The problem of the morphological noise in reconstructed dendritic arborizations

A biologically inspired repair mechanism for neuronal reconstructions with a focus on human dendrites

Investigating and modelling the functionality of human neurons remains challenging due to the technical limitations, resulting in scarce and incomplete 3D anatomical reconstructions. Here we used a morphological modelling approach based on optimal wiring to repair the parts of a dendritic morphology that were lost due to incomplete tissue samples. In Drosophila, where dendritic regrowth has been studied experimentally using laser ablation, we found that modelling the regrowth reproduced a bimodal distribution between regeneration of cut branches and invasion by neighbouring branches. Interestingly, our repair model followed growth rules similar to those for the generation of a new dendritic tree. To generalise the repair algorithm from Drosophila to mammalian neurons, we artificially sectioned reconstructed dendrites from mouse and human hippocampal pyramidal cell morphologies, and showed that the regrown dendrites were morphologically similar to the original ones. Furthermore, we were able to restore their electrophysiological functionality, as evidenced by the recovery of their firing behaviour. Importantly, we show that such repairs also apply to other neuron types including hippocampal granule cells and cerebellar Purkinje cells. We then extrapolated the repair to incomplete human CA1 pyramidal neurons, where the anatomical boundaries of the particular brain areas innervated by the neurons in question were known. Interestingly, the repair of incomplete human dendrites helped to simulate the recently observed increased synaptic thresholds for dendritic NMDA spikes in human versus mouse dendrites. To make the repair tool available to the neuroscience community, we have developed an intuitive and simple graphical user interface (GUI), which is available in the TREES toolbox (www.treestoolbox.org).

The importance of metadata to assess information content in digital reconstructions of neuronal morphology

Digital reconstructions of axonal and dendritic arbors provide a powerful representation of neuronal morphology in formats amenable to quantitative analysis, computational modeling, and data mining. Reconstructed files, however, require adequate metadata to identify the appropriate animal species, developmental stage, brain region, and neuron type. Moreover, experimental details about tissue processing, neurite visualization and microscopic imaging are essential to assess the information content of digital morphologies. Typical morphological reconstructions only partially capture the underlying biological reality. Tracings are often limited to certain domains (e.g., dendrites and not axons), may be incomplete due to tissue sectioning, imperfect staining, and limited imaging resolution, or can disregard aspects irrelevant to their specific scientific focus (such as branch thickness or depth). Gauging these factors is critical in subsequent data reuse and comparison. NeuroMorpho.Org is a central repository of reconstructions from many laboratories and experimental conditions. Here, we introduce substantial additions to the existing metadata annotation aimed to describe the completeness of the reconstructed neurons in NeuroMorpho.Org. These expanded metadata form a suitable basis for effective description of neuromorphological data.

Trigeminal intersubnuclear neurons: morphometry and input-dependent structural plasticity in adult rats

Intersubnuclear neurons in the caudal division of the spinal trigeminal nucleus that project to the principal nucleus (Pr5) play an active role in shaping the receptive fields of other neurons, at different levels in the ascending sensory system that processes information originating from the vibrissae. By using retrograde labeling and digital reconstruction, we investigated the morphometry and topology of the dendritic trees of these neurons and the changes induced by long-term experience-dependent plasticity in adult male rats. Primary afferent input was either eliminated by transection of the right infraorbital nerve (IoN), or selectively altered by repeated whisker clipping on the right side. These neurons do not display asymmetries between sides in basic metric and topologic parameters (global number of trees, nodes, spines, or dendritic ends), although neurons on the left tend to have longer terminal segments. Ipsilaterally, both deafferentation (IoN transection) and deprivation (whisker trimming) reduced the density of spines, and the former also caused a global increase in total dendritic length and a relative increase in more complex arbors. Contralaterally, deafferentation reduced more complex dendritic trees, and caused a moderate decline in dendritic length and spatial reach, and a loss of spines in number and density. Deprivation caused a similar, but more profound, effect on spines. Our findings provide original quantitative descriptions of a scarcely known cell population, and show that denervation- or deprivation-derived plasticity is expressed not only by neurons at higher levels of the sensory pathways, but also by neurons in key subcortical circuits for sensory processing.

Context-aware modeling of neuronal morphologies

Neuronal morphologies are pivotal for brain functioning: physical overlap between dendrites and axons constrain the circuit topology, and the precise shape and composition of dendrites determine the integration of inputs to produce an output signal. At the same time, morphologies are highly diverse and variant. The variance, presumably, originates from neurons developing in a densely packed brain substrate where they interact (e.g., repulsion or attraction) with other actors in this substrate. However, when studying neurons their context is never part of the analysis and they are treated as if they existed in isolation. Here we argue that to fully understand neuronal morphology and its variance it is important to consider neurons in relation to each other and to other actors in the surrounding brain substrate, i.e., their context. We propose a context-aware computational framework, NeuroMaC, in which large numbers of neurons can be grown simultaneously according to growth rules expressed in terms of interactions between the developing neuron and the surrounding brain substrate. As a proof of principle, we demonstrate that by using NeuroMaC we can generate accurate virtual morphologies of distinct classes both in isolation and as part of neuronal forests. Accuracy is validated against population statistics of experimentally reconstructed morphologies. We show that context-aware generation of neurons can explain characteristics of variation. Indeed, plausible variation is an inherent property of the morphologies generated by context-aware rules. We speculate about the applicability of this framework to investigate morphologies and circuits, to classify healthy and pathological morphologies, and to generate large quantities of morphologies for large-scale modeling.

A software tool for the analysis of neuronal morphology data

Anatomy plays a fundamental role in supporting and shaping nervous system activity. The remarkable progress of computer processing power within the last two decades has enabled the generation of electronic databases of complete three-dimensional (3D) dendritic and axonal morphology for neuroanatomical studies. Several laboratories are freely posting their reconstructions online after result publication v.gr. NeuroMorpho.Org (Nat Rev Neurosci7:318-324, 2006). These neuroanatomical archives represent a crucial resource to explore the relationship between structure and function in the brain (Front Neurosci6:49, 2012). However, such 'Cartesian' descriptions bear little intuitive information for neuroscientists. Here, we developed a simple prototype of a MATLAB-based software tool to quantitatively describe the 3D neuronal structures from public repositories. The program imports neuronal reconstructions and quantifies statistical distributions of basic morphological parameters such as branch length, tortuosity, branch's genealogy and bifurcation angles. Using these morphological distributions, our algorithm can generate a set of virtual neurons readily usable for network simulations.

Unveiling the neuromorphological space

This article proposes the concept of neuromorphological space as the multidimensional space defined by a set of measurements of the morphology of a representative set of almost 6000 biological neurons available from the NeuroMorpho database. For the first time, we analyze such a large database in order to find the general distribution of the geometrical features. We resort to McGhee's biological shape space concept in order to formalize our analysis, allowing for comparison between the geometrically possible tree-like shapes, obtained by using a simple reference model, and real neuronal shapes. Two optimal types of projections, namely, principal component analysis and canonical analysis, are used in order to visualize the originally 20-D neuron distribution into 2-D morphological spaces. These projections allow the most important features to be identified. A data density analysis is also performed in the original 20-D feature space in order to corroborate the clustering structure. Several interesting results are reported, including the fact that real neurons occupy only a small region within the geometrically possible space and that two principal variables are enough to account for about half of the overall data variability. Most of the measurements have been found to be important in representing the morphological variability of the real neurons.

Neuron tracing in perspective

The study of the structure and function of neuronal cells and networks is of crucial importance in the endeavor to understand how the brain works. A key component in this process is the extraction of neuronal morphology from microscopic imaging data. In the past four decades, many computational methods and tools have been developed for digital reconstruction of neurons from images, with limited success. As witnessed by the growing body of literature on the subject, as well as the organization of challenging competitions in the field, the quest for a robust and fully automated system of more general applicability still continues. The aim of this work, is to contribute by surveying recent developments in the field for anyone interested in taking up the challenge. Relevant aspects discussed in the article include proposed image segmentation methods, quantitative measures of neuronal morphology, currently available software tools for various related purposes, and morphology databases. (c) 2010 International Society for Advancement of Cytometry.

Trigeminothalamic barrelette neurons: natural structural side asymmetries and sensory input-dependent plasticity in adult rats

In the rodent trigeminal principal nucleus (Pr5) the barrelette thalamic-projecting neurons relay information from individual whiskers to corresponding contralateral thalamic barreloids. Here we investigated the presence of lateral asymmetries in the dendritic trees of these neurons, and the morphometric changes resulting from input-dependent plasticity in young adult rats. After retrograde labeling with dextran amines from the thalamus, neurons were digitally reconstructed with Neurolucida, and metrically and topologically analyzed with NeuroExplorer. The most unexpected and remarkable result was the observation of side-to-side asymmetries in the barrelette neurons of control rats. These asymmetries more significantly involved the number of low-grade trees and the total dendritic length, which were greater on the left side. Chronic global input loss resulting from infraorbital nerve (IoN) transection, or loss of active touch resulting from whisker clipping in the right neutralized, or even reversed, the observed lateral differences. While results after IoN transection have to be interpreted in the context of partial neuron death in this model, profound bilateral changes were found after haptic loss, which is achieved without inflicting any nerve damage. After whisker trimming, neurons on the left side closely resembled neurons on the right in controls, the natural dendritic length asymmetry being reversed mainly by a shortening of the left trees and a more moderate elongation of the right trees. These results demonstrate that dendritic morphometry is both side- and input-dependent, and that unilateral manipulation of the sensory periphery leads to bilateral morphometric changes in second order neurons of the whisker-barrel system. The presence of anatomical asymmetries in neural structures involved in early stages of somatosensory processing could help explain the expression of sensory input-dependent behavioral asymmetries.

Parameter sensitivity of distributed transfer properties of neuronal dendrites: a passive cable approximation

Geometry and membrane properties of the dendrites crucially determine input-output relations in neurons. Unlike geometry often available in detail from computer reconstruction, the membrane resistivity is fragmentarily known if at all. Moreover, it varies during ongoing activity. In this study we address the question: what is the impact of the variation in membrane resistivity on the transfer properties of dendrites? Following a standard approach of the control system theory, we derive and explore the sensitivity functions complementary to the transfer functions of the passive dendrites with arbitrary geometrical parameters (length and diameter) and boundary conditions. We use the location-dependent somatopetal current transfer ratio (the reciprocal of the somatofugal voltage) as the transfer function, and its membrane resistivity derivatives, as the sensitivity functions. In the dendrites, at every path distance from the origin, the sensitivity function in a common form relates the transfer function, membrane resistivity, characteristic input conductance of semi-infinite cable and directional somatofugal input conductances at the given internal site and origin, and the length. Plotted in membrane resistivity versus path distance coordinates, the sensitivity functions display common features: along any coordinate there are low and high ranges, in which the sensitivity, respectively, increases and decreases. The ranges and corresponding rates depend on morphology and boundary conditions in a characteristic manner. These features predict existence of the geometry-dependent range of membrane resistivity (the earlier unattended mid-conductance state), such that the dendrites with a given metrical asymmetry are most distinguished in their transfer properties and electrical states if membrane resistivity is within the range and are not otherwise.

Relationship between morphoelectrotonic properties of motoneuron dendrites and their trajectory

The distribution and geometry of the dendritic trees of spinal motoneurons obey several well-established rules. Some of these rules are based on systematic relationships between quantitative geometrical features (e.g., total dendritic length) and the three-dimensional trajectory followed by dendrites from their origin to their termination. Because dendritic geometry partially determines the transmission of current and voltage signals generated by synapses on the dendritic tree, our goal was to compare the efficacy of signal transmission by dendritic trajectories that followed different directions. To achieve this goal, we constructed detailed compartmental models of the dendritic trees of three intracellularly stained biventer cervicis/complexus (BCCM) motoneurons and calculated the electronic properties of 361 dendritic paths. Each trajectory was classified according to its orientation, e.g., rostral, rostral-dorsal-lateral. The attenuation of current and voltage signals en route to the soma was strongly related to trajectory orientation. Trajectories with similar attenuation factors formed functional subunits that were arranged in distinct domains within the ventral horn. Changes in R(m) or R(i) had little effect on which trajectories belonged to each functional subunit. However, differences in the efficacy of signal transmission between subunits increased during high network activity (mimicked by decreases in R(m)). The most efficient subunit delivered two times more current and four times more voltage to the soma than the least efficient subunit. These results indicate that the input-output properties of motoneurons depend on the direction of the path taken by dendrites from their origin at the cell body to their terminals.

Comparison of the morphological and electrotonic properties of Renshaw cells, Ia inhibitory interneurons, and motoneurons in the cat

The morphological and electrotonic properties of 4 motoneurons, 8 Ia inhibitory interneurons, and 4 Renshaw cells were compared. The morphological analysis, based on 3-D reconstructions of the cells, revealed that dendrites of motoneurons are longer and more extensively branched. Renshaw cells have dendrites that are shorter and simpler in structure. Dendrites of Ia inhibitory interneurons could be as long as those of motoneurons but the branching structure resembled that of Renshaw cells. Compartmental models were used to determine the electrotonic properties of the paths from each dendritic terminal to the soma. The attenuations of steady-state voltage changes in motoneurons were 3 and 7 times larger than in Ia inhibitory interneurons and Renshaw cells, respectively. The same relative order was observed for current attenuation and electrotonic length. The dendritic input resistances in Renshaw cells were 2 and 4 times larger than in Ia inhibitory interneurons and motoneurons, respectively. The difference in these electrotonic properties increased during higher synaptic activity as modeled by a decrease of Rm. The peak amplitudes of voltage transients at sites of brief, synaptic-like changes in conductance were highly dependent on cell class and were largest in Renshaw cells and smallest in motoneurons. In combination with class-specific differences in the attenuation of transient voltage signals, this led to large differences in the peak amplitudes of somatic voltage transients. Differences in the rise times and half-widths of the voltage transients were observed as well. Thus, based on passive properties, each cell class has a unique set of input/output properties.

Visualization of the dendritic arbor of neurons in intact 500 microm thick brain slices

Characterizing the structure and electrophysiological properties of single neurons is essential for understanding how individual cells contribute to the function of neuronal networks. Following intra-cellular recording from neurons in acute brain slices, the structure of the recorded cell has typically been examined by serial sectioning of the tissue slice and then reconstructing the neuron of interest; a labor-intensive and time-consuming process. Here, we have adapted a whole-mount immunohistochemical technique and used it to visualize the dendritic arbor of individual neurons in sections of adult CNS tissue up to 500 microm thick. Permeabilization of the slice and extensive washing allow histochemical reagents to penetrate and be washed from the section, producing limited background staining. Using this method, the cell within the slice can be sectioned optically and reconstructed using the optical sections. We present images of the dendritic trees of neurons in 500 microm thick slices of adult rat entorhinal cortex and hippocampus, labeled either immunohistochemically, or by biocytin injection following whole-cell patch clamp or sharp electrode recordings. The resolution obtained is sufficient to visualize dendritic spines deep within the section. The method is free from artifacts associated with cutting serial sections and is broadly applicable to tasks that require visualization of the fine structure of individual cells in thick slices of CNS tissue.