Autism Spectrum Disorder
Research Papers
Showing 6 of 17A new error-monitoring brain-computer interface based on reinforcement learning for people with autism spectrum disorders
Objective.Brain-computer interfaces (BCIs) are emerging as promising cognitive training tools in neurodevelopmental disorders, as they combine the advantages of traditional computerized interventions with real-time tailored feedback. We propose a gamified BCI based on non-volitional neurofeedback for cognitive training, aiming at reaching a neurorehabilitation tool for application in autism spectrum disorders (ASDs).Approach.The BCI consists of an emotional facial expression paradigm controlled by an intelligent agent that makes correct and wrong actions, while the user observes and judges the agent's actions. The agent learns through reinforcement learning (RL) an optimal strategy if the participant generates error-related potentials (ErrPs) upon incorrect agent actions. We hypothesize that this training approach will allow not only the agent to learn but also the BCI user, by participating through implicit error scrutiny in the process of learning through operant conditioning, making it of particular interest for disorders where error monitoring processes are altered/compromised such as in ASD. In this paper, the main goal is to validate the whole methodological BCI approach and assess whether it is feasible enough to move on to clinical experiments. A control group of ten neurotypical participants and one participant with ASD tested the proposed BCI approach.Main results.We achieved an online balanced-accuracy in ErrPs detection of 81.6% and 77.1%, respectively for two different game modes. Additionally, all participants achieved an optimal RL strategy for the agent at least in one of the test sessions.Significance.The ErrP classification results and the possibility of successfully achieving an optimal learning strategy, show the feasibility of the proposed methodology, which allows to move towards clinical experimentation with ASD participants to assess the effectiveness of the approach as hypothesized.
View Full Paper →Effects of an intensive slow cortical potentials neurofeedback training in female and male adolescents with autism spectrum disorder : Are there sex differences?
BACKGROUND: This study aims to compare the effects of neurofeedback training on male and female adolescents with autism spectrum disorder (ASD). Furthermore, it examines sex differences regarding improvements in co-occurring psychopathological symptoms, cognitive flexibility and emotion recognition abilities. The study might provide first hints whether there is an influence of sex on treatment outcomes. METHODS: Six female and six male adolescents with ASD were matched according to age, IQ and symptom severity. All participants received 24 sessions of electroencephalography-based neurofeedback training. Before and after the intervention, psychological data for measuring co-occurring psychopathological symptoms as well as behavioral data for measuring cognitive flexibility and emotion recognition abilities were recorded. RESULTS: Caregivers rated statistically significant higher psychopathological problems in female than in male adolescents with ASD at baseline. Apart from that, no statistically significant sex-related differences were revealed in this sample; however, male adolescents tended to report greater improvements of externalizing, internalizing and total symptoms, whereas females experienced smaller improvements of externalizing and total problems, but no improvements of internalizing problems. Regarding caregivers' assessments, more improvement of total problems was reported for females. For males, only improvements of internalizing and total problems were described. CONCLUSION: This study reveals preliminary results that sex-related differences might play a role when evaluating treatment outcomes after neurofeedback training regarding comorbid psychopathological symptoms. Adolescents' self-report and parental assessments, especially concerning psychopathological symptoms, should be combined and considered in future studies to help prevent sex bias in adolescents with ASD.
View Full Paper →Electrophysiological signatures of brain aging in autism spectrum disorder
Recent evidence suggests that structural and functional brain aging is atypical in adults with autism spectrum disorder (ASD). However, it remains unclear if oscillatory slowing, a key marker of neurophysiological aging, follows an atypical trajectory in this population. This study examines patterns of age-related oscillatory slowing in adults with ASD, captured by reductions in the brain's peak alpha frequency (PAF). Resting-state electroencephalography (EEG) data from adults (18-70 years) with ASD (N = 93) and non-ASD controls (N = 87) were pooled from three independent datasets. A robust curve-fitting procedure quantified the peak frequency of alpha oscillations (7-13 Hz) across all brain regions. Associations between PAF and age were assessed and compared between groups. Consistent with characteristic patterns of oscillatory slowing, PAF was negatively associated with age across the entire sample (p < .0001). A significant group-by-age interaction revealed that this relationship was more pronounced in adults with ASD (p < .01). These findings invite further longitudinal investigations of PAF in adults with ASD to confirm if age-related oscillatory slowing is accelerated.
View Full Paper →Practitioner's review: medication for children and adolescents with autism spectrum disorder (ASD) and comorbid conditions
Alleviating the multiple problems of children with autism spectrum disorder (ASD) and its comorbid conditions presents major challenges for the affected children, parents, and therapists. Because of a complex psychopathology, structured therapy and parent training are not always sufficient, especially for those patients with intellectual disability (ID) and multiple comorbidities. Moreover, structured therapy is not available for a large number of patients, and pharmacological support is often needed, especially in those children with additional attention deficit/hyperactivity and oppositional defiant, conduct, and sleep disorders.
View Full Paper →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%).
View Full Paper →Volitional modification of brain activity in adolescents with Autism Spectrum Disorder: A Bayesian analysis of Slow Cortical Potential neurofeedback
Autism spectrum disorder is (ASD) characterized by a persisting triad of impairments of social interaction, language as well as inflexible, stereotyped and ritualistic behaviors. Increasingly, scientific evidence suggests a neurobiological basis of these emotional, social and cognitive deficits in individuals with ASD. The aim of this randomized controlled brain self-regulation intervention study was to investigate whether the core symptomatology of ASD could be reduced via an electroencephalography (EEG) based brain self-regulation training of Slow Cortical Potentials (SCP). 41 male adolescents with ASD were recruited and allocated to a) an experimental group undergoing 24 sessions of EEG-based brain training (n1 = 21), or to b) an active control group undergoing conventional treatment (n2 = 20), that is, clinical counseling during a 3-months intervention period. We employed real-time neurofeedback training recorded from a fronto-central electrode intended to enable participants to volitionally regulate their brain activity. Core autistic symptomatology was measured at six time points during the intervention and analyzed with Bayesian multilevel approach to characterize changes in core symptomatology. Additional Bayesian models were formulated to describe the neural dynamics of the training process as indexed by SCP (time-domain) and power density (PSD, frequency-domain) measures. The analysis revealed a substantial improvement in the core symptomatology of ASD in the experimental group (reduction of 21.38 points on the Social Responsiveness Scale, SD = 5.29), which was slightly superior to that observed in the control group (evidence Ratio = 5.79). Changes in SCP manifested themselves as different trajectories depending on the different feedback conditions and tasks. Further, the model of PSD revealed a continuous decrease in delta power, parallel to an increase in alpha power. Most notably, a non-linear (quadratic) model turned out to be better at predicting the data than a linear model across all analyses. Taken together, our analyses suggest that behavioral and neural processes of change related to neurofeedback training are complex and non-linear. Moreover, they have implications for the design of future trials and training protocols.
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