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Individualised Clinical Neuroimaging in the Developing Brain: Abnormality Detection

发育中大脑的个体化临床神经影像：异常检测

基本信息

批准号：
2338628
负责人：
金额：
--
依托单位：
King's College London
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2018
资助国家：
英国
起止时间：
2018 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2338628
关键词：
Individualised Clinical Neuroimaging Developing Brain

项目摘要

The aim of this study is to be able to provide individualised markers of brain tissue abnormalities in complex disorders of neurodevelopment such as childhood epilepsy. This will not only provide treatment targets, it will guide individually tailored interventions (i.e. precision medicine). Detecting these markers using neuroimaging methods is hampered by the rapid developmental changes in the brain throughout infancy. In practice, this means that sensitivity to neuroanatomical changes in childhood disorders can vary depending on what age the child is. This heterogeneity can either mask true changes in clinical studies and trials or bias studies towards very circumscribed age-ranges. This project will develop and apply machine learning techniques to model typical brain development using large MRI datasets of >2000 infants, and new data collected in children with epilepsy. The resulting model will be used to maximise detection of abnormal tissue in individual children. This will include the creation of a 4D atlas of the developing brain from 28 weeks PMA (foetal) to 52 weeks PMA. Regression based algorithms will allow the creation of an individual atlas at any age within the first year PMM and be able incorporate the rapid growth and extensive changes during this period. Statistical/machine learning methods will be used to produce probability maps for the whole brain, regional and voxel locations for each template. These can then be used in conjunction with target scans to provide individual analysis of subject brains and assessment of abnormalities; i.e. variations outside the expected norm for each voxel and/or region.

这项研究的目的是能够为复杂的神经发育障碍（如儿童癫痫）提供脑组织异常的个体化标记。这不仅将提供治疗目标，还将指导量身定制的干预措施（即精准医疗）。在整个婴儿期，大脑的快速发育变化阻碍了使用神经成像方法检测这些标记。在实践中，这意味着对儿童疾病的神经解剖学变化的敏感性可能因儿童的年龄而异。这种异质性既可以掩盖临床研究和试验的真实变化，也可以使研究偏向于非常有限的年龄范围。该项目将开发和应用机器学习技术，利用bbbb2000婴儿的大型MRI数据集和癫痫儿童收集的新数据来模拟典型的大脑发育。由此产生的模型将用于最大限度地检测个别儿童的异常组织。这将包括创建一个从预产期28周（胎儿）到预产期52周的大脑发育4D图谱。基于回归的算法将允许在PMM的第一年创建任何年龄的单独地图集，并能够纳入这一时期的快速增长和广泛变化。统计/机器学习方法将用于为每个模板生成整个大脑、区域和体素位置的概率图。然后，这些可以与目标扫描结合使用，提供受试者大脑的个体分析和异常评估；即，超出每个体素和/或区域的预期规范的变化。