AxonDeepSeg: automatic axon and myelin segmentation from microscopy data using convolutional neural networks

A functional tacrolimus-releasing nerve wrap for enhancing nerve regeneration following surgical nerve repair

JOURNAL/nrgr/04.03/01300535-202501000-00036/figure1/v/2024-05-14T021156Z/r/image-tiff Axonal regeneration following surgical nerve repair is slow and often incomplete, resulting in poor functional recovery which sometimes contributes to lifelong disability. Currently, there are no FDA-approved therapies available to promote nerve regeneration. Tacrolimus accelerates axonal regeneration, but systemic side effects presently outweigh its potential benefits for peripheral nerve surgery. The authors describe herein a biodegradable polyurethane-based drug delivery system for the sustained local release of tacrolimus at the nerve repair site, with suitable properties for scalable production and clinical application, aiming to promote nerve regeneration and functional recovery with minimal systemic drug exposure. Tacrolimus is encapsulated into co-axially electrospun polycarbonate-urethane nanofibers to generate an implantable nerve wrap that releases therapeutic doses of bioactive tacrolimus over 31 days. Size and drug loading are adjustable for applications in small and large caliber nerves, and the wrap degrades within 120 days into biocompatible byproducts. Tacrolimus released from the nerve wrap promotes axon elongation in vitro and accelerates nerve regeneration and functional recovery in preclinical nerve repair models while off-target systemic drug exposure is reduced by 80% compared with systemic delivery. Given its surgical suitability and preclinical efficacy and safety, this system may provide a readily translatable approach to support axonal regeneration and recovery in patients undergoing nerve surgery.

Myelin basic protein mRNA levels affect myelin sheath dimensions, architecture, plasticity, and density of resident glial cells

Myelin Basic Protein (MBP) is essential for both elaboration and maintenance of CNS myelin, and its reduced accumulation results in hypomyelination. How different Mbp mRNA levels affect myelin dimensions across the lifespan and how resident glial cells may respond to such changes are unknown. Here, to investigate these questions, we used enhancer-edited mouse lines that accumulate Mbp mRNA levels ranging from 8% to 160% of wild type. In young mice, reduced Mbp mRNA levels resulted in corresponding decreases in Mbp protein accumulation and myelin sheath thickness, confirming the previously demonstrated rate-limiting role of Mbp transcription in the control of initial myelin synthesis. However, despite maintaining lower line specific Mbp mRNA levels into old age, both MBP protein levels and myelin thickness improved or fully normalized at rates defined by the relative Mbp mRNA level. Sheath length, in contrast, was affected only when mRNA levels were very low, demonstrating that sheath thickness and length are not equally coupled to Mbp mRNA level. Striking abnormalities in sheath structure also emerged with reduced mRNA levels. Unexpectedly, an increase in the density of all glial cell types arose in response to reduced Mbp mRNA levels. This investigation extends understanding of the role MBP plays in myelin sheath elaboration, architecture, and plasticity across the mouse lifespan and illuminates a novel axis of glial cell crosstalk.

aXonica: A support package for MRI based Neuroimaging

Magnetic Resonance Imaging (MRI) assists in studying the nervous system. MRI scans undergo significant processing before presenting the final images to medical practitioners. These processes are executed with ease due to excellent software pipelines. However, establishing software workstations is non-trivial and requires researchers in life sciences to be comfortable in downloading, installing, and scripting software that is non-user-friendly and may lack basic GUI. As researchers struggle with these skills, there is a dire need to develop software packages that can automatically install software pipelines speeding up building software workstations and laboratories. Previous solutions include NeuroDebian, BIDS Apps, Flywheel, QMENTA, Boutiques, Brainlife and Neurodesk. Overall, all these solutions complement each other. NeuroDebian covers neuroscience and has a wider scope, providing only 51 tools for MRI. Whereas, BIDS Apps is committed to the BIDS format, covering only 45 software related to MRI. Boutiques is more flexible, facilitating its pipelines to be easily installed as separate containers, validated, published, and executed. Whereas, both Flywheel and Qmenta are propriety, leaving four for users looking for 'free for use' tools, i.e., NeuroDebian, Brainlife, Neurodesk, and BIDS Apps. This paper presents an extensive survey of 317 tools published in MRI-based neuroimaging in the last ten years, along with 'aXonica,' an MRI-based neuroimaging support package that is unbiased towards any formatting standards and provides 130 applications, more than that of NeuroDebian (51), BIDS App (45), Flywheel (70), and Neurodesk (85). Using a technology stack that employs GUI as the front-end and shell scripted back-end, aXonica provides (i) 130 tools that span the entire MRI-based neuroimaging analysis, and allow the user to (ii) select the software of their choice, (iii) automatically resolve individual dependencies and (iv) installs them. Hence, aXonica can serve as an important resource for researchers and teachers working in the field of MRI-based Neuroimaging to (a) develop software workstations, and/or (b) install newer tools in their existing workstations.

Recordings of Superior Laryngeal Nerve Sensory Nerve Action Potentials in a Rat Model

OBJECTIVE

Superior laryngeal nerve (SLN) function is critical to laryngeal sensation. Sensory dysfunction in the larynx, mediated through the internal branch of the superior laryngeal nerve (iSLN), is thought to occur with aging and neurodegenerative disease. However, objective analysis of iSLN neurophysiology is difficult due to its anatomic location and small diameter. This study measures sensory nerve action potentials (SNAP) from the iSLN in a rat model.

METHODS

SNAP data were obtained from two adult rat strains (Sprague-Dawley, SD and Fischer 344 × Brown Norway F1 Hybrid rats, FBN). Evoked responses were obtained by stimulating the main trunk of the SLN and recording the response using a 160-μm cuff electrode placed around the iSLN. SNAP were averaged from 10 stimulations. Laryngeal adductor reflex (LAR) threshold measurements were obtained with stimulation of the iSLN and direct laryngoscopy. The sections of the iSLN were obtained for histologic analysis.

RESULTS

SLN-evoked responses were successfully obtained in 18 hemi-laryngeal preparations (SD n = 13 and FBN n = 5) with corresponding LAR threshold measurements. Mean(±SD) SNAP latency, total duration, amplitude, negative durations, and intensity were 2.28 ms (±0.56), 2.13 ms (±0.70), 879 μV (±535), and 0.69 mA (±0.25), respectively. SLN stimulation threshold to elicit an LAR was of 0.84 mA (±0.31).

CONCLUSION

It is feasible to record evoked SLN responses in two adult rat strains. This work may lead to a tractable animal model for objective measurements of SLN neurophysiology with various disease states.

LEVEL OF EVIDENCE

N/A Laryngoscope, 2024.

Age-related myelin deficits in the auditory brain stem contribute to cocktail-party deficits

Age-related hearing loss is a global health problem of increasing importance. While the role of peripheral hearing loss is well understood and treatments are available, central hearing loss, the ability of the brain to make sense of sound, is much less well understood and no treatments are available. We report on age-related alterations in the auditory brain stem which compromise a listener's ability to isolate a sound from competing background noises, for example in a crowded restaurant. Sound localization depends on extreme temporal precision on the order of microseconds, and the sound localization pathway shows several specializations towards temporal precision. The pathway from the cochlear nucleus to the medial nucleus of the trapezoid body (MNTB) is heavily myelinated and terminates in the calyx of Held. Using auditory brain stem response measurements (ABRs), we found that the physiological properties of MNTB changes with age. The mechanism is that in older animals, MNTB afferents demyelinate to various degrees, resulting in larger variability in the timing of responses. Myelin is produced by oligodendrocytes, and we found that fewer mature, but more precursor and immature oligodendrocytes are present in MNTB of aged animals, suggesting that the demyelination is an age-related deficit in oligodendrocyte maturation.

NRV: An open framework for in silico evaluation of peripheral nerve electrical stimulation strategies

Electrical stimulation of peripheral nerves has been used in various pathological contexts for rehabilitation purposes or to alleviate the symptoms of neuropathologies, thus improving the overall quality of life of patients. However, the development of novel therapeutic strategies is still a challenging issue requiring extensive in vivo experimental campaigns and technical development. To facilitate the design of new stimulation strategies, we provide a fully open source and self-contained software framework for the in silico evaluation of peripheral nerve electrical stimulation. Our modeling approach, developed in the popular and well-established Python language, uses an object-oriented paradigm to map the physiological and electrical context. The framework is designed to facilitate multi-scale analysis, from single fiber stimulation to whole multifascicular nerves. It also allows the simulation of complex strategies such as multiple electrode combinations and waveforms ranging from conventional biphasic pulses to more complex modulated kHz stimuli. In addition, we provide automated support for stimulation strategy optimization and handle the computational backend transparently to the user. Our framework has been extensively tested and validated with several existing results in the literature.

Transient demyelination causes long-term cognitive impairment, myelin alteration and network synchrony defects

In the adult brain, activity-dependent myelin plasticity is required for proper learning and memory consolidation. Myelin loss, alteration, or even subtle structural modifications can therefore compromise the network activity, leading to functional impairment. In multiple sclerosis, spontaneous myelin repair process is possible, but it is heterogeneous among patients, sometimes leading to functional recovery, often more visible at the motor level than at the cognitive level. In cuprizone-treated mouse model, massive brain demyelination is followed by spontaneous and robust remyelination. However, reformed myelin, although functional, may not exhibit the same morphological characteristics as developmental myelin, which can have an impact on the activity of neural networks. In this context, we used the cuprizone-treated mouse model to analyze the structural, functional, and cognitive long-term effects of transient demyelination. Our results show that an episode of demyelination induces despite remyelination long-term cognitive impairment, such as deficits in spatial working memory, social memory, cognitive flexibility, and hyperactivity. These deficits were associated with a reduction in myelin content in the medial prefrontal cortex (mPFC) and hippocampus (HPC), as well as structural myelin modifications, suggesting that the remyelination process may be imperfect in these structures. In vivo electrophysiological recordings showed that the demyelination episode altered the synchronization of HPC-mPFC activity, which is crucial for memory processes. Altogether, our data indicate that the myelin repair process following transient demyelination does not allow the complete recovery of the initial myelin properties in cortical structures. These subtle modifications alter network features, leading to prolonged cognitive deficits in mice.

Neurophysiology of the Superior Laryngeal Nerve in an In Vivo Rat Model

OBJECTIVE

The superior laryngeal nerve (SLN) is fundamental in laryngeal sensation, cough reflex, and pitch control. SLN injury has substantial consequences including altered sensation, aspiration, and dysphonia. To date, in vivo measurement of the SLN remains elusive. The purpose of this study was to assess the feasibility of recording motor and sensory evoked potentials in a rat SLN model.

METHODS

Twenty-two rat hemi-laryngeal preparations (n = 11) were obtained from 4-month-old Sprague-Dawley rats and included in this study. Compound motor action potentials (CMAPs) and motor unit number estimation (MUNE) were calculated by stimulating the SLN at the point of medial extension near the carotid artery and by placing a recording electrode on the cricothyroid muscle. Sensory response was determined through stimulation of the SLN and laryngoscopic visualization of a laryngeal adductor reflex (LAR). SLN and cricothyroid muscle cross-sections were stained and histologic morphometrics were quantified.

RESULTS

Laryngeal evoked potentials were successfully obtained in all trials. Mean CMAP latency and negative durations were 0.99 ± 0.57 ms and 1.49 ± 0.57 ms, respectively. The median MUNE was 2.06 (IQR 1.88, 3.51). LAR was induced with a mean intensity of 0.69 ± 0.20 mV. Mean axon count, myelin thickness, and g-ratio were 681 ± 192.2, 1.72 ± 0.26, and 0.45 ± 0.04, respectively.

CONCLUSIONS

This study demonstrates the feasibility of recording evoked response potentials following SLN stimulation. We hypothesize that this work will provide a tractable animal model to study changes in laryngeal sensation and cricothyroid motor function with aging, neurodegenerative disease, aspiration, or nerve injury.

LEVEL OF EVIDENCE

NA Laryngoscope, 134:1778-1784, 2024.

AxoDetect: an automated nerve image segmentation and quantification workflow for computational nerve modeling

Objective.Bioelectronic treatments targeting near-organ innervation have unprecedented clinical applications. Particularly in the spleen, the inhibition of the cholinergic inflammatory response by near-organ nerve stimulation has potential to replace pharmacological treatments in chronic and autoimmune diseases. A caveat is that the optimization of therapeutic stimulation parameters relies onin vivoexperimentation, which becomes challenging due to the small nerve diameters (2 μm), complex anatomy, and mixed axon type composition of the autonomic nerves. Effective development ofin silicomodels requires tools which allow for fast and efficient quantification of axonal composition of specific nerves. Current approaches to generate such information rely on manual image segmentation and quantification.Approach.We developed a combined image-segmentation and model-generation software called AxoDetect: a target- and format-agnostic computer vision algorithm which can segment myelin, endo/epineurium, and both myelinated and unmyelinated fibers from a nerve image without training.Main results.AxoDetect is over 10 times faster on average when compared with current automatic methods while maintaining flexibility through the use of tunable pixel threshold filters to detect different types of tissue. When compared to a distribution-based and a manually segmented model of the splenic nerve terminal branch 1, the model generated with AxoDetect had comparable threshold prediction and was able to accurately detect an increase in activation threshold caused by the addition of surrounding fat tissue to the modeled nerve.Significance.AxoDetect contributes to the acceleration of neuromodulation treatment development through faster model design and iteration without requiring training. Furthermore, the computer vision approach and tunable nature of the filters in our method allow for its use in a variety of histological applications. Our approach will impact not only the study of nerves but also the design of implantable neural interfaces to enhance bioelectronic therapeutic options.

Touch sensation requires the mechanically gated ion channel ELKIN1

Touch perception is enabled by mechanically activated ion channels, the opening of which excites cutaneous sensory endings to initiate sensation. In this study, we identify ELKIN1 as an ion channel likely gated by mechanical force, necessary for normal touch sensitivity in mice. Touch insensitivity in Elkin1-/- mice was caused by a loss of mechanically activated currents (MA currents) in around half of all sensory neurons activated by light touch (low-threshold mechanoreceptors). Reintroduction of Elkin1 into sensory neurons from Elkin1-/- mice restored MA currents. Additionally, small interfering RNA-mediated knockdown of ELKIN1 from induced human sensory neurons substantially reduced indentation-induced MA currents, supporting a conserved role for ELKIN1 in human touch. Our data identify ELKIN1 as a core component of touch transduction in mice and potentially in humans.

Insights and improvements in correspondence between axonal volume fraction measured with diffusion-weighted MRI and electron microscopy

Biophysical diffusion-weighted imaging (DWI) models are increasingly used in neuroscience to estimate the axonal water fraction (f AW ), which in turn is key for noninvasive estimation of the axonal volume fraction (f A ). These models require thorough validation by comparison with a reference method, for example, electron microscopy (EM). While EM studies often neglect the unmyelinated axons and solely report the fraction of myelinated axons, in DWI both myelinated and unmyelinated axons contribute to the DWI signal. However, DWI models often include simplifications, for example, the neglect of differences in the compartmental relaxation times or fixed diffusivities, which in turn might affect the estimation off AW . We investigate whether linear calibration parameters (scaling and offset) can improve the comparability between EM- and DWI-based metrics off A . To this end, we (a) used six DWI models based on the so-called standard model of white matter (WM), including two models with fixed compartmental diffusivities (e.g., neurite orientation dispersion and density imaging, NODDI) and four models that fitted the compartmental diffusivities (e.g., white matter tract integrity, WMTI), and (b) used a multimodal data set including ex vivo diffusion DWI and EM data in mice with a broad dynamic range of fibre volume metrics. We demonstrated that the offset is associated with the volume fraction of unmyelinated axons and the scaling factor is associated with different compartmentalT 2 and can substantially enhance the comparability between EM- and DWI-based metrics off A . We found that DWI models that fitted compartmental diffusivities provided the most accurate estimates of the EM-basedf A . Finally, we introduced a more efficient hybrid calibration approach, where only the offset is estimated but the scaling is fixed to a theoretically predicted value. Using this approach, a similar one-to-one correspondence to EM was achieved for WMTI. The method presented can pave the way for use of validated DWI-based models in clinical research and neuroscience.

Machine learning enables non-Gaussian investigation of changes to peripheral nerves related to electrical stimulation

Electrical stimulation of the peripheral nervous system (PNS) is becoming increasingly important for the therapeutic treatment of numerous disorders. Thus, as peripheral nerves are increasingly the target of electrical stimulation, it is critical to determine how, and when, electrical stimulation results in anatomical changes in neural tissue. We introduce here a convolutional neural network and support vector machines for cell segmentation and analysis of histological samples of the sciatic nerve of rats stimulated with varying current intensities. We describe the methodologies and present results that highlight the validity of the approach: machine learning enabled highly efficient nerve measurement collection, while multivariate analysis revealed notable changes to nerves' anatomy, even when subjected to levels of stimulation thought to be safe according to the Shannon current limits.

NRV: An open framework for in silico evaluation of peripheral nerve electrical stimulation strategies

Electrical stimulation of peripheral nerves has been used in various pathological contexts for rehabilitation purposes or to alleviate the symptoms of neuropathologies, thus improving the overall quality of life of patients. However, the development of novel therapeutic strategies is still a challenging issue requiring extensive in vivo experimental campaigns and technical development. To facilitate the design of new stimulation strategies, we provide a fully open source and self-contained software framework for the in silico evaluation of peripheral nerve electrical stimulation. Our modeling approach, developed in the popular and well-established Python language, uses an object-oriented paradigm to map the physiological and electrical context. The framework is designed to facilitate multi-scale analysis, from single fiber stimulation to whole multifascicular nerves. It also allows the simulation of complex strategies such as multiple electrode combinations and waveforms ranging from conventional biphasic pulses to more complex modulated kHz stimuli. In addition, we provide automated support for stimulation strategy optimization and handle the computational backend transparently to the user. Our framework has been extensively tested and validated with several existing results in the literature.

CellWalker: a user-friendly and modular computational pipeline for morphological analysis of microscopy images

SUMMARY

The implementation of computational tools for analysis of microscopy images has been one of the most important technological innovations in biology, providing researchers unmatched capabilities to comprehend cell shape and connectivity. While numerous tools exist for image annotation and segmentation, there is a noticeable gap when it comes to morphometric analysis of microscopy images. Most existing tools often measure features solely on 2D serial images, which can be difficult to extrapolate to 3D. For this reason, we introduce CellWalker, a computational toolbox that runs inside Blender, an open-source computer graphics software. This add-on improves the morphological analysis by seamlessly integrating analysis tools into the Blender workflow, providing visual feedback through a powerful 3D visualization, and leveraging the resources of Blender's community. CellWalker provides several morphometric analysis tools that can be used to calculate distances, volume, surface areas and to determine cross-sectional properties. It also includes tools to build skeletons, calculate distributions of subcellular organelles. In addition, this python-based tool contains 'visible-source' IPython notebooks accessories for segmentation of 2D/3D microscopy images using deep learning and visualization of the segmented images that are required as input to CellWalker. Overall, CellWalker provides practical tools for segmentation and morphological analysis of microscopy images in the form of an open-source and modular pipeline which allows a complete access to fine-tuning of algorithms through visible-source code while still retaining a result-oriented interface.

AVAILABILITY AND IMPLEMENTATION

CellWalker source code is available on GitHub (https://github.com/utraf-pasteur-institute/Cellwalker-blender and https://github.com/utraf-pasteur-institute/Cellwalker-notebooks) under a GPL-3 license.

AimSeg: A machine-learning-aided tool for axon, inner tongue and myelin segmentation

Electron microscopy (EM) images of axons and their ensheathing myelin from both the central and peripheral nervous system are used for assessing myelin formation, degeneration (demyelination) and regeneration (remyelination). The g-ratio is the gold standard measure of assessing myelin thickness and quality, and traditionally is determined from measurements made manually from EM images-a time-consuming endeavour with limited reproducibility. These measurements have also historically neglected the innermost uncompacted myelin sheath, known as the inner tongue. Nonetheless, the inner tongue has been shown to be important for myelin growth and some studies have reported that certain conditions can elicit its enlargement. Ignoring this fact may bias the standard g-ratio analysis, whereas quantifying the uncompacted myelin has the potential to provide novel insights in the myelin field. In this regard, we have developed AimSeg, a bioimage analysis tool for axon, inner tongue and myelin segmentation. Aided by machine learning classifiers trained on transmission EM (TEM) images of tissue undergoing remyelination, AimSeg can be used either as an automated workflow or as a user-assisted segmentation tool. Validation results on TEM data from both healthy and remyelinating samples show good performance in segmenting all three fibre components, with the assisted segmentation showing the potential for further improvement with minimal user intervention. This results in a considerable reduction in time for analysis compared with manual annotation. AimSeg could also be used to build larger, high quality ground truth datasets to train novel deep learning models. Implemented in Fiji, AimSeg can use machine learning classifiers trained in ilastik. This, combined with a user-friendly interface and the ability to quantify uncompacted myelin, makes AimSeg a unique tool to assess myelin growth.

Proceeding the categorization of microplastics through deep learning-based image segmentation

Microplastics (MPs) have been recognized as prominent anthropogenic pollutants that inflict significant harm to marine ecosystems. Various approaches have been proposed to mitigate the risks posed by MPs. Gaining an understanding of the morphology of plastic particles can provide valuable insights into the source and their interaction with marine organisms, which can assist the development of response measures. In this study, we present an automated technique for identifying MPs through segmentation of MPs in microscopic images using a deep convolutional neural network (DCNN) based on a shape classification nomenclature framework. We used MP images from diverse samples to train a Mask Region Convolutional Neural Network (Mask R-CNN) based model for classification. Erosion and dilation operations were added to the model to improve segmentation results. On the testing dataset, the mean F1-score (F1) of segmentation and shape classification was 0.7601 and 0.617, respectively. These results demonstrate the potential of proposed method for the automatic segmentation and shape classification of MPs. Furthermore, by adopting a specific nomenclature, our approach represents a practical step towards the global standardization of MPs categorization criteria. This work also identifies future research directions to improve accuracy and further explore the possibilities of using DCNN for MPs identification.

Self-supervised segmentation and characterization of fiber bundles in anatomic tracing data

Anatomic tracing is the gold standard tool for delineating brain connections and for validating more recently developed imaging approaches such as diffusion MRI tractography. A key step in the analysis of data from tracer experiments is the careful, manual charting of fiber trajectories on histological sections. This is a very time-consuming process, which limits the amount of annotated tracer data that are available for validation studies. Thus, there is a need to accelerate this process by developing a method for computer-assisted segmentation. Such a method must be robust to the common artifacts in tracer data, including variations in the intensity of stained axons and background, as well as spatial distortions introduced by sectioning and mounting the tissue. The method should also achieve satisfactory performance using limited manually charted data for training. Here we propose the first deeplearning method, with a self-supervised loss function, for segmentation of fiber bundles on histological sections from macaque brains that have received tracer injections. We address the limited availability of manual labels with a semi-supervised training technique that takes advantage of unlabeled data to improve performance. We also introduce anatomic and across-section continuity constraints to improve accuracy. We show that our method can be trained on manually charted sections from a single case and segment unseen sections from different cases, with a true positive rate of ~0.80. We further demonstrate the utility of our method by quantifying the density of fiber bundles as they travel through different white-matter pathways. We show that fiber bundles originating in the same injection site have different levels of density when they travel through different pathways, a finding that can have implications for microstructure-informed tractography methods. The code for our method is available at https://github.com/v-sundaresan/fiberbundle_seg_tracing.

Perineurial Window is Critical for Experimental Reverse End-to-Side Nerve Transfer

BACKGROUND

The depth of connective tissue window in the side of a recipient nerve in reverse end-to-side transfers (RETS) remains controversial.

OBJECTIVE

To test whether the depth of connective tissue disruption influences the efficiency of donor axonal regeneration in the context of RETS.

METHODS

Sprague-Dawley rats (n = 24) were assigned to 1 of the 3 groups for obturator nerve to motor femoral nerve RETS: group 1, without epineurium opening; group 2, with epineurium only opening; and group 3, with epineurium and perineurium opening. Triple retrograde labeling was used to assess the number of motor neurons that had regenerated into the recipient motor femoral branch. Thy1-GFP rats (n = 8) were also used to visualize the regeneration pathways in the nerve transfer networks at 2- and 8-week time point using light sheet fluorescence microscopy.

RESULTS

The number of retrogradely labeled motor neurons that had regenerated distally toward the target muscle was significantly higher in group 3 than that in groups 1 and 2. Immunohistochemistry validated the degree of connective tissue disruption among the 3 groups, and optical tissue clearing methods demonstrated donor axons traveling outside the fascicles in groups 1 and 2 but mostly within the fascicles in group 3.

CONCLUSION

Creating a perineurial window in the side of recipient nerves provides the best chances of robust donor axonal regeneration across the RETS repair site. This finding aids nerve surgeons by confirming that a deep window should be undertaken when doing a RETS procedure.

Electrical Stimulation Induced Current Distribution in Peripheral Nerves Varies Significantly with the Extent of Nerve Damage: A Computational Study Utilizing Convolutional Neural Network and Realistic Nerve Models

Electrical stimulation of the peripheral nervous system is a promising therapeutic option for several conditions; however, its effects on tissue and the safety of the stimulation remain poorly understood. In order to devise stimulation protocols that enhance therapeutic efficacy without the risk of causing tissue damage, we constructed computational models of peripheral nerve and stimulation cuffs based on extremely high-resolution cross-sectional images of the nerves using the most recent advances in computing power and machine learning techniques. We developed nerve models using nonstimulated (healthy) and over-stimulated (damaged) rat sciatic nerves to explore how nerve damage affects the induced current density distribution. Using our in-house computational, quasi-static, platform, and the Admittance Method (AM), we estimated the induced current distribution within the nerves and compared it for healthy and damaged nerves. We also estimated the extent of localized cell damage in both healthy and damaged nerve samples. When the nerve is damaged, as demonstrated principally by the decreased nerve fiber packing, the current penetrates deeper into the over-stimulated nerve than in the healthy sample. As safety limits for electrical stimulation of peripheral nerves still refer to the Shannon criterion to distinguish between safe and unsafe stimulation, the capability this work demonstrated is an important step toward the development of safety criteria that are specific to peripheral nerve and make use of the latest advances in computational bioelectromagnetics and machine learning, such as Python-based AM and CNN-based nerve image segmentation.

AxoNet 2.0: A Deep Learning-Based Tool for Morphometric Analysis of Retinal Ganglion Cell Axons

Purpose

Assessment of glaucomatous damage in animal models is facilitated by rapid and accurate quantification of retinal ganglion cell (RGC) axonal loss and morphologic change. However, manual assessment is extremely time- and labor-intensive. Here, we developed AxoNet 2.0, an automated deep learning (DL) tool that (i) counts normal-appearing RGC axons and (ii) quantifies their morphometry from light micrographs.

Methods

A DL algorithm was trained to segment the axoplasm and myelin sheath of normal-appearing axons using manually-annotated rat optic nerve (ON) cross-sectional micrographs. Performance was quantified by various metrics (e.g., soft-Dice coefficient between predicted and ground-truth segmentations). We also quantified axon counts, axon density, and axon size distributions between hypertensive and control eyes and compared to literature reports.

Results

AxoNet 2.0 performed very well when compared to manual annotations of rat ON (R2 = 0.92 for automated vs. manual counts, soft-Dice coefficient = 0.81 ± 0.02, mean absolute percentage error in axonal morphometric outcomes < 15%). AxoNet 2.0 also showed promise for generalization, performing well on other animal models (R2 = 0.97 between automated versus manual counts for mice and 0.98 for non-human primates). As expected, the algorithm detected decreased in axon density in hypertensive rat eyes (P ≪ 0.001) with preferential loss of large axons (P < 0.001).

Conclusions

AxoNet 2.0 provides a fast and nonsubjective tool to quantify both RGC axon counts and morphological features, thus assisting with assessing axonal damage in animal models of glaucomatous optic neuropathy.

Translational Relevance

This deep learning approach will increase rigor of basic science studies designed to investigate RGC axon protection and regeneration.

mTORC2 Loss in Oligodendrocyte Progenitor Cells Results in Regional Hypomyelination in the Central Nervous System

In the CNS, oligodendrocyte progenitor cells (OPCs) differentiate into mature oligodendrocytes to generate myelin, an essential component for normal nervous system function. OPC differentiation is driven by signaling pathways, such as mTOR, which functions in two distinct complexes: mTOR complex 1 (mTORC1) and mTOR complex 2 (mTORC2), containing Raptor or Rictor, respectively. In the current studies, mTORC2 signaling was selectively deleted from OPCs in PDGFRα-Cre X Rictor^fl/fl mice. This study examined developmental myelination in male and female mice, comparing the impact of mTORC2 deletion in the corpus callosum and spinal cord. In both regions, Rictor loss in OPCs resulted in early reduction in myelin RNAs and proteins. However, these deficits rapidly recovered in spinal cord, where normal myelin was noted at P21 and P45. By contrast, the losses in corpus callosum resulted in severe hypomyelination and increased unmyelinated axons. The hypomyelination may result from decreased oligodendrocytes in the corpus callosum, which persisted in animals as old as postnatal day 350. The current studies focus on uniquely altered signaling pathways following mTORC2 loss in developing oligodendrocytes. A major mTORC2 substrate is phospho-Akt-S473, which was significantly reduced throughout development in both corpus callosum and spinal cord at all ages measured, yet this had little impact in spinal cord. Loss of mTORC2 signaling resulted in decreased expression of actin regulators, such as gelsolin in corpus callosum, but only minimal loss in spinal cord. The current study establishes a regionally specific role for mTORC2 signaling in OPCs, particularly in the corpus callosum.SIGNIFICANCE STATEMENT mTORC1 and mTORC2 signaling has differential impact on myelination in the CNS. Numerous studies identify a role for mTORC1, but deletion of Rictor (mTORC2 signaling) in late-stage oligodendrocytes had little impact on myelination in the CNS. However, the current studies establish that deletion of mTORC2 signaling from oligodendrocyte progenitor cells results in reduced myelination of brain axons. These studies also establish a regional impact of mTORC2, with little change in spinal cord in these conditional Rictor deletion mice. Importantly, in both brain and spinal cord, mTORC2 downstream signaling targets were impacted by Rictor deletion. Yet, these signaling changes had little impact on myelination in spinal cord, while they resulted in long-term alterations in myelination in brain.

Volumetric Characterization of Microvasculature in Ex Vivo Human Brain Samples By Serial Sectioning Optical Coherence Tomography

OBJECTIVE

Serial sectioning optical coherence tomography (OCT) enables accurate volumetric reconstruction of several cubic centimeters of human brain samples. We aimed to identify anatomical features of the ex vivo human brain, such as intraparenchymal blood vessels and axonal fiber bundles, from the OCT data in 3D, using intrinsic optical contrast.

METHODS

We developed an automatic processing pipeline to enable characterization of the intraparenchymal microvascular network in human brain samples.

RESULTS

We demonstrated the automatic extraction of the vessels down to a 20 μm in diameter using a filtering strategy followed by a graphing representation and characterization of the geometrical properties of microvascular network in 3D. We also showed the ability to extend this processing strategy to extract axonal fiber bundles from the volumetric OCT image.

CONCLUSION

This method provides a viable tool for quantitative characterization of volumetric microvascular network as well as the axonal bundle properties in normal and pathological tissues of the ex vivo human brain.

Histology-informed automatic parcellation of white matter tracts in the rat spinal cord

The white matter is organized into “tracts” or “bundles,” which connect different parts of the central nervous system. Knowing where these tracts are located in each individual is important for understanding the cause of potential sensorial, motor or cognitive deficits and for developing appropriate treatments. Traditionally, tracts are found using tracer injection, which is a difficult, slow and poorly scalable technique. However, axon populations from a given tract exhibit specific characteristics in terms of morphometrics and myelination. Hence, the delineation of tracts could, in principle, be done based on their morphometry. The objective of this study was to generate automatic parcellation of the rat spinal white matter tracts using the manifold information from scanning electron microscopy images of the entire spinal cord. The axon morphometrics (axon density, axon diameter, myelin thickness and g-ratio) were computed pixelwise following automatic axon segmentation using AxonSeg. The parcellation was based on an agglomerative clustering algorithm to group the tracts. Results show that axon morphometrics provide sufficient information to automatically identify some white matter tracts in the spinal cord, however, not all tracts were correctly identified. Future developments of microstructure quantitative MRI even bring hope for a personalized clustering of white matter tracts in each individual patient. The generated atlas and the associated code can be found at https://github.com/neuropoly/tract-clustering.

Axon and Myelin Sheath Segmentation in Electron Microscopy Images using Meta Learning

Various neurological diseases affect the morphology of myelinated axons. Quantitative analysis of these structures and changes occurring due to neurodegeneration or neuroregeneration is of great importance for characterization of disease state and treatment response. This paper proposes a robust, meta-learning based pipeline for segmentation of axons and surrounding myelin sheaths in electron microscopy images. This is the first step towards computation of electron microscopy related bio-markers of hypoglossal nerve degeneration/regeneration. This segmentation task is challenging due to large variations in morphology and texture of myelinated axons at different levels of degeneration and very limited availability of annotated data. To overcome these difficulties, the proposed pipeline uses a meta learning-based training strategy and a U-net like encoder decoder deep neural network. Experiments on unseen test data collected at different magnification levels (i.e, trained on 500X and 1200X images, and tested on 250X and 2500X images) showed improved segmentation performance by 5% to 7% compared to a regularly trained, comparable deep learning network.

Enhanced Automatic Morphometry of Nerve Histological Sections Using Ensemble Learning

There is a need for an automated morphometry algorithm to facilitate the otherwise labor-intensive task of the quantitative histological analysis of neural microscopic images. A benchmark morphometry algorithm is the convolutional neural network Axondeepseg (ADS), which yields a high segmentation accuracy for scanning and transmission electron microscopy images. Nevertheless, it shows decreased accuracy when applied to optical microscopy images, and it has been observed to yield sizable false positives when identifying small-sized neurons within the slides. In this study, ensemble learning is used to enhance the performance of ADS by combining it with the paired image-to-image translation algorithm PairedImageTranslation (PIT). Here, 120 optical microscopy images of peripheral nerves were used to train and test the ensemble learning model and the two base models individually for comparison. The results showed weighted pixel-wise accuracy for the ensemble model of 95.5%, whereas the ADS and PIT yielded accuracies of 93.4% and 90%, respectively. The automated measurements of the axon diameters and myelin thicknesses from the manually marked ground truth images were not statistically different (p = 0.05) from the measurements taken from the same images when segmented using the developed ensemble model, while they were different when they were measured from the segmented images by the two base models individually. The automated measurement of the G ratios indicated a higher similarity to the ground truth testing images for the ensemble model in comparison with the individual base models. The proposed model yielded automated segmentation of the nerve slides, which were sufficiently equivalent to the manual annotations and could be employed for axon diameters and myelin thickness measurements for fully automated histological analysis of the neural images.

Long-Term Changes in Axon Calibers after Injury: Observations on the Mouse Corticospinal Tract

White matter pathology is common across a wide spectrum of neurological diseases. Characterizing this pathology is important for both a mechanistic understanding of neurological diseases as well as for the development of neuroimaging biomarkers. Although axonal calibers can vary by orders of magnitude, they are tightly regulated and related to neuronal function, and changes in axon calibers have been reported in several diseases and their models. In this study, we utilize the impact acceleration model of traumatic brain injury (IA-TBI) to assess early and late changes in the axon diameter distribution (ADD) of the mouse corticospinal tract using Airyscan and electron microscopy. We find that axon calibers follow a lognormal distribution whose parameters significantly change after injury. While IA-TBI leads to 30% loss of corticospinal axons by day 7 with a bias for larger axons, at 21 days after injury we find a significant redistribution of axon frequencies that is driven by a reduction in large-caliber axons in the absence of detectable degeneration. We postulate that changes in ADD features may reflect a functional adaptation of injured neural systems. Moreover, we find that ADD features offer an accurate way to discriminate between injured and non-injured mice. Exploring injury-related ADD signatures by histology or new emerging neuroimaging modalities may offer a more nuanced and comprehensive way to characterize white matter pathology and may also have the potential to generate novel biomarkers of injury.

gACSON software for automated segmentation and morphology analyses of myelinated axons in 3D electron microscopy

BACKGROUND AND OBJECTIVE

Advances in electron microscopy (EM) now allow three-dimensional (3D) imaging of hundreds of micrometers of tissue with nanometer-scale resolution, providing new opportunities to study the ultrastructure of the brain. In this work, we introduce a freely available Matlab-based gACSON software for visualization, segmentation, assessment, and morphology analysis of myelinated axons in 3D-EM volumes of brain tissue samples.

METHODS

The software is equipped with a graphical user interface (GUI). It automatically segments the intra-axonal space of myelinated axons and their corresponding myelin sheaths and allows manual segmentation, proofreading, and interactive correction of the segmented components. gACSON analyzes the morphology of myelinated axons, such as axonal diameter, axonal eccentricity, myelin thickness, or g-ratio.

RESULTS

We illustrate the use of the software by segmenting and analyzing myelinated axons in six 3D-EM volumes of rat somatosensory cortex after sham surgery or traumatic brain injury (TBI). Our results suggest that the equivalent diameter of myelinated axons in somatosensory cortex was decreased in TBI animals five months after the injury.

CONCLUSION

Our results indicate that gACSON is a valuable tool for visualization, segmentation, assessment, and morphology analysis of myelinated axons in 3D-EM volumes. It is freely available at https://github.com/AndreaBehan/g-ACSON under the MIT license.

Foretinib mitigates cutaneous nerve fiber loss in experimental diabetic neuropathy

Diabetes is by far, the most common cause of neuropathy, inducing neurodegeneration of terminal sensory nerve fibers associated with loss of sensation, paresthesia, and persistent pain. Foretinib prevents die-back degeneration in cultured sensory and sympathetic neurons by rescuing mitochondrial activity and has been proven safe in prospective clinical trials. Here we aimed at investigating a potential neuroprotective effect of Foretinib in experimental diabetic neuropathy. A mouse model of streptozotocin induced diabetes was used that expresses yellow fluorescent protein (YFP) in peripheral nerve fibers under the thy-1 promoter. Streptozotocin-injected mice developed a stable diabetic state (blood glucose > 270 mg/dl), with a significant reduction of intraepidermal nerve fiber density by 25% at 5 weeks compared to the non-diabetic controls. When diabetic mice were treated with Foretinib, a significantly greater volume of the cutaneous nerve fibers (67.3%) in the plantar skin was preserved compared to vehicle treated (37.8%) and non-treated (44.9%) diabetic mice while proximal nerve fiber morphology was not affected. Our results indicate a neuroprotective effect of Foretinib on cutaneous nerve fibers in experimental diabetic neuropathy. As Foretinib treated mice showed greater weight loss compared to vehicle treated controls, future studies may define more sustainable treatment regimen and thereby may allow patients to take advantage of this neuroprotective drug in chronic neurodegenerative diseases like diabetic neuropathy.

Electrical Stimulation Induced Current Distribution in Peripheral Nerves Varies Significantly with the Extent of Nerve Damage: A Computational Study Utilizing Convolutional Neural Network and Realistic Nerve Models

Although electrical stimulation is an established treatment option for multiple central nervous and peripheral nervous system diseases, its effects on the tissue and subsequent safety of the stimulation are not well understood. Therefore, it is crucial to design stimulation protocols that maximize therapeutic efficacy while avoiding any potential tissue damage. Further, the stimulation levels need to be adjusted regularly to ensure that they are safe even with the changes to the nerve due to long-term stimulation. Using the latest advances in computing capabilities and machine learning approaches, we developed computational models of peripheral nerve stimulation based on very high-resolution cross-sectional images of the nerves. We generated nerve models constructed from non-stimulated (healthy) and over-stimulated (damaged) rat sciatic nerves to examine how the current density distribution is affected by nerve damage. Using our in-house numerical solver, the Admittance Method (AM), we computed the induced current distribution inside the nerves and compared the current penetration for healthy and damaged nerves. Our computational results indicate that when the nerve is damaged, primarily evidenced by the decreased nerve fiber packing, the current penetrates deeper inside the nerve than in the healthy case. As safety limits for electrical stimulation of biological tissue are still debated, we ultimately aim to utilize our computational models to determine refined safety criteria and help design safer and more efficacious electrical stimulation protocols.

A Combined Conduit-Bioactive Hydrogel Approach for Regeneration of Transected Sciatic Nerves

Transected peripheral nerve injury (PNI) affects the quality of life of patients, which leads to socioeconomic burden. Despite the existence of autografts and commercially available nerve guidance conduits (NGCs), the complexity of peripheral nerve regeneration requires further research in bioengineered NGCs to improve surgical outcomes. In this work, we introduce multidomain peptide (MDP) hydrogels, as intraluminal fillers, into electrospun poly(ε-caprolactone) (PCL) conduits to bridge 10 mm rat sciatic nerve defects. The efficacy of treatment groups was evaluated by electromyography and gait analysis to determine their electrical and motor recovery. We then studied the samples' histomorphometry with immunofluorescence staining and automatic axon counting/measurement software. Comparison with negative control group shows that PCL conduits filled with an anionic MDP may improve functional recovery 16 weeks postoperation, displaying higher amplitude of compound muscle action potential, greater gastrocnemius muscle weight retention, and earlier occurrence of flexion contracture. In contrast, PCL conduits filled with a cationic MDP showed the least degree of myelination and poor functional recovery. This phenomenon may be attributed to MDPs' difference in degradation time. Electrospun PCL conduits filled with an anionic MDP may become an attractive tissue engineering strategy for treating transected PNI when supplemented with other bioactive modifications.

Microscopy-BIDS: An Extension to the Brain Imaging Data Structure for Microscopy Data

The Brain Imaging Data Structure (BIDS) is a specification for organizing, sharing, and archiving neuroimaging data and metadata in a reusable way. First developed for magnetic resonance imaging (MRI) datasets, the community-led specification evolved rapidly to include other modalities such as magnetoencephalography, positron emission tomography, and quantitative MRI (qMRI). In this work, we present an extension to BIDS for microscopy imaging data, along with example datasets. Microscopy-BIDS supports common imaging methods, including 2D/3D, ex/in vivo, micro-CT, and optical and electron microscopy. Microscopy-BIDS also includes comprehensible metadata definitions for hardware, image acquisition, and sample properties. This extension will facilitate future harmonization efforts in the context of multi-modal, multi-scale imaging such as the characterization of tissue microstructure with qMRI.

Rapid, automated nerve histomorphometry through open-source artificial intelligence

We aimed to develop and validate a deep learning model for automated segmentation and histomorphometry of myelinated peripheral nerve fibers from light microscopic images. A convolutional neural network integrated in the AxonDeepSeg framework was trained for automated axon/myelin segmentation using a dataset of light-microscopic cross-sectional images of osmium tetroxide-stained rat nerves including various axonal regeneration stages. In a second dataset, accuracy of automated segmentation was determined against manual axon/myelin labels. Automated morphometry results, including axon diameter, myelin sheath thickness and g-ratio were compared against manual straight-line measurements and morphometrics extracted from manual labels with AxonDeepSeg as a reference standard. The neural network achieved high pixel-wise accuracy for nerve fiber segmentations with a mean (± standard deviation) ground truth overlap of 0.93 (± 0.03) for axons and 0.99 (± 0.01) for myelin sheaths, respectively. Nerve fibers were identified with a sensitivity of 0.99 and a precision of 0.97. For each nerve fiber, the myelin thickness, axon diameter, g-ratio, solidity, eccentricity, orientation, and individual x -and y-coordinates were determined automatically. Compared to manual morphometry, automated histomorphometry showed superior agreement with the reference standard while reducing the analysis time to below 2.5% of the time needed for manual morphometry. This open-source convolutional neural network provides rapid and accurate morphometry of entire peripheral nerve cross-sections. Given its easy applicability, it could contribute to significant time savings in biomedical research while extracting unprecedented amounts of objective morphologic information from large image datasets.

Quantitative susceptibility mapping and R1 measurement: Determination of the myelin volume fraction in the aging ex vivo rat corpus callosum

In studies of the white matter (WM) in aging brains, both quantitative susceptibility mapping (QSM) and direct R₁ measurement offer potentially useful ex vivo MRI tools that allow volumetric characterization of myelin content changes. Despite the technical importance of such MRI methods in numerous age-related diseases, the supposed linear relationship between the estimates of either the QSM or R₁ method and age-affected myelin contents has not been validated. In this study, the absolute myelin volume fraction (MVF) was determined by transmission electron microscopy (TEM) as a gold standard measure for comparison with the values obtained by the aforementioned MR methods. To theoretically evaluate and understand the MR signal characteristics, QSM simulations were performed using the finite perturber method (FPM). Specifically, the simulation geometry modeling was based on TEM-derived structures aligned orthogonally to the main magnetic field, the construct of which was used to estimate the magnetic field shift (ΔB) changes arising from the conjectured myelin structures. Experimentally, ex vivo corpus callosum (CC) samples from rat brains obtained at 6 weeks (n = 3), 4 months (n = 3), and 20 months (n = 3) after birth were used to establish the relationship between changes quantified by either QSM or R₁ with the absolute MVF by TEM. From the ex vivo brain samples, the scatterplot of mean MVF versus R₁ was fitted to a linear equation, where R_1mean  = 0.7948 × MVF_mean  + 0.8118 (Pearson's correlation coefficient r = 0.9138; p < 0.01), while the scatterplot of mean MVF versus MRI-derived magnetic susceptibility (χ) was also fitted to a line where χ_{measured,mean}  = -0.1218 × MVF_mean  - 0.006345 (r = -0.8435; p < 0.01). As a result of the FPM-based QSM simulations, a linearly proportional relationship between the simulated magnetic susceptibility, χ_{simulated,mean} , and MVF (r = -0.9648; p < 0.01) was established. Such a statistically significant linear correlation between MRI-derived values by the QSM (or R₁ ) method and MVF demonstrated that variable myelin contents in the WM (i.e., CC) can be quantified across multiple stages of aging. These findings further support that both techniques based on QSM and R₁ provide an efficient means of studying the brain-aging process with accurate volumetric quantification of the myelin content in WM.

Deep Learning-Enabled Technologies for Bioimage Analysis

Deep learning (DL) is a subfield of machine learning (ML), which has recently demonstrated its potency to significantly improve the quantification and classification workflows in biomedical and clinical applications. Among the end applications profoundly benefitting from DL, cellular morphology quantification is one of the pioneers. Here, we first briefly explain fundamental concepts in DL and then we review some of the emerging DL-enabled applications in cell morphology quantification in the fields of embryology, point-of-care ovulation testing, as a predictive tool for fetal heart pregnancy, cancer diagnostics via classification of cancer histology images, autosomal polycystic kidney disease, and chronic kidney diseases.

High-throughput segmentation of unmyelinated axons by deep learning

Axonal characterizations of connectomes in healthy and disease phenotypes are surprisingly incomplete and biased because unmyelinated axons, the most prevalent type of fibers in the nervous system, have largely been ignored as their quantitative assessment quickly becomes unmanageable as the number of axons increases. Herein, we introduce the first prototype of a high-throughput processing pipeline for automated segmentation of unmyelinated fibers. Our team has used transmission electron microscopy images of vagus and pelvic nerves in rats. All unmyelinated axons in these images are individually annotated and used as labeled data to train and validate a deep instance segmentation network. We investigate the effect of different training strategies on the overall segmentation accuracy of the network. We extensively validate the segmentation algorithm as a stand-alone segmentation tool as well as in an expert-in-the-loop hybrid segmentation setting with preliminary, albeit remarkably encouraging results. Our algorithm achieves an instance-level [Formula: see text] score of between 0.7 and 0.9 on various test images in the stand-alone mode and reduces expert annotation labor by 80% in the hybrid setting. We hope that this new high-throughput segmentation pipeline will enable quick and accurate characterization of unmyelinated fibers at scale and become instrumental in significantly advancing our understanding of connectomes in both the peripheral and the central nervous systems.

An Overview of Organs-on-Chips Based on Deep Learning

Microfluidic-based organs-on-chips (OoCs) are a rapidly developing technology in biomedical and chemical research and have emerged as one of the most advanced and promising in vitro models. The miniaturization, stimulated tissue mechanical forces, and microenvironment of OoCs offer unique properties for biomedical applications. However, the large amount of data generated by the high parallelization of OoC systems has grown far beyond the scope of manual analysis by researchers with biomedical backgrounds. Deep learning, an emerging area of research in the field of machine learning, can automatically mine the inherent characteristics and laws of "big data" and has achieved remarkable applications in computer vision, speech recognition, and natural language processing. The integration of deep learning in OoCs is an emerging field that holds enormous potential for drug development, disease modeling, and personalized medicine. This review briefly describes the basic concepts and mechanisms of microfluidics and deep learning and summarizes their successful integration. We then analyze the combination of OoCs and deep learning for image digitization, data analysis, and automation. Finally, the problems faced in current applications are discussed, and future perspectives and suggestions are provided to further strengthen this integration.

Towards a representative reference for MRI-based human axon radius assessment using light microscopy

Non-invasive assessment of axon radii via MRI bears great potential for clinical and neuroscience research as it is a main determinant of the neuronal conduction velocity. However, there is a lack of representative histological reference data at the scale of the cross-section of MRI voxels for validating the MRI-visible, effective radius (r_eff). Because the current gold standard stems from neuroanatomical studies designed to estimate the bulk-determined arithmetic mean radius (r_arith) on small ensembles of axons, it is unsuited to estimate the tail-weighted r_eff. We propose CNN-based segmentation on high-resolution, large-scale light microscopy (lsLM) data to generate a representative reference for r_eff. In a human corpus callosum, we assessed estimation accuracy and bias of r_arith and r_eff. Furthermore, we investigated whether mapping anatomy-related variation of r_arith and r_eff is confounded by low-frequency variation of the image intensity, e.g., due to staining heterogeneity. Finally, we analyzed the error due to outstandingly large axons in r_eff. Compared to r_arith, r_eff was estimated with higher accuracy (maximum normalized-root-mean-square-error of r_eff: 8.5 %; r_arith: 19.5 %) and lower bias (maximum absolute normalized-mean-bias-error of r_eff: 4.8 %; r_arith: 13.4 %). While r_arith was confounded by variation of the image intensity, variation of r_eff seemed anatomy-related. The largest axons contributed between 0.8 % and 2.9 % to r_eff. In conclusion, the proposed method is a step towards representatively estimating r_eff at MRI voxel resolution. Further investigations are required to assess generalization to other brains and brain areas with different axon radii distributions.

Revisiting the T2 spectrum imaging inverse problem: Bayesian regularized non-negative least squares

Multi-echo T₂ magnetic resonance images contain information about the distribution of T₂ relaxation times of compartmentalized water, from which we can estimate relevant brain tissue properties such as the myelin water fraction (MWF). Regularized non-negative least squares (NNLS) is the tool of choice for estimating non-parametric T₂ spectra. However, the estimation is ill-conditioned, sensitive to noise, and highly affected by the employed regularization weight. The purpose of this study is threefold: first, we want to underline that the apparently innocuous use of two alternative parameterizations for solving the inverse problem, which we called the standard and alternative regularization forms, leads to different solutions; second, to assess the performance of both parameterizations; and third, to propose a new Bayesian regularized NNLS method (BayesReg). The performance of BayesReg was compared with that of two conventional approaches (L-curve and Chi-square (X²) fitting) using both regularization forms. We generated a large dataset of synthetic data, acquired in vivo human brain data in healthy participants for conducting a scan-rescan analysis, and correlated the myelin content derived from histology with the MWF estimated from ex vivo data. Results from synthetic data indicate that BayesReg provides accurate MWF estimates, comparable to those from L-curve and X², and with better overall stability across a wider signal-to-noise range. Notably, we obtained superior results by using the alternative regularization form. The correlations reported in this study are higher than those reported in previous studies employing the same ex vivo and histological data. In human brain data, the estimated maps from L-curve and BayesReg were more reproducible. However, the T₂ spectra produced by BayesReg were less affected by over-smoothing than those from L-curve. These findings suggest that BayesReg is a good alternative for estimating T₂ distributions and MWF maps.

AxonDeep: Automated Optic Nerve Axon Segmentation in Mice With Deep Learning

Purpose

Optic nerve damage is the principal feature of glaucoma and contributes to vision loss in many diseases. In animal models, nerve health has traditionally been assessed by human experts that grade damage qualitatively or manually quantify axons from sampling limited areas from histologic cross sections of nerve. Both approaches are prone to variability and are time consuming. First-generation automated approaches have begun to emerge, but all have significant shortcomings. Here, we seek improvements through use of deep-learning approaches for segmenting and quantifying axons from cross-sections of mouse optic nerve.

Methods

Two deep-learning approaches were developed and evaluated: (1) a traditional supervised approach using a fully convolutional network trained with only labeled data and (2) a semisupervised approach trained with both labeled and unlabeled data using a generative-adversarial-network framework.

Results

From comparisons with an independent test set of images with manually marked axon centers and boundaries, both deep-learning approaches outperformed an existing baseline automated approach and similarly to two independent experts. Performance of the semisupervised approach was superior and implemented into AxonDeep.

Conclusions

AxonDeep performs automated quantification and segmentation of axons from healthy-appearing nerves and those with mild to moderate degrees of damage, similar to that of experts without the variability and constraints associated with manual performance.

Translational Relevance

Use of deep learning for axon quantification provides rapid, objective, and higher throughput analysis of optic nerve that would otherwise not be possible.

Electrode Spacing and Current Distribution in Electrical Stimulation of Peripheral Nerve: A Computational Modeling Study using Realistic Nerve Models

Electrical stimulation of peripheral nerves has long been used and proven effective in restoring function caused by disease or injury. Accurate placement of electrodes is often critical to properly excite the nerve and yield the desired outcome. Computational modeling is becoming an important tool that can guide the rapid development and optimization of such implantable neural stimulation devices. Here, we developed a heterogeneous very high-resolution computational model of a realistic peripheral nerve stimulated by a current source through cuff electrodes. We then calculated the current distribution inside the nerve and investigated the effect of electrodes spacing on current penetration. In the present study, we first describe model implementation and calibration; we then detail the methodology we use to calculate current distribution and apply it to characterize the effect of electrodes distance on current penetration. Our computational results indicate that when the source and return cuff electrodes are placed close to each other, the penetration depth in the nerve is shallower than the cases in which the electrode distance is larger. This study outlines the utility of the proposed computational methods and anatomically correct high-resolution models in guiding and optimizing experimental nerve stimulation protocols.

Reduced Axon Calibre in the Associative Striatum of the Sapap3 Knockout Mouse

Pathological repetitive behaviours are a common feature of various neuropsychiatric disorders, including compulsions in obsessive-compulsive disorder or tics in Gilles de la Tourette syndrome. Clinical research suggests that compulsive-like symptoms are related to associative cortico-striatal dysfunctions, and tic-like symptoms to sensorimotor cortico-striatal dysfunctions. The Sapap3 knockout mouse (Sapap3-KO), the current reference model to study such repetitive behaviours, presents both associative as well as sensorimotor cortico-striatal dysfunctions. Previous findings point to deficits in both macro-, as well as micro-circuitry, both of which can be affected by neuronal structural changes. However, to date, structural connectivity has not been analysed. Hence, in the present study, we conducted a comprehensive structural characterisation of both associative and sensorimotor striatum as well as major cortical areas connecting onto these regions. Besides a thorough immunofluorescence study on oligodendrocytes, we applied AxonDeepSeg, an open source software, to automatically segment and characterise myelin thickness and axon area. We found that axon calibre, the main contributor to changes in conduction speed, is specifically reduced in the associative striatum of the Sapap3-KO mouse; myelination per se seems unaffected in associative and sensorimotor cortico-striatal circuits.

On the microstructural origin of brain white matter hydraulic permeability

Brain microstructure plays a key role in driving the transport of drug molecules directly administered to the brain tissue, as in Convection-Enhanced Delivery procedures. The proposed research analyzes the hydraulic permeability of two white matter (WM) areas (corpus callosum and fornix) whose three-dimensional microstructure was reconstructed starting from the acquisition of electron microscopy images. We cut the two volumes with 20 equally spaced planes distributed along two perpendicular directions, and, on each plane, we computed the corresponding permeability vector. Then, we considered that the WM structure is mainly composed of elongated and parallel axons, and, using a principal component analysis, we defined two principal directions, parallel and perpendicular, with respect to the axons' main direction. The latter were used to define a reference frame onto which the permeability vectors were projected to finally obtain the permeability along the parallel and perpendicular directions. The results show a statistically significant difference between parallel and perpendicular permeability, with a ratio of about two in both the WM structures analyzed, thus demonstrating their anisotropic behavior. Moreover, we find a significant difference between permeability in corpus callosum and fornix, which suggests that the WM heterogeneity should also be considered when modeling drug transport in the brain. Our findings, which demonstrate and quantify the anisotropic and heterogeneous character of the WM, represent a fundamental contribution not only for drug-delivery modeling, but also for shedding light on the interstitial transport mechanisms in the extracellular space.

MyelTracer: A Semi-Automated Software for Myelin g-Ratio Quantification

In the central and peripheral nervous systems, the myelin sheath promotes neuronal signal transduction. The thickness of the myelin sheath changes during development and in disease conditions like multiple sclerosis. Such changes are routinely detected using electron microscopy through g-ratio quantification. While g-ratio is one of the most critical measurements in myelin studies, a major drawback is that g-ratio quantification is extremely laborious and time-consuming. Here, we report the development and validation of MyelTracer, an installable, stand-alone software for semi-automated g-ratio quantification based on the Open Computer Vision Library (OpenCV). Compared with manual g-ratio quantification, using MyelTracer produces consistent results across multiple tissues and animal ages, as well as in remyelination after optic nerve crush, and reduces total quantification time by 40-60%. With g-ratio measurements via MyelTracer, a known hypomyelination phenotype can be detected in a Williams syndrome mouse model. MyelTracer is easy to use and freely available for Windows and Mac OS X (https://github.com/HarrisonAllen/MyelTracer).

A simple and robust method for automating analysis of naïve and regenerating peripheral nerves

BACKGROUND

Manual axon histomorphometry (AH) is time- and resource-intensive, which has inspired many attempts at automation. However, there has been little investigation on implementation of automated programs for widespread use. Ideally such a program should be able to perform AH across imaging modalities and nerve states. AxonDeepSeg (ADS) is an open source deep learning program that has previously been validated in electron microscopy. We evaluated the robustness of ADS for peripheral nerve axonal histomorphometry in light micrographs prepared using two different methods.

METHODS

Axon histomorphometry using ADS and manual analysis (gold-standard) was performed on light micrographs of naïve or regenerating rat median nerve cross-sections prepared with either toluidine-resin or osmium-paraffin embedding protocols. The parameters of interest included axon count, axon diameter, myelin thickness, and g-ratio.

RESULTS

Manual and automatic ADS axon counts demonstrated good agreement in naïve nerves and moderate agreement on regenerating nerves. There were small but consistent differences in measured axon diameter, myelin thickness and g-ratio; however, absolute differences were small. Both methods appropriately identified differences between naïve and regenerating nerves. ADS was faster than manual axon analysis.

CONCLUSIONS

Without any algorithm retraining, ADS was able to appropriately identify critical differences between naïve and regenerating nerves and work with different sample preparation methods of peripheral nerve light micrographs. While there were differences between absolute values between manual and ADS, ADS performed consistently and required much less time. ADS is an accessible and robust tool for AH that can provide consistent analysis across protocols and nerve states.

Disentangling Rotational Dynamics and Ordering Transitions in a System of Self-Organizing Protein Nanorods via Rotationally Invariant Latent Representations

The dynamics of complex ordering systems with active rotational degrees of freedom exemplified by protein self-assembly is explored using a machine learning workflow that combines deep learning-based semantic segmentation and rotationally invariant variational autoencoder-based analysis of orientation and shape evolution. The latter allows for disentanglement of the particle orientation from other degrees of freedom and compensates for lateral shifts. The disentangled representations in the latent space encode the rich spectrum of local transitions that can now be visualized and explored via continuous variables. The time dependence of ensemble averages allows insight into the time dynamics of the system and, in particular, illustrates the presence of the potential ordering transition. Finally, analysis of the latent variables along the single-particle trajectory allows tracing these parameters on a single-particle level. The proposed approach is expected to be universally applicable for the description of the imaging data in optical, scanning probe, and electron microscopy seeking to understand the dynamics of complex systems where rotations are a significant part of the process.

Beyond the diffusion standard model in fixed rat spinal cord with combined linear and planar encoding

Information about tissue on the microscopic and mesoscopic scales can be accessed by modelling diffusion MRI signals, with the aim of extracting microstructure-specific biomarkers. The standard model (SM) of diffusion, currently the most broadly adopted microstructural model, describes diffusion in white matter (WM) tissues by two Gaussian components, one of which has zero radial diffusivity, to represent diffusion in intra- and extra-axonal water, respectively. Here, we reappraise these SM assumptions by collecting comprehensive double diffusion encoded (DDE) MRI data with both linear and planar encodings, which was recently shown to substantially enhance the ability to estimate SM parameters. We find however, that the SM is unable to account for data recorded in fixed rat spinal cord at an ultrahigh field of 16.4 T, suggesting that its underlying assumptions are violated in our experimental data. We offer three model extensions to mitigate this problem: first, we generalize the SM to accommodate finite radii (axons) by releasing the constraint of zero radial diffusivity in the intra-axonal compartment. Second, we include intracompartmental kurtosis to account for non-Gaussian behaviour. Third, we introduce an additional (third) compartment. The ability of these models to account for our experimental data are compared based on parameter feasibility and Bayesian information criterion. Our analysis identifies the three-compartment description as the optimal model. The third compartment exhibits slow diffusion with a minor but non-negligible signal fraction (∼12%). We demonstrate how failure to take the presence of such a compartment into account severely misguides inferences about WM microstructure. Our findings bear significance for microstructural modelling at large and can impact the interpretation of biomarkers extracted from the standard model of diffusion.

A shape-adjusted ellipse approach corrects for varied axonal dispersion angles and myelination in primate nerve roots

Segmentation of axons in light and electron micrographs allows for quantitative high-resolution analysis of nervous tissues, but varied axonal dispersion angles result in over-estimates of fiber sizes. To overcome this technical challenge, we developed a novel shape-adjusted ellipse (SAE) determination of axonal size and myelination as an all-inclusive and non-biased tool to correct for oblique nerve fiber presentations. Our new resource was validated by light and electron microscopy against traditional methods of determining nerve fiber size and myelination in rhesus macaques as a model system. We performed detailed segmental mapping and characterized the morphological signatures of autonomic and motor fibers in primate lumbosacral ventral roots (VRs). An en bloc inter-subject variability for the preganglionic parasympathetic fibers within the L7-S2 VRs was determined. The SAE approach allows for morphological ground truth data collection and assignment of individual axons to functional phenotypes with direct implications for fiber mapping and neuromodulation studies.

Microfluidics in Biotechnology: Quo Vadis

The emerging technique of microfluidics offers new approaches for precisely controlling fluidic conditions on a small scale, while simultaneously facilitating data collection in both high-throughput and quantitative manners. As such, the so-called lab-on-a-chip (LOC) systems have the potential to revolutionize the field of biotechnology. But what needs to happen in order to truly integrate them into routine biotechnological applications? In this chapter, some of the most promising applications of microfluidic technology within the field of biotechnology are surveyed, and a few strategies for overcoming current challenges posed by microfluidic LOC systems are examined. In addition, we also discuss the intensifying trend (across all biotechnology fields) of using point-of-use applications which is being facilitated by new technological achievements.