CRCNS US-French Research Proposal: Architectural Principles and Predictive Modeling of the Mammalian Connectome

CRCNS 美法研究提案：哺乳动物连接组的架构原理和预测建模

基本信息

批准号：
1724297
负责人：
Zoltan Toroczkai
金额：
$ 53.42万
依托单位：
University of Notre Dame
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-10-01 至 2021-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1724297&HistoricalAwards=false
关键词：
CRCNS US French Research Proposal

项目摘要

This US-France collaborative project is aimed at discovering the fundamental properties of the structural/anatomical organization of cortical connections capable of supporting massive amounts of computations in the brain, despite the 100,000-fold variation in mass from the smallest mammals to the largest. Several independent empirical observations (such as sensory substitution experiments) suggest the existence of common network architectural principles in the mammalian cortex, critical for efficient and hierarchically modular information processing. Through capturing these fundamental structural and dynamical features in large-scale neuronal networks across several species, this project will help with our understanding of information processing in the human brain. It will also inform the emerging field of neuromorphic engineering, which focuses on bio-inspired computational devices. The outcomes of this project may also be relevant to neuro-degenerative diseases, given the growing evidence suggesting that disease progression often occurs via the breakdown of high-centrality, long-range connections between cortical areas, which will be characterized within this project.By extending empirical, consistent tract-tracing databases for the physical network of interareal cortical connections in the macaque and mouse (supplemented by dMRI tractography data) and exploiting recent discoveries related to the Exponential Distance Rule (EDR) (which has been empirically demonstrated in several mammals), this project aims to capture the network architectural invariants of the cortex. These invariants are graph theoretical properties of the connectome that are preserved across mammalian brains and across scales. Based on recent empirical evidence, the project puts forward the hypothesis that the EDR also plays a critical role in generating sparse encoding of highly correlated information streams in a scale-invariant manner, a hypothesis that will be tested within a predictive modeling approach. The work will also generate novel imputation algorithms suitable for dense networks and novel, efficient algorithms for comparing species connectomes, exploiting the spatial embeddedness of these networks.A companion project is being funded by the French National Research Agency (ANR).

这个US-France协作项目旨在发现能够支持大脑大量计算的皮质连接的结构/解剖组织的基本属性，尽管从最小的哺乳动物到最大的质量有100,000倍的质量变化。几种独立的经验观察（例如感觉替代实验）表明，哺乳动物皮质中存在共同的网络结构原理，对于有效且分层模块化信息处理至关重要。通过在几个物种的大规模神经元网络中捕获这些基本结构和动力学特征，该项目将有助于我们理解人脑中的信息处理。它还将为神经形态工程的新兴领域提供信息，该领域的重点是生物启发的计算设备。 The outcomes of this project may also be relevant to neuro-degenerative diseases, given the growing evidence suggesting that disease progression often occurs via the breakdown of high-centrality, long-range connections between cortical areas, which will be characterized within this project.By extending empirical, consistent tract-tracing databases for the physical network of interareal cortical connections in the macaque and mouse (supplemented by dMRI拖拉图数据）并利用与指数距离规则（EDR）（在几种哺乳动物中经验证明的）相关的最新发现，该项目旨在捕获皮质的网络体系结构不变性。这些不变的是连接组的图理论特性，它们保留在跨哺乳动物的大脑和范围内。根据最近的经验证据，该项目提出了以下假设：EDR在以规模不变的方式生成高度相关信息流的稀疏编码中起着至关重要的作用，该假设将在预测的建模方法中进行检验。这项工作还将生成适用于密集网络的新型归合算法和用于比较物种连接组的新型，有效的算法，利用这些网络的空间嵌入性。伴侣项目由法国国家研究机构（ANR）资助。