Individualised Clinical Neuroimaging in the Developing Brain: Abnormality Detection

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性化标记。这不仅将提供治疗目标，还将指导个别量身定做的干预措施(即精确医学)。使用神经成像方法检测这些标记物受到整个婴儿期大脑快速发育变化的阻碍。在实践中，这意味着儿童障碍对神经解剖学变化的敏感度可能会因儿童的年龄而异。这种异质性要么掩盖了临床研究和试验的真实变化，要么使研究偏向非常有限的年龄范围。这个项目将开发和应用机器学习技术来模拟典型的大脑发育，使用2000名婴儿的大量MRI数据集，以及在癫痫儿童中收集的新数据。由此产生的模型将被用来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胎龄28周到胎龄52周的发育中的大脑的4D图谱。基于回归的算法将允许在PMM第一年内的任何年龄创建单独的地图集，并能够纳入这一时期的快速增长和广泛变化。将使用统计/机器学习方法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，以提供对受试者大脑的单独分析和对异常的评估；即，每个体素和/或区域超出预期标准的变化。

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.