Hierarchical organization of cortical and thalamic connectivity Nature, 575, pages195–202(2019) Hierarchical organization of cortical and thalamic connectivity Julie A. Harris, et.al. Allen Institute for Brain Science, Seattle, WA, USA University of Washington, Department of Applied Mathematics, Seattle, WA, USA Laboratory of Systems Neuroscience, National Institute of Mental Health, Bethesda, MD, USA Wenzhou Medical University, Wenzhou, P. R. China [paraphrase] The mammalian cortex is a laminar structure containing many areas and cell types that are densely interconnected in complex ways, and for which generalizable principles of organization remain mostly unknown. Here we describe a major expansion of the Allen Mouse Brain Connectivity Atlas resource, involving around a thousand new tracer experiments in the cortex and its main satellite structure, the thalamus. We used Cre driver lines (mice expressing Cre recombinase) to comprehensively and selectively label brain-wide connections by layer and class of projection neuron. Through observations of axon termination patterns, we have derived a set of generalized anatomical rules to describe corticocortical, thalamocortical and corticothalamic projections. We have built a model to assign connection patterns between areas as either feedforward or feedback, and generated testable predictions of hierarchical positions for individual cortical and thalamic areas and for cortical network modules. Our results show that cell-class- specific connections are organized in a shallow hierarchy within the mouse corticothalamic network. Cognitive processes and voluntary control of behaviour originate in the cortex. To understand how incoming sensory information is processed and integrated with past experiences and current states in order to generate appropriate behaviour requires knowledge of the anatomical patterns and rules of connectivity between cortical areas. Connectomes—\complete descriptions of brain wiring—\exist at different levels of spatial granularity, from single cells to populations of cells and entire areas (micro-, meso-, and macro-scale). Common organizational features of macro- and meso-scale cortical connectivity have been distilled across data sets, often using graph theory approaches to describe network architecture. For example, cortical areas have unique patterns of connections (“fingerprints”), connection strengths follow a log-normal distribution spanning more than four orders of magnitude, and the organization of cortical areas is modular, with distinct modules corresponding to specific functions. The concept of hierarchical organization is important for understanding the cortex, and has inspired the development of neural network methods in deep machine learning. A hierarchy of cortical areas was first derived by mapping anatomical patterns of corticocortical (CC) connections onto feedforward and feedback directions. In primate, feedforward connections were characterized by dense axon terminations in layer (L)4 of the target area, and feedback connections as dense terminals in superficial and deep layers (avoiding L4). Differences in the layers of origin are also associated with feedforward and feedback connections. It is still unclear whether the concept of a cortical hierarchy, which has been derived largely from sensory systems, can be applied globally across the entire cortex, and how it arises from connections made by different classes of neurons. Each cortical region comprises distinct types of excitatory neurons that are largely organized by layers, but also by long-distance projection patterns: intratelencephalic (IT) in L2–L6, pyramidal tract (PT) in L5, and corticothalamic (CT) in L6. Thalamic nuclei make important contributions to cortical function. They serve as a “relay” for primary sensory information, and are well positioned to influence cortical information processing through reciprocal or transthalamic loops. Thalamocortical (TC) projection neurons are classified into three major classes: core, intralaminar, and matrix. Like CC projections, feedforward and feedback rules have been proposed for TC and CT projections. Core projections (to L4) are described as “driver” (feedforward) and matrix projections (to L1) as “modulator” (feedback). For CT connections, input from L6 is considered feedback, and from L5 feedforward. We hypothesize that a unifying hierarchical organization across the entire cortex and its major input structure, the thalamus, is governed by a set of anatomical rules for CC, CT and TC connections. By using diverse Cre driver mouse lines to selectively label cells from different cortical layers and classes, we have substantially expanded the Allen Mouse Brain Connectivity Atlas resource (http://connectivity.brain-map.org1), adding 1,256 new tracing experiments. Our findings follow analyses of projection patterns spanning nearly the entire mouse cortex and thalamus, and show how these patterns relate to layer and cell class. We test the above hypothesis by building a computational hierarchical model using anatomical rules derived from observations of axon termination patterns. Our results show that the mouse cortex and thalamus form an integrated hierarchical organization. We used a genetic viral tracing approach, building on our previously established whole- brain imaging and informatics pipeline, to map projections originating from unique cell populations in the same cortical area, and from distinct projection classes in the thalamus. Our study represents a big step towards a true mesoscale connectome. It will be informative for future connectome studies with more refined cell types and single cells, which will no doubt reveal additional principles of cell-type-specific brain connectivity.  With these mesoscale data, we derived several generalizable anatomical rules of cortical and thalamic connections, and tested whether the organizing principle of a hierarchy applies to mouse cortex and thalamus. The cortex is organized as a modular network, which provides a structural view of possible paths of information flow, but does not impose direction or order onto that flow. By contrast, a hierarchy implies that interareal connections belong to at least two general types: feedforward or feedback. Specific anatomical projection patterns were previously associated with information transmission in these directions in primate and rodent visual cortex. In our data, we observed many similar patterns. Two patterns that differed were the superficial layer projections (cluster 1) and the deep layer projections (cluster 9). Felleman and van Essen noted the occasional superficial-only pattern, but they called it feedback because it did not involve L4. Our results suggest this pattern is associated with feedforward. The strength and presence of projections between areas from the predominantly L4 Cre lines was also unexpected, given canonical circuit diagrams, and might be explained by varying degrees of layer selectivity. However, by reconstructing the complete dendritic and axonal morphology of single cells, we directly show that L4 neurons, even spiny stellate cells, can in fact have long-range projections. The hierarchy that we find is shallower than might have been expected, even with inclusion of thalamic regions. The difference between the lowest and the highest areas is less than two full levels, and the all-area hierarchy global score is at 19% between random and perfectly hierarchical. This might be characteristic of the mouse cortex, given its high connection density, particularly when considering all non-zero connection strengths. We did not explicitly include strengths in computing hierarchy, except that weak connections were removed. Notably, hierarchical position alone does not explain all of the connections of a given area. This complexity may be why some have argued that the concept of a hierarchy is overly simplistic for describing functional properties. Given the number of different connection types that arise from a single area, future computational models that incorporate more than feedforward and feedback labels will enable further insights into the organization of brain networks. Cortical hierarchies were previously derived from classic anterograde or retrograde tracing without cell-class resolution. Using Cre lines, we have mapped both layer of origin and target lamination pattern in the same experiment. We found that L2/3 and L4 neurons have predominantly feedforward layer projection patterns, whereas L5 and L6 neurons have both feedforward and feedback patterns. However, these general relationships depend on the specific source–target connection and Cre line. The Cre data set, with all this detail, produced the most robust hierarchy. However, our results from wild-type mice provide a solid benchmark for others interested in applying these hierarchical model algorithms to classic tracing data. The calculation of global hierarchy scores for other data sets will enable direct comparisons between species and quantitative assessments of how development or disease might affect hierarchical organization. Links to Consciousness Discussion