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Purpose High-speed videoendoscopy (HSV) is an emerging, but barely used, endoscopy technique in the clinic to assess and diagnose voice disorders because of the lack of dedicated software to analyze the data. HSV allows to quantify the vocal fold oscillations by segmenting the glottal area. This challenging task has been tackled by various studies; however, the proposed approaches are mostly limited and not suitable for daily clinical routine. Method We developed a user-friendly software in C# that allows the editing, motion correction, segmentation, and quantitative analysis of HSV data. We further provide pretrained deep neural networks for fully automatic glottis segmentation. Results We freely provide our software Glottis Analysis Tools (GAT). Using GAT, we provide a general threshold-based region growing platform that enables the user to analyze data from various sources, such as in vivo recordings, ex vivo recordings, and high-speed footage of artificial vocal folds. Additionally, especially for in vivo recordings, we provide three robust neural networks at various speed and quality settings to allow a fully automatic glottis segmentation needed for application by untrained personnel. GAT further evaluates video and audio data in parallel and is able to extract various features from the video data, among others the glottal area waveform, that is, the changing glottal area over time. In total, GAT provides 79 unique quantitative analysis parameters for video- and audio-based signals. Many of these parameters have already been shown to reflect voice disorders, highlighting the clinical importance and usefulness of the GAT software. Conclusion GAT is a unique tool to process HSV and audio data to determine quantitative, clinically relevant parameters for research, diagnosis, and treatment of laryngeal disorders. Supplemental Material https://doi.org/10.23641/asha.14575533

Background: Various voice assessment tools, such as questionnaires and aerodynamic voice characteristics, can be used to assess vocal function of individuals. However, not much is known about the best combinations of these parameters in identification of functional dysphonia in clinical settings. Methods: This study investigated six scores from clinically commonly used questionnaires and seven acoustic parameters. 514 females and 277 males were analyzed. The subjects were divided into three groups: one healthy group (N01) (49 females, 50 males) and two disordered groups with perceptually hoarse (FD23) (220 females, 96 males) and perceptually not hoarse (FD01) (245 females, 131 males) sounding voices. A tree stumps Adaboost approach was applied to find the subset of parameters that best separates the groups. Subsequently, it was determined if this parameter subset reflects treatment outcome for 120 female and 51 male patients by pairwise pre- and post-treatment comparisons of parameters. Results: The questionnaire “Voice-related-quality-of-Life” and three objective parameters (“maximum fundamental frequency”, “maximum Intensity” and “Jitter Percent”) were sufficient to separate the groups (accuracy ranging from 0.690 (FD01 vs. FD23, females) to 0.961 (N01 vs. FD23, females)). Our study suggests that a reduced parameter subset (4 out of 13) is sufficient to separate these three groups. All parameters reflected treatment outcome for patients with hoarse voices, Voice-related-quality-of-Life showed improvement for the not hoarse group (FD01). Conclusion: Results show that single parameters are insufficient to separate voice disorders but a set of several well-chosen parameters is. These findings will help to optimize and reduce clinical assessment time., In this study, we show that only a small set of four parameters is sufficient to differentiate between healthy subjects and patients with diagnosis of functional dysphonia. These four parameters also reflect treatment outcome for functional dysphonia and perceivable hoarse voices (success or failure). Thus, in future a functioning set of objective tools could be created that improves and accelerates assessment and therapy of voice disorders.

Summary: Stabilizing gaze and position within an environment constitutes an important task for the nervous system of many animals. The optomotor response (OMR) is a reflexive behavior, present across many species, in which animals move in the direction of perceived whole-field visual motion, therefore stabilizing themselves with respect to the visual environment. Although the OMR has been extensively used to probe visuomotor neuronal circuitry, the exact visual cues that elicit the behavior remain unidentified. In this study, we use larval zebrafish to identify spatiotemporal visual features that robustly elicit forward OMR swimming. These cues consist of a local, forward-moving, off edge together with on/off symmetric, similarly directed, global motion. Imaging experiments reveal neural units specifically activated by the forward-moving light-dark transition. We conclude that the OMR is driven not just by whole-field motion but by the interplay between global and local visual stimuli, where the latter exhibits a strong light-dark asymmetry. : Kist and Portugues use reverse correlation in an optomotor behavioral assay in larval zebrafish to identify the stereotypic filter that elicits swimming. It consists of a forward-moving local light-dark transition alongside global whole-field motion. The luminance profile strongly affects behavioral parameters, and filter-specific activity is spread across the brain. Keywords: zebrafish, optomotor response, visual motion, behavior, functional imaging

We present Stytra, a flexible, open-source software package, written in Python and designed to cover all the general requirements involved in larval zebrafish behavioral experiments. It provides timed stimulus presentation, interfacing with external devices and simultaneous real-time tracking of behavioral parameters such as position, orientation, tail and eye motion in both freely-swimming and head-restrained preparations. Stytra logs all recorded quantities, metadata, and code version in standardized formats to allow full provenance tracking, from data acquisition through analysis to publication. The package is modular and expandable for different experimental protocols and setups. Current releases can be found at https://github.com/portugueslab/stytra. We also provide complete documentation with examples for extending the package to new stimuli and hardware, as well as a schema and parts list for behavioral setups. We showcase Stytra by reproducing previously published behavioral protocols in both head-restrained and freely-swimming larvae. We also demonstrate the use of the software in the context of a calcium imaging experiment, where it interfaces with other acquisition devices. Our aims are to enable more laboratories to easily implement behavioral experiments, as well as to provide a platform for sharing stimulus protocols that permits easy reproduction of experiments and straightforward validation. Finally, we demonstrate how Stytra can serve as a platform to design behavioral experiments involving tracking or visual stimulation with other animals and provide an example integration with the DeepLabCut neural network-based tracking method.

SUMMARYThe cerebellum integrates sensory stimuli and motor actions to enable smooth coordination and motor learning. Here we harness the innate behavioral repertoire of the larval zebrafish to characterize the spatiotemporal dynamics of feature coding across the entire Purkinje cell population during visual stimuli and the reflexive behaviors that they elicit. Population imaging reveals three spatially-clustered regions of Purkinje cell activity along the rostrocaudal axis. Complementary single-cell electrophysiological recordings assign these Purkinje cells to one of three functional phenotypes that encode a specific visual, and not motor, signal via complex spikes. In contrast, simple spike output of most Purkinje cells is strongly driven by motor-related tail and eye signals. Interactions between complex and simple spikes show heterogeneous modulation patterns across different Purkinje cells, which become temporally restricted during swimming episodes. Our findings reveal how sensorimotor information is encoded by individual Purkinje cells and organized into behavioral modules across the entire cerebellum.

In the mid-twentieth century, the development of electrophysiology revolutionized the way that the brain could be studied, allowing scientists to advance beyond anatomy and neuroethology and address questions involving brain function. These recordings offered a temporally and spatially high-resolution readout of the activity of single cells and enabled a detailed understanding of the input–output function of individual neurons. Nevertheless, understanding the brain one neuron at a time seems like a daunting task. Over the last two decades, a considerable amount of research has focused on understanding the brain at the mesoscale of brain circuits and networks, trying to bridge the gap from single neurons to the function of the whole brain in generating behavior. This is a large, open and exciting field that encompasses theory, computational models, behavioral studies, genetic manipulations and many more approaches. Importantly, the current interest in brain circuits is fueled by the development of new techniques that allow us to acquire data relevant to addressing network function and the activity of large populations of neurons. In this chapter, we present an introduction to whole-brain, single-cell resolution imaging in a behaving vertebrate model organism, the larval zebrafish. We describe the fundamental concepts developed during the last five years that are important for understanding large-scale imaging techniques in vertebrates from experimental design to data acquisition and analysis.

PLoS one 11(9), e0161789 (2016). doi:10.1371/journal.pone.0161789, Published by PLoS, Lawrence, Kan.