Decreasing time consumption of microscopy image segmentation through parallel processing on the GPU

The computational performance of graphical processing units (GPUs) has improved significantly. Achieving speedup factors of more than 50x compared to single-threaded CPU execution are not uncommon due to parallel processing. This makes their use for high throughput microscopy image analysis very appealing. Unfortunately, GPU programming is not straightforward and requires a lot of programming skills and effort. Additionally, the attainable speedup factor is hard to predict, since it depends on the type of algorithm, input data and the way in which the algorithm is implemented. In this paper, we identify the characteristic algorithm and data-dependent properties that significantly relate to the achievable GPU speedup. We find that the overall GPU speedup depends on three major factors: (1) the coarse-grained parallelism of the algorithm, (2) the size of the data and (3) the computation/memory transfer ratio. This is illustrated on two types of well-known segmentation methods that are extensively used in microscopy image analysis: SLIC superpixels and high-level geometric active contours. In particular, we find that our used geometric active contour segmentation algorithm is very suitable for parallel processing, resulting in acceleration factors of 50x for 0.1 megapixel images and 100x for 10 megapixel images

Local processing in neurites of VGluT3- expressing amacrine cells differentially organizes visual information

Neurons receive synaptic inputs on extensive neurite arbors. How information is organized across arbors and how local processing in neurites contributes to circuit function is mostly unknown. Here, we used two-photon Ca2+ imaging to study visual processing in VGluT3-expressing amacrine cells (VG3-ACs) in the mouse retina. Contrast preferences (ON vs. OFF) varied across VG3-AC arbors depending on the laminar position of neurites, with ON responses preferring larger stimuli than OFF responses. Although arbors of neighboring cells overlap extensively, imaging population activity revealed continuous topographic maps of visual space in the VG3-AC plexus. All VG3-AC neurites responded strongly to object motion, but remained silent during global image motion. Thus, VG3-AC arbors limit vertical and lateral integration of contrast and location information, respectively. We propose that this local processing enables the dense VG3-AC plexus to contribute precise object motion signals to diverse targets without distorting target-specific contrast preferences and spatial receptive fields.</jats:p

Pharmacological Blockade of the Calcium Plateau Provides Neuroprotection Following Organophosphate Paraoxon Induced Status Epilepticus in Rats

Organophosphate (OP) compounds which include nerve agents and pesticides are considered chemical threat agents. Currently approved antidotes are crucial in limiting OP mediated acute mortality. However, survivors of lethal OP exposure exhibit delayed neuronal injury and chronic behavioral morbidities. In this study, we investigated neuroprotective capabilities of dantrolene and carisbamate in a rat survival model of paraoxon (POX) induced status epilepticus (SE). Significant elevations in hippocampal calcium levels were observed 48-h post POX SE survival, and treatment with dantrolene (10 mg/kg, i.m.) and carisbamate (90 mg/kg, i.m.) lowered these protracted calcium elevations. POX SE induced delayed neuronal injury as characterized by Fluoro Jade C labeling was observed in critical brain areas including the dentate gyrus, parietal cortex, amygdala, and thalamus. Dantrolene and carisbamate treatment provided significant neuroprotection against delayed neuronal damage in these brain regions when administered one-hour after POX-SE. These results indicate that dantrolene or carisbamate could be effective adjuvant therapies to the existing countermeasures to reduce neuronal injury and behavioral morbidities post OP SE survival

Synaptic Cleft Segmentation in Non-Isotropic Volume Electron Microscopy of the Complete Drosophila Brain

Neural circuit reconstruction at single synapse resolution is increasingly recognized as crucially important to decipher the function of biological nervous systems. Volume electron microscopy in serial transmission or scanning mode has been demonstrated to provide the necessary resolution to segment or trace all neurites and to annotate all synaptic connections. Automatic annotation of synaptic connections has been done successfully in near isotropic electron microscopy of vertebrate model organisms. Results on non-isotropic data in insect models, however, are not yet on par with human annotation. We designed a new 3D-U-Net architecture to optimally represent isotropic fields of view in non-isotropic data. We used regression on a signed distance transform of manually annotated synaptic clefts of the CREMI challenge dataset to train this model and observed significant improvement over the state of the art. We developed open source software for optimized parallel prediction on very large volumetric datasets and applied our model to predict synaptic clefts in a 50 tera-voxels dataset of the complete Drosophila brain. Our model generalizes well to areas far away from where training data was available

General features of the retinal connectome determine the computation of motion anticipation

Motion anticipation allows the visual system to compensate for the slow speed of phototransduction so that a moving object can be accurately located. This correction is already present in the signal that ganglion cells send from the retina but the biophysical mechanisms underlying this computation are not known. Here we demonstrate that motion anticipation is computed autonomously within the dendritic tree of each ganglion cell and relies on feedforward inhibition. The passive and non-linear interaction of excitatory and inhibitory synapses enables the somatic voltage to encode the actual position of a moving object instead of its delayed representation. General rather than specific features of the retinal connectome govern this computation: an excess of inhibitory inputs over excitatory, with both being randomly distributed, allows tracking of all directions of motion, while the average distance between inputs determines the object velocities that can be compensated for

Automated tracing of myelinated axons and detection of the nodes of Ranvier in serial images of peripheral nerves

The development of realistic neuroanatomical models of peripheral nerves for simulation purposes requires the reconstruction of the morphology of the myelinated fibres in the nerve, including their nodes of Ranvier. Currently, this information has to be extracted by semimanual procedures, which severely limit the scalability of the experiments. In this contribution, we propose a supervised machine learning approach for the detailed reconstruction of the geometry of fibres inside a peripheral nerve based on its high-resolution serial section images. Learning from sparse expert annotations, the algorithm traces myelinated axons, even across the nodes of Ranvier. The latter are detected automatically. The approach is based on classifying the myelinated membranes in a supervised fashion, closing the membrane gaps by solving an assignment problem, and classifying the closed gaps for the nodes of Ranvier detection. The algorithm has been validated on two very different datasets: (i) rat vagus nerve subvolume, SBFSEM microscope, 200 × 200 × 200 nm resolution, (ii) rat sensory branch subvolume, confocal microscope, 384 × 384 × 800 nm resolution. For the first dataset, the algorithm correctly reconstructed 88% of the axons (241 out of 273) and achieved 92% accuracy on the task of Ranvier node detection. For the second dataset, the gap closing algorithm correctly closed 96.2% of the gaps, and 55% of axons were reconstructed correctly through the whole volume. On both datasets, training the algorithm on a small data subset and applying it to the full dataset takes a fraction of the time required by the currently used semiautomated protocols. Our software, raw data and ground truth annotations are available at http://hci.iwr.uni-heidelberg.de/Benchmarks/. The development version of the code can be found at https://github.com/RWalecki/ATMA

Large-scale circuit reconstruction in medial entorhinal cortex

Es ist noch weitgehend ungeklärt, mittels welcher Mechanismen die elektrische Aktivität von Nervenzellpopulationen des Gehirns Verhalten ermöglicht. Die Orientierung im Raum ist eine Fähigkeit des Gehirns, für die im Säugetier der mediale entorhinale Teil der Großhirnrinde als entscheidende Struktur identifiziert wurde. Hier wurden Nervenzellen gefunden, die die Umgebung des Individuums in einer gitterartigen Anordnung repräsentieren. Die neuronalen Schaltkreise, welche diese geordnete Nervenzellaktivität im medialen entorhinalen Kortex (MEK) ermöglichen, sind noch wenig verstanden. Die vorliegende Dissertation hat eine Klärung der zellulären Architektur und der neuronalen Schaltkreise in der zweiten Schicht des MEK der Ratte zum Ziel. Zunächst werden die Beiträge zur Entdeckung der hexagonal angeordneten zellulären Anhäufungen in Schicht 2 des MEK sowie zur Beschreibung der Dichotomie der Haupt-Nervenzelltypen dargestellt. Im zweiten Teil wird erstmalig eine konnektomische Analyse des MEK beschrieben. Die detaillierte Untersuchung der Architektur einzelner exzitatorischer Axone ergab das überraschende Ergebnis der präzisen Sortierung von Synapsen entlang axonaler Pfade. Die neuronalen Schaltkreise, in denen diese Neurone eingebettet sind, zeigten eine starke zeitliche Bevorzugung der hemmenden Neurone. Die hier erhobenen Daten tragen zu einem detaillierteren Verständnis der neuronalen Schaltkreise im MEK bei. Sie enthalten die erste Beschreibung überraschend präziser axonaler synaptischer Ordnung im zerebralen Kortex der Säugetiere. Diese Schaltkreisarchitektur lässt einen Effekt auf die Weiterleitung synchroner elektrischer Populationsaktivität im MEK vermuten. In zukünftigen Studien muss insbesondere geklärt werden, ob es sich bei den hier berichteten Ergebnissen um eine Besonderheit des MEK oder ein generelles Verschaltungsprinzip der Hirnrinde des Säugetiers handelt.The mechanisms by which the electrical activity of ensembles of neurons in the brain give rise to an individual’s behavior are still largely unknown. Navigation in space is one important capacity of the brain, for which the medial entorhinal cortex (MEC) is a pivotal structure in mammals. At the cellular level, neurons that represent the surrounding space in a grid-like fashion have been identified in MEC. These so-called grid cells are located predominantly in layer 2 (L2) of MEC. The detailed neuronal circuits underlying this unique activity pattern are still poorly understood. This thesis comprises studies contributing to a mechanistic description of the synaptic architecture in rat MEC L2. First, this thesis describes the discovery of hexagonally arranged cell clusters and anatomical data on the dichotomy of the two principle cell types in L2 of the MEC. Then, the first connectomic study of the MEC is reported. An analysis of the axonal architecture of excitatory neurons revealed synaptic positional sorting along axons, integrated into precise microcircuits. These microcircuits were found to involve interneurons with a surprising degree of axonal specialization for effective and fast inhibition. Together, these results contribute to a detailed understanding of the circuitry in MEC. They provide the first description of highly precise synaptic arrangements along axons in the cerebral cortex of mammals. The functional implications of these anatomical features were explored using numerical simulations, suggesting effects on the propagation of synchronous activity in L2 of the MEC. These findings motivate future investigations to clarify the contribution of precise synaptic architecture to computations underlying spatial navigation. Further studies are required to understand whether the reported synaptic specializations are specific for the MEC or represent a general wiring principle in the mammalian cortex

The evolution of epilepsy surgery between 1991 and 2011 in nine major epilepsy centers across the United States, Germany, and Australia.

OBJECTIVE: Epilepsy surgery is the most effective treatment for select patients with drug-resistant epilepsy. In this article, we aim to provide an accurate understanding of the current epidemiologic characteristics of this intervention, as this knowledge is critical for guiding educational, academic, and resource priorities. METHODS: We profile the practice of epilepsy surgery between 1991 and 2011 in nine major epilepsy surgery centers in the United States, Germany, and Australia. Clinical, imaging, surgical, and histopathologic data were derived from the surgical databases at various centers. RESULTS: Although five of the centers performed their highest number of surgeries for mesial temporal sclerosis (MTS) in 1991, and three had their highest number of MTS surgeries in 2001, only one center achieved its peak number of MTS surgeries in 2011. The most productive year for MTS surgeries varied then by center; overall, the nine centers surveyed performed 48% (95% confidence interval [CI] -27.3% to -67.4%) fewer such surgeries in 2011 compared to either 1991 or 2001, whichever was higher. There was a parallel increase in the performance of surgery for nonlesional epilepsy. Further analysis of 5/9 centers showed a yearly increase of 0.6 ± 0.07% in the performance of invasive electroencephalography (EEG) without subsequent resections. Overall, although MTS was the main surgical substrate in 1991 and 2001 (proportion of total surgeries in study centers ranging from 33.3% to 70.2%); it occupied only 33.6% of all resections in 2011 in the context of an overall stable total surgical volume. SIGNIFICANCE: These findings highlight the major aspects of the evolution of epilepsy surgery across the past two decades in a sample of well-established epilepsy surgery centers, and the critical current challenges of this treatment option in addressing complex epilepsy cases requiring detailed evaluations. Possible causes and implications of these findings are discussed

Quantitative neuroanatomy for connectomics in Drosophila.

Neuronal circuit mapping using electron microscopy demands laborious proofreading or reconciliation of multiple independent reconstructions. Here, we describe new methods to apply quantitative arbor and network context to iteratively proofread and reconstruct circuits and create anatomically enriched wiring diagrams. We measured the morphological underpinnings of connectivity in new and existing reconstructions of Drosophila sensorimotor (larva) and visual (adult) systems. Synaptic inputs were preferentially located on numerous small, microtubule-free 'twigs' which branch off a single microtubule-containing 'backbone'. Omission of individual twigs accounted for 96% of errors. However, the synapses of highly connected neurons were distributed across multiple twigs. Thus, the robustness of a strong connection to detailed twig anatomy was associated with robustness to reconstruction error. By comparing iterative reconstruction to the consensus of multiple reconstructions, we show that our method overcomes the need for redundant effort through the discovery and application of relationships between cellular neuroanatomy and synaptic connectivity.Funding came from the HHMI Janelia Visiting Scientist program (AC), Swiss National Science Foundation grant 31003A 132969 (AC), HHMI, and the Institute of Neuroinformatics of the University of Zurich and ETH Zurich.This is the final version of the article. It first appeared from eLife via http://dx.doi.org/10.7554/eLife.12059.00