Neuron tracing in perspective

Early central and peripheral corneal microstructural changes in type 2 diabetes mellitus patients identified using in vivo confocal microscopy: A case-control study

The aim of this study was to find early central and peripheral corneal microstructural changes in healthy subjects and type 2 diabetes mellitus (T2DM) patients with/without cornea fluorescein dot staining.This is a prospective case-control study of T2DM patients with/without cornea fluorescein dot staining. Age, sex, duration of diabetes, and serum glycosylated hemoglobin A1c (HbA1c) levels were recorded. Keratograph 5 M (K5 M) and in vivo confocal microscopy were performed on all subjects. The cornea was divided into 5 zones: central, superior, temporal, nasal, and inferior. Basal epithelial cell (BEC) density, the area of BEC, sub-basal nerve plexus (SBN) density, Langerhans cell (LC), and endothelial cell (EC) density were quantitatively analyzed.This study included a total of 87 individuals (28 males and 59 females; mean age, 62.30 ± 9.93 years) with T2DM, without (n = 48; T2DM group 1) and with (n = 39; T2DM group 2) cornea fluorescein staining, as well as 51 age- and sex-matched healthy subjects (18 males and 33 females; mean age, 61.53 ± 10.15 years). Ocular Surface Disease Index scores, Schirmer Ι test, tear meniscus height, the first breakup of tear film occurrence (NIKBUT-first), and the average time of all breakup incidents (NIKBUT-average) values were significantly lower for the T2DM groups than for the healthy group. The corneal sensations of all cornea positions in the T2DM groups were significantly different from the control group. The HbA1c in the T2DM groups showed a negative correlation with central BEC density (R = 0.348, P = .015; R = 0.91, P = .001); there was no correlation of HbA1c with BEC density in the control group. The BEC density, the area of BEC, SBN, and LC density of T2DM group 1 and T2DM group 2 were significantly different compared with the control group in all corneal positions (P < .001). The BEC density of T2DM group 2 was significantly different from T2DM group 1 in the central (P = .044) and inferior (P = .013) zones. The area of BEC in T2DM group 2 was significantly different from T2DM group 1 in inferior zone (P = .014) and other corneal positions showed was no significant difference (P > .05). The SBN density of T2DM group 2 was not significantly different from T2DM group 1 in all corneal positions (P > .05). The LC density of T2DM group 2 was significantly different from T2DM group 1 in the central (P = .006) and inferior (P = .006) zones. Although the LC density in the T2DM groups showed no significant difference in all corneal zones (P > .05), the LC density in the central zone was significantly lower compared with the peripheral zone in the control group (P = .001). The central ECs in the 3 groups were not significantly different (P > .05).LC induced an immune-mediated contribution to corneal nerve damage and may influence the early stages of BEC proliferation and differentiation in T2DM. BEC density was the reliable index for evaluating the early condition of diabetic corneal epitheliopathy. The BEC density of the central and inferior corneal zones was more sensitive.

NeuronRead, an open source semi-automated tool for morphometric analysis of phase contrast and fluorescence neuronal images

Neurons are specialized cells of the Central Nervous System whose function is intricately related to the neuritic network they develop to transmit information. Morphological evaluation of this network and other neuronal structures is required to establish relationships between neuronal morphology and function, and may allow monitoring physiological and pathophysiologic alterations. Fluorescence-based microphotographs are the most widely used in cellular bioimaging, but phase contrast (PhC) microphotographs are easier to obtain, more affordable, and do not require invasive, complicated and disruptive techniques. Despite the various freeware tools available for fluorescence-based images analysis, few exist that can tackle the more elusive and harder-to-analyze PhC images. To surpass this, an interactive semi-automated image processing workflow was developed to easily extract relevant information (e.g. total neuritic length, average cell body area) from both PhC and fluorescence neuronal images. This workflow, named 'NeuronRead', was developed in the form of an ImageJ macro. Its robustness and adaptability were tested and validated on rat cortical primary neurons under control and differentiation inhibitory conditions. Validation included a comparison to manual determinations and to a golden standard freeware tool for fluorescence image analysis. NeuronRead was subsequently applied to PhC images of neurons at distinct differentiation days and exposed or not to DAPT, a pharmacological inhibitor of the γ-secretase enzyme, which cleaves the well-known Alzheimer's amyloid precursor protein (APP) and the Notch receptor. Data obtained confirms a neuritogenic regulatory role for γ-secretase products and validates NeuronRead as a time- and cost-effective useful monitoring tool.

Absence of the neurogenesis-dependent nuclear receptor TLX induces inflammation in the hippocampus

The orphan nuclear receptor TLX (Nr2e1) is a key regulator of hippocampal neurogenesis. Impaired adult hippocampal neurogenesis has been reported in neurodegenerative and psychiatric conditions including dementia and stress-related depression. Neuroinflammation is also implicated in the neuropathology of these disorders, and has been shown to negatively affect hippocampal neurogenesis. To investigate a role for TLX in hippocampal neuroinflammation, we assessed microglial activation in the hippocampus of mice with a spontaneous deletion of TLX. Results from our study suggest that a lack of TLX is implicated in deregulation of microglial phenotype and that consequently, the survival and function of newborn cells in the hippocampus is impaired. TLX may be an important target in understanding inflammatory-associated impairments in neurogenesis.

System-wide organization of actin cytoskeleton determines organelle transport in hypocotyl plant cells

The actin cytoskeleton is an essential intracellular filamentous structure that underpins cellular transport and cytoplasmic streaming in plant cells. However, the system-level properties of actin-based cellular trafficking remain tenuous, largely due to the inability to quantify key features of the actin cytoskeleton. Here, we developed an automated image-based, network-driven framework to accurately segment and quantify actin cytoskeletal structures and Golgi transport. We show that the actin cytoskeleton in both growing and elongated hypocotyl cells has structural properties facilitating efficient transport. Our findings suggest that the erratic movement of Golgi is a stable cellular phenomenon that might optimize distribution efficiency of cell material. Moreover, we demonstrate that Golgi transport in hypocotyl cells can be accurately predicted from the actin network topology alone. Thus, our framework provides quantitative evidence for system-wide coordination of cellular transport in plant cells and can be readily applied to investigate cytoskeletal organization and transport in other organisms.

Automated neuron tracing using probability hypothesis density filtering

Motivation

The functionality of neurons and their role in neuronal networks is tightly connected to the cell morphology. A fundamental problem in many neurobiological studies aiming to unravel this connection is the digital reconstruction of neuronal cell morphology from microscopic image data. Many methods have been developed for this, but they are far from perfect, and better methods are needed.

Results

Here we present a new method for tracing neuron centerlines needed for full reconstruction. The method uses a fundamentally different approach than previous methods by considering neuron tracing as a Bayesian multi-object tracking problem. The problem is solved using probability hypothesis density filtering. Results of experiments on 2D and 3D fluorescence microscopy image datasets of real neurons indicate the proposed method performs comparably or even better than the state of the art.

Availability and Implementation

Software implementing the proposed neuron tracing method was written in the Java programming language as a plugin for the ImageJ platform. Source code is freely available for non-commercial use at https://bitbucket.org/miroslavradojevic/phd .

Contact

meijering@imagescience.org.

Supplementary information

Supplementary data are available at Bioinformatics online.

A Manual Segmentation Tool for Three-Dimensional Neuron Datasets

To date, automated or semi-automated software and algorithms for segmentation of neurons from three-dimensional imaging datasets have had limited success. The gold standard for neural segmentation is considered to be the manual isolation performed by an expert. To facilitate the manual isolation of complex objects from image stacks, such as neurons in their native arrangement within the brain, a new Manual Segmentation Tool (ManSegTool) has been developed. ManSegTool allows user to load an image stack, scroll down the images and to manually draw the structures of interest stack-by-stack. Users can eliminate unwanted regions or split structures (i.e., branches from different neurons that are too close each other, but, to the experienced eye, clearly belong to a unique cell), to view the object in 3D and save the results obtained. The tool can be used for testing the performance of a single-neuron segmentation algorithm or to extract complex objects, where the available automated methods still fail. Here we describe the software's main features and then show an example of how ManSegTool can be used to segment neuron images acquired using a confocal microscope. In particular, expert neuroscientists were asked to segment different neurons from which morphometric variables were subsequently extracted as a benchmark for precision. In addition, a literature-defined index for evaluating the goodness of segmentation was used as a benchmark for accuracy. Neocortical layer axons from a DIADEM challenge dataset were also segmented with ManSegTool and compared with the manual “gold-standard” generated for the competition.

Transcriptomic Profiling Discloses Molecular and Cellular Events Related to Neuronal Differentiation in SH-SY5Y Neuroblastoma Cells

Human SH-SY5Y neuroblastoma cells are widely utilized in in vitro studies to dissect out pathogenetic mechanisms of neurodegenerative disorders. These cells are considered as neuronal precursors and differentiate into more mature neuronal phenotypes under selected growth conditions. In this study, in order to decipher the pathways and cellular processes underlying neuroblastoma cell differentiation in vitro, we performed systematic transcriptomic (RNA-seq) and bioinformatic analysis of SH-SY5Y cells differentiated according to a two-step paradigm: retinoic acid treatment followed by enriched neurobasal medium. Categorization of 1989 differentially expressed genes (DEGs) identified in differentiated cells functionally linked them to changes in cell morphology including remodelling of plasma membrane and cytoskeleton, and neuritogenesis. Seventy-three DEGs were assigned to axonal guidance signalling pathway, and the expression of selected gene products such as neurotrophin receptors, the functionally related SLITRK6, and semaphorins, was validated by immunoblotting. Along with these findings, the differentiated cells exhibited an ability to elongate longer axonal process as assessed by the neuronal cytoskeletal markers biochemical characterization and morphometric evaluation. Recognition of molecular events occurring in differentiated SH-SY5Y cells is critical to accurately interpret the cellular responses to specific stimuli in studies on disease pathogenesis.

Track structure model of microscopic energy deposition by protons and heavy ions in segments of neuronal cell dendrites represented by cylinders or spheres

Changes to cognition, including memory, following radiation exposure are a concern for cosmic ray exposures to astronauts and in Hadron therapy with proton and heavy ion beams. The purpose of the present work is to develop computational methods to evaluate microscopic energy deposition (ED) in volumes representative of neuron cell structures, including segments of dendrites and spines, using a stochastic track structure model. A challenge for biophysical models of neuronal damage is the large sizes (> 100µm) and variability in volumes of possible dendritic segments and pre-synaptic elements (spines and filopodia). We consider cylindrical and spherical microscopic volumes of varying geometric parameters and aspect ratios from 0.5 to 5 irradiated by protons, and ³He and ¹²C particles at energies corresponding to a distance of 1cm to the Bragg peak, which represent particles of interest in Hadron therapy as well as space radiation exposure. We investigate the optimal axis length of dendritic segments to evaluate microscopic ED and hit probabilities along the dendritic branches at a given macroscopic dose. Because of large computation times to analyze ED in volumes of varying sizes, we developed an analytical method to find the mean primary dose in spheres that can guide numerical methods to find the primary dose distribution for cylinders. Considering cylindrical segments of varying aspect ratio at constant volume, we assess the chord length distribution, mean number of hits and ED profiles by primary particles and secondary electrons (δ-rays). For biophysical modeling applications, segments on dendritic branches are proposed to have equal diameters and axes lengths along the varying diameter of a dendritic branch.

Fast assembling of neuron fragments in serial 3D sections

Reconstructing neurons from 3D image-stacks of serial sections of thick brain tissue is very time-consuming and often becomes a bottleneck in high-throughput brain mapping projects. We developed NeuronStitcher, a software suite for stitching non-overlapping neuron fragments reconstructed in serial 3D image sections. With its efficient algorithm and user-friendly interface, NeuronStitcher has been used successfully to reconstruct very large and complex human and mouse neurons.

M-AMST: an automatic 3D neuron tracing method based on mean shift and adapted minimum spanning tree

BACKGROUND

Understanding the working mechanism of the brain is one of the grandest challenges for modern science. Toward this end, the BigNeuron project was launched to gather a worldwide community to establish a big data resource and a set of the state-of-the-art of single neuron reconstruction algorithms. Many groups contributed their own algorithms for the project, including our mean shift and minimum spanning tree (M-MST). Although M-MST is intuitive and easy to implement, the MST just considers spatial information of single neuron and ignores the shape information, which might lead to less precise connections between some neuron segments. In this paper, we propose an improved algorithm, namely M-AMST, in which a rotating sphere model based on coordinate transformation is used to improve the weight calculation method in M-MST.

RESULTS

Two experiments are designed to illustrate the effect of adapted minimum spanning tree algorithm and the adoptability of M-AMST in reconstructing variety of neuron image datasets respectively. In the experiment 1, taking the reconstruction of APP2 as reference, we produce the four difference scores (entire structure average (ESA), different structure average (DSA), percentage of different structure (PDS) and max distance of neurons' nodes (MDNN)) by comparing the neuron reconstruction of the APP2 and the other 5 competing algorithm. The result shows that M-AMST gets lower difference scores than M-MST in ESA, PDS and MDNN. Meanwhile, M-AMST is better than N-MST in ESA and MDNN. It indicates that utilizing the adapted minimum spanning tree algorithm which took the shape information of neuron into account can achieve better neuron reconstructions. In the experiment 2, 7 neuron image datasets are reconstructed and the four difference scores are calculated by comparing the gold standard reconstruction and the reconstructions produced by 6 competing algorithms. Comparing the four difference scores of M-AMST and the other 5 algorithm, we can conclude that M-AMST is able to achieve the best difference score in 3 datasets and get the second-best difference score in the other 2 datasets.

CONCLUSIONS

We develop a pathway extraction method using a rotating sphere model based on coordinate transformation to improve the weight calculation approach in MST. The experimental results show that M-AMST utilizes the adapted minimum spanning tree algorithm which takes the shape information of neuron into account can achieve better neuron reconstructions. Moreover, M-AMST is able to get good neuron reconstruction in variety of image datasets.

Temporal relation between neural activity and neurite pruning on a numerical model and a microchannel device with micro electrode array

Synapse elimination and neurite pruning are essential processes for the formation of neuronal circuits. These regressive events depend on neural activity and occur in the early postnatal days known as the critical period, but what makes this temporal specificity is not well understood. One possibility is that the neural activities during the developmentally regulated shift of action of GABA inhibitory transmission lead to the critical period. Moreover, it has been reported that the shifting action of the inhibitory transmission on immature neurons overlaps with synapse elimination and neurite pruning and that increased inhibitory transmission by drug treatment could induce temporal shift of the critical period. However, the relationship among these phenomena remains unclear because it is difficult to experimentally show how the developmental shift of inhibitory transmission influences neural activities and whether the activities promote synapse elimination and neurite pruning. In this study, we modeled synapse elimination in neuronal circuits using the modified Izhikevich's model with functional shifting of GABAergic transmission. The simulation results show that synaptic pruning within a specified period like the critical period is spontaneously generated as a function of the developmentally shifting inhibitory transmission and that the specific firing rate and increasing synchronization of neural circuits are seen at the initial stage of the critical period. This temporal relationship was experimentally supported by an in vitro primary culture of rat cortical neurons in a microchannel on a multi-electrode array (MEA). The firing rate decreased remarkably between the 18-25 days in vitro (DIV), and following these changes in the firing rate, the neurite density was slightly reduced. Our simulation and experimental results suggest that decreasing neural activity due to developing inhibitory synaptic transmission could induce synapse elimination and neurite pruning at particular time such as the critical period. Additionally, these findings indicate that we can estimate the maturity level of inhibitory transmission and the critical period by measuring the firing rate and the degree of synchronization in engineered neural networks.

Semi-automatic 3D morphological reconstruction of neurons with densely branching morphology: Application to retinal AII amacrine cells imaged with multi-photon excitation microscopy

BACKGROUND

Accurate reconstruction of the morphology of single neurons is important for morphometric studies and for developing compartmental models. However, manual morphological reconstruction can be extremely time-consuming and error-prone and algorithms for automatic reconstruction can be challenged when applied to neurons with a high density of extensively branching processes.

NEW METHOD

We present a procedure for semi-automatic reconstruction specifically adapted for densely branching neurons such as the AII amacrine cell found in mammalian retinas. We used whole-cell recording to fill AII amacrine cells in rat retinal slices with fluorescent dyes and acquired digital image stacks with multi-photon excitation microscopy. Our reconstruction algorithm combines elements of existing procedures, with segmentation based on adaptive thresholding and reconstruction based on a minimal spanning tree. We improved this workflow with an algorithm that reconnects neuron segments that are disconnected after adaptive thresholding, using paths extracted from the image stacks with the Fast Marching method.

RESULTS

By reducing the likelihood that disconnected segments were incorrectly connected to neighboring segments, our procedure generated excellent morphological reconstructions of AII amacrine cells.

COMPARISON WITH EXISTING METHODS

Reconstructing an AII amacrine cell required about 2h computing time, compared to 2-4days for manual reconstruction. To evaluate the performance of our method relative to manual reconstruction, we performed detailed analysis using a measure of tree structure similarity (DIADEM score), the degree of projection area overlap (Dice coefficient), and branch statistics.

CONCLUSIONS

We expect our procedure to be generally useful for morphological reconstruction of neurons filled with fluorescent dyes.

Extracting information from imaging cytometry: a review

The extraction of statistically meaningful quantitative information from microscopy images is increasingly important for modern biological research. Obtaining accurate, quantitative information from biological specimens, however, is a complex process that requires optimization of several parameters. One must consider the number of probes, fluorescent channels required, type of plate to be used, number of fields to be acquired and optimal resolution for image acquisition. The extraction of information from images is dependent on and can be aided greatly by careful consideration of the factors involved in the image acquisition process. I summarize here the general principles behind the imaging and software technology that is used to quantify images and highlight particular issues of concern for critically applying image quantitation techniques for research.

Three-Dimensional Segmentation of the Ex-Vivo Anterior Lamina Cribrosa From Second-Harmonic Imaging Microscopy

The lamina cribrosa (LC) is a connective tissue in the posterior eye with a complex mesh-like trabecular microstructure, through which all the retinal ganglion cell axons and central retinal vessels pass. Recent studies have demonstrated that changes in the structure of the LC correlate with glaucomatous damage. Thus, accurate segmentation and reconstruction of the LC is of utmost importance. This paper presents a new automated method for segmenting the microstructure of the anterior LC in the images obtained via multiphoton microscopy using a combination of ideas. In order to reduce noise, we first smooth the input image using a 4-D collaborative filtering scheme. Next, we enhance the beam-like trabecular microstructure of the LC using wavelet multiresolution analysis. The enhanced LC microstructure is then automatically extracted using a combination of histogram thresholding and graph-cut binarization. Finally, we use morphological area opening as a postprocessing step to remove the small and unconnected 3-D regions in the binarized images. The performance of the proposed method is evaluated using mutual overlap accuracy, Tanimoto index, F-score, and Rand index. Quantitative and qualitative results show that the proposed algorithm provides improved segmentation accuracy and computational efficiency compared to the other recent algorithms.

Segment 2D and 3D Filaments by Learning Structured and Contextual Features

We focus on the challenging problem of filamentary structure segmentation in both 2D and 3D images, including retinal vessels and neurons, among others. Despite the increasing amount of efforts in learning based methods to tackle this problem, there still lack proper data-driven feature construction mechanisms to sufficiently encode contextual labelling information, which might hinder the segmentation performance. This observation prompts us to propose a data-driven approach to learn structured and contextual features in this paper. The structured features aim to integrate local spatial label patterns into the feature space, thus endowing the follow-up tree classifiers capability to grouping training examples with similar structure into the same leaf node when splitting the feature space, and further yielding contextual features to capture more of the global contextual information. Empirical evaluations demonstrate that our approach outperforms state-of-the-arts on well-regarded testbeds over a variety of applications. Our code is also made publicly available in support of the open-source research activities.

Fuzzy-Logic Based Detection and Characterization of Junctions and Terminations in Fluorescence Microscopy Images of Neurons

Digital reconstruction of neuronal cell morphology is an important step toward understanding the functionality of neuronal networks. Neurons are tree-like structures whose description depends critically on the junctions and terminations, collectively called critical points, making the correct localization and identification of these points a crucial task in the reconstruction process. Here we present a fully automatic method for the integrated detection and characterization of both types of critical points in fluorescence microscopy images of neurons. In view of the majority of our current studies, which are based on cultured neurons, we describe and evaluate the method for application to two-dimensional (2D) images. The method relies on directional filtering and angular profile analysis to extract essential features about the main streamlines at any location in an image, and employs fuzzy logic with carefully designed rules to reason about the feature values in order to make well-informed decisions about the presence of a critical point and its type. Experiments on simulated as well as real images of neurons demonstrate the detection performance of our method. A comparison with the output of two existing neuron reconstruction methods reveals that our method achieves substantially higher detection rates and could provide beneficial information to the reconstruction process.

Measuring Process Dynamics and Nuclear Migration for Clones of Neural Progenitor Cells

Neural stem and progenitor cells (NPCs) generate processes that extend from the cell body in a dynamic manner. The NPC nucleus migrates along these processes with patterns believed to be tightly coupled to mechanisms of cell cycle regulation and cell fate determination. Here, we describe a new segmentation and tracking approach that allows NPC processes and nuclei to be reliably tracked across multiple rounds of cell division in phase-contrast microscopy images. Results are presented for mouse adult and embryonic NPCs from hundreds of clones, or lineage trees, containing tens of thousands of cells and millions of segmentations. New visualization approaches allow the NPC nuclear and process features to be effectively visualized for an entire clone. Significant differences in process and nuclear dynamics were found among type A and type C adult NPCs, and also between embryonic NPCs cultured from the anterior and posterior cerebral cortex.

Improved detection of soma location and morphology in fluorescence microscopy images of neurons

BACKGROUND

Automated detection and segmentation of somas in fluorescent images of neurons is a major goal in quantitative studies of neuronal networks, including applications of high-content-screenings where it is required to quantify multiple morphological properties of neurons. Despite recent advances in image processing targeted to neurobiological applications, existing algorithms of soma detection are often unreliable, especially when processing fluorescence image stacks of neuronal cultures.

NEW METHOD

In this paper, we introduce an innovative algorithm for the detection and extraction of somas in fluorescent images of networks of cultured neurons where somas and other structures exist in the same fluorescent channel. Our method relies on a new geometrical descriptor called Directional Ratio and a collection of multiscale orientable filters to quantify the level of local isotropy in an image. To optimize the application of this approach, we introduce a new construction of multiscale anisotropic filters that is implemented by separable convolution.

RESULTS

Extensive numerical experiments using 2D and 3D confocal images show that our automated algorithm reliably detects somas, accurately segments them, and separates contiguous ones.

COMPARISON WITH EXISTING METHODS

We include a detailed comparison with state-of-the-art existing methods to demonstrate that our algorithm is extremely competitive in terms of accuracy, reliability and computational efficiency.

CONCLUSIONS

Our algorithm will facilitate the development of automated platforms for high content neuron image processing. A Matlab code is released open-source and freely available to the scientific community.

Tomographic brain imaging with nucleolar detail and automatic cell counting

Brain tissue evaluation is essential for gaining in-depth insight into its diseases and disorders. Imaging the human brain in three dimensions has always been a challenge on the cell level. In vivo methods lack spatial resolution, and optical microscopy has a limited penetration depth. Herein, we show that hard X-ray phase tomography can visualise a volume of up to 43 mm³ of human post mortem or biopsy brain samples, by demonstrating the method on the cerebellum. We automatically identified 5,000 Purkinje cells with an error of less than 5% at their layer and determined the local surface density to 165 cells per mm² on average. Moreover, we highlight that three-dimensional data allows for the segmentation of sub-cellular structures, including dendritic tree and Purkinje cell nucleoli, without dedicated staining. The method suggests that automatic cell feature quantification of human tissues is feasible in phase tomograms obtained with isotropic resolution in a label-free manner.

Inner-Retinal Irregularity Index Predicts Postoperative Visual Prognosis in Idiopathic Epiretinal Membrane

PURPOSE

To investigate the correlation between the inner-retinal irregularity index and visual outcomes before and after idiopathic epiretinal membrane (ERM) surgery.

DESIGN

Retrospective cohort study.

METHODS

We analyzed 66 eyes of 66 patients with idiopathic ERM. Ophthalmic examinations included best-corrected visual acuity (BCVA) measurements, metamorphopsia assessment, and spectral-domain optical coherence tomography before surgery and 1, 3, and 6 months post-surgery. Correlations between the inner-retinal irregularity index, defined as the length ratio between the inner plexiform layer and retinal pigment epithelium, and visual outcomes before and after ERM surgery were evaluated and compared with the correlation between the central foveal thickness, ganglion cell-inner plexiform layer (GC-IPL) thickness, interdigitation zone defect, and visual outcomes.

RESULTS

Inner-retinal irregularity index and central foveal thickness were significantly correlated with BCVA and metamorphopsia at each follow-up examination (all P < .05). The interdigitation zone defect correlated with BCVA at 3 and 6 months post-surgery (P < .001 and P < .015, respectively). However, GC-IPL thickness was not correlated with visual outcomes at any follow-up examination. The preoperative interdigitation zone defect was correlated with 6-month BCVA (P = .035) and the preoperative inner-retinal irregularity index was significantly correlated with the 6-month BCVA and marginally correlated with the 6-month metamorphopsia (P = .018 and P = .097, respectively).

CONCLUSION

The inner-retinal irregularity index was significantly correlated with visual outcomes before and after ERM surgery. This index can be used as a new surrogate marker for inner-retinal damage and a predictive prognostic marker in ERM.

High-throughput dual-colour precision imaging for brain-wide connectome with cytoarchitectonic landmarks at the cellular level

The precise annotation and accurate identification of neural structures are prerequisites for studying mammalian brain function. The orientation of neurons and neural circuits is usually determined by mapping brain images to coarse axial-sampling planar reference atlases. However, individual differences at the cellular level likely lead to position errors and an inability to orient neural projections at single-cell resolution. Here, we present a high-throughput precision imaging method that can acquire a co-localized brain-wide data set of both fluorescent-labelled neurons and counterstained cell bodies at a voxel size of 0.32 × 0.32 × 2.0 μm in 3 days for a single mouse brain. We acquire mouse whole-brain imaging data sets of multiple types of neurons and projections with anatomical annotation at single-neuron resolution. The results show that the simultaneous acquisition of labelled neural structures and cytoarchitecture reference in the same brain greatly facilitates precise tracing of long-range projections and accurate locating of nuclei.

High-throughput imaging methods for brain-wide connectome mapping with precise location reference have been lacking. Here authors report a method that allows simultaneous acquisition of fluorescently labelled neurons and cytoarchitectural landmarks in the same mouse brain at the single-cell resolution.

The Rac-GAP alpha2-chimaerin regulates hippocampal dendrite and spine morphogenesis

Dendritic spines are fine neuronal processes where spatially restricted input can induce activity-dependent changes in one spine, while leaving neighboring spines unmodified. Morphological spine plasticity is critical for synaptic transmission and is thought to underlie processes like learning and memory. Significantly, defects in dendritic spine stability and morphology are common pathogenic features found in several neurodevelopmental and neuropsychiatric disorders. The remodeling of spines relies on proteins that modulate the underlying cytoskeleton, which is primarily composed of filamentous (F)-actin. The Rho-GTPase Rac1 is a major regulator of F-actin and is essential for the development and plasticity of dendrites and spines. However, the key molecules and mechanisms that regulate Rac1-dependent pathways at spines and synapses are not well understood. We have identified the Rac1-GTPase activating protein, α2-chimaerin, as a critical negative regulator of Rac1 in hippocampal neurons. The loss of α2-chimaerin significantly increases the levels of active Rac1 and induces the formation of aberrant polymorphic dendritic spines. Further, disruption of α2-chimaerin signaling simplifies dendritic arbor complexity and increases the presence of dendritic spines that appear poly-innervated. Our data suggests that α2-chimaerin serves as a "brake" to constrain Rac1-dependent signaling to ensure that the mature morphology of spines is maintained in response to network activity.

Tropomodulin isoforms utilize specific binding functions to modulate dendrite development

Tropomodulins (Tmods) cap F-actin pointed ends and have altered expression in the brain in neurological diseases. The function of Tmods in neurons has been poorly studied and their role in neurological diseases is entirely unknown. In this article, we show that Tmod1 and Tmod2, but not Tmod3, are positive regulators of dendritic complexity and dendritic spine morphology. Tmod1 increases dendritic branching distal from the cell body and the number of filopodia/thin spines. Tmod2 increases dendritic branching proximal to the cell body and the number of mature dendritic spines. Tmods utilize two actin-binding sites and two tropomyosin (Tpm)-binding sites to cap F-actin. Overexpression of Tmods with disrupted Tpm-binding sites indicates that Tmod1 and Tmod2 differentially utilize their Tpm- and actin-binding sites to affect morphology. Disruption of Tmod1's Tpm-binding sites abolished the overexpression phenotype. In contrast, overexpression of the mutated Tmod2 caused the same phenotype as wild type overexpression. Proximity ligation assays indicate that the mutated Tmods are shuttled similarly to wild type Tmods. Our data begins to uncover the roles of Tmods in neural development and the mechanism by which Tmods alter neural morphology. These observations in combination with altered Tmod expression found in several neurological diseases also suggest that dysregulation of Tmod expression may be involved in the pathology of these diseases. © 2016 Wiley Periodicals, Inc.

Neurite Tracing With Object Process

In this paper we present a pipeline for automatic analysis of neuronal morphology: from detection, modeling to digital reconstruction. First, we present an automatic, unsupervised object detection framework using stochastic marked point process. It extracts connected neuronal networks by fitting special configuration of marked objects to the centreline of the neurite branches in the image volume giving us position, local width and orientation information. Semantic modeling of neuronal morphology in terms of critical nodes like bifurcations and terminals, generates various geometric and morphology descriptors such as branching index, branching angles, total neurite length, internodal lengths for statistical inference on characteristic neuronal features. From the detected branches we reconstruct neuronal tree morphology using robust and efficient numerical fast marching methods. We capture a mathematical model abstracting out the relevant position, shape and connectivity information about neuronal branches from the microscopy data into connected minimum spanning trees. Such digital reconstruction is represented in standard SWC format, prevalent for archiving, sharing, and further analysis in the neuroimaging community. Our proposed pipeline outperforms state of the art methods in tracing accuracy and minimizes the subjective variability in reconstruction, inherent to semi-automatic methods.

Multi-scale segmentation of neurons based on one-class classification

BACKGROUND

High resolution multiphoton and confocal microscopy has allowed the acquisition of large amounts of data to be analyzed by neuroscientists. However, manual processing of these images has become infeasible. Thus, there is a need to create automatic methods for the morphological reconstruction of 3D neuronal image stacks.

NEW METHOD

An algorithm to extract the 3D morphology from a neuron is presented. The main contribution of the paper is the segmentation of the neuron from the background. Our segmentation method is based on one-class classification where the 3D image stack is analyzed at different scales. First, a multi-scale approach is proposed to compute the Laplacian of the 3D image stack. The Laplacian is used to select a training set consisting of background points. A decision function is learned for each scale from the training set that allows determining how similar an unlabeled point is to the points in the background class. Foreground points (dendrites and axons) are assigned as those points that are rejected as background. Finally, the morphological reconstruction of the neuron is extracted by applying a state-of-the-art centerline tracing algorithm on the segmentation.

RESULTS

Quantitative and qualitative results on several datasets demonstrate the ability of our algorithm to accurately and robustly segment and trace neurons.

COMPARISON WITH EXISTING METHOD(S)

Our method was compared to state-of-the-art neuron tracing algorithms.

CONCLUSIONS

Our approach allows segmentation of thin and low contrast dendrites that are usually difficult to segment. Compared to our previous approach, this algorithm is more accurate and much faster.

Quantitative and semi-quantitative measurements of axonal degeneration in tissue and primary neuron cultures

BACKGROUND

Axon viability is critical for maintaining neural connectivity, which is central to neural functionality. Many neurodegenerative diseases (e.g., Parkinson's disease (PD) and Alzheimer's disease) appear to involve extensive axonal degeneration that often precedes somatic loss in affected neural populations. Axonal degeneration involves a number of intracellular pathways and characteristic changes in axon morphology (i.e., swelling, fragmentation, and loss).

NEW METHOD

We describe a relatively simple set of methods to quantify the axonal degeneration using the 6-hydroxydopamine neurotoxin model of PD in rats and a colchicine-induced model in primary rat neurons. Specifically, approaches are described that use the spaceballs stereological probe for tissue sections and petrimetrics stereological probe for cultured neurons, and image analysis techniques in both tissue sections and cultured neurons.

RESULTS

These methods provide a mechanism for obtaining quantitative and semi-quantitative data to track the extent of axonal degeneration and may prove useful as outcome measures in studies aimed at preventing or slowing axonal degeneration in disease models.

COMPARISON WITH EXISTING METHODS

Existing methods of quantification of axonal degeneration use densitometry and manual counts of axonal projections, but they do not utilize the random, unbiased systematic sampling approaches that are characteristic of stereological methods. The ImageJ thresholding analyses described here provide a descriptive method for quantifying the state of axonal degeneration.

CONCLUSIONS

These methods provide an efficient and effective means to quantify the extent and state of axonal degeneration in animal tissue and cultured neurons and can be used in other models for the same purposes.

Automatic Morphological Reconstruction of Neurons from Multiphoton and Confocal Microscopy Images Using 3D Tubular Models

The challenges faced in analyzing optical imaging data from neurons include a low signal-to-noise ratio of the acquired images and the multiscale nature of the tubular structures that range in size from hundreds of microns to hundreds of nanometers. In this paper, we address these challenges and present a computational framework for an automatic, three-dimensional (3D) morphological reconstruction of live nerve cells. The key aspects of this approach are: (i) detection of neuronal dendrites through learning 3D tubular models, and (ii) skeletonization by a new algorithm using a morphology-guided deformable model for extracting the dendritic centerline. To represent the neuron morphology, we introduce a novel representation, the Minimum Shape-Cost (MSC) Tree that approximates the dendrite centerline with sub-voxel accuracy and demonstrate the uniqueness of such a shape representation as well as its computational efficiency. We present extensive quantitative and qualitative results that demonstrate the accuracy and robustness of our method.

Semi-automated counting of axon regeneration in poly(lactide co-glycolide) spinal cord bridges

BACKGROUND

Spinal cord injury (SCI) is a debilitating event with multiple mechanisms of degeneration leading to life-long paralysis. Biomaterial strategies, including bridges that span the injury and provide a pathway to reconnect severed regions of the spinal cord, can promote partial restoration of motor function following SCI. Axon growth through the bridge is essential to characterizing regeneration, as recovery can occur via other mechanisms such as plasticity. Quantitative analysis of axons by manual counting of histological sections can be slow, which can limit the number of bridge designs evaluated. In this study, we report a semi-automated process to resolve axon numbers in histological sections, which allows for efficient analysis of large data sets.

NEW METHOD

Axon numbers were estimated in SCI cross-sections from animals implanted with poly(lactide co-glycolide) (PLG) bridges with multiple channels for guiding axons. Immunofluorescence images of histological sections were filtered using a Hessian-based approach prior to threshold detection to improve the signal-to-noise ratio and filter out background staining associated with PLG polymer.

RESULTS

Semi-automated counting successfully recapitulated average axon densities and myelination in a blinded PLG bridge implantation study.

COMPARISON WITH EXISTING METHODS

Axon counts obtained with the semi-automated technique correlated well with manual axon counts from blinded independent observers across sections with a wide range of total axons.

CONCLUSIONS

This semi-automated detection of Hessian-filtered axons provides an accurate and significantly faster alternative to manual counting of axons for quantitative analysis of regeneration following SCI.

A Graph-Theoretical Approach for Tracing Filamentary Structures in Neuronal and Retinal Images

The aim of this study is about tracing filamentary structures in both neuronal and retinal images. It is often crucial to identify single neurons in neuronal networks, or separate vessel tree structures in retinal blood vessel networks, in applications such as drug screening for neurological disorders or computer-aided diagnosis of diabetic retinopathy. Both tasks are challenging as the same bottleneck issue of filament crossovers is commonly encountered, which essentially hinders the ability of existing systems to conduct large-scale drug screening or practical clinical usage. To address the filament crossovers' problem, a two-step graph-theoretical approach is proposed in this paper. The first step focuses on segmenting filamentary pixels out of the background. This produces a filament segmentation map used as input for the second step, where they are further separated into disjointed filaments. Key to our approach is the idea that the problem can be reformulated as label propagation over directed graphs, such that the graph is to be partitioned into disjoint sub-graphs, or equivalently, each of the neurons (vessel trees) is separated from the rest of the neuronal (vessel) network. This enables us to make the interesting connection between the tracing problem and the digraph matrix-forest theorem in algebraic graph theory for the first time. Empirical experiments on neuronal and retinal image datasets demonstrate the superior performance of our approach over existing methods.

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. Amongst the neuronal structures that show morphological plasticity are neurites, synapses, dendritic spines and even nuclei. This structural remodelling is directly connected with functional changes such as intercellular communication and the associated calcium bursting behaviour. In vitro cultured neuronal networks are valuable models for studying these morpho-functional changes. Owing to the automation and standardization of both image acquisition and image analysis, it has become possible to extract statistically relevant readouts 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.

Growth/differentiation factor-15 deficiency compromises dopaminergic neuron survival and microglial response in the 6-hydroxydopamine mouse model of Parkinson's disease

Growth/differentiation factor-15 (Gdf-15) is a member of the TGF-β superfamily and a pleiotropic, widely distributed cytokine, which has been shown to play roles in various pathologies, including inflammation. Analysis of Gdf-15(-/-) mice has revealed that it serves the postnatal maintenance of spinal cord motor neurons and sensory neurons. In a previous study, exogenous Gdf-15 rescued 6-hydroxydopamine (6-OHDA) lesioned Gdf-15(+/+) nigrostriatal dopaminergic (DAergic) neurons in vitro and in vivo. Whether endogenous Gdf-15 serves the physiological maintenance of nigrostriatal DAergic neurons in health and disease is not known and was addressed in the present study. Stereotactic injection of 6-OHDA into the medial forebrain bundle (MFB) led to a significant decline in the numbers of DAergic neurons in both Gdf-15(+/+) and Gdf-15(-/-) mice over a time-period of 14days. However, this decrease was exacerbated in the Gdf-15(-/-) mice, with only 5.5% surviving neurons as compared to 24% in the Gdf-15(+/+) mice. Furthermore, the microglial response to the 6-OHDA lesion was reduced in Gdf-15(-/-) mice, with significantly lower numbers of total and activated microglia and a differential cytokine expression as compared to the Gdf-15(+/+) mice. Using in vitro models, we could demonstrate the importance of endogenous Gdf-15 in promoting DAergic neuron survival thus highlighting its relevance in a direct neurotrophic supportive role. Taken together, these results indicate the importance of Gdf-15 in promoting survival of DAergic neurons and regulating the inflammatory response post 6-OHDA lesion.

DeFiNe: an optimisation-based method for robust disentangling of filamentous networks

Thread-like structures are pervasive across scales, from polymeric proteins to root systems to galaxy filaments, and their characteristics can be readily investigated in the network formalism. Yet, network links usually represent only parts of filaments, which, when neglected, may lead to erroneous conclusions from network-based analyses. The existing alternatives to detect filaments in network representations require tuning of parameters over a large range of values and treat all filaments equally, thus, precluding automated analysis of diverse filamentous systems. Here, we propose a fully automated and robust optimisation-based approach to detect filaments of consistent intensities and angles in a given network. We test and demonstrate the accuracy of our solution with contrived, biological, and cosmic filamentous structures. In particular, we show that the proposed approach provides powerful automated means to study properties of individual actin filaments in their network context. Our solution is made publicly available as an open-source tool, "DeFiNe", facilitating decomposition of any given network into individual filaments.

Insulin resistance-induced hyperglycemia decreased the activation of Akt/CREB in hippocampus neurons: Molecular evidence for mechanism of diabetes-induced cognitive dysfunction

Several previous studies have indicated that diabetic have higher risk of suffering from Alzheimer's disease, which severely induced cognitive dysfunction. However, the underlying molecular mechanism and more details on the cognitive deficits induced by hyperglycemia have not been elucidated. Here in our present study, on the basis of Goto-Kakizaki (GK) rats and streptozotocin (STZ)-induced diabetic model, we detected the variation of dendritic spine density in hippocampus as well as the differential expression of some important signal transduction molecules that were of relevance in learning and memory function. We found that the magnitude of escape latency time was significantly increased in such diabetic animals; the phosphorylated Akt/CREB; SYP and BDNF as well as other downstream molecules in hippocampus neurons were also downregulated in both diabetic groups compared to the normal groups. Thus, all of these data indicate the obstacle of neuronal pathology and the Akt/CREB signaling pathway caused by hyperglycemia that may suppress cognitive behavior, which may provide a novel way for the prevention of diabetic encephalopathy and the cognitive deficits of Alzheimer's disease.

Neuron Image Analyzer: Automated and Accurate Extraction of Neuronal Data from Low Quality Images

Image analysis software is an essential tool used in neuroscience and neural engineering to evaluate changes in neuronal structure following extracellular stimuli. Both manual and automated methods in current use are severely inadequate at detecting and quantifying changes in neuronal morphology when the images analyzed have a low signal-to-noise ratio (SNR). This inadequacy derives from the fact that these methods often include data from non-neuronal structures or artifacts by simply tracing pixels with high intensity. In this paper, we describe Neuron Image Analyzer (NIA), a novel algorithm that overcomes these inadequacies by employing Laplacian of Gaussian filter and graphical models (i.e., Hidden Markov Model, Fully Connected Chain Model) to specifically extract relational pixel information corresponding to neuronal structures (i.e., soma, neurite). As such, NIA that is based on vector representation is less likely to detect false signals (i.e., non-neuronal structures) or generate artifact signals (i.e., deformation of original structures) than current image analysis algorithms that are based on raster representation. We demonstrate that NIA enables precise quantification of neuronal processes (e.g., length and orientation of neurites) in low quality images with a significant increase in the accuracy of detecting neuronal changes post-stimulation.

NeuroGPS-Tree: automatic reconstruction of large-scale neuronal populations with dense neurites

The reconstruction of neuronal populations, a key step in understanding neural circuits, remains a challenge in the presence of densely packed neurites. Here we achieved automatic reconstruction of neuronal populations by partially mimicking human strategies to separate individual neurons. For populations not resolvable by other methods, we obtained recall and precision rates of approximately 80%. We also demonstrate the reconstruction of 960 neurons within 3 h.

Whole-body tissue stabilization and selective extractions via tissue-hydrogel hybrids for high-resolution intact circuit mapping and phenotyping

To facilitate fine-scale phenotyping of whole specimens, we describe here a set of tissue fixation-embedding, detergent-clearing and staining protocols that can be used to transform excised organs and whole organisms into optically transparent samples within 1-2 weeks without compromising their cellular architecture or endogenous fluorescence. PACT (passive CLARITY technique) and PARS (perfusion-assisted agent release in situ) use tissue-hydrogel hybrids to stabilize tissue biomolecules during selective lipid extraction, resulting in enhanced clearing efficiency and sample integrity. Furthermore, the macromolecule permeability of PACT- and PARS-processed tissue hybrids supports the diffusion of immunolabels throughout intact tissue, whereas RIMS (refractive index matching solution) grants high-resolution imaging at depth by further reducing light scattering in cleared and uncleared samples alike. These methods are adaptable to difficult-to-image tissues, such as bone (PACT-deCAL), and to magnified single-cell visualization (ePACT). Together, these protocols and solutions enable phenotyping of subcellular components and tracing cellular connectivity in intact biological networks.

Automatic neuron segmentation and neural network analysis method for phase contrast microscopy images

Phase Contrast Microscopy (PCM) is an important tool for the long term study of living cells. Unlike fluorescence methods which suffer from photobleaching of fluorophore or dye molecules, PCM image contrast is generated by the natural variations in optical index of refraction. Unfortunately, the same physical principles which allow for these studies give rise to complex artifacts in the raw PCM imagery. Of particular interest in this paper are neuron images where these image imperfections manifest in very different ways for the two structures of specific interest: cell bodies (somas) and dendrites. To address these challenges, we introduce a novel parametric image model using the level set framework and an associated variational approach which simultaneously restores and segments this class of images. Using this technique as the basis for an automated image analysis pipeline, results for both the synthetic and real images validate and demonstrate the advantages of our approach.

Imaging of the 3D dynamics of flagellar beating in human sperm

The study of the mechanical and environmental factors that regulate a fundamental event such as fertilization have been subject of multiple studies. Nevertheless, the microscopical size of the spermatozoa and the high beating frequency of their flagella (up to 20 Hz) impose a series of technological challenges for the study of the mechanical factors implicated. Traditionally, due to the inherent characteristics of the rapid sperm movement, and to the technological limitations of microscopes (optical or confocal) to follow in three dimensions (3D) their movement, the analysis of their dynamics has been studied in two dimensions, when the head is confined to a surface. Flagella propel sperm and while their head can be confined to a surface, flagellar movement is not restricted to 2D, always displaying 3D components. In this work, we present a highly novel and useful tool to analyze sperm flagella dynamics in 3D. The basis of the method is a 100 Hz oscillating objective mounted on a bright field optical microscope covering a 16 microns depth space at a rate of ~ 5000 images per second. The best flagellum focused subregions were associated to their respective Z real 3D position. Unprecedented graphical results making evident the 3D movement of the flagella are shown in this work and supplemental material illustrating a 3D animation using the obtained experimental results is also included.

Automatic Dendritic Length Quantification for High Throughput Screening of Mature Neurons

High-throughput automated fluorescent imaging and screening are important for studying neuronal development, functions, and pathogenesis. An automatic approach of analyzing images acquired in automated fashion, and quantifying dendritic characteristics is critical for making such screens high-throughput. However, automatic and effective algorithms and tools, especially for the images of mature mammalian neurons with complex arbors, have been lacking. Here, we present algorithms and a tool for quantifying dendritic length that is fundamental for analyzing growth of neuronal network. We employ a divide-and-conquer framework that tackles the challenges of high-throughput images of neurons and enables the integration of multiple automatic algorithms. Within this framework, we developed algorithms that adapt to local properties to detect faint branches. We also developed a path search that can preserve the curvature change to accurately measure dendritic length with arbor branches and turns. In addition, we proposed an ensemble strategy of three estimation algorithms to further improve the overall efficacy. We tested our tool on images for cultured mouse hippocampal neurons immunostained with a dendritic marker for high-throughput screen. Results demonstrate the effectiveness of our proposed method when comparing the accuracy with previous methods. The software has been implemented as an ImageJ plugin and available for use.

Sharing Neuron Data: Carrots, Sticks, and Digital Records

Routine data sharing is greatly benefiting several scientific disciplines, such as molecular biology, particle physics, and astronomy. Neuroscience data, in contrast, are still rarely shared, greatly limiting the potential for secondary discovery and the acceleration of research progress. Although the attitude toward data sharing is non-uniform across neuroscience subdomains, widespread adoption of data sharing practice will require a cultural shift in the community. Digital reconstructions of axonal and dendritic morphology constitute a particularly "sharable" kind of data. The popularity of the public repository NeuroMorpho.Org demonstrates that data sharing can benefit both users and contributors. Increased data availability is also catalyzing the grassroots development and spontaneous integration of complementary resources, research tools, and community initiatives. Even in this rare successful subfield, however, more data are still unshared than shared. Our experience as developers and curators of NeuroMorpho.Org suggests that greater transparency regarding the expectations and consequences of sharing (or not sharing) data, combined with public disclosure of which datasets are shared and which are not, may expedite the transition to community-wide data sharing.

SmartTracing: self-learning-based Neuron reconstruction

In this work, we propose SmartTracing, an automatic tracing framework that does not require substantial human intervention. There are two major novelties in SmartTracing. First, given an input image, SmartTracing invokes a user-provided existing neuron tracing method to produce an initial neuron reconstruction, from which the likelihood of every neuron reconstruction unit is estimated. This likelihood serves as a confidence score to identify reliable regions in a neuron reconstruction. With this score, SmartTracing automatically identifies reliable portions of a neuron reconstruction generated by some existing neuron tracing algorithms, without human intervention. These reliable regions are used as training exemplars. Second, from the training exemplars the most characteristic wavelet features are automatically selected and used in a machine learning framework to predict all image areas that most probably contain neuron signal. Since the training samples and their most characterizing features are selected from each individual image, the whole process is automatically adaptive to different images. Notably, SmartTracing can improve the performance of an existing automatic tracing method. In our experiment, with SmartTracing we have successfully reconstructed complete neuron morphology of 120 Drosophila neurons. In the future, the performance of SmartTracing will be tested in the BigNeuron project (bigneuron.org). It may lead to more advanced tracing algorithms and increase the throughput of neuron morphology-related studies.

BigNeuron: Large-Scale 3D Neuron Reconstruction from Optical Microscopy Images

Understanding the structure of single neurons is critical for understanding how they function within neural circuits. BigNeuron is a new community effort that combines modern bioimaging informatics, recent leaps in labeling and microscopy, and the widely recognized need for openness and standardization to provide a community resource for automated reconstruction of dendritic and axonal morphology of single neurons.

Quantification of nonlinear elasticity for the evaluation of submillimeter crack length in cortical bone

The objective of this study was to investigate the sensitivity of the nonlinear elastic properties of cortical bone to the presence of a single submillimetric crack. Nonlinear elasticity was measured by nonlinear resonant ultrasound spectroscopy (NRUS) in 14 human cortical bone specimens. The specimens were parallelepiped beams (50×2×2 mm(3)). A central notch of 500 µm was made to control crack initiation and propagation during four-point bending. The nonlinear hysteretic elastic and dissipative parameters αf and αQ, and Young׳s modulus Eus were measured in dry condition for undamaged (control) specimens and in dry and wet conditions for damaged specimens. The length of the crack was assessed using synchrotron radiation micro-computed tomography (SR-μCT) with a voxel size of 1.4 μm. The initial values of αf, measured on the intact specimens, were remarkably similar for all the specimens (αf =-5.5±1.5). After crack propagation, the nonlinear elastic coefficient αf increased significantly (p<0.006), with values ranging from -4.0 to -296.7. Conversely, no significant variation was observed for αQ and Eus. A more pronounced nonlinear elastic behavior was observed in hydrated specimens compared to dry specimens (p<0.001) after propagation of a single submillimetric crack. The nonlinear elastic parameter αf was found to be significantly correlated to the crack length both in dry (R=0.79, p<0.01) and wet (R=0.84, p<0.005) conditions. Altogether these results show that nonlinear elasticity assessed by NRUS is sensitive to a single submillimetric crack induced mechanically and suggest that the humidity must be strictly controlled during measurements.