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

肯尼迪-诺布劳赫实验室已经建立了一个广泛的猕猴皮质区域间通路数据库。该数据库使开发具有许多有趣特征的皮质的预测性大规模模型成为可能，包括层级组织(马尔科夫等人，2013b；Song等人，2014年)。Fries团队以开发用于大规模电生理学的尖端技术而闻名于世，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(Bastos等人，2015a；Bastos等人，2012年)。Fries和Kennedy-Knoblauch的团队合作比较了从结构和功能数据得出的猕猴视觉皮质的层次结构(Bastos等人，2015b)。他们表明，定向影响用结构层次的关键特征来约束功能层次，同时表现出任务依赖的动态。本项目将扩展和深化这一合作，结合我们的互补技能，将大规模结构和功能数据集整合到脑部光滑的绒猴中。我们将使用与我们早先在猕猴身上所做的工作相同的逆行示踪技术来推导出恒河猴皮质的加权和定向连接矩阵。示踪剂将被注射到广泛分布的绒猴皮质部位，特别是视觉区域。这些区域的生理特征将使用高密度皮层脑电图仪(hdECog，200个电极/平方厘米)进行表征。示踪剂实验将在已经进行hdECog记录的动物身上进行，以便共同记录电生理和解剖学地图。数据将被用来以前所未有的详细程度构建视觉预测编码机制的DCMS。开发用于结构网络完善的贝叶斯框架将增强我们的解剖数据。我们将通过在程序中加入距离和权重来改进现有的数据完成和不确定度估计算法。这些程序将对猴、猕猴和老鼠网络的完善至关重要。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，将我们的皮质矩阵从91个区域增加到131个区域。此外，改进我们现有的重量将实现灵活的分割，并提高与电生理和成像数据的相关性的特异性。目前的提案将研究猕猴、绒猴和小鼠的大规模结构模型，以深入了解EDR模型如何随大脑大小而变化。这三个物种的重量-距离关系将使我们能够确定皮质的比例特征，并将我们目前的发现外推到人脑。

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.