Exploring early fetal brain development: a deep learning approach

博士项目的目的：在这个项目中，我们将开发深度学习图像分析工具，以了解多模态MRI所描绘的早期大脑发育。我们将开发方法来描绘短暂的和新兴的大脑结构，并在平均健康的大脑发育以及个人水平上观察它们的微观结构。我们将提取正常和异常早期大脑发育的相关生物标志物，包括已知与癫痫和自闭症谱系障碍有关的解剖学。项目描述/背景：在妊娠的后半段，大脑经历快速发育，包括白色物质束的形成，髓鞘形成和皮质折叠的开始（图1）。了解这些发育过程的精确时间和变化将有助于了解各种疾病的起源，例如自闭症谱系障碍，癫痫以及先天性异常或子宫内感染的影响。使用多模态MRI的神经成像可以提供对胎儿大脑发育的见解[1，2]。然而，对母亲体内移动的胎儿进行成像是困难的，导致图像质量变化，并且快速的大脑发育干扰了对胎儿大脑MRI图像的解释，需要专门的胎儿图像分析技术[3]。在这个项目中，我们建议构建深度学习工具，专门用于分析通过多模态MRI观察到的快速早期胎儿大脑发育。我们将建立在开发人类连接组项目的基础上，该项目产生了最先进的运动校正多模态胎儿MRI的大型数据库[4]。我们将使用人工智能来检测和描绘快速发展的瞬态胎儿结构，通过融合从结构和扩散MRI提取的形态，微观结构和连接信息。获得定量指标，包括体积、形状和微观结构，以准确地对胎儿大脑发育进行分期开发用于胎儿MRI图像增强的先进深度学习技术，以减少伪影，并对个体婴儿进行可靠的定量评估获得海马等大脑区域结构发育的定量指标（旋转/折叠指数）和大脑外侧裂（盖指数），癫痫病的病因有哪些？时间深度学习模型来预测ASD和早产的风险，并解释这些模型，以找到诊断这些疾病的多模态MRI生物标志物。

Aim of the PhD Project:In this project we will develop deep learning image analysis tools to enable understanding of early brain development as depicted by multi-modal MRI. We will develop methods to delineate transient and emerging brain structures and characterise their microstructure in average healthy brain development as well as on individual level. We will extract relevant biomarkers of normal and abnormal early brain development, including anatomy known to be involved in epilepsy and autism spectrum disorder. Project Description / Background:During the second half of pregnancy the brain undergoes rapid development, including formation of white matter tracts, the onset of myelination and cortical folding (Figure 1). Understanding precise timing and variation of these developmental processes would shed light on origins of various conditions, such as Autism Spectrum Disorder, Epilepsy, and effects of congenital abnormalities or infection in in womb. Neuroimaging using multi-modal MRI can offer insights into fetal brain development [1,2]. However, imaging moving fetus inside the mother is difficult, resulting in variable image quality, and fast brain development interferes with interpretation of the fetal brain MRI images, requiring dedicated fetal image analysis techniques [3]. In this project we propose to build deep learning tools dedicated to analysis of rapid early fetal brain development as observed by multi-modal MRI. We will build on Developing Human Connectome project that produced a large database of state-of-the-art motion-corrected multi-modal fetal MRI [4]. We will use artificial intelligence to Detect and delineate rapidly evolving transient fetal structures, by fusing morphological, microstructural and connectivity information extracted from structural and diffusion MRI. Derive quantitative indices, including volumes, shape and microstructure to accurately stage the fetal brain development Develop advance deep learning techniques for fetal MRI image enhancement to reduce artefacts and enable reliable quantitative assessment of individual babies Derive quantitative indices of structural development for brain regions like hippocampus (rotation/folding indices) and Sylvian fissure (opercularisation indices), known to be involved in Epilepsy Develop spatio-temporal deep learning models to predict risk of ASD and preterm birth and interpret these models to find multi-modal MRI biomarkers that characterise these conditions.