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The comprehensive characterization of neuronal morphology requires tracing extensive axonal and dendritic arbors imaged with light microscopy into digital reconstructions. Considerable effort is ongoing to automate this greatly labor-intensive and currently rate-determining process. Experimental data in the form of manually traced digital reconstructions and corresponding image stacks play a vital role in developing increasingly more powerful reconstruction algorithms. The DIADEM challenge (short for DIgital reconstruction of Axonal and DEndritic Morphology) successfully stimulated progress in this area by utilizing six data set collections from different animal species, brain regions, neuron types, and visualization methods. The original research projects that provided these data are representative of the diverse scientific questions addressed in this field. At the same time, these data provide a benchmark for the types of demands automated software must meet to achieve the quality of manual reconstructions while minimizing human involvement. The DIADEM data underwent extensive curation, including quality control, metadata annotation, and format standardization, to focus the challenge on the most substantial technical obstacles. This data set package is now freely released ( http://diademchallenge.org ) to train, test, and aid development of automated reconstruction algorithms.

To better understand the relationship between input and output connectivity for neurons of interest in specific brain regions, a viral-genetic tracing approach is used to identify input based on a combination of neurons’ projection and cell type, as illustrated in a study of locus coeruleus noradrenaline neurons. Noradrenaline circuit architecture New circuit tracing techniques have steadily increased our knowledge of the connectivity between brain regions and how such links may contribute to function and information processing. Here, Liqun Luo and colleagues expand this toolbox to include TRIO, a new strategy designed to characterize the input–output relationships between genetically specified populations of neurons. As a proof of concept, input–output tracing relationships and projection patterns were completed for the noradrenaline neurons of the locus coeruleus. Deciphering how neural circuits are anatomically organized with regard to input and output is instrumental in understanding how the brain processes information. For example, locus coeruleus noradrenaline (also known as norepinephrine) (LC-NE) neurons receive input from and send output to broad regions of the brain and spinal cord, and regulate diverse functions including arousal, attention, mood and sensory gating 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 . However, it is unclear how LC-NE neurons divide up their brain-wide projection patterns and whether different LC-NE neurons receive differential input. Here we developed a set of viral-genetic tools to quantitatively analyse the input–output relationship of neural circuits, and applied these tools to dissect the LC-NE circuit in mice. Rabies-virus-based input mapping indicated that LC-NE neurons receive convergent synaptic input from many regions previously identified as sending axons to the locus coeruleus, as well as from newly identified presynaptic partners, including cerebellar Purkinje cells. The ‘tracing the relationship between input and output’ method (or TRIO method) enables trans-synaptic input tracing from specific subsets of neurons based on their projection and cell type. We found that LC-NE neurons projecting to diverse output regions receive mostly similar input. Projection-based viral labelling revealed that LC-NE neurons projecting to one output region also project to all brain regions we examined. Thus, the LC-NE circuit overall integrates information from, and broadcasts to, many brain regions, consistent with its primary role in regulating brain states. At the same time, we uncovered several levels of specificity in certain LC-NE sub-circuits. These tools for mapping output architecture and input–output relationship are applicable to other neuronal circuits and organisms. More broadly, our viral-genetic approaches provide an efficient intersectional means to target neuronal populations based on cell type and projection pattern.

Functionally and anatomically distinct cortical substructures, such as areas or layers, contain different principal neuron (PN) subtypes that generate output signals representing particular information. Various types of cortical inhibitory interneurons (INs) differentially but coordinately regulate PN activity. Despite a potential determinant for functional specialization of PN subtypes, the spatial organization of IN subtypes that innervate defined PN subtypes remains unknown. Here we develop a genetic strategy combining a recombinase-based intersectional labeling method and rabies viral monosynaptic tracing, which enables subtype-specific visualization of cortical IN ensembles sending inputs to defined PN subtypes. Our approach reveals not only cardinal but also underrepresented connections between broad, non-overlapping IN subtypes and PNs. Furthermore, we demonstrate that distinct PN subtypes defined by areal or laminar positions display different organization of input IN subtypes. Our genetic strategy will facilitate understanding of the wiring and developmental principles of cortical inhibitory circuits at unparalleled levels.Inhibitory interneurons shape neuronal activity of cortical principal neurons. Yetman et al. developed a genetic strategy to elucidate the organization of inhibitory neuron subtypes sending inputs to principal neurons.

The authors describe a technique for delivering DNA vectors through a patch-pipette following characterization of the neuron by whole-cell recording. They demonstrate the utility of the approach by mapping the synaptic and anatomical receptive fields of several visual cortical neurons. Single-cell genetic manipulation is expected to substantially advance the field of systems neuroscience. However, existing gene delivery techniques do not allow researchers to electrophysiologically characterize cells and to thereby establish an experimental link between physiology and genetics for understanding neuronal function. In the mouse brain in vivo , we found that neurons remained intact after 'blind' whole-cell recording, that DNA vectors could be delivered through the patch-pipette during such recordings and that these vectors drove protein expression in recorded cells for at least 7 d. To illustrate the utility of this approach, we recorded visually evoked synaptic responses in primary visual cortical cells while delivering DNA plasmids that allowed retrograde, monosynaptic tracing of each neuron's presynaptic inputs. By providing a biophysical profile of a cell before its specific genetic perturbation, this combinatorial method captures the synaptic and anatomical receptive field of a neuron.

We describe our connectomics pipeline for processing anterograde tracer injection data for the brain of the common marmoset (Callithrix jacchus). Brain sections were imaged using a batch slide scanner (NanoZoomer 2.0-HT) and we used artificial intelligence to precisely segment the tracer signal from the background in the fluorescence images. The shape of each brain was reconstructed by reference to a block-face and all data were mapped into a common 3D brain space with atlas and 2D cortical flat map. To overcome the effect of using a single template atlas to specify cortical boundaries, brains were cyto- and myelo-architectonically annotated to create individual 3D atlases. Registration between the individual and common brain cortical boundaries in the flat map space was done to absorb the variation of each brain and precisely map all tracer injection data into one cortical brain space. We describe the methodology of our pipeline and analyze the accuracy of our tracer segmentation and brain registration approaches. Results show our pipeline can successfully process and normalize tracer injection experiments into a common space, making it suitable for large-scale connectomics studies with a focus on the cerebral cortex.

This paper presents a broadly applicable algorithm and a comprehensive open-source software implementation for automated tracing of neuronal structures in 3-D microscopy images. The core 3-D neuron tracing algorithm is based on three-dimensional (3-D) open-curve active Contour (Snake). It is initiated from a set of automatically detected seed points. Its evolution is driven by a combination of deforming forces based on the Gradient Vector Flow (GVF), stretching forces based on estimation of the fiber orientations, and a set of control rules. In this tracing model, bifurcation points are detected implicitly as points where multiple snakes collide. A boundariness measure is employed to allow local radius estimation. A suite of pre-processing algorithms enable the system to accommodate diverse neuronal image datasets by reducing them to a common image format. The above algorithms form the basis for a comprehensive, scalable, and efficient software system developed for confocal or brightfield images. It provides multiple automated tracing modes. The user can optionally interact with the tracing system using multiple view visualization, and exercise full control to ensure a high quality reconstruction. We illustrate the utility of this tracing system by presenting results from a synthetic dataset, a brightfield dataset and two confocal datasets from the DIADEM challenge.

Neurotropic viral transsynaptic tracing is an increasingly powerful technique for dissecting the structure and function of neural circuits. Herpes simplex virus type 1 strain H129 has been widely used as an anterograde tracer. However, HSV tracers still have several shortcomings, including high toxicity, low sensitivity and non-specific retrograde labeling. Here, we aimed to construct high-brightness HSV anterograde tracers by increasing the expression of exogenous genes carried by H129 viruses. Using a Trojan horse-like strategy, a HSV/AAV (adeno-associated virus) chimaera termed H8 was generated to enhance the expression of a fluorescent marker. In vitro and in vivo assays showed that the exogenous gene was efficiently replicated and amplified by the synergism of the HSV vector and introduced AAV replication system. H8 reporting fluorescence was brighter than that of currently available H129 tracers, and H8 could be used for fast and effective anterograde tracing without additional immunostaining. These results indicated that foreign gene expression in HSV tracers could be enhanced by integrating HSV with AAV replication system. This approach may be useful as a general enhanced expression strategy for HSV-based tracing tools or gene delivery vectors.

Throughout history, researchers who examine the structure and function of the brain debate one another about how cortical areas are defined, as well as how these areas should be named. Different pieces of empirical evidence are used to define brain areas and it is important to preserve the accurate history of this evidence and the timeline of studies that lead to areal definitions that are either still used today or have been modified. As such, this paper traces the early history of a brain area located at the junction between the occipital and temporal lobes of the macaque known as TEO. This historical analysis leads to four main findings. First, even though Bonin and Bailey are credited with the definition of area TEO in 1947, they did not have the cytoarchitectonic evidence to support the distinction of TEO from adjacent areas. Second, the first evidence definitively separating area TEO from TE was actually based on connectivity as identified with strychnine neuronography by Petr et al. in 1949. Third, causal evidence from ablation studies conducted by Iwai and Mishkin (Experimental Neurology 25(4):585–594, 1969) supported this distinction by showing that TEO and TE were functionally distinct from one another. Fourth, researchers in the 1970s began referring to TEO as posterior inferotemporal (PIT) and TE as anterior inferotemporal (AIT), which is an important historical clarification as the PIT/AIT nomenclature is presently attributed to studies conducted more than a decade later. Altogether, this paper aims to preserve the historical origin of area TEO, as well as the empirical evidence that was used to originally differentiate this cortical expanse from surrounding areas.

Automating the process of neural circuit reconstruction on a large-scale is one of the foremost challenges in the field of neuroscience. In this study we examine the methodology for circuit reconstruction from three-dimensional light microscopy (LM) stacks of images. We show how the minimal error-rate of an ideal reconstruction procedure depends on the density of labeled neurites, giving rise to the fundamental limitation of an LM based approach for neural circuit research. Circuit reconstruction procedures typically involve steps related to neuron labeling and imaging, and subsequent image pre-processing and tracing of neurites. In this study, we focus on the last step—detection of traces of neurites from already pre-processed stacks of images. Our automated tracing algorithm, implemented as part of the Neural Circuit Tracer software package, consists of the following main steps. First, image stack is filtered to enhance labeled neurites. Second, centerline of the neurites is detected and optimized. Finally, individual branches of the optimal trace are merged into trees based on a cost minimization approach. The cost function accounts for branch orientations, distances between their end-points, curvature of the merged structure, and its intensity. The algorithm is capable of connecting branches which appear broken due to imperfect labeling and can resolve situations where branches appear to be fused due the limited resolution of light microscopy. The Neural Circuit Tracer software is designed to automatically incorporate ImageJ plug-ins and functions written in MatLab and provides roughly a 10-fold increases in speed in comparison to manual tracing.

Developments in image acquisition technology make high volumes of neuron images available to neuroscientists for analysis. However, manual processing of these images is not practical and is infeasible for larger and larger scale studies. Reliable interpretation and analysis of high volume data requires accurate quantitative measures. This requires analysis algorithms to use mathematical models that inherit the underlying geometry of biological structures in order to extract topological information. In this paper, we first introduce principal curves as a model for the underlying skeleton of axons and branches, then describe a recursive principal curve tracing (RPCT) method to extract this topology information from 3D microscopy imagery. RPCT first finds samples on the one dimensional principal set of the intensity function in space. Then, given an initial direction and location, the algorithm iteratively traces the principal curve in space using our principal curve tracing (PCT) method. Recursive implementation of PCT provides a compact solution for extracting complex tubular structures that exhibit bifurcations.