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结构脑组织中神经发育多样性的生成网络模型。

A generative network model of neurodevelopmental diversity in structural brain organization.

机构信息

MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.

Department of Psychiatry, University of Cambridge, Cambridge, UK.

出版信息

Nat Commun. 2021 Jul 9;12(1):4216. doi: 10.1038/s41467-021-24430-z.

Abstract

The formation of large-scale brain networks, and their continual refinement, represent crucial developmental processes that can drive individual differences in cognition and which are associated with multiple neurodevelopmental conditions. But how does this organization arise, and what mechanisms drive diversity in organization? We use generative network modeling to provide a computational framework for understanding neurodevelopmental diversity. Within this framework macroscopic brain organization, complete with spatial embedding of its organization, is an emergent property of a generative wiring equation that optimizes its connectivity by renegotiating its biological costs and topological values continuously over time. The rules that govern these iterative wiring properties are controlled by a set of tightly framed parameters, with subtle differences in these parameters steering network growth towards different neurodiverse outcomes. Regional expression of genes associated with the simulations converge on biological processes and cellular components predominantly involved in synaptic signaling, neuronal projection, catabolic intracellular processes and protein transport. Together, this provides a unifying computational framework for conceptualizing the mechanisms and diversity in neurodevelopment, capable of integrating different levels of analysis-from genes to cognition.

摘要

大规模脑网络的形成及其不断完善是关键的发育过程,能够导致认知个体差异,并与多种神经发育状况相关。但是,这种组织是如何产生的,以及哪些机制驱动了组织的多样性?我们使用生成网络模型为理解神经发育多样性提供了一个计算框架。在这个框架内,宏观的大脑组织及其组织的空间嵌入是生成布线方程的涌现性质,该方程通过不断重新协商其生物成本和拓扑值来优化其连接性。控制这些迭代布线属性的规则受一组紧密框架参数的控制,这些参数的细微差异将网络的生长引导向不同的神经多样性结果。与模拟相关的基因的区域表达集中在与突触信号、神经元投射、分解代谢细胞内过程和蛋白质运输等主要参与的生物过程和细胞成分上。总的来说,这为理解神经发育的机制和多样性提供了一个统一的计算框架,能够整合从基因到认知的不同分析层次。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3047/8270998/d3785d07153c/41467_2021_24430_Fig1_HTML.jpg

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