Antecedents of aberrant cognitive development in early life

两岁前异常的神经发育是日后负面精神、社会和教育结果的早期预测指标。然而，了解发育轨迹的个体差异是如何发生的是困难的，多种社会、临床和遗传因素对生命早期大脑成熟已经复杂的过程产生影响。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际关系项目（dHCP）的数据。dHCP获得了数百名婴儿出生后不久的多模态MRI图像和相关的人口统计学和临床数据，并在18个月时进行了随后的神经发育评估。尽管新生儿神经影像学不再是一个年轻的领域，但很少有已发表的研究将早期生活中的神经影像学表型与后来的神经发育结果联系起来。造成这种情况的一个可能原因是难以调整影响早期生活结果的不同社会风险因素。在我的项目中，我将在可用的人口统计数据中使用数据驱动的方法识别社会风险较高和较低的人群。然后，我将试图了解社会风险如何调节出生时神经影像学表型、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，如相似网络融合（SNF）和非负矩阵分解（NNMF）。

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).