The Promise of Neurotechnology in Clinical Translational Science

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.

A critical study on the researches about the application of neurotechnology in education

INTRODUCTION

The education and pedagogy have been adopted with the development of technology in order to achieve efficient consequences and the new methods in neuroscience and neurotechnology have influenced the educational systems and the classrooms. A great number of researches have been projected in this field to demonstrate the advantages and desirable effects of neurotechnology in education and the classrooms. These researches are examinable in terms of considering both advantages and disadvantages of technology.

OBJECTS

The aim of this study is to demonstrate the advantages and undesirable effects of neurotechnology in education and the classrooms.

METHODS

This article surveys the fourteen recent researches about using neurotechnology in education and the classrooms in the framework of critical theory to discuss the adverse and undesired effects of neurotechnology as well as their neglected aspects in education and the classrooms.

FINDINGS

The findings illuminate that crucial disadvantages of neurotechnology are neglected in using computerbased tools in education and the classrooms and their side effects on the participants in the process of learning.

CONCLUSIONS

The new methods in neuroscience and neurotechnology have influenced the educational systems and the classrooms. A considerable number of researches have been projected in this field that all try to demonstrate the advantages and desirable effects of neurotechnology in education and the classrooms, but they consciously or unconsciously neglect the immoral and unscrupulous effects of such technologies.

Brain Data in Context: Are New Rights the Way to Mental and Brain Privacy?

The potential to collect brain data more directly, with higher resolution, and in greater amounts has heightened worries about mental and brain privacy. In order to manage the risks to individuals posed by these privacy challenges, some have suggested codifying new privacy rights, including a right to "mental privacy." In this paper, we consider these arguments and conclude that while neurotechnologies do raise significant privacy concerns, such concerns are-at least for now-no different from those raised by other well-understood data collection technologies, such as gene sequencing tools and online surveillance. To better understand the privacy stakes of brain data, we suggest the use of a conceptual framework from information ethics, Helen Nissenbaum's "contextual integrity" theory. To illustrate the importance of context, we examine neurotechnologies and the information flows they produce in three familiar contexts-healthcare and medical research, criminal justice, and consumer marketing. We argue that by emphasizing what is distinct about brain privacy issues, rather than what they share with other data privacy concerns, risks weakening broader efforts to enact more robust privacy law and policy.

An optogenetics device with smartphone video capture to introduce neurotechnology and systems neuroscience to high school students

Although neurotechnology careers are on the rise, and neuroscience curriculums have significantly grown at the undergraduate and graduate levels, increasing neuroscience and neurotechnology exposure in high school curricula has been an ongoing challenge. This is due, in part, to difficulties in converting cutting-edge neuroscience research into hands-on activities that are accessible for high school students and affordable for high school educators. Here, we describe and characterize a low-cost, easy-to-construct device to enable students to record rapid Drosophila melanogaster (fruit fly) behaviors during optogenetics experiments. The device is generated from inexpensive Arduino kits and utilizes a smartphone for video capture, making it easy to adopt in a standard biology laboratory. We validate this device is capable of replicating optogenetics experiments performed with more sophisticated setups at leading universities and institutes. We incorporate the device into a high school neuroengineering summer workshop. We find student participation in the workshop significantly enhances their understanding of key neuroscience and neurotechnology concepts, demonstrating how this device can be utilized in high school settings and undergraduate research laboratories seeking low-cost alternatives.

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.

Trial by trial EEG based BCI for distress versus non distress classification in individuals with ASD

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that is often accompanied by impaired emotion regulation (ER). There has been increasing emphasis on developing evidence-based approaches to improve ER in ASD. Electroencephalography (EEG) has shown success in reducing ASD symptoms when used in neurofeedback-based interventions. Also, certain EEG components are associated with ER. Our overarching goal is to develop a technology that will use EEG to monitor real-time changes in ER and perform intervention based on these changes. As a first step, an EEG-based brain computer interface that is based on an Affective Posner task was developed to identify patterns associated with ER on a single trial basis, and EEG data collected from 21 individuals with ASD. Accordingly, our aim in this study is to investigate EEG features that could differentiate between distress and non-distress conditions. Specifically, we investigate if the EEG time-locked to the visual feedback presentation could be used to classify between WIN (non-distress) and LOSE (distress) conditions in a game with deception. Results showed that the extracted EEG features could differentiate between WIN and LOSE conditions (average accuracy of 81%), LOSE and rest-EEG conditions (average accuracy 94.8%), and WIN and rest-EEG conditions (average accuracy 94.9%).

Validating Biobehavioral Technologies for Use in Clinical Psychiatry

The last decade has witnessed the development of sophisticated biobehavioral and genetic, ambulatory, and other measures that promise unprecedented insight into psychiatric disorders. As yet, clinical sciences have struggled with implementing these objective measures and they have yet to move beyond "proof of concept." In part, this struggle reflects a traditional, and conceptually flawed, application of traditional psychometrics (i.e., reliability and validity) for evaluating them. This paper focuses on "resolution," concerning the degree to which changes in a signal can be detected and quantified, which is central to measurement evaluation in informatics, engineering, computational and biomedical sciences. We define and discuss resolution in terms of traditional reliability and validity evaluation for psychiatric measures, then highlight its importance in a study using acoustic features to predict self-injurious thoughts/behaviors (SITB). This study involved tracking natural language and self-reported symptoms in 124 psychiatric patients: (a) over 5-14 recording sessions, collected using a smart phone application, and (b) during a clinical interview. Importantly, the scope of these measures varied as a function of time (minutes, weeks) and spatial setting (i.e., smart phone vs. interview). Regarding reliability, acoustic features were temporally unstable until we specified the level of temporal/spatial resolution. Regarding validity, accuracy based on machine learning of acoustic features predicting SITB varied as a function of resolution. High accuracy was achieved (i.e., ~87%), but only when the acoustic and SITB measures were "temporally-matched" in resolution was the model generalizable to new data. Unlocking the potential of biobehavioral technologies for clinical psychiatry will require careful consideration of resolution.

Attention Modification to Attenuate Facial Emotion Recognition Deficits in Children with Autism: A Pilot Study

Diminished attending to faces may contribute to the impairments in emotion recognition and expression in autism spectrum disorder (ASD). The current study evaluated the acceptability, feasibility, and preliminary efficacy of an attention modification intervention designed to attenuate deficits in facial emotion recognition (FER). During the 10-session experimental treatment, children (n = 8) with ASD watched dynamic videos of people expressing different emotions with the facial features highlighted to guide children's attention. Children and their parents generally rated the treatment as acceptable and helpful. Although FER improvement was not apparent on task-based measures, parents reported slight improvements and decreased socioemotional problems following treatment. Results suggest that further research on visual attention retraining for ASD, within an experimental therapeutic program, may be promising.

Feasibility of Automated Training for Facial Emotion Expression and Recognition in Autism

Impairment in facial emotion recognition (FER) and facial emotion expression (FEE), often documented in autism spectrum disorder (ASD), are believed to contribute to the observed core social-communication disability that characterizes this disorder. Moreover, impaired FER and FEE are frequently seen in other disorders and problem behaviors. We describe the development of a novel system to detect and give real-time feedback on these processes, termed facial emotion expression training (FEET), an automated, gamelike system that is based on 3-dimensional sensing (Kinect) technology. A sample of 40 children (n = 20 ASD, n = 20 typically developing) interacted with our prototype system, which presented audiovisual stimuli and assessed responses of participants. Overall, consumer satisfaction ratings were high, and youth with ASD reported enjoying interacting with the system more than did the typical youth. Results suggest that new technology-based interventions are acceptable to consumers and viable for use in remediation of transdiagnostic processes, such as FER and FEE. Implications for future technology-based intervention to target transdiagnostic processes are discussed.

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.