on brain development remains poorly understood. METHODS: We conducted a prospective study of term-born infants with and without prenatal opioid exposure. Structural brain MRI was performed between 40 and 48 weeks postmenstrual age. T2-weighted images were processed using the Developing Human Connectome Project structural pipeline. We compared 63 relative regional brain volumes between groups. RESULTS: Twenty-nine infants with prenatal opioid exposure and 42 unexposed controls were included. The groups had similar demographics, except exposed infants had lower birth weights, more maternal smoking and maternal Hepatitis C, fewer mothers with a college degree, and were more likely non-Hispanic White. After controlling for sex, postmenstrual age at scan, birth weight, and maternal education, exposed infants had significantly smaller relative volumes of the deep gray matter, bilateral thalamic ventrolateral nuclei, bilateral insular white matter, bilateral subthalamic nuclei, brainstem, and cerebrospinal fluid. Exposed infants had larger relative volumes of the right cingulate gyrus white matter and left occipital lobe white matter. CONCLUSIONS: Infants with prenatal opioid exposure had smaller brain volumes in multiple regions compared to controls, with two regions larger in the opioid-exposed group. Further research should focus on the relative contributions of maternal opioids and other exposures. IMPACT: Prenatal opioid exposure is associated with developmental and behavioral consequences, but the direct effects of opioids on the developing human brain are poorly understood. Prior small studies using MRI have shown smaller regional brain volumes in opioid-exposed infants and children. After controlling for covariates, infants with prenatal opioid exposure scanned at 40-48 weeks postmenstrual age had smaller brain volumes in multiple regions compared to controls, with two regions larger in the opioid-exposed group. This adds to the literature showing potential impact of prenatal opioid exposure on the developing brain.

Effects of intraventricular hemorrhage on white matter microstructural changes at term and early developmental outcomes in infants born very preterm.

PURPOSE: Very preterm (VPT) infants are at high risk for motor and behavioral deficits. We investigated microstructural differences using diffusion tensor imaging (DTI) among VPT infants with different grades of intraventricular hemorrhage (IVH), their association with early motor function and temperament ratings, and the potential moderating effect of IVH severity on the above structure-function relations. METHODS: Fifty-seven VPT (≤32 weeks gestational age) infants with IVH (Low Grade (Papile grading I/II): 42; High Grade (III/IV): 15) were studied. DTI was acquired between 39 and 44 weeks postmenstrual age and was analyzed using the tract-based spatial statistics approach. Early motor function and temperament were assessed at 3-month corrected age based on the Hammersmith Infant Neurological Examination (HINE) and Infant Behavioral Questionnaire - Revised, Short Version (IBQ-R-S), respectively. RESULTS: Significantly lower fractional anisotropy and higher mean, axial, and/or radial diffusivity were found in VPT infants with High Grade IVH compared to Low Grade IVH (p < 0.05). Significant associations were found between DTI metrics and motor function in both IVH groups and between DTI and Fear temperament ratings in the High Grade IVH Group (all p < 0.05). IVH severity had a significant moderating effect on the relation between DTI and motor and Fear ratings (p < 0.05). CONCLUSION: DTI is a sensitive neuroimaging biomarker providing a refined understanding of the impact and location of differing severities of IVH on the developing white matter of VPT infants. Early motor and behavioral outcomes are associated with microstructural changes that are influenced by severity of IVH.

DeepLiverNet: a deep transfer learning model for classifying liver stiffness using clinical and T2-weighted magnetic resonance imaging data in children and young adults.

BACKGROUND: Although MR elastography allows for quantitative evaluation of liver stiffness to assess chronic liver diseases, it has associated drawbacks related to additional scanning time, patient discomfort, and added costs. OBJECTIVE: To develop a machine learning model that can categorically classify the severity of liver stiffness using both anatomical T2-weighted MRI and clinical data for children and young adults with known or suspected pediatric chronic liver diseases. MATERIALS AND METHODS: We included 273 subjects with known or suspected chronic liver disease. We extracted data including axial T2-weighted fast spin-echo fat-suppressed images, clinical data (e.g., demographic/anthropomorphic data, particular medical diagnoses, laboratory values) and MR elastography liver stiffness measurements. We propose DeepLiverNet (a deep transfer learning model) to classify patients into one of two groups: no/mild liver stiffening (<3 kPa) or moderate/severe liver stiffening (≥3 kPa). We conducted internal cross-validation using 178 subjects, and external validation using an independent cohort of 95 subjects. We assessed diagnostic performance using accuracy, sensitivity, specificity and area under the receiver operating characteristic curve (AuROC). RESULTS: In the internal cross-validation experiment, the combination of clinical and imaging data produced the best performance (AuROC=0.86) compared to clinical (AuROC=0.83) or imaging (AuROC=0.80) data alone. Using both clinical and imaging data, the DeepLiverNet correctly classified patients with accuracy of 88.0%, sensitivity of 74.3% and specificity of 94.6%. In our external validation experiment, this same deep learning model achieved an accuracy of 80.0%, sensitivity of 61.1%, specificity of 91.5% and AuROC of 0.79. CONCLUSION: A deep learning model that incorporates clinical data and anatomical T2-weighted MR images might provide a means of risk-stratifying liver stiffness and directing the use of MR elastography.