Automatic characterization and classification of ganglion cells from the salamander retina

Microglial Metamorphosis in Three Dimensions in Virus Limbic Encephalitis: An Unbiased Pictorial Representation Based on a Stereological Sampling Approach of Surveillant and Reactive Microglia

Microglia influence pathological progression in neurological diseases, reacting to insults by expressing multiple morphofunctional phenotypes. However, the complete morphological spectrum of reactive microglia, as revealed by three-dimensional microscopic reconstruction, has not been detailed in virus limbic encephalitis. Here, using an anatomical series of brain sections, we expanded on an earlier Piry arbovirus encephalitis study to include CA1/CA2 and assessed the morphological response of homeostatic and reactive microglia at eight days post-infection. Hierarchical cluster and linear discriminant function analyses of multimodal morphometric features distinguished microglial morphology between infected animals and controls. For a broad representation of the spectrum of microglial morphology in each defined cluster, we chose representative cells of homeostatic and reactive microglia, using the sum of the distances of each cell in relation to all the others. Based on multivariate analysis, reactive microglia of infected animals showed more complex trees and thicker branches, covering a larger volume of tissue than in control animals. This approach offers a reliable representation of microglia dispersion in the Euclidean space, revealing the morphological kaleidoscope of surveillant and reactive microglia morphotypes. Because form precedes function in nature, our findings offer a starting point for research using integrative methods to understand microglia form and function.

Density Visualization Pipeline: A Tool for Cellular and Network Density Visualization and Analysis

Neuron classification is an important component in analyzing network structure and quantifying the effect of neuron topology on signal processing. Current quantification and classification approaches rely on morphology projection onto lower-dimensional spaces. In this paper a 3D visualization and quantification tool is presented. The Density Visualization Pipeline (DVP) computes, visualizes and quantifies the density distribution, i.e., the "mass" of interneurons. We use the DVP to characterize and classify a set of GABAergic interneurons. Classification of GABAergic interneurons is of crucial importance to understand on the one hand their various functions and on the other hand their ubiquitous appearance in the neocortex. 3D density map visualization and projection to the one-dimensional x, y, z subspaces show a clear distinction between the studied cells, based on these metrics. The DVP can be coupled to computational studies of the behavior of neurons and networks, in which network topology information is derived from DVP information. The DVP reads common neuromorphological file formats, e.g., Neurolucida XML files, NeuroMorpho.org SWC files and plain ASCII files. Full 3D visualization and projections of the density to 1D and 2D manifolds are supported by the DVP. All routines are embedded within the visual programming IDE VRL-Studio for Java which allows the definition and rapid modification of analysis workflows.

Morphological Neuron Classification Based on Dendritic Tree Hierarchy

The shape of a neuron can reveal interesting properties about its function. Therefore, morphological neuron characterization can contribute to a better understanding of how the brain works. However, one of the great challenges of neuroanatomy is the definition of morphological properties that can be used for categorizing neurons. This paper proposes a new methodology for neuron morphological analysis by considering different hierarchies of the dendritic tree for characterizing and categorizing neuronal cells. The methodology consists in using different strategies for decomposing the dendritic tree along its hierarchies, allowing the identification of relevant parts (possibly related to specific neuronal functions) for classification tasks. A set of more than 5000 neurons corresponding to 10 classes were examined with supervised classification algorithms based on this strategy. It was found that classification accuracies similar to those obtained by using whole neurons can be achieved by considering only parts of the neurons. Branches close to the soma were found to be particularly relevant for classification.

Nonlinear dynamics support a linear population code in a retinal target-tracking circuit

A basic task faced by the visual system of many organisms is to accurately track the position of moving prey. The retina is the first stage in the processing of such stimuli; the nature of the transformation here, from photons to spike trains, constrains not only the ultimate fidelity of the tracking signal but also the ease with which it can be extracted by other brain regions. Here we demonstrate that a population of fast-OFF ganglion cells in the salamander retina, whose dynamics are governed by a nonlinear circuit, serve to compute the future position of the target over hundreds of milliseconds. The extrapolated position of the target is not found by stimulus reconstruction but is instead computed by a weighted sum of ganglion cell outputs, the population vector average (PVA). The magnitude of PVA extrapolation varies systematically with target size, speed, and acceleration, such that large targets are tracked most accurately at high speeds, and small targets at low speeds, just as is seen in the motion of real prey. Tracking precision reaches the resolution of single photoreceptors, and the PVA algorithm performs more robustly than several alternative algorithms. If the salamander brain uses the fast-OFF cell circuit for target extrapolation as we suggest, the circuit dynamics should leave a microstructure on the behavior that may be measured in future experiments. Our analysis highlights the utility of simple computations that, while not globally optimal, are efficiently implemented and have close to optimal performance over a limited but ethologically relevant range of stimuli.

Shape, connectedness and dynamics in neuronal networks

The morphology of neurons is directly related to several aspects of the nervous system, including its connectedness, health, development, evolution, dynamics and, ultimately, behavior. Such interplays of the neuronal morphology can be understood within the more general shape-function paradigm. The current article reviews, in an introductory way, some key issues regarding the role of neuronal morphology in the nervous system, with emphasis on works developed in the authors' group. The following topics are addressed: (a) characterization of neuronal shape; (b) stochastic synthesis of neurons and neuronal systems; (c) characterization of the connectivity of neuronal networks by using complex networks concepts; and (d) investigations of influences of neuronal shape on network dynamics. The presented concepts and methods are useful also for several other multiple object systems, such as protein-protein interaction, tissues, aggregates and polymers.

A multivariate approach to structural heterogeneity of retinal ganglion cells

Knowing neuronal types is essential for understanding the structural and functional organization of the nervous system. It has long been recognized that neuronal types should be discovered and not defined. This can be done using cluster analysis (CA). Despite there being many studies using CA to classify neurons, only a few of them meet its formal prerequisites. In the present study, we provide an example of using CA in combination with other multivariate techniques for examining neuronal diversity. A special emphasis is put on formal prerequisites to the data and procedure. The data under scrutiny are a sample of ganglion cells projecting to the basal optic nucleus [accessory optic system-projecting ganglion cells (AOS GCs)] in the common frog. There is physiological evidence that these cells comprise at least two functional types but their structural heterogeneity has not been addressed. Cells were labeled with horseradish peroxidase in vivo and examined in whole-mounted retinae using light microscopy. A sample of well-stained cells was obtained and used to estimate 18 structural parameters. A variety of clustering algorithms were used to classify the cells. The joint polar distribution of dendrite mass was monomodal. CA did not reveal a statistically reliable cluster structure in the sample. The clusters were not cohesive and well isolated. ANOVA-on-Ranks revealed no significant between-cluster differences. Our formal conclusion is that functionally distinct frog AOS GCs do not differ in morphology or dendritic arbor orientation. The advantages and limitations of the adopted approach are discussed.

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.

A retinal circuit that computes object motion

Certain ganglion cells in the retina respond sensitively to differential motion between the receptive field center and surround, as produced by an object moving over the background, but are strongly suppressed by global image motion, as produced by the observer's head or eye movements. We investigated the circuit basis for this object motion sensitive (OMS) response by recording intracellularly from all classes of retinal interneurons while simultaneously recording the spiking output of many ganglion cells. Fast, transient bipolar cells respond linearly to motion in the receptive field center. The synaptic output from their terminals is rectified and then pooled by the OMS ganglion cell. A type of polyaxonal amacrine cell is driven by motion in the surround, again via pooling of rectified inputs, but from a different set of bipolar cell terminals. By direct intracellular current injection, we found that these polyaxonal amacrine cells selectively suppress the synaptic input of OMS ganglion cells. A quantitative model of these circuit elements and their interactions explains how an important visual computation is accomplished by retinal neurons and synapses.

L-Measure: a web-accessible tool for the analysis, comparison and search of digital reconstructions of neuronal morphologies

L-Measure (LM) is a freely available software tool for the quantitative characterization of neuronal morphology. LM computes a large number of neuroanatomical parameters from 3D digital reconstruction files starting from and combining a set of core metrics. After more than six years of development and use in the neuroscience community, LM enables the execution of commonly adopted analyses as well as of more advanced functions. This report illustrates several LM protocols: (i) extraction of basic morphological parameters, (ii) computation of frequency distributions, (iii) measurements from user-specified subregions of the neuronal arbors, (iv) statistical comparison between two groups of cells and (v) filtered selections and searches from collections of neurons based on any Boolean combination of the available morphometric measures. These functionalities are easily accessed and deployed through a user-friendly graphical interface and typically execute within few minutes on a set of approximately 20 neurons. The tool is available at http://krasnow.gmu.edu/cn3 for either online use on any Java-enabled browser and platform or download for local execution under Windows and Linux.

Successes and rewards in sharing digital reconstructions of neuronal morphology

The computer-assisted three-dimensional reconstruction of neuronal morphology is becoming an increasingly popular technique to quantify the arborization patterns of dendrites and axons. The resulting digital files are suitable for comprehensive morphometric analyses as well as for building anatomically realistic compartmental models of membrane biophysics and neuronal electrophysiology. The digital tracings acquired in a lab for a specific purpose can be often re-used by a different research group to address a completely unrelated scientific question, if the original investigators are willing to share the data. Since reconstructing neuronal morphology is a labor-intensive process, data sharing and re-analysis is particularly advantageous for the neuroscience and biomedical communities. Here we present numerous cases of "success stories" in which digital reconstructions of neuronal morphology were shared and re-used, leading to additional, independent discoveries and publications, and thus amplifying the impact of the "source" study for which the data set was first collected. In particular, we overview four main applications of this kind of data: comparative morphometric analyses, statistical estimation of potential synaptic connectivity, morphologically accurate electrophysiological simulations, and computational models of neuronal shape and development.

Current trends in shape and texture analysis in neurology: aspects of the morphological substrate of volume and wiring transmission

Morphological and morphometrical studies of neural shape and texture are becoming more and more important in the field of neurosciences due to the recognized close link between shape and function at molecular, cellular and tissutal level. Indeed, some different morphological classes of neurons are known to be correlated to well defined functional classes; several neurological pathologies are associated with modification of neuronal shape; during a neural development, cells impose geometrical and physical constrains at one another. The understanding of these fundamental processes requires morphological/morphometrical analysis. In addition, the geometric properties at the individual level plays a relevant role in defining the actual and the potential global connectivity of the system. From this standpoint it will be important to study the relationship between the shape descriptors illustrated in this paper and the potential global connectivity of the system. In such a context, the classical multivariate statistical tools of analysis (and probably new ones) will become necessary to correctly utilize huge set of information provided from shape and texture descriptors. In this review, avoiding to consider some common measures of shape, such as area, perimeter, perimeter/area ratio, eccentricity, we considered only shape and texture analysis methods classified within the set of scalar transform techniques, discussing their advantages and limitations, especially regarding their application to neuromorphometry.

An integrated approach to the characterization of cell movement

BACKGROUND

Most phenomena in developmental biology involve or depend upon cell migration. This article describes a comprehensive framework for the characterization and analysis of trajectories defined by cell movement. The following two perspectives are considered: (a) the behavior of each individual cell and (b) interactions between neighboring pairs of cells.

METHODS

The measurements considered for individual trajectories include the velocity magnitude and orientation, maximum spatial dispersion, displacement effectiveness, and displacement entropies. Interactions between two trajectories are characterized by comparing the respective velocities.

RESULTS

The potential of the overall framework is illustrated using data of moving cells in different biological environments. The work shows that it is possible to use the new algorithm presented here to characterize cell motility.

CONCLUSIONS

The features of the algorithm were successful in determining the motility changes under different experimental conditions.

Functional organization of ganglion cells in the salamander retina

Recently, we reported a novel technique for recording all of the ganglion cells in a retinal patch and showed that their receptive fields cover visual space roughly 60 times over in the tiger salamander. Here, we carry this analysis further and divide the population of ganglion cells into functional classes using quantitative clustering algorithms that combine several response characteristics. Using only the receptive field to classify ganglion cells revealed six cell types, in agreement with anatomical studies. Adding other response measures served to blur the distinctions between these cell types rather than resolve further classes. Only the biphasic off type had receptive fields that tiled the retina. Even when we attempted to split these classes more finely, ganglion cells with almost identical functional properties were found to have strongly overlapping spatial receptive fields. A territorial spatial organization, where ganglion cell receptive fields tend to avoid those of other cells of the same type, was only found for the biphasic off cell. We further studied the functional segregation of the ganglion cell population by computing the amount of visual information shared between pairs of cells under natural movie stimulation. This analysis revealed an extensive mixing of visual information among cells of different functional type. Together, our results indicate that the salamander retina uses a population code in which every point in visual space is represented by multiple neurons with subtly different visual sensitivities.

New methods for the computer-assisted 3-D reconstruction of neurons from confocal image stacks

Exact geometrical reconstructions of neuronal architecture are indispensable for the investigation of neuronal function. Neuronal shape is important for the wiring of networks, and dendritic architecture strongly affects neuronal integration and firing properties as demonstrated by modeling approaches. Confocal microscopy allows to scan neurons with submicron resolution. However, it is still a tedious task to reconstruct complex dendritic trees with fine structures just above voxel resolution. We present a framework assisting the reconstruction. User time investment is strongly reduced by automatic methods, which fit a skeleton and a surface to the data, while the user can interact and thus keeps full control to ensure a high quality reconstruction. The reconstruction process composes a successive gain of metric parameters. First, a structural description of the neuron is built, including the topology and the exact dendritic lengths and diameters. We use generalized cylinders with circular cross sections. The user provides a rough initialization by marking the branching points. The axes and radii are fitted to the data by minimizing an energy functional, which is regularized by a smoothness constraint. The investigation of proximity to other structures throughout dendritic trees requires a precise surface reconstruction. In order to achieve accuracy of 0.1 microm and below, we additionally implemented a segmentation algorithm based on geodesic active contours that allow for arbitrary cross sections and uses locally adapted thresholds. In summary, this new reconstruction tool saves time and increases quality as compared to other methods, which have previously been applied to real neurons.

Classification of HIV-1-mediated neuronal dendritic and synaptic damage using multiple criteria linear programming

The ability to identify neuronal damage in the dendritic arbor during HIV-1-associated dementia (HAD) is crucial for designing specific therapies for the treatment of HAD. To study this process, we utilized a computer-based image analysis method to quantitatively assess HIV-1 viral protein gp120 and glutamate-mediated individual neuronal damage in cultured cortical neurons. Changes in the number of neurites, arbors, branch nodes, cell body area, and average arbor lengths were determined and a database was formed (http://dm.ist.unomaha. edu/database.htm). We further proposed a two-class model of multiple criteria linear programming (MCLP) to classify such HIV-1-mediated neuronal dendritic and synaptic damages. Given certain classes, including treatments with brain-derived neurotrophic factor (BDNF), glutamate, gp120 or non-treatment controls from our in vitro experimental systems, we used the two-class MCLP model to determine the data patterns between classes in order to gain insight about neuronal dendritic damages. This knowledge can be applied in principle to the design and study of specific therapies for the prevention or reversal of neuronal damage associated with HAD. Finally, the MCLP method was compared with a well-known artificial neural network algorithm to test for the relative potential of different data mining applications in HAD research.

A percolation approach to neural morphometry and connectivity

This article addresses the issues of neural shape characterization and analysis from the perspective of one of the main roles played by neural shapes, namely, connectivity. This study is oriented toward the geometry at the individual cell level and involves the use of the percolation concept from statistical mechanics, which is reviewed in an accessible fashion. The characterization of the neural cell geometry with respect to connectivity is performed in terms of critical percolation probability obtained experimentally while considering several types of geometrical interactions between cells, therefore directly expressing the potential for connections defined by each situation. Two basic situations are considered: dendrite-dendrite and dendrite-axon interactions. The obtained results corroborate the potential of the critical percolation probability as a valuable resource for characterizing, classifying, and analyzing the morphology of neural cells.

Characterization of Melanophore Morphology by Fractal Dimension Analysis

Fractal or focal dimension (FD) analysis is a valuable tool to identify physiologic stimuli at the cellular and tissue levels that allows for quantification of cell perimeter complexity. The FD analysis was determined on fluorescence images of caffeine- or epinephrine-treated (or untreated control) killifish Fundulus heteroclitus (Linneaus) melanophores in culture. Cell perimeters were indicated by rhodamine-phalloidin labeling of cortical microfilaments using box-counting FD analysis. Caffeine-treated melanophores displayed dispersed melanosomes in cells with less serrated edges and reduced FD and complexity. Complexity in epinephrine-treated cells was significantly higher than the caffeine-treated cells or in the control. Cytoarchitectural variability of the cell perimeter is expected because cells change shape when cued with agents. Epinephrine-treated melanophores demonstrated aggregated melanosomes in cells with more serrated edges, significantly higher FD and thus complexity. Melanophores not treated with caffeine or epinephrine produced variable distributions of melanosomes and resulted in cells with variably serrated edges and intermediate FD with a larger SE of the regression and greater range of complexity. Dispersion of melanosomes occurs with rearrangements of the cytoskeleton to accommodate centrifugal distribution of melanosomes throughout the cell and to the periphery. The loading of melanosomes onto cortical microfilaments may provide a less complex cell contour, with the even distribution of the cytoskeleton and melanosomes. Aggregation of melanosomes occurs with rearrangements of the cytoskeleton to accommodate centripetal distribution of melanosomes. The aggregation of melanosomes may contribute to centripetal retraction of the cytoskeleton and plasma membrane. The FD analysis is, therefore, a convenient method to measure contrasting morphologic changes within stimulated cells.

Neuromorphometric characterization with shape functionals

This work presents a procedure to extract morphological information from neuronal cells based on the variation of shape functionals as the cell geometry undergoes a dilation through a wide interval of spatial scales. The targeted shapes are alpha and beta cat retinal ganglion cells, which are characterized by different ranges of dendritic field diameter. Image functionals are expected to act as descriptors of the shape, gathering relevant geometric and topological features of the complex cell form. We present a comparative study of classification performance of additive shape descriptors, namely, Minkowski functionals, and the nonadditive multiscale fractal. We found that the proposed measures perform efficiently the task of identifying the two main classes alpha and beta based solely on scale invariant information, while also providing intraclass morphological assessment.

INVESTIGATING SHAPE AND FUNCTION RELATIONSHIP IN RETINAL GANGLION CELLS

This article addresses the investigation of the relationship between neural shape and function in cat retinal ganglion cells in terms of representative morphological features. More specifically, a series of geometrical measures is extracted from two-dimensional images of these cells, and pattern recognition methods are applied in order to quantify the differentiation between the two classes (i.e., alpha, beta). The morphological measures cover several of the more meaningful geometrical features of neuronal cells, including: (a) the distribution of angles along the cell contours considering several smoothing degrees; (b) the overall interaction between the cell arborization and the surrounding space, quantified in terms of the multiscale fractal dimension; and (c) the distribution of width and extent of the dendritic processes. Several combinations of such morphological measures are assessed with respect to the separability of the classes. The obtained results indicate that the methods based on statistic relation between segment length and segment diameter, and the method of multiscale angle entropy not only successfully encapsulated a large amount of experimental data into relatively compact patterns but also marked off various ganglion cells into befit groups. On the other hand, the method of neuron classification based on fractal dimension resulted relatively less effective for class separation.

Generation of fractal dimension images and its application to automatic edge detection in brain MRI

We have developed four methods to generate a fractal dimension image and have applied them to the brain MRI. We have adopted four types of scanning methods, "CONVENTIONAL", "OVERLAPPING", "SYMMETRIC" and "FOLDED" to estimate the fractal dimension. The first three methods show almost the same fractal dimension images and their values were between two and three. In the "FOLDED" method, we were able to obtain the images in which the edge of a narrow region including dura and scalp surrounding the brain was selectively enhanced in the T1-weighted MRI. This is found to be a new edge-enhancing filter. We could remove the surrounding structure of the brain by using these filtered images and detect the edge of the brain surface automatically. The brain surface data can be used for various applications such as three-dimensional surface display and registration of inter-modal brain images.

Impulse encoding across the dendritic morphologies of retinal ganglion cells

Nerve impulse entrainment and other excitation and passive phenomena are analyzed for a morphologically diverse and exhaustive data set (n = 57) of realistic (3-dimensional computer traced) soma-dendritic tree structures of ganglion cells in the tiger salamander (Ambystoma tigrinum) retina. The neurons, including axon and an anatomically specialized thin axonal segment that is observed in every ganglion cell, were supplied with five voltage- or ligand-gated ion channels (plus leakage), which were distributed in accordance with those found in a recent study that employed an equivalent dendritic cylinder. A wide variety of impulse-entrainment responses was observed, including regular low-frequency firing, impulse doublets, and more complex patterns involving impulse propagation failures (or aborted spikes) within the encoder region, all of which have been observed experimentally. The impulse-frequency response curves of the cells fell into three groups called FAST, MEDIUM, and SLOW in approximate proportion as seen experimentally. In addition to these, a new group was found among the traced cells that exhibited an impulse-frequency response twice that of the FAST category. The total amount of soma-dendritic surface area exhibited by a given cell is decisive in determining its electrophysiological classification. On the other hand, we found only a weak correlation between the electrophysiological group and the morphological classification of a given cell, which is based on the complexity of dendritic branching and the physical reach or "receptive field" area of the cell. Dendritic morphology determines discharge patterns to dendritic (synaptic) stimulation. Orthodromic impulses can be initiated on the axon hillock, the thin axonal segment, the soma, or even the proximal axon beyond the thin segment, depending on stimulus magnitude, soma-dendritic membrane area, channel distribution, and state within the repetitive impulse cycle. Although a sufficiently high dendritic Na-channel density can lead to dendritic impulse initiation, this does not occur with our "standard" channel densities and is not seen experimentally. Even so, impulses initiated elsewhere do invade all except very thin dendritic processes. Impulse-encoding irregularities increase when channel conductances are reduced in the encoder region, and the F/I properties of the cells are a strong function of the calcium- and Ca-activated K-channel densities. Use of equivalent dendritic cylinders requires more soma-dendritic surface area than real dendritic trees, and the source of the discrepancy is discussed.