Neonatal Brain Microstructure and Machine-Learning-Based Prediction of Early Language Development in Children Born VeryPreterm.
BACKGROUND: Very-low-birth-weight preterm infants have a higher rate of language impairments compared with children born full term. Early identification of preterm infants at risk for language delay is essential to guide early intervention at the time of optimal neuroplasticity. This study examined near-term structural brain magnetic resonance imaging (MRI) and white matter microstructure assessed on diffusion tensor imaging (DTI) in relation to early language development in children born very preterm.METHODS: A total of 102 very-low-birth-weight neonates (birthweight?1500g, gestational age ?32-weeks) were recruited to participate from 2010 to 2011. Near-term structural MRI was evaluated for white matter and cerebellar abnormalities. DTI fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity were assessed. Language development was assessed with Bayley Scales of Infant-Toddler Development-III at 18 to 22months adjusted age. Multivariate models with leave-one-out cross-validation and exhaustive feature selection identified three brain regions most predictive of language function. Distinct logistic regression models predicted high-risk infants, defined by language scores >1 S.D. below average.RESULTS: Of 102 children, 92 returned for neurodevelopmental testing. Composite language score mean±S.D. was 89.0±16.0; 31 of 92 children scored <85, including 15 of 92 scoring<70, suggesting moderate-to-severe delay. Children with cerebellar asymmetry had lower receptive language subscores (P=0.016). Infants at high risk for language impairments were predicted based on regional white matter microstructure on DTI with high accuracy (sensitivity, specificity) for composite (89%, 86%), expressive (100%, 90%), and receptive language (100%, 90%).CONCLUSIONS: Multivariate models of near-term structural MRI and white matter microstructure on DTI may assist in identification of preterm infants at risk for language impairment, guiding early intervention.
View details for DOI 10.1016/j.pediatrneurol.2020.02.007
View details for PubMedID 32279900
- Prediction of Gait Impairment in Toddlers Born Preterm From Near-Term Brain Microstructure Assessed With DTI, Using Exhaustive Feature Selection and Cross-Validation FRONTIERS IN HUMAN NEUROSCIENCE 2019; 13
Prediction of Gait Impairment in Toddlers Born Preterm From Near-Term Brain Microstructure Assessed With DTI, Using Exhaustive Feature Selection and Cross-Validation.
Frontiers in human neuroscience
2019; 13: 305
To predict gait impairment in toddlers born preterm with very-low-birth-weight (VLBW), from near-term white-matter microstructure assessed with diffusion tensor imaging (DTI), using exhaustive feature selection, and cross-validation.Near-term MRI and DTI of 48 bilateral and corpus callosum regions were assessed in 66 VLBW preterm infants; at 18-22 months adjusted-age, 52/66 participants completed follow-up gait assessment of velocity, step length, step width, single-limb support and the Toddle Temporal-spatial Deviation Index (TDI). Multiple linear models with exhaustive feature selection and leave-one-out cross-validation were employed in this prospective cohort study: linear and logistic regression identified three brain regions most correlated with gait outcome.Logistic regression of near-term DTI correctly classified infants high-risk for impaired gait velocity (93% sensitivity, 79% specificity), right and left step length (91% and 93% sensitivity, 85% and 76% specificity), single-limb support (100% and 100% sensitivity, 100% and 100% specificity), step width (85% sensitivity, 80% specificity), and Toddle TDI (85% sensitivity, 75% specificity). Linear regression of near-term brain DTI and toddler gait explained 32%-49% variance in gait temporal-spatial parameters. Traditional MRI methods did not predict gait in toddlers.Near-term brain microstructure assessed with DTI and statistical learning methods predicted gait impairment, explaining substantial variance in toddler gait. Results indicate that at near term age, analysis of a set of brain regions using statistical learning methods may offer more accurate prediction of outcome at toddler age. Infants high risk for single-limb support impairment were most accurately predicted. As a fundamental element of biped gait, single-limb support may be a sensitive marker of gait impairment, influenced by early neural correlates that are evolutionarily and developmentally conserved. For infants born preterm, early prediction of gait impairment can help guide early, more effective intervention to improve quality of life.? Accurate prediction of toddler gait from near-term brain microstructure on DTI.? Use of machine learning analysis of neonatal neuroimaging to predict gait.? Early prediction of gait impairment to guide early treatment for children born preterm.
View details for DOI 10.3389/fnhum.2019.00305
View details for PubMedID 31619977
View details for PubMedCentralID PMC6760000
- Prediction of cognitive and motor development in preterm children using exhaustive feature selection and cross-validation of near-term white matter microstructure NEUROIMAGE-CLINICAL 2018; 17: 667?79
Prediction of cognitive and motor development in preterm children using exhaustive feature selection and cross-validation of near-term white matter microstructure.
2018; 17: 667?79
Advanced neuroimaging and computational methods offer opportunities for more accurate prognosis. We hypothesized that near-term regional white matter (WM) microstructure, assessed on diffusion tensor imaging (DTI), using exhaustive feature selection with cross-validation would predict neurodevelopment in preterm children.Near-term MRI and DTI obtained at 36.6 ± 1.8 weeks postmenstrual age in 66 very-low-birth-weight preterm neonates were assessed. 60/66 had follow-up neurodevelopmental evaluation with Bayley Scales of Infant-Toddler Development, 3rd-edition (BSID-III) at 18-22 months. Linear models with exhaustive feature selection and leave-one-out cross-validation computed based on DTI identified sets of three brain regions most predictive of cognitive and motor function; logistic regression models were computed to classify high-risk infants scoring one standard deviation below mean.Cognitive impairment was predicted (100% sensitivity, 100% specificity; AUC = 1) by near-term right middle-temporal gyrus MD, right cingulate-cingulum MD, left caudate MD. Motor impairment was predicted (90% sensitivity, 86% specificity; AUC = 0.912) by left precuneus FA, right superior occipital gyrus MD, right hippocampus FA. Cognitive score variance was explained (29.6%, cross-validated R?2 = 0.296) by left posterior-limb-of-internal-capsule MD, Genu RD, right fusiform gyrus AD. Motor score variance was explained (31.7%, cross-validated R?2 = 0.317) by left posterior-limb-of-internal-capsule MD, right parahippocampal gyrus AD, right middle-temporal gyrus AD.Search in large DTI feature space more accurately identified neonatal neuroimaging correlates of neurodevelopment.
View details for PubMedID 29234600