NeuroEditor: a tool to edit and visualize neuronal morphologies.

The digital extraction of detailed neuronal morphologies from microscopy data is an essential step in the study of neurons. Ever since Cajal's work, the acquisition and analysis of neuron anatomy has yielded invaluable insight into the nervous system, which has led to our present understanding of many structural and functional aspects of the brain and the nervous system, well beyond the anatomical perspective. Obtaining detailed anatomical data, though, is not a simple task. Despite recent progress, acquiring neuron details still involves using labor-intensive, error prone methods that facilitate the introduction of inaccuracies and mistakes. In consequence, getting reliable morphological tracings usually needs the completion of post-processing steps that require user intervention to ensure the extracted data accuracy. Within this framework, this paper presents NeuroEditor, a new software tool for visualization, editing and correction of previously reconstructed neuronal tracings. This tool has been developed specifically for alleviating the burden associated with the acquisition of detailed morphologies. NeuroEditor offers a set of algorithms that can automatically detect the presence of potential errors in tracings. The tool facilitates users to explore an error with a simple mouse click so that it can be corrected manually or, where applicable, automatically. In some cases, this tool can also propose a set of actions to automatically correct a particular type of error. Additionally, this tool allows users to visualize and compare the original and modified tracings, also providing a 3D mesh that approximates the neuronal membrane. The approximation of this mesh is computed and recomputed on-the-fly, reflecting any instantaneous changes during the tracing process. Moreover, NeuroEditor can be easily extended by users, who can program their own algorithms in Python and run them within the tool. Last, this paper includes an example showing how users can easily define a customized workflow by applying a sequence of editing operations. The edited morphology can then be stored, together with the corresponding 3D mesh that approximates the neuronal membrane.

Attention and impulsivity assessment using virtual reality games.

The assessment of cognitive functions is mainly based on standardized neuropsychological tests, widely used in various fields such as personnel recruitment, education, or health. This paper presents a virtual reality game that allows collecting continuous measurements of both the performance and behaviour of the subject in an immersive, controllable, and naturalistic experience. The application registers variables related to the user's eye movements through the use of virtual reality goggles, as well as variables of the game performance. We study how virtual reality can provide data to help predict scores on the Attention Control Scale Test and the Barratt Impulsiveness Scale. We design the application and test it with a pilot group. We build a random forest regressor model to predict the attention and impulsivity scales' total score. When evaluating the performance of the model, we obtain a positive correlation with attention (0.434) and with impulsivity (0.382). In addition, our model identified that the most significant variables are the time spent looking at the target or at distractors, the eye movements variability, the number of blinks and the pupil dilation in both attention and impulsivity. Our results are consistent with previous results in the literature showing that it is possible to use data collected in virtual reality to predict the degree of attention and impulsivity.

Attention Deficit Hyperactivity Disorder Assessment Based on Patient Behavior Exhibited in a Car Video Game: A Pilot Study.

Symptoms of Attention Deficit Hyperactivity Disorder (ADHD) include excessive activity, difficulty sustaining attention, and inability to act in a reflective manner. Early diagnosis and treatment of ADHD is key but may be influenced by the observation and communication skills of caregivers, and the experience of the medical professional. Attempts to obtain additional measures to support the medical diagnosis, such as reaction time when performing a task, can be found in the literature. We propose an information recording system that allows to study in detail the behavior shown by children already diagnosed with ADHD during a car driving video game. We continuously record the participants' activity throughout the task and calculate the error committed. Studying the trajectory graphs, some children showed uniform patterns, others lost attention from one point onwards, and others alternated attention/inattention intervals. Results show a dependence between the age of the children and their performance. Moreover, by analyzing the positions by age over time using clustering, we show that it is possible to classify children according to their performance. Future studies will examine whether this detailed information about each child's performance pattern can be used to fine-tune treatment.

Automatic Personality Assessment through Movement Analysis.

Obtaining accurate and objective assessments of an individual's personality is vital in many areas including education, medicine, sports and management. Currently, most personality assessments are conducted using scales and questionnaires. Unfortunately, it has been observed that both scales and questionnaires present various drawbacks. Their limitations include the lack of veracity in the answers, limitations in the number of times they can be administered, or cultural biases. To solve these problems, several articles have been published in recent years proposing the use of movements that participants make during their evaluation as personality predictors. In this work, a multiple linear regression model was developed to assess the examinee's personality based on their movements. Movements were captured with the low-cost Microsoft Kinect camera, which facilitates its acceptance and implementation. To evaluate the performance of the proposed system, a pilot study was conducted aimed at assessing the personality traits defined by the Big-Five Personality Model. It was observed that the traits that best fit the model are Extroversion and Conscientiousness. In addition, several patterns that characterize the five personality traits were identified. These results show that it is feasible to assess an individual's personality through his or her movements and open up pathways for several research.

Neuronize v2: Bridging the Gap Between Existing Proprietary Tools to Optimize Neuroscientific Workflows.

Knowledge about neuron morphology is key to understanding brain structure and function. There are a variety of software tools that are used to segment and trace the neuron morphology. However, these tools usually utilize proprietary formats. This causes interoperability problems since the information extracted with one tool cannot be used in other tools. This article aims to improve neuronal reconstruction workflows by facilitating the interoperability between two of the most commonly used software tools-Neurolucida (NL) and Imaris (Filament Tracer). The new functionality has been included in an existing tool-Neuronize-giving rise to its second version. Neuronize v2 makes it possible to automatically use the data extracted with Imaris Filament Tracer to generate a tracing with dendritic spine information that can be read directly by NL. It also includes some other new features, such as the ability to unify and/or correct inaccurately-formed meshes (i.e., dendritic spines) and to calculate new metrics. This tool greatly facilitates the process of neuronal reconstruction, bridging the gap between existing proprietary tools to optimize neuroscientific workflows.

NeuroTessMesh: A Tool for the Generation and Visualization of Neuron Meshes and Adaptive On-the-Fly Refinement.

Gaining a better understanding of the human brain continues to be one of the greatest challenges for science, largely because of the overwhelming complexity of the brain and the difficulty of analyzing the features and behavior of dense neural networks. Regarding analysis, 3D visualization has proven to be a useful tool for the evaluation of complex systems. However, the large number of neurons in non-trivial circuits, together with their intricate geometry, makes the visualization of a neuronal scenario an extremely challenging computational problem. Previous work in this area dealt with the generation of 3D polygonal meshes that approximated the cells' overall anatomy but did not attempt to deal with the extremely high storage and computational cost required to manage a complex scene. This paper presents NeuroTessMesh, a tool specifically designed to cope with many of the problems associated with the visualization of neural circuits that are comprised of large numbers of cells. In addition, this method facilitates the recovery and visualization of the 3D geometry of cells included in databases, such as NeuroMorpho, and provides the tools needed to approximate missing information such as the soma's morphology. This method takes as its only input the available compact, yet incomplete, morphological tracings of the cells as acquired by neuroscientists. It uses a multiresolution approach that combines an initial, coarse mesh generation with subsequent on-the-fly adaptive mesh refinement stages using tessellation shaders. For the coarse mesh generation, a novel approach, based on the Finite Element Method, allows approximation of the 3D shape of the soma from its incomplete description. Subsequently, the adaptive refinement process performed in the graphic card generates meshes that provide good visual quality geometries at a reasonable computational cost, both in terms of memory and rendering time. All the described techniques have been integrated into NeuroTessMesh, available to the scientific community, to generate, visualize, and save the adaptive resolution meshes.

Improving impulsivity assessment using movement recognition: A pilot study.

The Continuous Performance Test (CPT) is a widely used computerized test to assess impulsivity. This article proposes the use of a CPT variant based on movement recognition to obtain more accurate measurements of impulsivity. In this pilot study, 22 volunteers participated in a CPT experiment responding to the stimuli by raising his or her dominant hand instead of pressing the space bar in a keyboard. Using this method, correlations of self-reported impulsivity with number of commission errors and average reaction time improved those obtained with standard CPT.

A new user-adapted search haptic algorithm to navigate along filiform structures.

One of the mayor research challenges of this century is the understanding of the human brain. Regarding this field line, simulation based research is gaining importance. A large amount of money is being spent in huge international projects such as The Human Brain Project [1] and The Blue Brain [2]. The behavior of the brain and, therefore, the behavior of brain simulations depend to a large extend on the neural topology. Neural elements are organized in a connected, dense, complex network of thread-like (i.e., filiform) structures. The analysis of a computer-based simulation using just the visual modality is a highly complex task due to the complexity of the neural topology and the large amounts of multi-variable and multi-modal data generated by computer simulations. In this paper, we propose the use of haptic devices to aid in the navigation along these neural structures, helping neurobiologists in the analysis of neural network topologies. However, haptic navigation constrained to complex filiform networks entails problems when these structures have high frequency features, noise and/or complex branching nodes. We address these issues by presenting a new user-adapted search haptic method that uses the forces exerted by the users to infer their intentions. In addition, we propose a specific calibration technique to adapt the haptic navigation to the user's skills and to the data. We validate this approach through a perceptual study. Finally, we show in this paper the application of our method to the analysis of dense and complex filiform structures in the neurobiology context. Additionally, our technique could be applied to other problems such as electronic circuits and graph exploration.

Haptically assisted connection procedure for the reconstruction of dendritic spines.

Dendritic spines are thin protrusions that cover the dendritic surface of numerous neurons in the brain and whose function seems to play a key role in neural circuits. The correct segmentation of those structures is difficult due to their small size and the resulting spines can appear incomplete. This paper presents a four-step procedure for the complete reconstruction of dendritic spines. The haptically driven procedure is intended to work as an image processing stage before the automatic segmentation step giving the final representation of the dendritic spines. The procedure is designed to allow both the navigation and the volume image editing to be carried out using a haptic device. A use case employing our procedure together with a commercial software package for the segmentation stage is illustrated. Finally, the haptic editing is evaluated in two experiments; the first experiment concerns the benefits of the force feedback and the second checks the suitability of the use of a haptic device as input. In both cases, the results shows that the procedure improves the editing accuracy.

Assessing Performance in Shoulder Arthroscopy: The Imperial Global Arthroscopy Rating Scale (IGARS).

BACKGROUND: Surgical training is undergoing major changes with reduced resident work hours and an increasing focus on patient safety and surgical aptitude. The aim of this study was to create a valid, reliable method for an assessment of arthroscopic skills that is independent of time and place and is designed for both real and simulated settings. The validity of the scale was tested using a virtual reality shoulder arthroscopy simulator. METHODS: The study consisted of two parts. In the first part, an Imperial Global Arthroscopy Rating Scale for assessing technical performance was developed using a Delphi method. Application of this scale required installing a dual-camera system to synchronously record the simulator screen and body movements of trainees to allow an assessment that is independent of time and place. The scale includes aspects such as efficient portal positioning, angles of instrument insertion, proficiency in handling the arthroscope and adequately manipulating the camera, and triangulation skills. In the second part of the study, a validation study was conducted. Two experienced arthroscopic surgeons, blinded to the identities and experience of the participants, each assessed forty-nine subjects performing three different tests using the Imperial Global Arthroscopy Rating Scale. Results were analyzed using two-way analysis of variance with measures of absolute agreement. The intraclass correlation coefficient was calculated for each test to assess inter-rater reliability. RESULTS: The scale demonstrated high internal consistency (Cronbach alpha, 0.918). The intraclass correlation coefficient demonstrated high agreement between the assessors: 0.91 (p < 0.001). Construct validity was evaluated using Kruskal-Wallis one-way analysis of variance (chi-square test, 29.826; p < 0.001), demonstrating that the Imperial Global Arthroscopy Rating Scale distinguishes significantly between subjects with different levels of experience utilizing a virtual reality simulator. CONCLUSIONS: The Imperial Global Arthroscopy Rating Scale has a high internal consistency and excellent inter-rater reliability and offers an approach for assessing technical performance in basic arthroscopy on a virtual reality simulator. CLINICAL RELEVANCE: The Imperial Global Arthroscopy Rating Scale provides detailed information on surgical skills. Although it requires further validation in the operating room, this scale, which is independent of time and place, offers a robust and reliable method for assessing arthroscopic technical skills.

Neuronize: a tool for building realistic neuronal cell morphologies.

This study presents a tool, Neuronize, for building realistic three-dimensional models of neuronal cells from the morphological information extracted through computer-aided tracing applications. Neuronize consists of a set of methods designed to build 3D neural meshes that approximate the cell membrane at different resolution levels, allowing a balance to be reached between the complexity and the quality of the final model. The main contribution of the present study is the proposal of a novel approach to build a realistic and accurate 3D shape of the soma from the incomplete information stored in the digitally traced neuron, which usually consists of a 2D cell body contour. This technique is based on the deformation of an initial shape driven by the position and thickness of the first order dendrites. The addition of a set of spines along the dendrites completes the model, building a final 3D neuronal cell suitable for its visualization in a wide range of 3D environments.

Assessment study of insight ARTHRO VR (®) arthroscopy virtual training simulator: face, content, and construct validities.

The aims of this study were to test the face, content, and construct validities of a virtual-reality haptic arthroscopy simulator and to validate four assessment hypothesis. The participants in our study were 94 arthroscopists attending an international conference on arthroscopy. The interviewed surgeons had been performing arthroscopies for a mean of 8.71 years (σ = 6.94 years). We explained the operation, functionality, instructions for use, and the exercises provided by the simulator. They performed a trial exercise and then an exercise in which performance was recorded. After having using it, the arthroscopists answered a questionnaire. The simulator was classified as one of the best training methods (over phantoms), and obtained a mark of 7.10 out of 10 as an evaluation tool. The simulator was considered more useful for inexperienced surgeons than for surgeons with experience (mean difference 1.88 out of 10, P value < 0.001). The participants valued the simulator at 8.24 as a tool for learning skills, its fidelity at 7.41, the quality of the platform at 7.54, and the content of the exercises at 7.09. It obtained a global score of 7.82. Of the subjects, 30.8% said they would practise with the simulator more than 6 h per week. Of the surgeons, 89.4% affirmed that they would recommend the simulator to their colleagues. The data gathered support the first three hypotheses, as well as face and content validities. Results show statistically significant differences between experts and novices, thus supporting the construct validity, but studies with a larger sample must be carried out to verify this. We propose concrete solutions and an equation to calculate economy of movement. Analogously, we analyze competence measurements and propose an equation to provide a single measurement that contains them all and that, according to the surgeons' criteria, is as reliable as the judgment of experts observing the performance of an apprentice.