Image informatics strategies for deciphering neuronal network connectivity

Brain function relies on an intricate network of highly dynamic neuronal connections that rewires dramatically under the impulse of various external cues and pathological conditions. Among the neuronal structures that show morphologi- cal plasticity are neurites, synapses, dendritic spines and even nuclei. This structural remodelling is directly connected with functional changes such as intercellular com- munication and the associated calcium-bursting behaviour. In vitro cultured neu- ronal networks are valuable models for studying these morpho-functional changes. Owing to the automation and standardisation of both image acquisition and image analysis, it has become possible to extract statistically relevant readout from such networks. Here, we focus on the current state-of-the-art in image informatics that enables quantitative microscopic interrogation of neuronal networks. We describe the major correlates of neuronal connectivity and present workflows for analysing them. Finally, we provide an outlook on the challenges that remain to be addressed, and discuss how imaging algorithms can be extended beyond in vitro imaging studies

Reading out a spatiotemporal population code by imaging neighbouring parallel fibre axons in vivo.

The spatiotemporal pattern of synaptic inputs to the dendritic tree is crucial for synaptic integration and plasticity. However, it is not known if input patterns driven by sensory stimuli are structured or random. Here we investigate the spatial patterning of synaptic inputs by directly monitoring presynaptic activity in the intact mouse brain on the micron scale. Using in vivo calcium imaging of multiple neighbouring cerebellar parallel fibre axons, we find evidence for clustered patterns of axonal activity during sensory processing. The clustered parallel fibre input we observe is ideally suited for driving dendritic spikes, postsynaptic calcium signalling, and synaptic plasticity in downstream Purkinje cells, and is thus likely to be a major feature of cerebellar function during sensory processing

Computational geometry analysis of dendritic spines by structured illumination microscopy

We are currently short of methods that can extract objective parameters of dendritic spines useful for their categorization. Authors present in this study an automatic analytical pipeline for spine geometry using 3D-structured illumination microscopy, which can effectively extract many geometrical parameters of dendritic spines without bias and automatically categorize spine population based on their morphological feature

Automated Three-Dimensional Detection and Shape Classification of Dendritic Spines from Fluorescence Microscopy Images

A fundamental challenge in understanding how dendritic spine morphology controls learning and memory has been quantifying three-dimensional (3D) spine shapes with sufficient precision to distinguish morphologic types, and sufficient throughput for robust statistical analysis. The necessity to analyze large volumetric data sets accurately, efficiently, and in true 3D has been a major bottleneck in deriving reliable relationships between altered neuronal function and changes in spine morphology. We introduce a novel system for automated detection, shape analysis and classification of dendritic spines from laser scanning microscopy (LSM) images that directly addresses these limitations. The system is more accurate, and at least an order of magnitude faster, than existing technologies. By operating fully in 3D the algorithm resolves spines that are undetectable with standard two-dimensional (2D) tools. Adaptive local thresholding, voxel clustering and Rayburst Sampling generate a profile of diameter estimates used to classify spines into morphologic types, while minimizing optical smear and quantization artifacts. The technique opens new horizons on the objective evaluation of spine changes with synaptic plasticity, normal development and aging, and with neurodegenerative disorders that impair cognitive function

Experience-dependent structural plasticity at pre- and postsynaptic sites of layer 2/3 cells in developing visual cortex

The developing brain can respond quickly to altered sensory experience by circuit reorganization. During a critical period in early life, neurons in the primary visual cortex rapidly lose responsiveness to an occluded eye and come to respond better to the open eye. While physiological and some of the molecular mechanisms of this process have been characterized, its structural basis, except for the well-known changes in the thalamocortical projection, remains obscure. To elucidate the relationship between synaptic remodeling and functional changes during this experience-dependent process, we used 2-photon microscopy to image synaptic structures of sparsely labeled layer 2/3 neurons in the binocular zone of mouse primary visual cortex. Anatomical changes at presynaptic and postsynaptic sites in mice undergoing monocular visual deprivation (MD) were compared to those in control mice with normal visual experience. We found that postsynaptic spines remodeled quickly in response to MD, with neurons more strongly dominated by the deprived eye losing more spines. These postsynaptic changes parallel changes in visual responses during MD and their recovery after restoration of binocular vision. In control animals with normal visual experience, the formation of presynaptic boutons increased during the critical period and then declined. MD affected bouton formation, but with a delay, blocking it after 3 d. These findings reveal intracortical anatomical changes in cellular layers of the cortex that can account for rapid activity-dependent plasticity

Maternal Loss of Ube3a Impairs Experience-Driven Dendritic Spine Maintenance in the Developing Visual Cortex

Dendritic spines are a morphological feature of the majority of excitatory synapses in the mammalian neocortex and are motile structures with shapes and lifetimes that change throughout development. Proper cortical development and function, including cortical contributions to learning and memory formation, require appropriate experience-dependent dendritic spine remodeling. Dendritic spine abnormalities have been reported for many neurodevelopmental disorders, including Angelman syndrome (AS), which is caused by the loss of the maternally inherited UBE3A allele (encoding ubiquitin protein ligase E3A). Prior studies revealed that UBE3A protein loss leads to reductions in dendritic spine density and diminished excitatory synaptic transmission. However, the decrease in spine density could come from either a reduction in spine formation or an increase in spine elimination. Here, we used acute and longitudinal in vivo two-photon microscopy to investigate developmental and experience-dependent changes in the numbers, dynamics, and morphology of layer 5 pyramidal neuron apical dendritic spines in the primary visual cortex of control and AS model mice (Ube3am−/p+ mice). We found that neurons in AS model mice undergo a greater elimination of dendritic spines than wild-type mice during the end of the first postnatal month. However, when raised in darkness, spine density and dynamics were indistinguishable between control and AS model mice, which indicates that decreased spine density in AS model mice reflects impaired experience-driven spine maintenance. Our data thus demonstrate an experience-dependent anatomical substrate by which the loss of UBE3A reduces dendritic spine density and disrupts cortical circuitry

Imaging the Impact of Cortical Microcirculation on Synaptic Structure and Sensory-Evoked Hemodynamic Responses In Vivo

In vivo two-photon microscopy was used to image in real time dendrites and their spines in a mouse photothrombotic stroke model that reduced somatosensory cortex blood flow in discrete regions of cortical functional maps. This approach allowed us to define relationships between blood flow, cortical structure, and function on scales not previously achieved with macroscopic imaging techniques. Acute ischemic damage to dendrites was triggered within 30 min when blood flow over >0.2 mm(2) of cortical surface was blocked. Rapid damage was not attributed to a subset of clotted or even leaking vessels (extravasation) alone. Assessment of stroke borders revealed a remarkably sharp transition between intact and damaged synaptic circuitry that occurred over tens of μm and was defined by a transition between flowing and blocked vessels. Although dendritic spines were normally ~13 μm from small flowing vessels, we show that intact dendritic structure can be maintained (in areas without flowing vessels) by blood flow from vessels that are on average 80 μm away. Functional imaging of intrinsic optical signals associated with activity-evoked hemodynamic responses in somatosensory cortex indicated that sensory-induced changes in signal were blocked in areas with damaged dendrites, but were present ~400 μm away from the border of dendritic damage. These results define the range of influence that blood flow can have on local cortical fine structure and function, as well as to demonstrate that peri-infarct tissues can be functional within the first few hours after stroke and well positioned to aid in poststroke recovery

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

Modeling Brain Circuitry over a Wide Range of Scales

If we are ever to unravel the mysteries of brain function at its most fundamental level, we will need a precise understanding of how its component neurons connect to each other. Electron Microscopes (EM) can now provide the nanometer resolution that is needed to image synapses, and therefore connections, while Light Microscopes (LM) see at the micrometer resolution required to model the 3D structure of the dendritic network. Since both the topology and the connection strength are integral parts of the brain's wiring diagram, being able to combine these two modalities is critically important. In fact, these microscopes now routinely produce high-resolution imagery in such large quantities that the bottleneck becomes automated processing and interpretation, which is needed for such data to be exploited to its full potential. In this paper, we briefly review the Computer Vision techniques we have developed at EPFL to address this need. They include delineating dendritic arbors from LM imagery, segmenting organelles from EM, and combining the two into a consistent representation