Cortical layering is a hallmark of the mammalian neocortex and a major determinant of local synaptic circuit organization in sensory systems. In motor cortex, the laminar organization of cortical circuits has not been resolved, yet their input-output operations are crucial for motor control. Here, we developed a general approach for estimating layer-specific connectivity in cortical circuits, and applied it to mouse motor cortex. From these data we computed a laminar presynaptic→postsynaptic connectivity matrix, W post,pre , revealing a complement of stereotypic pathways dominated by layer 2 outflow to deeper layers. Network modeling predicted, and experiments with disinhibited slices confirmed, that stimuli targeting upper but not lower cortical layers effectively evoked network-wide events. Thus, in motor cortex, descending excitation from a preamplifier-like network of upper-layer neurons drives output neurons in lower layers. Our analysis provides a quantitative wiring diagram framework for further investigation of the excitatory networks mediating cortical mechanisms of motor control.Motor cortex is centrally involved in mammalian motor behavior and the cortical control of movement. Motor cortex has long been studied in terms of its motor output, and much is known about how corticospinal neuron activity correlates with movement. The large-scale networks linking motor cortex with other cortical and subcortical motor systems are also well characterized. Multiple long-range excitatory input pathways converge on primary somatic motor cortex (M1). These include corticocortical projections (carrying ipsi-and contralateral sensorimotor information), thalamocortical projections (carrying cerebellar and basal ganglionic information via the thalamic ventrolateral nucleus, as well as sensory and other information via multiple additional thalamocortical pathways), and neuromodulatory projections from brainstem and other areas (reviewed in 1 ). Major outputs from M1 include projections to spinal/bulbar motor centers, striatum, thalamus, subthalamus, red nucleus, and pons. All excitatory neurons in M1 are pyramidal neurons projecting to one or several of these long-distance targets. Corticofugal outflow to the pyramidal/extrapyramidal motor systems and thalamus originates primarily from cells in middle and deeper layers, while corticocortical connections arise from cells in both upper and deeper layers.

Summary A lack of methods for measuring the protein compositions of individual synapses in situ has so far hindered the exploration and exploitation of synapse molecular diversity. Here we describe the use of array tomography, a new high-resolution proteomic imaging method, to determine the composition of glutamate and GABA synapses in somatosensory cortex of Line-H-YFP Thy-1 transgenic mice. We find that virtually all synapses are recognized by antibodies to the presynaptic phosphoprotein synapsin I, while antibodies to 16 other synaptic proteins discriminate amongst 4 subtypes of glutamatergic synapses and GABAergic synapses. Cell-specific YFP expression in the YFP-H mouse line allows synapses to be assigned to specific presynaptic and postsynaptic partners and reveals that a subpopulation of spines on layer 5 pyramidal cells receives both VGluT1-subtype glutamatergic and GABAergic synaptic inputs. These results establish a means for the high-throughput acquisition of proteomic data from individual cortical synapses in situ.

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A major question in neuroscience is how diverse subsets of synaptic connections in neural circuits are affected by experience dependent plasticity to form the basis for behavioral learning and memory. Differences in protein expression patterns at individual synapses could constitute a key to understanding both synaptic diversity and the effects of plasticity at different synapse populations. Our approach to this question leverages the immunohistochemical multiplexing capability of array tomography (ATomo) and the columnar organization of mouse barrel cortex to create a dataset comprising high resolution volumetric images of spared and deprived cortical whisker barrels stained for over a dozen synaptic molecules each. These dataset has been made available through the Open Connectome Project for interactive online viewing, and may also be downloaded for offline analysis using web, Matlab, and other interfaces.

We describe a scalable database cluster for the spatial analysis and annotation of high-throughput brain imaging data, initially for 3-d electron microscopy image stacks, but for time-series and multi-channel data as well. The system was designed primarily for workloads that build connectomes— neural connectivity maps of the brain—using the parallel execution of computer vision algorithms on high-performance compute clusters. These services and open-science data sets are publicly available at openconnecto.me. The system design inherits much from NoSQL scale-out and data-intensive computing architectures. We distribute data to cluster nodes by partitioning a spatial index. We direct I/O to different systems—reads to parallel disk arrays and writes to solid-state storage—to avoid I/O interference and maximize throughput. All programming interfaces are RESTful Web services, which are simple and stateless, improving scalability and usability. We include a performance evaluation of the production system, highlighting the effec-tiveness of spatial data organization.