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

这个美法合作项目旨在发现皮层连接的结构/解剖组织的基本特性，这些结构/解剖组织能够支持大脑中大量的计算，尽管从最小的哺乳动物到最大的哺乳动物的质量相差10万倍。一些独立的经验观察（如感官替代实验）表明，哺乳动物皮层中存在共同的网络架构原则，这对于有效和分层模块化的信息处理至关重要。通过在多个物种的大规模神经网络中捕捉这些基本结构和动态特征，该项目将有助于我们理解人类大脑中的信息处理。它还将为新兴的神经形态工程领域提供信息，该领域专注于生物启发的计算设备。这个项目的结果也可能与神经退行性疾病有关，因为越来越多的证据表明，疾病的进展通常是通过皮层区域之间的高中心性、远距离连接的破坏而发生的，这将在这个项目中被描述。通过扩展猕猴和小鼠的区域间皮层连接物理网络的经验，一致的通道追踪数据库（辅以dMRI通道成像数据），并利用与指数距离规则（EDR）相关的最新发现（已在几种哺乳动物中得到实证证明），该项目旨在捕捉皮层的网络结构不变性。这些不变量是连接组的图理论性质，在哺乳动物的大脑和不同的尺度上都被保留下来。基于最近的经验证据，该项目提出了一个假设，即EDR在以尺度不变的方式生成高度相关信息流的稀疏编码方面也起着关键作用，这一假设将在预测建模方法中进行测试。这项工作还将产生适用于密集网络的新颖的输入算法，以及用于比较物种连接体的新颖、高效的算法，利用这些网络的空间嵌入性。法国国家研究机构（ANR）正在资助一个伙伴项目。

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).