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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.

Alfredy-Knoblauch实验室已经建立了一个广泛的猕猴皮层区域间通路数据库。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质模型，包括分层组织（Markov等人，2013 b; Song等人，2014年）。Fries团队以开发大规模电生理学的尖端技术而闻名，导致了关于神经元动力学和结构连接之间相互作用的重要发现（Fries，2009）。最近，Fries团队开发了动态因果模型（DCM），用于观察与预测编码相关的前馈和反馈途径中的不对称性（巴斯托斯等人，2015 a;巴斯托斯等人，2012年）。Fries和Schmiddy-Knoblauch的团队合作比较了来自结构和功能数据的猕猴视觉皮层中的层次结构（巴斯托斯等人，2015年b）。他们表明，直接影响约束的功能层次结构的关键功能层次结构，同时表现出任务依赖的动态。本项目将通过结合我们的互补技能来扩展和深化这种合作，以整合光滑大脑绒猴中的大规模结构和功能数据集。我们将使用相同的逆行示踪技术，从我们早期的工作在猕猴，以获得一个加权和定向的连接矩阵的绒猴皮层。示踪剂将注射到绒猴皮层的广泛分布部位，特别关注视觉区域。将使用高密度皮质电描记术（hdECog，200个电极/cm 2）表征这些区域的生理学。将在已进行hdECog记录的动物中进行示踪剂实验，以便共同配准电生理和解剖图。数据将被用来构建一个前所未有的细节水平的视觉预测编码机制的DCM。为结构网络完成开发贝叶斯框架将增加我们的解剖数据。我们将改进现有的算法，数据完成和估计的不确定性，将距离和重量在我们的程序。这些程序将是在绒猴，猕猴和小鼠的网络完成至关重要的。这些发展将使我们能够改进和扩展我们现有的猕猴数据库，将我们的皮质矩阵从91个增加到131个区域。此外，改进我们现有的权重将实现灵活的分组，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，绒猴和小鼠的大规模结构模型，以深入了解EDR模型如何与大脑大小进行缩放。这三个物种的体重-距离关系将使我们能够确定皮层的尺度特征，并将对我们目前的发现外推到大型人类大脑具有重要意义。