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

Kennedy-Knoblauch实验室已经建立了猕猴皮层区域间通路的广泛数据库。该数据库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 et al., 2013b; Song et al., 2014）。Fries团队因开发大规模电生理学的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通路的不对称性（Bastos等人，2015；Bastos等人，2012）。Fries和Kennedy-Knoblauch团队合作比较了从结构和功能数据中得出的猕猴视觉皮层的层次结构（Bastos et al., 2015b）。他们表明，定向影响约束了具有结构层次关键特征的功能层次，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，将大型结构和功能数据集整合到光滑大脑的狨猴中。我们将使用相同的逆行示踪技术，从我们早期在猕猴身上的工作中得到一个加权和定向的狨猴皮层连接矩阵。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。这些区域的生理学将使用高密度皮质电图（hdECog， 200个电极/cm2）进行表征。对hdECog记录的动物进行示踪实验，共同登记电生理图和解剖图。数据将用于构建视觉预测编码机制的dcm，其细节达到前所未有的水平。开发结构网络完成的贝叶斯框架将增加我们的解剖数据。我们将通过在我们的程序中加入距离和权重来改进现有的数据补全和不确定性估计算法。这些步骤将对狨猴、猕猴和小鼠的网络完成至关重要。这些发展将使我们能够完善和扩展我们现有的猕猴数据库，将我们的皮质基质从91个区域增加到131个区域。此外，改进我们现有的权重将实现灵活的包裹，并提高与电生理和成像数据相关的特异性。本提案将研究猕猴、狨猴和小鼠的大规模结构模型，以深入了解EDR模型如何随脑大小缩放。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，并将对我们目前的发现外推到大的人类大脑有重要的影响。

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.