CORNET - Integrating top-down and bottom-up processing in the marmoset and macaque cortex

CORNET - 在狨猴和猕猴皮层中集成自上而下和自下而上的处理

基本信息

批准号：
287010018
负责人：
Professor Dr. Pascal Fries
金额：
--
依托单位：
Stem Cell and Brain Research Institute (SBRI)
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2015
资助国家：
德国
起止时间：
2014-12-31 至 2019-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/287010018?language=en
关键词：
CORNET Integrating top down bottom

项目摘要

The Kennedy-Knoblauch lab has established an extensive database of inter-areal pathways in the macaque cortex. This database enabled the development of a predictive, large-scale model of the cortex with numerous interesting features, including hierarchical organization (Markov et al., 2013b; Song et al., 2014). The Fries team is internationally known for developing cutting-edge technology for large-scale electrophysiology, leading to important findings on the interplay between neuronal dynamics and structural connectivity (Fries, 2009). Recently, the Fries team has developed dynamic causal models (DCMs) for looking at asymmetries in feedforward and feedback pathways in relation to predictive coding (Bastos et al., 2015a; Bastos et al., 2012). The teams of Fries and Kennedy-Knoblauch have collaborated to compare hierarchies in macaque visual cortex derived from structural and functional data (Bastos et al., 2015b). They showed that directed influences constrain a functional hierarchy with critical features of the structural hierarchy, while exhibiting task dependent dynamics. The present project will extend and deepen this collaboration, by combining our complementary skills to integrate large-scale structural and functional datasets in the smooth-brained marmoset. We will use identical retrograde tracer technology from our earlier work in macaques to derive a weighted and directed connectivity matrix for the marmoset cortex. Tracers will be injected in widely distributed sites of the marmoset cortex, with a particular focus on visual areas. The physiology of these areas will be characterized using high-density electrocorticography (hdECog, 200 electrodes/cm2). Tracer experiments will be carried out in animals that have undergone hdECog recording so as to co-register electrophysiological and anatomical maps. Data will be used to construct DCMs of the mechanics of visual predictive coding at an unprecedented level of detail. Developing a Bayesian framework for structural network completion will augment our anatomical data. We will improve existing algorithms for data completion and the estimation of uncertainty by incorporating distance and weight in our procedures. These procedures will be of critical importance for network completion in marmoset, macaque and mouse. These developments will allow us to refine and extend our existing macaque database, by increasing our cortical matrix from 91 to 131 areas. Additionally, refining our existing weights will enable flexible parcellation and improve specificity for correlation to electrophysiology and imaging data. The present proposal will investigate large-scale structural models in macaque, marmoset and mouse providing insight into how the EDR model scales with brain size. The weight-distance relationships in these three species will allow us to determine the scaling features of cortex and will have important consequences for extrapolating our present findings to the large human brain.

肯尼迪 - 诺布拉赫（Kennedy-Knoblauch）实验室已建立了猕猴皮层中全面途径的广泛数据库。该数据库实现了具有许多有趣的特征，包括等级组织的预测性大规模模型（Markov等，2013b; Song等，2014）。弗里斯队以开发用于大规模电生理学的尖端技术而闻名，这导致了关于神经元动力学和结构连接性之间相互作用的重要发现（Fries，2009）。最近，弗里斯团队开发了动态因果模型（DCMS），用于查看与预测编码有关的进料和反馈途径中的不对称（Bastos等，2015a; Bastos等，2012）。弗里斯和肯尼迪·斯诺布拉赫的团队合作比较了从结构和功能数据衍生的猕猴视觉皮层中的层次结构（Bastos等，2015b）。他们表明，指示的影响会以结构层次结构的关键特征来限制功能层次结构，同时表现出任务依赖性动态。本项目将通过结合我们的补充技能，以将大规模的结构和功能数据集整合在光滑脑的摩尔马斯群岛中，从而扩展和加深这种合作。我们将使用猕猴的早期工作中使用相同的逆行示踪技术来得出Marmoset Cortex的加权和定向连接矩阵。示踪剂将被注入广泛分布的Marmoset Cortex位置，特别关注视觉区域。这些区域的生理学将使用高密度的皮质学（HDECOG，200个电极/CM2）来表征。示踪剂实验将在经历HDECOG记录的动物中进行，以共同注册电生理图和解剖图。数据将用于以前所未有的细节级别构建视觉预测编码力学的DCM。为结构网络完成开发贝叶斯框架将增加我们的解剖学数据。我们将通过将距离和权重纳入我们的过程中来改善数据完成的现有算法和不确定性的估计。对于Marmoset，Macaque和Mouse的网络完成，这些过程至关重要。这些发展将使我们能够通过将皮质矩阵从91个区域增加到131个区域来完善和扩展现有的猕猴数据库。此外，精炼我们现有的权重将使灵活的分割并提高与电生理学和成像数据相关的特异性。本提案将研究猕猴，果果和小鼠中的大规模结构模型，以提供有关EDR模型如何缩放脑大小的洞察力。这三个物种的重量距离关系将使我们能够确定皮质的缩放特征，并将推断出我们目前的发现到大脑大脑的重要后果。