权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Integrating cognition and systems neuroscience to understand child development

整合认知和系统神经科学来了解儿童发展

基本信息

批准号：
MC_UU_00030/2
负责人：
Duncan Astle
金额：
$ 272.81万
依托单位：
University of Cambridge
依托单位国家：
英国
项目类别：
Intramural
财政年份：
2022
资助国家：
英国
起止时间：
2022 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=MC_UU_00030%2F2
关键词：
Integrating cognition systems neuroscience understand

项目摘要

The human brain has a characteristic organisation. This organisation emerges early in life, even before we are born, and continues to change through adolescence and into early adulthood. The emergence and continual refinement of brain networks, connecting different regions of the brain across large distances, allows for increasing ‘functional specialisation’. That is, these networks become more dedicated to particular cognitive processes. But this gradual process of network emergence is incredibly variable across individuals, and it is not clear why this diversity exists, what consequences it holds for cognition, or what factors might shape it over time. The purpose of this programme is to address these gaps in our understanding. We develop and apply multiple different analysis tools, including computational models inspired by artificial intelligence, and integrate these with brain imaging data. This combination allows us to unpack typical and atypical developmental processes. This allows us to address the following questions: Firstly, how do we capture the incredible variability in brain development that exists across childhood and adolescence, and does this variability map to established diagnostic categories? Secondly, can we build developmental models that formalise simple biological principles in order to capture complex developmental phenomena? Thirdly, can we use these models to bridge scales and species to establish fundamental and causal mechanisms that shape development?

人类的大脑有一个独特的组织。这种组织在生命早期就出现了，甚至在我们出生之前，并在青春期和成年早期继续变化。大脑网络的出现和不断完善，跨越很远的距离连接大脑的不同区域，允许增加“功能专门化”。也就是说，这些网络变得更加专注于特定的认知过程。但是，这种网络出现的渐进过程在个体之间是难以置信的变化，并且不清楚为什么这种多样性存在，它对认知有什么影响，或者什么因素可能随着时间的推移而形成它。本计划的目的是解决我们在理解上的这些差距。我们开发和应用多种不同的分析工具，包括受人工智能启发的计算模型，并将这些工具与脑成像数据相结合。这种结合使我们能够解开典型和非典型的发育过程。这使我们能够解决以下问题：首先，我们如何捕捉童年和青春期存在的大脑发育的不可思议的可变性，这种可变性是否映射到已建立的诊断类别？其次，我们能否建立发展模型，将简单的生物学原理形式化，从而捕捉复杂的发展现象？第三，我们能否利用这些模型来连接尺度和物种，以建立影响发展的基本和因果机制？