A Randomized Trial Utilizing EEG Brain Computer Interface to Improve Facial Emotion Recognition in Autistic Adults.

PURPOSE: Many individuals with autism spectrum disorder (ASD) experience challenges with facial emotion recognition (FER), which may exacerbate social difficulties in ASD. Few studies have examined whether FER can be experimentally manipulated and improved for autistic people. This study utilized a randomized controlled trial design to examine acceptability and preliminary clinical impact of a novel mixed reality-based neurofeedback program, FER Assistant, using EEG brain computer interface (BCI)-assisted technology to improve FER for autistic adolescents and adults. METHODS: Twenty-seven autistic male participants (M age: 21.12 years; M IQ: 105.78; 85% white) were randomized to the active condition to receive FER Assistant (n = 17) or waitlist control (n = 10). FER Assistant participants received ten sessions utilizing BCI-assisted neurofeedback training in FER. All participants, regardless of randomization, completed a computerized FER task at baseline and endpoint. RESULTS: Results partially indicated that FER Assistant was acceptable to participants. Regression analyses demonstrated that participation in FER Assistant led to group differences in FER at endpoint, compared to a waitlist control. However, analyses examining reliable change in FER indicated no reliable improvement or decline for FER Assistant participants, whereas two waitlist participants demonstrated reliable decline. CONCLUSION: Given the preliminary nature of this work, results collectively suggest that FER Assistant may be an acceptable intervention. Results also suggest that FER may be a potential mechanism that is amenable to intervention for autistic individuals, although additional trials using larger sample sizes are warranted.

Social Anxiety Symptoms Predict Poorer Facial Emotion Recognition in Autistic Male Adolescents and Young Adults Without Intellectual Disability.

Utilizing a novel computerized task, we aimed to examine whether social anxiety symptoms would be related to individual differences in facial emotion recognition (FER) in a sample of autistic male adolescents and young adults without intellectual disability. Results indicated that social anxiety and IQ predicted poorer FER, irrespective of specific emotion type. When probing specific effects within emotion and condition types, social anxiety impacted surprise and disgust FER during a truncated viewing condition and not full viewing condition. Collectively, results suggest that social anxiety in autism may play a larger role in FER than previously thought. Future work should consider the role of social anxiety within autism as a factor that may meaningfully relate to FER assessment and intervention.

Interactive Visual Analysis of Structure-borne Noise Data.

Numerical simulation has become omnipresent in the automotive domain, posing new challenges such as high-dimensional parameter spaces and large as well as incomplete and multi-faceted data. In this design study, we show how interactive visual exploration and analysis of high-dimensional, spectral data from noise simulation can facilitate design improvements in the context of conflicting criteria. Here, we focus on structure-borne noise, i.e., noise from vibrating mechanical parts. Detecting problematic noise sources early in the design and production process is essential for reducing a product's development costs and its time to market. In a close collaboration of visualization and automotive engineering, we designed a new, interactive approach to quickly identify and analyze critical noise sources, also contributing to an improved understanding of the analyzed system. Several carefully designed, interactive linked views enable the exploration of noises, vibrations, and harshness at multiple levels of detail, both in the frequency and spatial domain. This enables swift and smooth changes of perspective; selections in the frequency domain are immediately reflected in the spatial domain, and vice versa. Noise sources are quickly identified and shown in the context of their neighborhood, both in the frequency and spatial domain. We propose a novel drill-down view, especially tailored to noise data analysis. Split boxplots and synchronized 3D geometry views support comparison tasks. With this solution, engineers iterate over design optimizations much faster, while maintaining a good overview at each iteration. We evaluated the new approach in the automotive industry, studying noise simulation data for an internal combustion engine.

Neural Mechanisms of Facial Emotion Recognition in Autism: Distinct Roles for Anterior Cingulate and dlPFC.

OBJECTIVE: The present study sought to measure and internally validate neural markers of facial emotion recognition (FER) in adolescents and young adults with ASD to inform targeted intervention. METHOD: We utilized fMRI to measure patterns of brain activity among individuals with ASD (N = 21) and matched controls (CON; N = 20) 2 s prior to judgments about the identity of six distinct facial emotions (happy, sad, angry, surprised, fearful, disgust). RESULTS: Predictive modeling of fMRI data (support vector classification; SVC) identified mechanistic roles for brain regions that forecasted correct and incorrect identification of facial emotion as well as sources of errors over these decisions. BOLD signal activation in bilateral insula, anterior cingulate (ACC) and right dorsolateral prefrontal cortex (dlPFC) preceded accurate FER in both controls and ASD. Predictive modeling utilizing SVC confirmed the utility of ACC in forecasting correct decisions in controls but not ASD, and further indicated that a region within the right dlPFC was the source of a type 1 error signal in ASD (i.e. neural marker reflecting an impending correct judgment followed by an incorrect behavioral response) approximately two seconds prior to emotion judgments during fMRI. CONCLUSIONS: ACC forecasted correct decisions only among control participants. Right dlPFC was the source of a false-positive signal immediately prior to an error about the nature of a facial emotion in adolescents and young adults with ASD, potentially consistent with prior work indicating that dlPFC may play a role in attention to and regulation of emotional experience.

Measuring change in facial emotion recognition in individuals with autism spectrum disorder: A systematic review.

LAY ABSTRACT: Children and adults with autism spectrum disorder show difficulty recognizing facial emotions in others, which makes social interaction challenging. While there are many treatments developed to improve facial emotion recognition, there is no agreement on the best way to measure such abilities in individuals with autism spectrum disorder. The purpose of this review is to examine studies that were published between January 1998 and November 2019 and have measured change in facial emotion recognition to evaluate the effectiveness of different treatments. Our search yielded 65 studies, and within these studies, 36 different measures were used to evaluate facial emotion recognition in individuals with autism spectrum disorder. Only six of these measures, however, were used in different studies and by different investigators. In this review, we summarize the different measures and outcomes of the studies, in order to identify promising assessment tools and inform future research.

Access to Autism Spectrum Disorder Services for Rural Appalachian Citizens.

Background: Low-resource rural communities face significant challenges regarding availability and adequacy of evidence-based services. Purposes: With respect to accessing evidence-based services for Autism Spectrum Disorder (ASD), this brief report summarizes needs of rural citizens in the South-Central Appalachian region, an area notable for persistent health disparities. Methods: A mixed-methods approach was used to collect quantitative and qualitative data during focus groups with 33 service providers and 15 caregivers of children with ASD in rural southwest Virginia. Results: Results supported the barriers of availability and affordability of ASD services in this region, especially relating to the need for more ASD-trained providers, better coordination and navigation of services, and addition of programs to assist with family financial and emotional stressors. Results also suggested cultural attitudes related to autonomy and trust towards outside professionals that may prevent families from engaging in treatment. Implications: Relevant policy recommendations are discussed related to provider incentives, insurance coverage, and telehealth. Integration of autism services into already existing systems and multicultural sensitivity of providers are also implicated.

A Comparison of Radial and Linear Charts for Visualizing Daily Patterns.

Radial charts are generally considered less effective than linear charts. Perhaps the only exception is in visualizing periodical time-dependent data, which is believed to be naturally supported by the radial layout. It has been demonstrated that the drawbacks of radial charts outweigh the benefits of this natural mapping. Visualization of daily patterns, as a special case, has not been systematically evaluated using radial charts. In contrast to yearly or weekly recurrent trends, the analysis of daily patterns on a radial chart may benefit from our trained skill on reading radial clocks that are ubiquitous in our culture. In a crowd-sourced experiment with 92 non-expert users, we evaluated the accuracy, efficiency, and subjective ratings of radial and linear charts for visualizing daily traffic accident patterns. We systematically compared juxtaposed 12-hours variants and single 24-hours variants for both layouts in four low-level tasks and one high-level interpretation task. Our results show that over all tasks, the most elementary 24-hours linear bar chart is most accurate and efficient and is also preferred by the users. This provides strong evidence for the use of linear layouts - even for visualizing periodical daily patterns.

The Role of Perspective-Taking on Ability to Recognize Fear.

Impairment in the ability to detect certain emotions, such as fear, is linked to multiple disorders and follows a pattern of inter-individual variability and intra-individual stability over time. Deficits in fear recognition are often related to social and interpersonal difficulties but the mechanisms by which this processing deficit might occur are not well understood. One potential mechanism through which impaired fear detection may influence social competency is through diminished perspective-taking, the ability to perceive and consider the point of view of another individual. In the current study, we hypothesized that intra-individual variability in the accuracy of facial emotion recognition is linked to perspective-taking abilities in a well-characterized, non-clinical adult sample. Results indicated that the ability to accurately detect fear in the faces of others was positively correlated with perspective-taking, consistent with initial hypotheses. This relationship appeared to be unique to recognition of fear, as perspective-taking was not significantly associated with recognition of the other basic emotions. Results from this study represent an initial step towards establishing a potential mechanism between some processes of FER and perspective-taking difficulties. It is important to establish the relationship between these processes in a non-clinical adult sample so that we can consider the possibility of a developmental or pathological influence of impoverished perspective-taking on fear perception.

Psychosocial and Computer-Assisted Intervention for College Students with Autism Spectrum Disorder: Preliminary Support for Feasibility.

The number of young adults with Autism Spectrum Disorders (ASD) enrolled in higher education institutions has steadily increased over the last decade. Despite this, there has been little research on how to most effectively support this growing population. The current study presents data from a pilot trial of two novel intervention programs developed for college students with ASD. In this small randomized controlled trial, college students with ASD (n = 8) were assigned to one of two new programs - either an intervention based on a virtual reality-Brain-Computer Interface for ASD (BCI-ASD) or a psychosocial intervention, the College and Living Success (CLS) program. Preliminary evidence supports the feasibility and acceptability of both programs, although behavioral outcomes were inconsistent across participants and interventions. Results indicate that expanded research on psychosocial and computer-assisted intervention approaches for this population is warranted, given the preliminary support found in this pilot study.

The Promise of Neurotechnology in Clinical Translational Science.

Neurotechnology is broadly defined as a set of devices used to understand neural processes and applications that can potentially facilitate the brain's ability to repair itself. In the past decade, an increasingly explicit understanding of basic biological mechanisms of brain-related illnesses has produced applications that allow a direct yet noninvasive method to index and manipulate the functioning of the human nervous system. Clinical scientists are poised to apply this technology to assess, treat, and better understand complex socioemotional processes that underlie many forms of psychopathology. In this review, we describe the potential benefits and hurdles, both technical and methodological, of neurotechnology in the context of clinical dysfunction. We also offer a framework for developing and evaluating neurotechnologies that is intended to expedite progress at the nexus of clinical science and neural interface designs by providing a comprehensive vocabulary to describe the necessary features of neurotechnology in the clinic.

Visual Analytics for Complex Engineering Systems: Hybrid Visual Steering of Simulation Ensembles.

In this paper we propose a novel approach to hybrid visual steering of simulation ensembles. A simulation ensemble is a collection of simulation runs of the same simulation model using different sets of control parameters. Complex engineering systems have very large parameter spaces so a naïve sampling can result in prohibitively large simulation ensembles. Interactive steering of simulation ensembles provides the means to select relevant points in a multi-dimensional parameter space (design of experiment). Interactive steering efficiently reduces the number of simulation runs needed by coupling simulation and visualization and allowing a user to request new simulations on the fly. As system complexity grows, a pure interactive solution is not always sufficient. The new approach of hybrid steering combines interactive visual steering with automatic optimization. Hybrid steering allows a domain expert to interactively (in a visualization) select data points in an iterative manner, approximate the values in a continuous region of the simulation space (by regression) and automatically find the "best" points in this continuous region based on the specified constraints and objectives (by optimization). We argue that with the full spectrum of optimization options, the steering process can be improved substantially. We describe an integrated system consisting of a simulation, a visualization, and an optimization component. We also describe typical tasks and propose an interactive analysis workflow for complex engineering systems. We demonstrate our approach on a case study from automotive industry, the optimization of a hydraulic circuit in a high pressure common rail Diesel injection system.

Interactive visual analysis of multiple simulation runs using the simulation model view: understanding and tuning of an electronic unit injector.

Multiple simulation runs using the same simulation model with different values of control parameters generate a large data set that captures the behavior of the modeled phenomenon. However, there is a conceptual and visual gap between the simulation model behavior and the data set that makes data analysis more difficult. We propose a simulation model view that helps to bridge that gap by visually combining the simulation model description and the generated data. The simulation model view provides a visual outline of the simulation process and the corresponding simulation model. The view is integrated in a Coordinated Multiple Views ;(CMV) system. As the simulation model view provides a limited display space, we use three levels of details. We explored the use of the simulation model view, in close collaboration with a domain expert, to understand and tune an electronic unit injector (EUI). We also developed analysis procedures based on the view. The EUI is mostly used in heavy duty Diesel engines. We were mainly interested in understanding the model and how to tune it for three different operation modes: low emission, low consumption, and high power. Very positive feedback from the domain expert shows that the use of the simulation model view and the corresponding ;analysis procedures within a CMV system represents an effective technique for interactive visual analysis of multiple simulation runs. We also developed new analysis procedures based on these results.

Interactive visual analysis of complex scientific data as families of data surfaces.

The widespread use of computational simulation in science and engineering provides challenging research opportunities. Multiple independent variables are considered and large and complex data are computed, especially in the case of multi-run simulation. Classical visualization techniques deal well with 2D or 3D data and also with time-dependent data. Additional independent dimensions, however, provide interesting new challenges. We present an advanced visual analysis approach that enables a thorough investigation of families of data surfaces, i.e., datasets, with respect to pairs of independent dimensions. While it is almost trivial to visualize one such data surface, the visual exploration and analysis of many such data surfaces is a grand challenge, stressing the users' perception and cognition. We propose an approach that integrates projections and aggregations of the data surfaces at different levels (one scalar aggregate per surface, a 1D profile per surface, or the surface as such). We demonstrate the necessity for a flexible visual analysis system that integrates many different (linked) views for making sense of this highly complex data. To demonstrate its usefulness, we exemplify our approach in the context of a meteorological multi-run simulation data case and in the context of the engineering domain, where our collaborators are working with the simulation of elastohydrodynamic (EHD) lubrication bearing in the automotive industry.

Interactive visual steering--rapid visual prototyping of a common rail injection system.

Interactive steering with visualization has been a common goal of the visualization research community for twenty years, but it is rarely ever realized in practice. In this paper we describe a successful realization of a tightly coupled steering loop, integrating new simulation technology and interactive visual analysis in a prototyping environment for automotive industry system design. Due to increasing pressure on car manufacturers to meet new emission regulations, to improve efficiency, and to reduce noise, both simulation and visualization are pushed to their limits. Automotive system components, such as the powertrain system or the injection system have an increasing number of parameters, and new design approaches are required. It is no longer possible to optimize such a system solely based on experience or forward optimization. By coupling interactive visualization with the simulation back-end (computational steering), it is now possible to quickly prototype a new system, starting from a non-optimized initial prototype and the corresponding simulation model. The prototyping continues through the refinement of the simulation model, of the simulation parameters and through trial-and-error attempts to an optimized solution. The ability to early see the first results from a multidimensional simulation space--thousands of simulations are run for a multidimensional variety of input parameters--and to quickly go back into the simulation and request more runs in particular parameter regions of interest significantly improves the prototyping process and provides a deeper understanding of the system behavior. The excellent results which we achieved for the common rail injection system strongly suggest that our approach has a great potential of being generalized to other, similar scenarios.

Interactive visual analysis of families of function graphs.

The analysis and exploration of multidimensional and multivariate data is still one of the most challenging areas in the field of visualization. In this paper, we describe an approach to visual analysis of an especially challenging set of problems that exhibit a complex internal data structure. We describe the interactive visual exploration and analysis of data that includes several (usually large) families of function graphs fi (x, t). We describe analysis procedures and practical aspects of the interactive visual analysis specific to this type of data (with emphasis on the function graph characteristic of the data). We adopted the well-proven approach of multiple, linked views with advanced interactive brushing to assess the data. Standard views such as histograms, scatterplots, and parallel coordinates are used to jointly visualize data. We support iterative visual analysis by providing means to create complex, composite brushes that span multiple views and that are constructed using different combination schemes. We demonstrate that engineering applications represent a challenging but very applicable area for visual analytics. As a case study, we describe the optimization of a fuel injection system in diesel engines of passenger cars.