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.

Kennedy-Knoblauch 实验室建立了一个关于猕猴皮层区域间通路的广泛数据库。该数据库使得能够开发具有许多有趣特征的预测性大规模皮层模型，包括分层组织（Markov 等人，2013b；Song 等人，2014）。 Fries 团队因开发大规模电生理学尖端技术而享誉国际，在神经元动力学和结构连接之间的相互作用方面取得了重要发现（Fries，2009）。最近，Fries 团队开发了动态因果模型 (DCM)，用于研究与预测编码相关的前馈和反馈路径中的不对称性（Bastos 等人，2015a；Bastos 等人，2012）。 Fries 和 Kennedy-Knoblauch 团队合作比较了源自结构和功能数据的猕猴视觉皮层的层次结构（Bastos 等，2015b）。他们表明，定向影响限制了具有结构层次结构关键特征的功能层次结构，同时表现出任务依赖的动态。目前的项目将通过结合我们的互补技能来扩展和深化这种合作，以整合光滑大脑狨猴中的大规模结构和功能数据集。我们将使用与我们早期在猕猴身上的研究相同的逆行示踪技术来导出狨猴皮层的加权和定向连接矩阵。示踪剂将被注射到狨猴皮层广泛分布的部位，特别是视觉区域。这些区域的生理学特征将使用高密度皮质电图（hdECog，200 电极/cm2）进行表征。示踪剂实验将在经过 hdECog 记录的动物中进行，以便共同记录电生理和解剖图。数据将用于以前所未有的详细程度构建视觉预测编码机制的 DCM。开发用于结构网络完成的贝叶斯框架将增强我们的解剖数据。我们将通过在我们的程序中纳入距离和重量来改进现有的数据补全和不确定性估计算法。这些程序对于狨猴、猕猴和小鼠的网络完善至关重要。这些进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我们现有的猕猴数据库。此外，改进我们现有的权重将实现灵活的分割并提高与电生理学和成像数据相关的特异性。目前的提案将研究猕猴、狨猴和小鼠的大规模结构模型，以深入了解 EDR 模型如何随大脑尺寸变化。这三个物种的重量-距离关系将使我们能够确定皮层的尺度特征，并将对将我们目前的发现外推到人类大脑的结果产生重要影响。