Neonatal Brain Microstructure and Machine-Learning-Based Prediction of Early Language Development in Children Born Very Preterm.,Pediatric Neurology

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Neonatal Brain Microstructure and Machine-Learning-Based Prediction of Early Language Development in Children Born Very Preterm.
Pediatric Neurology ( IF 3.2 ) Pub Date : 2020-03-03 , DOI: 10.1016/j.pediatrneurol.2020.02.007
Rachel Vassar ₁ , Kornél Schadl ₂ , Katelyn Cahill-Rowley ₃ , Kristen Yeom ₄ , David Stevenson ₅ , Jessica Rose ₆

Affiliation

Department of Orthopaedic Surgery, Stanford University School of Medicine, Stanford, California; Neonatal Neuroimaging Research Laboratory, Stanford University School of Medicine, Stanford, California; Division of Pediatric Neurology, Department of Neurology, University of California San Francisco, San Francisco, California.
Department of Orthopaedic Surgery, Stanford University School of Medicine, Stanford, California; Neonatal Neuroimaging Research Laboratory, Stanford University School of Medicine, Stanford, California; Semmelweis University School of Medicine, Budapest, Hungary.
Department of Orthopaedic Surgery, Stanford University School of Medicine, Stanford, California; Neonatal Neuroimaging Research Laboratory, Stanford University School of Medicine, Stanford, California; Motion Analysis Laboratory, Lucile Packard Children's Hospital, Stanford, California; Department of Bioengineering, Stanford University, Stanford, California.
Division of Pediatric Neuroradiology, Department of Radiology, Stanford University, Stanford, California.
Neonatal Neuroimaging Research Laboratory, Stanford University School of Medicine, Stanford, California; Division of Pediatric Neonatology, Department of Pediatrics, Stanford University, Stanford, California.
Department of Orthopaedic Surgery, Stanford University School of Medicine, Stanford, California; Neonatal Neuroimaging Research Laboratory, Stanford University School of Medicine, Stanford, California; Motion Analysis Laboratory, Lucile Packard Children's Hospital, Stanford, California.

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 22 months 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.

中文翻译：

早产儿的新生儿脑微结构和基于机器学习的早期语言发展预测。

背景

与足月出生的儿童相比，极低出生体重的早产儿的语言障碍发生率更高。尽早发现有语言延迟风险的早产儿，对于指导最佳神经可塑性时的早期干预至关重要。这项研究检查了早产儿的近期结构性脑磁共振成像（MRI）和弥散张量成像（DTI）评估的白质微结构与早期语言发展的关系。

方法

从2010年至2011年，共招募了102名极低出生体重的新生儿（出生体重≤1500g，胎龄≤32周）。对近期结构性MRI进行了白质和小脑异常的评估。评估了DTI分数各向异性，平均扩散率，轴向扩散率和径向扩散率。在18到22个月的调整年龄下，使用贝利量表的婴幼儿发展等级III评估语言发展。具有留一法交叉验证和详尽特征选择的多元模型确定了三个最能预测语言功能的大脑区域。不同的逻辑回归模型预测了高危婴儿，这是由语言得分比平均水平低1 SD所定义的。

结果

在102名儿童中，有92名返回了神经发育测试。综合语言分数平均值±SD为89.0±16.0；92名儿童中有31名得分<85，其中92名得分中有15名得分<70，表明中度至重度延迟。小脑不对称患儿的接受语言评分较低（P = 0.016）。根据DTI上的区域白质微观结构预测了具有语言障碍高风险的婴儿，其中复合材料（89％，86％），表达性（100％，90％）和接受性语言（100％）具有较高的准确性（敏感性，特异性），90％）。