Towards three-dimensional non-invasive recording of incised rock art

Ancient art cut into rock is difficult to research and manage off-site without precise three-dimensional records. Experiments with photographic modelling by the authors led to a relatively accessible and economical way of making them

Observed network dynamics from altering the balance between excitatory and inhibitory neurons in cultured networks

Complexity in the temporal organization of neural systems may be a reflection of the diversity of its neural constituents. These constituents, excitatory and inhibitory neurons, comprise an invariant ratio in vivo and form the substrate for rhythmic oscillatory activity. To begin to elucidate the dynamical mechanisms that underlie this balance, we construct novel neural circuits not ordinarily found in nature. We culture several networks of neurons composed of excitatory and inhibitory cells and use a multi-electrode array to study their temporal dynamics as the balance is modulated. We use the electrode burst as the temporal imprimatur to signify the presence of network activity. Burst durations, inter-burst intervals, and the number of spikes participating within a burst are used to illustrate the vivid dynamical differences between the various cultured networks. When the network consists largely of excitatory neurons, no network temporal structure is apparent. However, the addition of inhibitory neurons evokes a temporal order. Calculation of the temporal autocorrelation shows that when the number of inhibitory neurons is a major fraction of the network, a striking network pattern materializes when none was previously present

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

Dyrk1A Influences Neuronal Morphogenesis Through Regulation of Cytoskeletal Dynamics in Mammalian Cortical Neurons

Down syndrome (DS) is the most frequent genetic cause of mental retardation. Cognitive dysfunction in these patients is correlated with reduced dendritic branching and complexity, along with fewer spines of abnormal shape that characterize the cortical neuronal profile of DS. DS phenotypes are caused by the disruptive effect of specific trisomic genes. Here, we report that overexpression of dual-specificity tyrosine phosphorylation-regulated kinase 1A, DYRK1A, is sufficient to produce the dendritic alterations observed in DS patients. Engineered changes in Dyrk1A gene dosage in vivo strongly alter the postnatal dendritic arborization processes with a similar progression than in humans. In cultured mammalian cortical neurons, we determined a reduction of neurite outgrowth and synaptogenesis. The mechanism underlying neurite dysgenesia involves changes in the dynamic reorganization of the cytoskeleton

Synaptic Transmission Optimization Predicts Expression Loci of Long-Term Plasticity

Long-term modifications of neuronal connections are critical for reliable memory storage in the brain. However, their locus of expression—pre- or postsynaptic—is highly variable. Here we introduce a theoretical framework in which long-term plasticity performs an optimization of the postsynaptic response statistics toward a given mean with minimal variance. Consequently, the state of the synapse at the time of plasticity induction determines the ratio of pre- and postsynaptic modifications. Our theory explains the experimentally observed expression loci of the hippocampal and neocortical synaptic potentiation studies we examined. Moreover, the theory predicts presynaptic expression of long-term depression, consistent with experimental observations. At inhibitory synapses, the theory suggests a statistically efficient excitatory-inhibitory balance in which changes in inhibitory postsynaptic response statistics specifically target the mean excitation. Our results provide a unifying theory for understanding the expression mechanisms and functions of long-term synaptic transmission plasticity

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

The p53 Tumor Suppressor-Like Protein nvp63 Mediates Selective Germ Cell Death in the Sea Anemone Nematostella vectensis

Here we report the identification and molecular function of the p53 tumor suppressor-like protein nvp63 in a non-bilaterian animal, the starlet sea anemone Nematostella vectensis. So far, p53-like proteins had been found in bilaterians only. The evolutionary origin of p53-like proteins is highly disputed and primordial p53-like proteins are variably thought to protect somatic cells from genotoxic stress. Here we show that ultraviolet (UV) irradiation at low levels selectively induces programmed cell death in early gametes but not somatic cells of adult N. vectensis polyps. We demonstrate with RNA interference that nvp63 mediates this cell death in vivo. Nvp63 is the most archaic member of three p53-like proteins found in N. vectensis and in congruence with all known p53-like proteins, nvp63 binds to the vertebrate p53 DNA recognition sequence and activates target gene transcription in vitro. A transactivation inhibitory domain at its C-terminus with high homology to the vertebrate p63 may regulate nvp63 on a molecular level. The genotoxic stress induced and nvp63 mediated apoptosis in N. vectensis gametes reveals an evolutionary ancient germ cell protective pathway which relies on p63-like proteins and is conserved from cnidarians to vertebrates

Advances in diffusion MRI acquisition and processing in the Human Connectome Project

The Human Connectome Project (HCP) is a collaborative 5-year effort to map human brain connections and their variability in healthy adults. A consortium of HCP investigators will study a population of 1200 healthy adults using multiple imaging modalities, along with extensive behavioral and genetic data. In this overview, we focus on diffusion MRI (dMRI) and the structural connectivity aspect of the project. We present recent advances in acquisition and processing that allow us to obtain very high-quality in-vivo MRI data, whilst enabling scanning of a very large number of subjects. These advances result from 2 years of intensive efforts in optimising many aspects of data acquisition and processing during the piloting phase of the project. The data quality and methods described here are representative of the datasets and processing pipelines that will be made freely available to the community at quarterly intervals, beginning in 2013

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