BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets.

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

Network-neuron interactions underlying sensory responses of layer 5 pyramidal tract neurons in barrel cortex.

Neurons in the cerebral cortex receive thousands of synaptic inputs per second from thousands of presynaptic neurons. How the dendritic location of inputs, their timing, strength, and presynaptic origin, in conjunction with complex dendritic physiology, impact the transformation of synaptic input into action potential (AP) output remains generally unknown for in vivo conditions. Here, we introduce a computational approach to reveal which properties of the input causally underlie AP output, and how this neuronal input-output computation is influenced by the morphology and biophysical properties of the dendrites. We demonstrate that this approach allows dissecting of how different input populations drive in vivo observed APs. For this purpose, we focus on fast and broadly tuned responses that pyramidal tract neurons in layer 5 (L5PTs) of the rat barrel cortex elicit upon passive single whisker deflections. By reducing a multi-scale model that we reported previously, we show that three features are sufficient to predict with high accuracy the sensory responses and receptive fields of L5PTs under these specific in vivo conditions: the count of active excitatory versus inhibitory synapses preceding the response, their spatial distribution on the dendrites, and the AP history. Based on these three features, we derive an analytically tractable description of the input-output computation of L5PTs, which enabled us to dissect how synaptic input from thalamus and different cell types in barrel cortex contribute to these responses. We show that the input-output computation is preserved across L5PTs despite morphological and biophysical diversity of their dendrites. We found that trial-to-trial variability in L5PT responses, and cell-to-cell variability in their receptive fields, are sufficiently explained by variability in synaptic input from the network, whereas variability in biophysical and morphological properties have minor contributions. Our approach to derive analytically tractable models of input-output computations in L5PTs provides a roadmap to dissect network-neuron interactions underlying L5PT responses across different in vivo conditions and for other cell types.

Simulation-based inference for efficient identification of generative models in computational connectomics.

Recent advances in connectomics research enable the acquisition of increasing amounts of data about the connectivity patterns of neurons. How can we use this wealth of data to efficiently derive and test hypotheses about the principles underlying these patterns? A common approach is to simulate neuronal networks using a hypothesized wiring rule in a generative model and to compare the resulting synthetic data with empirical data. However, most wiring rules have at least some free parameters, and identifying parameters that reproduce empirical data can be challenging as it often requires manual parameter tuning. Here, we propose to use simulation-based Bayesian inference (SBI) to address this challenge. Rather than optimizing a fixed wiring rule to fit the empirical data, SBI considers many parametrizations of a rule and performs Bayesian inference to identify the parameters that are compatible with the data. It uses simulated data from multiple candidate wiring rule parameters and relies on machine learning methods to estimate a probability distribution (the 'posterior distribution over parameters conditioned on the data') that characterizes all data-compatible parameters. We demonstrate how to apply SBI in computational connectomics by inferring the parameters of wiring rules in an in silico model of the rat barrel cortex, given in vivo connectivity measurements. SBI identifies a wide range of wiring rule parameters that reproduce the measurements. We show how access to the posterior distribution over all data-compatible parameters allows us to analyze their relationship, revealing biologically plausible parameter interactions and enabling experimentally testable predictions. We further show how SBI can be applied to wiring rules at different spatial scales to quantitatively rule out invalid wiring hypotheses. Our approach is applicable to a wide range of generative models used in connectomics, providing a quantitative and efficient way to constrain model parameters with empirical connectivity data.

Robustness of sensory-evoked excitation is increased by inhibitory inputs to distal apical tuft dendrites.

Cortical inhibitory interneurons (INs) are subdivided into a variety of morphologically and functionally specialized cell types. How the respective specific properties translate into mechanisms that regulate sensory-evoked responses of pyramidal neurons (PNs) remains unknown. Here, we investigated how INs located in cortical layer 1 (L1) of rat barrel cortex affect whisker-evoked responses of L2 PNs. To do so we combined in vivo electrophysiology and morphological reconstructions with computational modeling. We show that whisker-evoked membrane depolarization in L2 PNs arises from highly specialized spatiotemporal synaptic input patterns. Temporally L1 INs and L2-5 PNs provide near synchronous synaptic input. Spatially synaptic contacts from L1 INs target distal apical tuft dendrites, whereas PNs primarily innervate basal and proximal apical dendrites. Simulations of such constrained synaptic input patterns predicted that inactivation of L1 INs increases trial-to-trial variability of whisker-evoked responses in L2 PNs. The in silico predictions were confirmed in vivo by L1-specific pharmacological manipulations. We present a mechanism-consistent with the theory of distal dendritic shunting-that can regulate the robustness of sensory-evoked responses in PNs without affecting response amplitude or latency.

Beyond Columnar Organization: Cell Type- and Target Layer-Specific Principles of Horizontal Axon Projection Patterns in Rat Vibrissal Cortex.

Vertical thalamocortical afferents give rise to the elementary functional units of sensory cortex, cortical columns. Principles that underlie communication between columns remain however unknown. Here we unravel these by reconstructing in vivo-labeled neurons from all excitatory cell types in the vibrissal part of rat primary somatosensory cortex (vS1). Integrating the morphologies into an exact 3D model of vS1 revealed that the majority of intracortical (IC) axons project far beyond the borders of the principal column. We defined the corresponding innervation volume as the IC-unit. Deconstructing this structural cortical unit into its cell type-specific components, we found asymmetric projections that innervate columns of either the same whisker row or arc, and which subdivide vS1 into 2 orthogonal [supra-]granular and infragranular strata. We show that such organization could be most effective for encoding multi whisker inputs. Communication between columns is thus organized by multiple highly specific horizontal projection patterns, rendering IC-units as the primary structural entities for processing complex sensory stimuli.

Cellular organization of cortical barrel columns is whisker-specific.

The cellular organization of the cortex is of fundamental importance for elucidating the structural principles that underlie its functions. It has been suggested that reconstructing the structure and synaptic wiring of the elementary functional building block of mammalian cortices, the cortical column, might suffice to reverse engineer and simulate the functions of entire cortices. In the vibrissal area of rodent somatosensory cortex, whisker-related "barrel" columns have been referred to as potential cytoarchitectonic equivalents of functional cortical columns. Here, we investigated the structural stereotypy of cortical barrel columns by measuring the 3D neuronal composition of the entire vibrissal area in rat somatosensory cortex and thalamus. We found that the number of neurons per cortical barrel column and thalamic "barreloid" varied substantially within individual animals, increasing by ∼2.5-fold from dorsal to ventral whiskers. As a result, the ratio between whisker-specific thalamic and cortical neurons was remarkably constant. Thus, we hypothesize that the cellular architecture of sensory cortices reflects the degree of similarity in sensory input and not columnar and/or cortical uniformity principles.

Three-dimensional axon morphologies of individual layer 5 neurons indicate cell type-specific intracortical pathways for whisker motion and touch.

The cortical output layer 5 contains two excitatory cell types, slender- and thick-tufted neurons. In rat vibrissal cortex, slender-tufted neurons carry motion and phase information during active whisking, but remain inactive after passive whisker touch. In contrast, thick-tufted neurons reliably increase spiking preferably after passive touch. By reconstructing the 3D patterns of intracortical axon projections from individual slender- and thick-tufted neurons, filled in vivo with biocytin, we were able to identify cell type-specific intracortical circuits that may encode whisker motion and touch. Individual slender-tufted neurons showed elaborate and dense innervation of supragranular layers of large portions of the vibrissal area (total length, 86.8 ± 5.5 mm). During active whisking, these long-range projections may modulate and phase-lock the membrane potential of dendrites in layers 2 and 3 to the whisking cycle. Thick-tufted neurons with soma locations intermingling with those of slender-tufted ones display less dense intracortical axon projections (total length, 31.6 ± 14.3 mm) that are primarily confined to infragranular layers. Based on anatomical reconstructions and previous measurements of spiking, we put forward the hypothesis that thick-tufted neurons in rat vibrissal cortex receive input of whisker motion from slender-tufted neurons onto their apical tuft dendrites and input of whisker touch from thalamic neurons onto their basal dendrites. During tactile-driven behavior, such as object location, near-coincident input from these two pathways may result in increased spiking activity of thick-tufted neurons and thus enhanced signaling to their subcortical targets.

3D reconstruction and standardization of the rat vibrissal cortex for precise registration of single neuron morphology.

The three-dimensional (3D) structure of neural circuits is commonly studied by reconstructing individual or small groups of neurons in separate preparations. Investigation of structural organization principles or quantification of dendritic and axonal innervation thus requires integration of many reconstructed morphologies into a common reference frame. Here we present a standardized 3D model of the rat vibrissal cortex and introduce an automated registration tool that allows for precise placement of single neuron reconstructions. We (1) developed an automated image processing pipeline to reconstruct 3D anatomical landmarks, i.e., the barrels in Layer 4, the pia and white matter surfaces and the blood vessel pattern from high-resolution images, (2) quantified these landmarks in 12 different rats, (3) generated an average 3D model of the vibrissal cortex and (4) used rigid transformations and stepwise linear scaling to register 94 neuron morphologies, reconstructed from in vivo stainings, to the standardized cortex model. We find that anatomical landmarks vary substantially across the vibrissal cortex within an individual rat. In contrast, the 3D layout of the entire vibrissal cortex remains remarkably preserved across animals. This allows for precise registration of individual neuron reconstructions with approximately 30 µm accuracy. Our approach could be used to reconstruct and standardize other anatomically defined brain areas and may ultimately lead to a precise digital reference atlas of the rat brain.

Cell type-specific three-dimensional structure of thalamocortical circuits in a column of rat vibrissal cortex.

Soma location, dendrite morphology, and synaptic innervation may represent key determinants of functional responses of individual neurons, such as sensory-evoked spiking. Here, we reconstruct the 3D circuits formed by thalamocortical afferents from the lemniscal pathway and excitatory neurons of an anatomically defined cortical column in rat vibrissal cortex. We objectively classify 9 cortical cell types and estimate the number and distribution of their somata, dendrites, and thalamocortical synapses. Somata and dendrites of most cell types intermingle, while thalamocortical connectivity depends strongly upon the cell type and the 3D soma location of the postsynaptic neuron. Correlating dendrite morphology and thalamocortical connectivity to functional responses revealed that the lemniscal afferents can account for some of the cell type- and location-specific subthreshold and spiking responses after passive whisker touch (e.g., in layer 4, but not for other cell types, e.g., in layer 5). Our data provides a quantitative 3D prediction of the cell type-specific lemniscal synaptic wiring diagram and elucidates structure-function relationships of this physiologically relevant pathway at single-cell resolution.

Large-scale automated histology in the pursuit of connectomes.

How does the brain compute? Answering this question necessitates neuronal connectomes, annotated graphs of all synaptic connections within defined brain areas. Further, understanding the energetics of the brain's computations requires vascular graphs. The assembly of a connectome requires sensitive hardware tools to measure neuronal and neurovascular features in all three dimensions, as well as software and machine learning for data analysis and visualization. We present the state of the art on the reconstruction of circuits and vasculature that link brain anatomy and function. Analysis at the scale of tens of nanometers yields connections between identified neurons, while analysis at the micrometer scale yields probabilistic rules of connection between neurons and exact vascular connectivity.

The impact of neuron morphology on cortical network architecture.

The neurons in the cerebral cortex are not randomly interconnected. This specificity in wiring can result from synapse formation mechanisms that connect neurons, depending on their electrical activity and genetically defined identity. Here, we report that the morphological properties of the neurons provide an additional prominent source by which wiring specificity emerges in cortical networks. This morphologically determined wiring specificity reflects similarities between the neurons' axo-dendritic projections patterns, the packing density, and the cellular diversity of the neuropil. The higher these three factors are, the more recurrent is the topology of the network. Conversely, the lower these factors are, the more feedforward is the network's topology. These principles predict the empirically observed occurrences of clusters of synapses, cell type-specific connectivity patterns, and nonrandom network motifs. Thus, we demonstrate that wiring specificity emerges in the cerebral cortex at subcellular, cellular, and network scales from the specific morphological properties of its neuronal constituents.

Circuit-selective cell-autonomous regulation of inhibition in pyramidal neurons by Ste20-like kinase.

Maintaining an appropriate balance between excitation and inhibition is critical for neuronal information processing. Cortical neurons can cell-autonomously adjust the inhibition they receive to individual levels of excitatory input, but the underlying mechanisms are unclear. We describe that Ste20-like kinase (SLK) mediates cell-autonomous regulation of excitation-inhibition balance in the thalamocortical feedforward circuit, but not in the feedback circuit. This effect is due to regulation of inhibition originating from parvalbumin-expressing interneurons, while inhibition via somatostatin-expressing interneurons is unaffected. Computational modeling shows that this mechanism promotes stable excitatory-inhibitory ratios across pyramidal cells and ensures robust and sparse coding. Patch-clamp RNA sequencing yields genes differentially regulated by SLK knockdown, as well as genes associated with excitation-inhibition balance participating in transsynaptic communication and cytoskeletal dynamics. These data identify a mechanism for cell-autonomous regulation of a specific inhibitory circuit that is critical to ensure that a majority of cortical pyramidal cells participate in information coding.

High-frequency burst spiking in layer 5 thick-tufted pyramids of rat primary somatosensory cortex encodes exploratory touch.

Diversity of cell-types that collectively shape the cortical microcircuit ensures the necessary computational richness to orchestrate a wide variety of behaviors. The information content embedded in spiking activity of identified cell-types remain unclear to a large extent. Here, we recorded spike responses upon whisker touch of anatomically identified excitatory cell-types in primary somatosensory cortex in naive, untrained rats. We find major differences across layers and cell-types. The temporal structure of spontaneous spiking contains high-frequency bursts (≥100 Hz) in all morphological cell-types but a significant increase upon whisker touch is restricted to layer L5 thick-tufted pyramids (L5tts) and thus provides a distinct neurophysiological signature. We find that whisker touch can also be decoded from L5tt bursting, but not from other cell-types. We observed high-frequency bursts in L5tts projecting to different subcortical regions, including thalamus, midbrain and brainstem. We conclude that bursts in L5tts allow accurate coding and decoding of exploratory whisker touch.

Cortical Output Is Gated by Horizontally Projecting Neurons in the Deep Layers.

Pyramidal tract neurons (PTs) represent the major output cell type of the mammalian neocortex. Here, we report the origins of the PTs' ability to respond to a broad range of stimuli with onset latencies that rival or even precede those of their intracortical input neurons. We find that neurons with extensive horizontally projecting axons cluster around the deep-layer terminal fields of primary thalamocortical axons. The strategic location of these corticocortical neurons results in high convergence of thalamocortical inputs, which drive reliable sensory-evoked responses that precede those in other excitatory cell types. The resultant fast and horizontal stream of excitation provides PTs throughout the cortical area with input that acts to amplify additional inputs from thalamocortical and other intracortical populations. The fast onsets and broadly tuned characteristics of PT responses hence reflect a gating mechanism in the deep layers, which assures that sensory-evoked input can be reliably transformed into cortical output.

Morphological characterization of HVC projection neurons in the zebra finch (Taeniopygia guttata).

Singing behavior in the adult male zebra finch is dependent upon the activity of a cortical region known as HVC (proper name). The vast majority of HVC projection neurons send primary axons to either the downstream premotor nucleus RA (robust nucleus of the arcopallium, or primary motor cortex) or Area X (basal ganglia), which play important roles in song production or song learning, respectively. In addition to these long-range outputs, HVC neurons also send local axon collaterals throughout that nucleus. Despite their implications for a range of circuit models, these local processes have never been completely reconstructed. Here, we use in vivo single-neuron Neurobiotin fills to examine 40 projection neurons across 31 birds with somatic positions distributed across HVC. We show that HVC(RA) and HVC(X) neurons have categorically distinct dendritic fields. Additionally, these cell classes send axon collaterals that are either restricted to a small portion of HVC ("local neurons") or broadly distributed throughout the entire nucleus ("broadcast neurons"). Overall, these processes within HVC offer a structural basis for significant local processing underlying behaviorally relevant population activity.

3D reconstruction and standardization of the rat facial nucleus for precise mapping of vibrissal motor networks.

The rodent facial nucleus (FN) comprises motoneurons (MNs) that control the facial musculature. In the lateral part of the FN, populations of vibrissal motoneurons (vMNs) innervate two groups of muscles that generate movements of the whiskers. Vibrissal MNs thus represent the terminal point of the neuronal networks that generate rhythmic whisking during exploratory behaviors and that modify whisker movements based on sensory-motor feedback during tactile-based perception. Here, we combined retrograde tracer injections into whisker-specific muscles, with large-scale immunohistochemistry and digital reconstructions to generate an average model of the rat FN. The model incorporates measurements of the FN geometry, its cellular organization and a whisker row-specific map formed by vMNs. Furthermore, the model provides a digital 3D reference frame that allows registering structural data - obtained across scales and animals - into a common coordinate system with a precision of ∼60 µm. We illustrate the registration method by injecting replication competent rabies virus into the muscle of a single whisker. Retrograde transport of the virus to vMNs enabled reconstruction of their dendrites. Subsequent trans-synaptic transport enabled mapping the presynaptic neurons of the reconstructed vMNs. Registration of these data to the FN reference frame provides a first account of the morphological and synaptic input variability within a population of vMNs that innervate the same muscle.

Transmitted light brightfield mosaic microscopy for three-dimensional tracing of single neuron morphology.

A fundamental challenge in neuroscience is the determination of the three-dimensional (3D) morphology of neurons in the cortex. Here we describe a semiautomated method to trace single biocytin-filled neurons using a transmitted light brightfield microscope. The method includes 3D tracing of dendritic trees and axonal arbors from image stacks of serial 100-microm-thick tangential brain sections. Key functionalities include mosaic scanning and optical sectioning, high-resolution image restoration, and fast, parallel computing for neuron tracing. The mosaic technique compensates for the limited field of view at high magnification, allowing the acquisition of high-resolution image stacks on a scale of millimeters. The image restoration by deconvolution is based on experimentally verified assumptions about the optical system. Restoration yields a significant improvement of signal-to-noise ratio and resolution of neuronal structures in the image stack. Application of local threshold and thinning filters result in a 3D graph representation of dendrites and axons in a section. The reconstructed branches are then manually edited and aligned. Branches from adjacent sections are spliced, resulting in a complete 3D reconstruction of a neuron. A comparison with 3D reconstructions from manually traced neurons shows that the semiautomated system is a fast and reliable alternative to the manual tracing systems currently available.

Cell Type-Specific Structural Organization of the Six Layers in Rat Barrel Cortex.

The cytoarchitectonic subdivision of the neocortex into six layers is often used to describe the organization of the cortical circuitry, sensory-evoked signal flow or cortical functions. However, each layer comprises neuronal cell types that have different genetic, functional and/or structural properties. Here, we reanalyze structural data from some of our recent work in the posterior-medial barrel-subfield of the vibrissal part of rat primary somatosensory cortex (vS1). We quantify the degree to which somata, dendrites and axons of the 10 major excitatory cell types of the cortex are distributed with respect to the cytoarchitectonic organization of vS1. We show that within each layer, somata of multiple cell types intermingle, but that each cell type displays dendrite and axon distributions that are aligned to specific cytoarchitectonic landmarks. The resultant quantification of the structural composition of each layer in terms of the cell type-specific number of somata, dendritic and axonal path lengths will aid future studies to bridge between layer- and cell type-specific analyses.

Relationships between structure, in vivo function and long-range axonal target of cortical pyramidal tract neurons.

Pyramidal tract neurons (PTs) represent the major output cell type of the neocortex. To investigate principles of how the results of cortical processing are broadcasted to different downstream targets thus requires experimental approaches, which provide access to the in vivo electrophysiology of PTs, whose subcortical target regions are identified. On the example of rat barrel cortex (vS1), we illustrate that retrograde tracer injections into multiple subcortical structures allow identifying the long-range axonal targets of individual in vivo recorded PTs. Here we report that soma depth and dendritic path lengths within each cortical layer of vS1, as well as spiking patterns during both periods of ongoing activity and during sensory stimulation, reflect the respective subcortical target regions of PTs. We show that these cellular properties result in a structure-function parameter space that allows predicting a PT's subcortical target region, without the need to inject multiple retrograde tracers.The major output cell type of the neocortex - pyramidal tract neurons (PTs) - send axonal projections to various subcortical areas. Here the authors combined in vivo recordings, retrograde tracings, and reconstructions of PTs in rat somatosensory cortex to show that PT structure and activity can predict specific subcortical targets.

EM connectomics reveals axonal target variation in a sequence-generating network.

The sequential activation of neurons has been observed in various areas of the brain, but in no case is the underlying network structure well understood. Here we examined the circuit anatomy of zebra finch HVC, a cortical region that generates sequences underlying the temporal progression of the song. We combined serial block-face electron microscopy with light microscopy to determine the cell types targeted by HVC(RA) neurons, which control song timing. Close to their soma, axons almost exclusively targeted inhibitory interneurons, consistent with what had been found with electrical recordings from pairs of cells. Conversely, far from the soma the targets were mostly other excitatory neurons, about half of these being other HVC(RA) cells. Both observations are consistent with the notion that the neural sequences that pace the song are generated by global synaptic chains in HVC embedded within local inhibitory networks.