Fast extraction of neuron morphologies from large-scale SBFSEM image stacks

Neuron morphology is frequently used to classify cell-types in the mammalian cortex. Apart from the shape of the soma and the axonal projections, morphological classification is largely defined by the dendrites of a neuron and their subcellular compartments, referred to as dendritic spines. The dimensions of a neuron’s dendritic compartment, including its spines, is also a major determinant of the passive and active electrical excitability of dendrites. Furthermore, the dimensions of dendritic branches and spines change during postnatal development and, possibly, following some types of neuronal activity patterns, changes depending on the activity of a neuron. Due to their small size, accurate quantitation of spine number and structure is difficult to achieve (Larkman, J Comp Neurol 306:332, 1991). Here we follow an analysis approach using high-resolution EM techniques. Serial block-face scanning electron microscopy (SBFSEM) enables automated imaging of large specimen volumes at high resolution. The large data sets generated by this technique make manual reconstruction of neuronal structure laborious. Here we present NeuroStruct, a reconstruction environment developed for fast and automated analysis of large SBFSEM data sets containing individual stained neurons using optimized algorithms for CPU and GPU hardware. NeuroStruct is based on 3D operators and integrates image information from image stacks of individual neurons filled with biocytin and stained with osmium tetroxide. The focus of the presented work is the reconstruction of dendritic branches with detailed representation of spines. NeuroStruct delivers both a 3D surface model of the reconstructed structures and a 1D geometrical model corresponding to the skeleton of the reconstructed structures. Both representations are a prerequisite for analysis of morphological characteristics and simulation signalling within a neuron that capture the influence of spines

Impact of Dendritic Size and Dendritic Topology on Burst Firing in Pyramidal Cells

Neurons display a wide range of intrinsic firing patterns. A particularly relevant pattern for neuronal signaling and synaptic plasticity is burst firing, the generation of clusters of action potentials with short interspike intervals. Besides ion-channel composition, dendritic morphology appears to be an important factor modulating firing pattern. However, the underlying mechanisms are poorly understood, and the impact of morphology on burst firing remains insufficiently known. Dendritic morphology is not fixed but can undergo significant changes in many pathological conditions. Using computational models of neocortical pyramidal cells, we here show that not only the total length of the apical dendrite but also the topological structure of its branching pattern markedly influences inter- and intraburst spike intervals and even determines whether or not a cell exhibits burst firing. We found that there is only a range of dendritic sizes that supports burst firing, and that this range is modulated by dendritic topology. Either reducing or enlarging the dendritic tree, or merely modifying its topological structure without changing total dendritic length, can transform a cell's firing pattern from bursting to tonic firing. Interestingly, the results are largely independent of whether the cells are stimulated by current injection at the soma or by synapses distributed over the dendritic tree. By means of a novel measure called mean electrotonic path length, we show that the influence of dendritic morphology on burst firing is attributable to the effect both dendritic size and dendritic topology have, not on somatic input conductance, but on the average spatial extent of the dendritic tree and the spatiotemporal dynamics of the dendritic membrane potential. Our results suggest that alterations in size or topology of pyramidal cell morphology, such as observed in Alzheimer's disease, mental retardation, epilepsy, and chronic stress, could change neuronal burst firing and thus ultimately affect information processing and cognition

Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties

The thick-tufted layer 5b pyramidal cell extends its dendritic tree to all six layers of the mammalian neocortex and serves as a major building block for the cortical column. L5b pyramidal cells have been the subject of extensive experimental and modeling studies, yet conductance-based models of these cells that faithfully reproduce both their perisomatic Na+-spiking behavior as well as key dendritic active properties, including Ca2+ spikes and back-propagating action potentials, are still lacking. Based on a large body of experimental recordings from both the soma and dendrites of L5b pyramidal cells in adult rats, we characterized key features of the somatic and dendritic firing and quantified their statistics. We used these features to constrain the density of a set of ion channels over the soma and dendritic surface via multi-objective optimization with an evolutionary algorithm, thus generating a set of detailed conductance-based models that faithfully replicate the back-propagating action potential activated Ca2+ spike firing and the perisomatic firing response to current steps, as well as the experimental variability of the properties. Furthermore, we show a useful way to analyze model parameters with our sets of models, which enabled us to identify some of the mechanisms responsible for the dynamic properties of L5b pyramidal cells as well as mechanisms that are sensitive to morphological changes. This automated framework can be used to develop a database of faithful models for other neuron types. The models we present provide several experimentally-testable predictions and can serve as a powerful tool for theoretical investigations of the contribution of single-cell dynamics to network activity and its computational capabilities

Bidirectional Coupling between Astrocytes and Neurons Mediates Learning and Dynamic Coordination in the Brain: A Multiple Modeling Approach

In recent years research suggests that astrocyte networks, in addition to nutrient and waste processing functions, regulate both structural and synaptic plasticity. To understand the biological mechanisms that underpin such plasticity requires the development of cell level models that capture the mutual interaction between astrocytes and neurons. This paper presents a detailed model of bidirectional signaling between astrocytes and neurons (the astrocyte-neuron model or AN model) which yields new insights into the computational role of astrocyte-neuronal coupling. From a set of modeling studies we demonstrate two significant findings. Firstly, that spatial signaling via astrocytes can relay a “learning signal” to remote synaptic sites. Results show that slow inward currents cause synchronized postsynaptic activity in remote neurons and subsequently allow Spike-Timing-Dependent Plasticity based learning to occur at the associated synapses. Secondly, that bidirectional communication between neurons and astrocytes underpins dynamic coordination between neuron clusters. Although our composite AN model is presently applied to simplified neural structures and limited to coordination between localized neurons, the principle (which embodies structural, functional and dynamic complexity), and the modeling strategy may be extended to coordination among remote neuron clusters

Computational subunits in thin dendrites of pyramidal cells

The thin basal and oblique dendrites of cortical pyramidal neurons receive most of the cells' synaptic input, but their integrative properties remain uncertain. Previous studies have most often reported global linear or sublinear summation. An alternative view,supported by biophysical modeling studies, holds that thin dendrites provide a layer of independent computational 'subunits' that sigmoidally modulate their inputs prior to global summation. To distinguish these possibilities, we combined confocal imaging and dual-site focal synaptic stimulation of identified thin dendrites in rat neocortical pyramidal neurons. We found that nearby inputs on the same branch summed sigmoidally, whereas widely separated inputs or inputs to different branches summed linearly. This strong spatial compartmentalization effect is incompatible with a global summation rule and provides the first experimental support for a two-layer "neural network" [The quotes are left in to refer to a standard architecture in the artificial neural network field] model of pyramidal neuron thin-branch integration. Our findings could have important implications for the computing and memory-related functions of cortical tissue

Gametogenic and reproductive cycles of the sea anemone, Entacmaea quadricolor

Host sea anemones are ecologically important as they provide habitat for obligate symbiotic anemonefish in many areas of the Indo-Pacific. Despite their importance, no information is available on their gametogenic cycles. This study aimed to address this lack of knowledge by determining the gametogenic cycles of Entacmaea quadricolor. Gonad samples were taken from January 2003 to February 2005 at North Solitary Island, Solitary Islands Marine Park, Australia using a specially developed non-lethal field biopsy sampling technique. Sampling was done 17 times during the study period, with 15–20 individuals being sampled on each occasion. Samples were examined prior to fixation, and then histologically sectioned to determine the reproductive activity of each individual. Female anemones were significantly more abundant than males, and had asynchronous oocyte development both within and among individuals. Male anemones showed a single annual cycle of spermary growth, development and spawning. Data from the 26-month study indicated that spawning occurred in the austral summer and autumn between January and April, which coincided with the observed spawning periods that have previously been documented for this species in outdoor flow-through seawater tanks at the study location. The biopsy sampling technique used during this study provides an opportunity to gain a more thorough understanding of the gametogenic cycles and sexual pattern of host sea anemones throughout their distribution

L'aide aux aidants en Alsace Bossue

STRASBOURG ILLKIRCH-Pharmacie (672182101) / SudocSudocFranceF

Properties of basal dendrites of layer 5 pyramidal neurons: a direct patch-clamp recording study

Basal dendrites receive the majority of synapses that contact neocortical pyramidal neurons, yet our knowledge of synaptic processing in these dendrites has been hampered by their inaccessibility for electrical recordings. A new approach to patch-clamp recordings enabled us to characterize the integrative properties of these cells. Despite the short physical length of rat basal dendrites, synaptic inputs were electrotonically remote from the soma (>30-fold excitatory postsynaptic potential (EPSP) attenuation) and back-propagating action potentials were significantly attenuated. Unitary EPSPs were location dependent, reaching large amplitudes distally (>8 mV), yet their somatic contribution was relatively location independent. Basal dendrites support sodium and NMDA spikes, but not calcium spikes, for 75% of their length. This suggests that basal dendrites, despite their proximity to the site of action potential initiation, do not form a single basal-somatic region but rather should be considered as a separate integrative compartment favoring two integration modes: subthreshold, location-independent summation versus local amplification of incoming spatiotemporally clustered information