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 selecting 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.

Characterization of the complex branching architecture of cerebral arteries across a representative sample of the human population is important for diagnosing, analyzing, and predicting pathological states. Brain arterial vasculature can be visualized by magnetic resonance angiography (MRA). However, most MRA studies are limited to qualitative assessments, partial morphometric analyses, individual (or small numbers of) subjects, proprietary datasets, or combinations of the above limitations. Neuroinformatics tools, developed for neuronal arbor analysis, were used to quantify vascular morphology from 3 T time-of-flight MRA high-resolution (620 μm isotropic) images collected in 61 healthy volunteers (36/25 F/M, average age = 31.2 ± 10.7, range = 19–64 years). We present in-depth morphometric analyses of the global and local anatomical features of these arbors. The overall structure and size of the vasculature did not significantly differ across genders, ages, or hemispheres. The total length of the three major arterial trees stemming from the circle of Willis (from smallest to largest: the posterior, anterior, and middle cerebral arteries; or PCAs, ACAs, and MCAs, respectively) followed an approximate 1:2:4 proportion. Arterial size co-varied across individuals: subjects with one artery longer than average tended to have all other arteries also longer than average. There was no net right–left difference across the population in any of the individual arteries, but ACAs were more lateralized than MCAs. MCAs, ACAs, and PCAs had similar branch-level properties such as bifurcation angles. Throughout the arterial vasculature, there were considerable differences between branch types: bifurcating branches were significantly shorter and straighter than terminating branches. Furthermore, the length and meandering of bifurcating branches increased with age and with path distance from the circle of Willis. All reconstructions are freely distributed through a public database to enable additional analyses and modeling (cng.gmu.edu/brava).

Digital reconstructions of neuronal morphology are used to study neuron function, development, and responses to various conditions. Although many measures exist to analyze differences between neurons, none is particularly suitable to compare the same arborizing structure over time (morphological change) or reconstructed by different people and/or software (morphological error). The metric introduced for the DIADEM (DIgital reconstruction of Axonal and DEndritic Morphology) Challenge quantifies the similarity between two reconstructions of the same neuron by matching the locations of bifurcations and terminations as well as their topology between the two reconstructed arbors. The DIADEM metric was specifically designed to capture the most critical aspects in automating neuronal reconstructions, and can function in feedback loops during algorithm development. During the Challenge, the metric scored the automated reconstructions of best-performing algorithms against manually traced gold standards over a representative data set collection. The metric was compared with direct quality assessments by neuronal reconstruction experts and with clocked human tracing time saved by automation. The results indicate that relevant morphological features were properly quantified in spite of subjectivity in the underlying image data and varying research goals. The DIADEM metric is freely released open source (http://diademchallenge.org) as a flexible instrument to measure morphological error or change in high-throughput reconstruction projects.

Neurons vary greatly in size, shape, and complexity depending on their underlying function. Overall size of neuronal trees affects connectivity, area of influence, and other biophysical properties. Relative distributions of neuronal extent, such as the difference between subtrees at branch points, are also critically related to function and activity. This review covers neuromorphological research that analyzes shape and size to elucidate their functional role for different neuron types. We also introduce a novel morphometric, "caulescence", capturing the extent to which trees exhibit a main path. Neuronal tree types differ vastly in caulescence, suggesting potential neurocomputational correlates of this property.

The morphological and electrophysiological diversity of inhibitory cells in hippocampal area CA3 may underlie specific computational roles and is not yet fully elucidated. In particular, interneurons with somata in strata radiatum (R) and lacunosum-moleculare (L-M) receive converging stimulation from the dentate gyrus and entorhinal cortex as well as within CA3. Although these cells express different forms of synaptic plasticity, their axonal trees and connectivity are still largely unknown. We investigated the branching and spatial patterns, plus the membrane and synaptic properties, of rat CA3b R and L-M interneurons digitally reconstructed after intracellular labeling. We found considerable variability within but no difference between the two layers, and no correlation between morphological and biophysical properties. Nevertheless, two cell types were identified based on the number of dendritic bifurcations, with significantly different anatomical and electrophysiological features. Axons generally branched an order of magnitude more than dendrites. However, interneurons on both sides of the R/L-M boundary revealed surprisingly modular axo-dendritic arborizations with consistently uniform local branch geometry. Both axons and dendrites followed a lamellar organization, and axons displayed a spatial preference towards the fissure. Moreover, only a small fraction of the axonal arbor extended to the outer portion of the invaded volume, and tended to return towards the proximal region. In contrast, dendritic trees demonstrated more limited but isotropic volume occupancy. These results suggest a role of predominantly local feedforward and lateral inhibitory control for both R and L-M interneurons. Such role may be essential to balance the extensive recurrent excitation of area CA3 underlying hippocampal autoassociative memory function.

Digital reconstruction of neuronal arborizations is an important step in the quantitative investigation of cellular neuroanatomy. In this process, neurites imaged by microscopy are semi-manually traced through the use of specialized computer software and represented as binary trees of branching cylinders (or truncated cones). Such form of the reconstruction files is efficient and parsimonious, and allows extensive morphometric analysis as well as the implementation of biophysical models of electrophysiology. Here, we describe Neuron_ Morpho, a plugin for the popular Java application ImageJ that mediates the digital reconstruction of neurons from image stacks. Both the executable and code of Neuron_ Morpho are freely distributed (www.maths. soton.ac.uk/staff/D'Alessandro/morpho or www.krasnow.gmu.edu/L-Neuron), and are compatible with all major computer platforms (including Windows, Mac, and Linux). We tested Neuron_Morpho by reconstructing two neurons from each of the two preparations representing different brain areas (hippocampus and cerebellum), neuritic type (pyramidal cell dendrites and olivar axonal projection terminals), and labeling method (rapid Golgi impregnation and anterograde dextran amine), and quantitatively comparing the resulting morphologies to those of the same cells reconstructed with the standard commercial system, Neurolucida. None of the numerous morphometric measures that were analyzed displayed any significant or systematic difference between the two reconstructing systems.

Concomitant with the publication of this Special Issue of Neuroinformatics, a substantially updated version of the DIADEM web site has been released at http://diademchall enge.org. This web site was originally designed to host the challenge for automating the digital reconstruction of axonal and dendritic morphology (hence the DIADEM acronym). This post-competition version features additional content for continued use as the access point for DIADEM-related material. From the very beginning, one of the spirits of DIADEM has been to share data and resources with the neuroscience research community at large. The resources available from or linked to the DIADEM website constitute a substantial scientific legacy of the 2009/2010 competition. The new content includes finalist algorithms, image stack data, gold standard reconstructions, an updated DIADEM metric, and a retrospective on the competition in text and images. The website continuing intent is to facilitate development of automated reconstruction algorithms.The DIADEM Data Sets include image stacks, manually reconstructed digital tracings (the "gold standards"), and metadata. The six extensively curated, diverse data sets can be used to train, test and aid in the development or tuning of existing automated reconstruction algorithms. The previously posted image stacks (training and qualifier sets for the DIADEM competition) are still available, and are augmented with the addition of the Final Round image stacks. 1 The Final Round sets include a previously unreleased type of data, a visual cortical pyramidal cell, used as a surprise set in the competition.The DIADEM metric (including source code) also has a new release with a wider set of user options and extended documentation. 2 These options provide flexibility for use on data beyond the DIADEM data sets and for alternative scoring schemes. Integration of the metric with automated reconstruction development has been made easier with more output options and methods to make results accessible to an interfacing program. These and other changes make the DIADEM metric viable in both algorithm development and evaluation. Since it was designed as a machine-derived surrogate to humans for quantifying the differences between two reconstructions of the same neuronal structure, the DIADEM metric can be used as a reliable tool to evaluate reconstruction quality of either newly developed or expansions of existing automated reconstruction algorithms.Most importantly, the DIADEM finalists made their algorithms freely available on the website. These include code, executables, and documentation, in addition to links

Cerebellar climbing fibers provide powerful excitatory input to Purkinje cells, which represent the sole output of the cerebellar cortex. Recent discoveries suggest that climbing fibers have information-rich signaling properties important for cerebellar function, beyond eliciting the well-known all-or-none Purkinje cell complex spike. Climbing fiber morphology has not been quantitatively analyzed at the same level of detail as their biophysical properties. Because morphology can greatly influence function, including the capacity for information processing, it is important to understand climbing fiber branching structure in detail, as well as its variability across and within arbors. We have digitally reconstructed 68 rat climbing fibers labeled using biotinylated dextran amine (BDA) injected into the inferior olive and comprehensively quantified their morphology. Climbing fiber structure was considerably diverse even within the same anatomical regions. Distinctly identifiable primary, tendril, and distal branches could be operationally differentiated by the relative size of the subtrees at their initial bifurcations. Additionally, primary branches were more directed toward the cortical surface and had fewer and less pronounced synaptic boutons, suggesting they prioritize efficient and reliable signal propagation. Tendril and distal branches were spatially segregated and bouton dense, indicating specialization in signal transmission. Furthermore, climbing fibers systematically targeted molecular layer interneuron cell bodies, especially at terminal boutons, potentially instantiating feed-forward inhibition on Purkinje cells. This study offers the most detailed and comprehensive characterization of climbing fiber morphology to date. The reconstruction files and metadata are publicly distributed at NeuroMorpho.Org.