Antecedents of aberrant cognitive development in early life

早年认知发展异常的前因

• 批准号: 2339131
• 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-2339131
• 关键词: Antecedents; aberrant; cognitive; development; early

Aberrant neurodevelopment in the first two years of life is an early predictor of later negative psychiatric, social and educational outcome. Understanding how inter-individual differences in developmental trajectory occur is difficult however, with multiple social, clinical and genetic factors exerting an influence on the already complex process of early life brain maturation. To better understand the interplay between these factors I will primarily use data from the Developing Human Connection Project (dHCP). The dHCP has acquired multi-modal MRI images and associated demographic and clinical data from several hundred infants soon after birth, with subsequent neurodevelopmental assessment at 18 months. Although neonatal neuroimaging is no longer a young field there are remarkably few published studies which correlate neuroimaging phenotype in early life to later neurodevelopmental outcome. One possible reason for this is the difficulty in adjusting for the different social risk factors which contribute to early life outcome. During my project I will identify groups of higher and lower social risk individuals using data-driven methods in available demographic data. I will then seek to understand how social risk modulates the relationship between neuroimaging phenotype at birth, polygenic risk factors and later neurodevelopmental outcome. To do so, I will use methods that integrate complex multi-modal data, such as similarity network fusion (SNF) and non-negative matrix factorization (NNMF).

在生命的头两年异常的神经发育是一个早期的预测，后来的负面精神，社会和教育的结果。然而，理解个体间发育轨迹的差异是很困难的，因为多种社会，临床和遗传因素对已经复杂的早期大脑成熟过程产生影响。为了更好地理解这些因素之间的相互作用，我将主要使用来自发展人类连接项目（dHCP）的数据。dHCP已经从出生后不久的数百名婴儿中获得了多模态MRI图像和相关的人口统计学和临床数据，随后在18个月时进行了神经发育评估。虽然新生儿神经影像学不再是一个年轻的领域，但很少有研究表明早期的神经影像学表型与以后的神经发育结果相关。其中一个可能的原因是难以调整不同的社会风险因素，这些因素有助于早期生活的结果。在我的项目期间，我将使用可用人口统计数据中的数据驱动方法来识别较高和较低社会风险的人群。然后，我将试图了解社会风险如何调节出生时的神经影像表型，多基因风险因素和后来的神经发育结果之间的关系。为此，我将使用集成复杂多模态数据的方法，例如相似性网络融合（SNF）和非负矩阵分解（NNMF）。