Design and implementation of multi-signal and time-varying neural reconstructions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.Several efficient procedures exist to digitally trace neuronal structure from light microscopy, and mature community resources have emerged to store, share, and analyze these datasets. In contrast, the quantification of intracellular distributions and morphological dynamics is not yet standardized. Current widespread descriptions of neuron morphology are static and inadequate for subcellular characterizations. We introduce a new file format to represent multichannel information as well as an open-source Vaa3D plugin to acquire this type of data. Next we define a novel data structure to capture morphological dynamics, and demonstrate its application to different time-lapse experiments. Importantly, we designed both innovations as judicious extensions of the classic SWC format, thus ensuring full back-compatibility with popular visualization and modeling tools. We then deploy the combined multichannel/time-varying reconstruction system on developing neurons in live Drosophila larvae by digitally tracing fluorescently labeled cytoskeletal components along with overall dendritic morphology as they changed over time. This same design is also suitable for quantifying dendritic calcium dynamics and tracking arbor-wide movement of any subcellular substrate of interest.Peer reviewe

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

A realistic morpho-anatomical connection strategy for modelling full-scale point-neuron microcircuits

The modeling of extended microcircuits is emerging as an effective tool to simulate the neurophysiological correlates of brain activity and to investigate brain dysfunctions. However, for specific networks, a realistic modeling approach based on the combination of available physiological, morphological and anatomical data is still an open issue. One of the main problems in the generation of realistic networks lies in the strategy adopted to build network connectivity. Here we propose a method to implement a neuronal network at single cell resolution by using the geometrical probability volumes associated with pre- and postsynaptic neurites. This allows us to build a network with plausible connectivity properties without the explicit use of computationally intensive touch detection algorithms using full 3D neuron reconstructions. The method has been benchmarked for the mouse hippocampus CA1 area, and the results show that this approach is able to generate full-scale brain networks at single cell resolution that are in good agreement with experimental findings. This geometric reconstruction of axonal and dendritic occupancy, by effectively reflecting morphological and anatomical constraints, could be integrated into structured simulators generating entire circuits of different brain areas facilitating the simulation of different brain regions with realistic models

Increased axonal bouton dynamics in the aging mouse cortex

Aging is a major risk factor for many neurological diseases and is associated with mild cognitive decline. Previous studies suggest that aging is accompanied by reduced synapse number and synaptic plasticity in specific brain regions. However, most studies, to date, used either postmortem or ex vivo preparations and lacked key in vivo evidence. Thus, whether neuronal arbors and synaptic structures remain dynamic in the intact aged brain and whether specific synaptic deficits arise during aging remains unknown. Here we used in vivo two-photon imaging and a unique analysis method to rigorously measure and track the size and location of axonal boutons in aged mice. Unexpectedly, the aged cortex shows circuit-specific increased rates of axonal bouton formation, elimination, and destabilization. Compared with the young adult brain, large (i.e., strong) boutons show 10-fold higher rates of destabilization and 20-fold higher turnover in the aged cortex. Size fluctuations of persistent boutons, believed to encode long-term memories, also are larger in the aged brain, whereas bouton size and density are not affected. Our data uncover a striking and unexpected increase in axonal bouton dynamics in the aged cortex. The increased turnover and destabilization rates of large boutons indicate that learning and memory deficits in the aged brain arise not through an inability to form new synapses but rather through decreased synaptic tenacity. Overall our study suggests that increased synaptic structural dynamics in specific cortical circuits may be a mechanism for age-related cognitive decline

Na+ imaging reveals little difference in action potential–evoked Na+ influx between axon and soma

Author Posting. © The Authors, 2010. This is the author's version of the work. It is posted here by permission of Nature Publishing Group for personal use, not for redistribution. The definitive version was published in Nature Neuroscience 13 (2010): 852-860, doi:10.1038/nn.2574.In cortical pyramidal neurons, the axon initial segment (AIS) plays a pivotal role in synaptic integration. It has been asserted that this property reflects a high density of Na+ channels in AIS. However, we here report that AP–associated Na+ flux, as measured by high–speed fluorescence Na+ imaging, is about 3 times larger in the rat AIS than in the soma. Spike evoked Na+ flux in the AIS and the first node of Ranvier is about the same, and in the basal dendrites it is about 8 times lower. At near threshold voltages persistent Na+ conductance is almost entirely axonal. Finally, we report that on a time scale of seconds, passive diffusion and not pumping is responsible for maintaining transmembrane Na+ gradients in thin axons during high frequency AP firing. In computer simulations, these data were consistent with the known features of AP generation in these neurons.Supported by US– Israel BSF Grant (2003082), Grass Faculty Grant from the MBL, NIH Grant (NS16295), Multiple Sclerosis Society Grant (PP1367), and a fellowship from the Gruss Lipper Foundation

Gotta trace ‘em all: A mini-review on tools and procedures for segmenting single neurons toward deciphering the structural connectome

Decoding the morphology and physical connections of all the neurons populating a brain is necessary for predicting and studying the relationships between its form and function, as well as for documenting structural abnormalities in neuropathies. Digitizing a complete and high-fidelity map of the mammalian brain at the micro-scale will allow neuroscientists to understand disease, consciousness, and ultimately what it is that makes us humans. The critical obstacle for reaching this goal is the lack of robust and accurate tools able to deal with 3D datasets representing dense-packed cells in their native arrangement within the brain. This obliges neuroscientist to manually identify the neurons populating an acquired digital image stack, a notably time-consuming procedure prone to human bias. Here we review the automatic and semi-automatic algorithms and software for neuron segmentation available in the literature, as well as the metrics purposely designed for their validation, highlighting their strengths and limitations. In this direction, we also briefly introduce the recent advances in tissue clarification that enable significant improvements in both optical access of neural tissue and image stack quality, and which could enable more efficient segmentation approaches. Finally, we discuss new methods and tools for processing tissues and acquiring images at sub-cellular scales, which will require new robust algorithms for identifying neurons and their sub-structures (e.g., spines, thin neurites). This will lead to a more detailed structural map of the brain, taking twenty-first century cellular neuroscience to the next level, i.e., the Structural Connectome