Early Prediction of Cognitive Deficits in Very Preterm Infants using Machine Learning and Brain Connectome

项目总结/摘要 神经发育障碍的高风险是父母和临床医生关心的主要问题。 早产儿每年大约有22,000名早产儿 (i.e.≤32周胎龄） 美国发展认知缺陷。 努力采取有针对性的干预措施， 我们目前无法在3至5岁之前诊断残疾，这阻碍了后遗症的诊断。 研究支持认知缺陷可能是由于神经连接的干扰， 通信最近对成年人和年龄较大的儿童的大脑连接体研究表明， 这些性质可用作预后生物标志物。其中许多研究都利用了机器学习模型 基于大脑连接体数据预测各种神经疾病，然而， 这方面的进展尚未完全扩大到早产儿。我们的初步研究表明， 使用基于机器学习的模型，可以在个人层面上准确预测认知缺陷。 在足月校正年龄（CA）的脑连接体特征。我们已经正确分类了91.3%的极早产儿 对认知缺陷高危婴儿的特异性为90%，敏感性为92.3%。我们的总体目标是 开发一个强大的机器学习模型，可以分析整合的结构和功能大脑 在足月CA时获得的连接体数据，以预测极早产儿后期的认知缺陷。 我们的中心假设是，机器学习技术分析整合的结构和功能的大脑 出生时的连接体特征可以预测极早产儿在个体水平上2年CA时的认知缺陷 婴儿的准确性大于90%，超过了目前经典的多变量 分析。测试中心假设的两个具体目标是：1）开发和实现一台机器 学习模型来提取高级大脑连接体特征，以及2）开发并验证机器学习 预测认知缺陷的框架。完成第一个目标后，我们将解释大脑连接体， 并提取最能代表大脑连接体的高维连接体特征。在第二 目的是，我们提出的机器学习模型将应用于预测认知缺陷（即2级） 分类）和连续量表上的认知得分（即，2年CA时的回归。量化 模型的区分度，我们还将验证其在不用于模型的数据中的性能 发展，并与目前的传统多元方法进行比较。拟议的研究是 意义重大，因为它将增加关于早产儿大脑连接体发育的科学知识。 婴儿和促进早期识别婴儿在高风险的神经发育缺陷，允许及时 临床干预以获得最佳认知结果。

Project Summary/Abstract The high risk of neurodevelopmental impairments is a major concern for parents and clinicians caring for premature babies. Annually, approximately 22,000 very preterm infants (i.e. ≤32 weeks gestational age) in the United States develop cognitive deficits. Efforts to target interventions to prevent neurodevelopmental sequelae are hampered by our current inability to diagnose disabilities before the age of 3 to 5 years. Research supports the notion that cognitive deficits may result from a perturbation of neural connection and communication. Recent brain connectome studies in adults and older children show that abnormal network properties are useful as prognostic biomarkers. Many of these studies have exploited machine learning models based on brain connectome data for the prediction of a variety of neurological conditions, however this progress has not been fully extended to the preterm population. Our preliminary studies suggest that early and accurate prediction of cognitive deficits at an individual level is possible using machine learning models based on brain connectome features at term corrected age (CA). We have correctly classified 91.3% of very preterm infants at high risk of cognitive deficits with 90% specificity and 92.3% sensitivity. Our overall objective is to develop a robust machine learning model that can analyze integrated structural and functional brain connectome data obtained at term CA to make a prediction of later cognitive deficits in very preterm infants. Our central hypothesis is that machine learning techniques analyzing integrated structural and functional brain connectome features at birth can predict cognitive deficits at 2 years CA at an individual level in very preterm infants with accuracy of greater than 90%, exceeding the performance of current classical multivariate analyses. The two specific aims to test the central hypothesis are: 1) Develop and implement a machine learning model to extract high-level brain connectome features and 2) Develop and validate a machine learning framework to predict cognitive deficits. On completion of the first aim, we will explicate the brain connectome, and extract high-dimensional connectome features that best represent the brain connectome. In the second aim, the machine learning model we proposed will be applied in predicting both cognitive deficit (i.e. 2-class classification) and cognitive scores on a continuous scale (i.e., regression) at 2 years CA. To quantify the model's discrimination, we will also validate its performance in data that are not used for the model development, and compare with the current conventional multivariate approach. The proposed research is significant because it will increase scientific knowledge about the developing brain connectome in very preterm infants and facilitate earlier identification of babies at high risk of neurodevelopmental deficits, allowing timely clinical interventions for optimal cognitive outcome.