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

使用机器学习和脑连接组对极早产儿认知缺陷进行早期预测

基本信息

批准号：
9759972
负责人：
Lili He
金额：
$ 23.85万
依托单位：
CINCINNATI CHILDRENS HOSP MED CTR
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-08 至 2020-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9759972
关键词：
5 year old Adult Age Artificial Intelligence Birth Brain Caring Child Child Rearing Childhood Acute Lymphocytic Leukemia Classification Clinical Cognitive Cognitive deficits Communication Data Diagnosis Diffusion Magnetic Resonance Imaging Discrimination Early Intervention Early identification Environmental Risk Factor Exhibits Funding Gestational Age Goals Human Image Individual Infant Institution Intervention Knowledge Learning Machine Learning Magnetic Resonance Imaging Medical Mind Modeling Multivariate Analysis Neonatal Neural Network Simulation Neurodevelopmental Deficit Neurodevelopmental Impairment Neurologic Neurologic Deficit Neuronal Plasticity Outcome Parents Pediatric Hospitals Performance Physiology Population Premature Infant Prognostic Marker Property Research Research Support Rest Scanning Scientist Socioeconomic Status Specificity Statistical Data Interpretation Structure System Techniques Testing Time United States Validation artificial neural network autoencoder base cognitive testing connectome connectome data disability high dimensionality high risk high risk infant interest life-long learning medical complication model development neonate neurodevelopment neuroimaging novel strategies outcome prediction prevent public health relevance relating to nervous system sex social

项目摘要

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.

项目概要/摘要神经发育障碍的高风险是父母和照顾者的主要担忧每年约有 22,000 名严重早产儿。（即孕周≤32周）美国出现认知缺陷。努力采取有针对性的干预措施来预防神经发育障碍由于我们目前无法在 3 至 5 岁之前诊断残疾，因此阻碍了后遗症的治疗。研究支持这样一种观点，即认知缺陷可能是由于神经连接和神经元的扰动造成的。最近对成人和年龄较大儿童的大脑连接组研究表明，网络异常。这些特性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。基于大脑连接组数据来预测各种神经系统疾病，但是这我们的初步研究表明，这一进展尚未完全推广到早产儿人群。使用基于机器学习的模型可以准确预测个体水平的认知缺陷我们对 91.3% 的极早产儿进行了正确分类。认知缺陷高风险婴儿的特异性为 90%，敏感性为 92.3%。开发一个强大的机器学习模型，可以分析大脑的整体结构和功能在足月 CA 获得的连接组数据可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大脑的整体结构和功能出生时的连接组特征可以预测极早产儿 2 岁时个体水平上的认知缺陷婴儿准确率大于90%，超过当前经典多元的表现检验中心假设的两个具体目标是：1）开发并实现一台机器。提取高级大脑连接组特征的学习模型，以及 2) 开发和验证机器学习预测认知缺陷的框架在完成第一个目标后，我们将开发大脑连接组，并提取最能代表大脑连接组的高维连接组特征。目标是，我们提出的机器学习模型将应用于预测认知缺陷（即 2 类分类）和 2 年 CA 连续量表（即回归）的认知得分。模型的区分度，我们还将在模型未使用的数据中验证其性能发展，并与当前传统的多元方法进行比较。意义重大，因为它将增加有关早产儿大脑连接组发育的科学知识婴儿并促进早期识别神经发育缺陷高风险婴儿，从而及时发现最佳认知结果的临床干预。