Neural Substrate of Outcomes after Neonatal Hypoxic Ischemic Encephalopathy

新生儿缺氧缺血性脑病后结局的神经基质

基本信息

批准号：
10577865
负责人：
Yangming Ou
金额：
$ 22.56万
依托单位：
BOSTON CHILDREN'S HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-03-01 至 2025-02-28
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10577865
关键词：
2 year old 3-Dimensional Adverse event Affect Age Agreement Algorithms Anatomy Apgar Score Area Under Curve Attention Basal Ganglia Biological Markers Birth Weight Brain Brain Diseases Brain Injuries Brain Neoplasms Brain region Caffeine Caring Cessation of life Clinical Clinical Data Cognitive Comprehension Computer software Corpus Callosum Corticospinal Tracts Cytidine Diphosphate Choline Data Data Set Fiber Funding Generations Hour Hyperbaric Oxygen Individual Infant Infant Development Injury Internal Capsule Language Left Lesion Machine Learning Magnetic Resonance Imaging Maps Measures Modeling Motor Multicenter Neonatal Research Network National Institute of Child Health and Human Development Neonatal Neonatal Brain Injury Neuroanatomy Newborn Infant Outcome Patients Pattern Population Predictive Value Reader Receiver Operating Characteristics Risk Sensory Site Specificity Symptoms System Techniques Technology Testing Thalamic structure Therapeutic Therapeutic Effect Therapeutic Trials Time United States National Institutes of Health Validation Work adverse outcome brain magnetic resonance imaging clinical research site connectome deep learning deep learning algorithm high risk improved innovation language outcome machine learning algorithm magnetic resonance imaging biomarker natural hypothermia neonatal brain neonatal hypoxic-ischemic brain injury neonate neural novel novel therapeutics outcome prediction post stroke postnatal potential biomarker sex somatosensory statistics stem cell therapy success survival outcome tool topiramate voice recognition

项目摘要

Abstract Neonatal brain injury caused by hypoxic ischemic encephalopathy (HIE) affects 1-5/1000 term-born infants. HIE often causes adverse outcomes, defined as death or cognitive Bayley Scales of Infant Development (cBSID)<85 by 2 years. Current therapeutic trials are testing whether late, deeper, and/or longer versions of the standard therapeutic hypothermia could further reduce the adverse 2-year outcome. However, therapeutic innovation is slow and inconclusive, because the exact neuroanatomic substrate (i.e., the anatomy that underlies outcomes) is unknown and there is a lack of a reliable tool to predict 2-year adverse outcomes after HIE. Understanding the neural substrate and hence prediction of the adverse 2-year outcome can: (a) before therapy in the neonatal stage, identify those patients at high risk to develop adverse 2-year outcomes, so our therapeutic trials can focus on those high-risk patients and avoid unnecessary therapies to low-risk patients; and (b) right after therapy, to evaluate therapeutic effects early (in the neonatal stage), avoiding the wait until 2 years to observe the outcome and therefore expediting therapeutic innovations. Brain magnetic resonance imaging (MRI) is being explored by NIH Neonatal Research Network (NRN) as a potential biomarker to quantify neural substrate and predict the 2- year adverse outcome. However, expert interpretation and scoring systems, the current norm, only consider a selected set of brain regions (e.g., thalamus, basal ganglia, internal capsules, and others usually along the corticospinal tract), and only consider MRI metrics, not optimally combining MRI with clinical variables. The recent rise of novel lesion-symptom mapping (LSM) technologies allows us to conduct the first study to quantify voxel-, region-, and fiber-wise neural substrate throughout the brain, in a much more comprehensive and multivariate manner than the NRN scoring systems. Our preliminary results using the first-generation of LSM (voxel-wise LSM, or V-LSM) have led to novel findings of new regions and left hemisphere dominance in the neural substrate that were previously not considered in the NRN scoring system. In this R21, our Aim 1 will further use the second-generation LSM (i.e., multivariate LSM, or M-LSM) and the third-generation LSM (i.e., connectome LSM, or C-LSM) to more comprehensively explore neural substrate underlying 2-year adverse HIE outcome. Our Aim 2 will propose a novel machine learning and deep learning algorithm, termed predictive LSM or P-LSM, to compare and combine the findings from three generations of LSM, and apply this novel algorithm to predict individual outcomes in HIE patients, without and with adding clinical variables (Aims 2a and 2b, respectively). Our overall hypothesis is that quantitative and thorough characterization of the neural substrate in our to-be-developed machine learning and deep learning framework can predict 2-year adverse HIE outcomes more accurately than the current NRN scores.

抽象的缺氧缺血性脑病 (HIE) 引起的新生儿脑损伤影响 1-5/1000 名足月出生的婴儿。哈伊通常会导致不良后果，定义为死亡或婴儿认知贝利发育量表 (cBSID)<85 2年。目前的治疗试验正在测试标准的后期、更深和/或更长的版本低温治疗可以进一步减少两年的不良后果。然而，治疗创新缓慢且不确定，因为确切的神经解剖学基础（即结果背后的解剖结构）尚不清楚，并且缺乏可靠的工具来预测 HIE 后 2 年的不良后果。了解神经基质以及因此对 2 年不良结果的预测可以： (a) 在新生儿治疗前阶段，识别出那些在 2 年中出现不良结果的高风险患者，以便我们的治疗试验可以重点关注针对高风险患者，避免对低风险患者进行不必要的治疗； (b) 治疗后立即尽早评估治疗效果（在新生儿阶段），避免等待 2 年才能观察结果从而加速治疗创新。脑磁共振成像（MRI）正在探索 NIH 新生儿研究网络 (NRN) 作为量化神经底物和预测 2- 的潜在生物标志物年不良结果。然而，专家解释和评分系统，目前的规范，只考虑选定的一组大脑区域（例如丘脑、基底神经节、内囊和通常沿着大脑的其他区域）皮质脊髓束），并且只考虑 MRI 指标，而不是 MRI 与临床变量的最佳结合。这最近兴起的新型病变症状图 (LSM) 技术使我们能够进行第一项研究来量化整个大脑的体素、区域和纤维神经基质，以更全面和更全面的方式与 NRN 评分系统相比，多变量方式。我们使用第一代 LSM 的初步结果（体素LSM，或V-LSM）导致了新区域的新发现，并在左半球中占据主导地位之前 NRN 评分系统中未考虑的神经底物。在这个 R21 中，我们的目标 1 将进一步使用第二代LSM（即多元LSM，或M-LSM）和第三代LSM（即，连接组 LSM 或 C-LSM）更全面地探索 2 年不良 HIE 背后的神经基质结果。我们的目标 2 将提出一种新颖的机器学习和深度学习算法，称为预测 LSM 或 P-LSM，比较并结合三代 LSM 的发现，并应用这种新颖的算法在不添加和添加临床变量的情况下预测 HIE 患者的个体结果（目标 2a 和 2b，分别）。我们的总体假设是，对神经基质的定量和彻底表征我们即将开发的机器学习和深度学习框架可以预测 2 年不良 HIE 结果比当前的 NRN 分数更准确。