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

• 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
• 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

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.

摘要 新生儿缺氧缺血性脑病(HIE)造成的新生儿脑损伤影响1-5/1000足月儿。HIE 通常导致不良后果，定义为婴儿死亡或认知贝利婴儿发育量表(CBSID) 两年前。目前的治疗试验正在测试标准的晚期、更深和/或更长版本 治疗性亚低温可进一步减少不良的2年预后。然而，治疗创新是 缓慢且不确定，因为确切的神经解剖学基础(即，作为结果基础的解剖学) 尚不清楚，而且缺乏可靠的工具来预测HIE后2年的不良后果。了解 神经底物和因此对不良2年结局的预测可以：(A)新生儿治疗前 阶段，确定那些有2年不良结局的高危患者，这样我们的治疗试验就可以专注于 并避免对低风险患者进行不必要的治疗；及(B)在治疗后立即 早期评估疗效(在新生儿期)，避免等到两年才观察结果 因此加快了治疗创新。脑部磁共振成像(MRI)正在被 NIH新生儿研究网络(NRN)作为一种潜在的生物标记物来量化神经底物和预测2- 一年的不利结果。然而，专家口译和评分系统，目前的规范，只考虑一个 选定的一组大脑区域(例如，丘脑、基底节、内囊和其他通常沿 皮质脊髓束)，并且只考虑MRI指标，而不是将MRI与临床变量进行最佳结合。这个 最近兴起的新的病变-症状标测(LSM)技术使我们能够进行第一项量化研究 整个大脑中的体素、区域和纤维神经基质，在一个更全面和 多变量方式优于NRN评分系统。我们使用第一代最小二乘法的初步结果 (体素LSM，或V-LSM)导致了新的发现，即新的区域和左半球在大脑 以前在NRN评分系统中没有考虑的神经基质。在这部R21中，我们的目标1将 进一步使用第二代LSM(即，多变量LSM，或M-LSM)和第三代LSM(即， Connectome LSM，或C-LSM)，以更全面地探索2年不良HIE背后的神经基质 结果。我们的目标2将提出一种新的机器学习和深度学习算法，称为预测最小二乘法 或者P-LSM，比较和结合三代LSM的结果，并应用这种新的算法 在没有和有增加临床变量的情况下预测HIE患者的个体结果(目标2a和2b， )。我们的总体假设是对神经底物的定量和彻底的描述 我们有待开发的机器学习和深度学习框架可以预测两年内HIE的不良结果 比目前的NRN分数更准确。