An integrated EEG and eye-tracking approach for the study of responding and initiating joint attention in Autism Spectrum Disorders

Repetitive active and passive cognitive stimulations induce EEG changes in patients with Rett syndrome

BACKGROUND

Despite being considered a rare disease, Rett syndrome is a leading cause of profound cognitive impairment in females. This study explores game-based cognitive stimulation to enhance attention during learning tasks, offering an alternative treatment perspective.

METHODS

Fifteen diagnosed Rett syndrome girls participated in four 24-minute sessions, including a 5-minute initial resting state recording. Primary indicators for analysis included relative power and spectral entropy.

RESULTS

Significant findings indicated variations among conditions (resting state, active task, passive task) in response to stimulation. Notably, over four days, evolution occurred, characterized by decreasing delta power and increasing theta and beta power. Topographic maps confirmed these shifts, highlighting affected brain areas. Linear regression emphasized the most significant impact on the first day, with subsequent shifts towards higher frequencies, particularly during the resting state. By the fourth day, resting-state patterns resembled those during cognitive activities.

CONCLUSION

Findings suggest cognitive stimulation induces substantial EEG spectral changes, potentially linked to cognitive enhancements in Rett syndrome. The shift towards higher frequency bands and increased spectral entropy align with enhanced brain activation during cognitive sessions, underscoring the potential of cognitive stimulation therapies and calling for further research to optimize abilities in individuals with Rett syndrome.

IMPACT

Game-based cognitive stimulation induces substantial EEG changes in individuals with Rett syndrome, enhancing cognitive functions, notably attention during learning. This study conducts a distinctive examination, assessing the habituation paradigm through the combination of game-based cognitive stimulation and learning, providing valuable insights into enhancing attention in Rett syndrome. Impacting understanding of cognitive processes in Rett syndrome, this research reveals significant EEG variations during tasks, emphasizing the potential of cognitive stimulation for attention enhancement and the need for further research in tailored interventions.

Feasibility of a 2-minute eye-tracking protocol to support the early identification of autism

We tested the potential for Gazefinder eye-tracking to support early autism identification, including feasible use with infants, and preliminary concurrent validity of trial-level gaze data against clinical assessment scores. We embedded the ~ 2-min 'Scene 1S4' protocol within a comprehensive clinical assessment for 54 consecutively-referred, clinically-indicated infants (prematurity-corrected age 9-14 months). Alongside % tracking rate as a broad indicator of feasible assessment/data capture, we report infant gaze data to pre-specified regions of interest (ROI) across four trial types and associations with scores on established clinical/behavioural tools. Most infants tolerated Gazefinder eye-tracking well, returning high overall % tracking rate. As a group, infants directed more gaze towards social vs. non-social (or more vs. less socially-salient) ROIs within trials. Behavioural autism features were correlated with increased gaze towards non-social/geometry (vs. social/people) scenes. No associations were found for gaze directed to ROIs within other stimulus types. Notably, there were no associations between developmental/cognitive ability or adaptive behaviour with gaze towards any ROI. Gazefinder assessment seems highly feasible with clinically-indicated infants, and the people vs. geometry stimuli show concurrent predictive validity for behavioural autism features. Aggregating data across the ~ 2-min autism identification protocol might plausibly offer greater utility than stimulus-level analysis alone.

Identification of diagnostic markers for ASD: a restrictive interest analysis based on EEG combined with eye tracking

Electroencephalography (EEG) functional connectivity (EFC) and eye tracking (ET) have been explored as objective screening methods for autism spectrum disorder (ASD), but no study has yet evaluated restricted and repetitive behavior (RRBs) simultaneously to infer early ASD diagnosis. Typically developing (TD) children (n = 27) and ASD (n = 32), age- and sex-matched, were evaluated with EFC and ET simultaneously, using the restricted interest stimulus paradigm. Network-based machine learning prediction (NBS-predict) was used to identify ASD. Correlations between EFC, ET, and Autism Diagnostic Observation Schedule-Second Edition (ADOS-2) were performed. The Area Under the Curve (AUC) of receiver-operating characteristics (ROC) was measured to evaluate the predictive performance. Under high restrictive interest stimuli (HRIS), ASD children have significantly higher α band connectivity and significantly more total fixation time (TFT)/pupil enlargement of ET relative to TD children (p = 0.04299). These biomarkers were not only significantly positively correlated with each other (R = 0.716, p = 8.26e-4), but also with ADOS total scores (R = 0.749, p = 34e-4) and RRBs sub-score (R = 0.770, p = 1.87e-4) for EFC (R = 0.641, p = 0.0148) for TFT. The accuracy of NBS-predict in identifying ASD was 63.4%. ROC curve demonstrated TFT with 91 and 90% sensitivity, and 78.7% and 77.4% specificity for ADOS total and RRB sub-scores, respectively. Simultaneous EFC and ET evaluation in ASD is highly correlated with RRB symptoms measured by ADOS-2. NBS-predict of EFC offered a direct prediction of ASD. The use of both EFC and ET improve early ASD diagnosis.

Eye-tracking correlates of response to joint attention in preschool children with autism spectrum disorder

BACKGROUND

A number of differences in joint attention behaviour between children with autism spectrum disorder (ASD) and typically developing (TD) individuals have previously been documented.

METHOD

We use eye-tracking technology to assess response to joint attention (RJA) behaviours in 77 children aged 31 to 73 months. We conducted a repeated-measures analysis of variance to identify differences between groups. In addition, we analysed correlations between eye-tracking and clinical measures using Spearman's correlation.

RESULTS

The children diagnosed with ASD were less likely to follow gaze compared to TD children. Children with ASD were less accurate at gaze following when only eye gaze information was available, compared to when eye gaze with head movement was observed. Higher accuracy gaze-following profiles were associated with better early cognition and more adaptive behaviours in children with ASD. Less accurate gaze-following profiles were associated with more severe ASD symptomatology.

CONCLUSION

There are differences in RJA behaviours between ASD and TD preschool children. Several eye-tracking measures of RJA behaviours in preschool children were found to be associated with clinical measures for ASD diagnosis. This study also highlights the construct validity of using eye-tracking measures as potential biomarkers in the assessment and diagnosis of ASD in preschool children.

Research on social attention in autism and the challenges of the research domain criteria (RDoC) framework

The fuzzy nature of categories of psychopathology, such as autism, leads to significant research challenges. Alternatively, focusing research on the study of a common set of important and well-defined psychological constructs across psychiatric conditions may make the fundamental etiological processes of psychopathology easier to discern and treat (Cuthbert, 2022). The development of the research domain criteria (RDoC) framework is designed to guide this new research approach (Insel et al., 2010). However, progress in research may be expected to continually refine and reorganize the understanding of the specifics of these mental processes (Cuthbert & Insel, 2013). Moreover, knowledge gleaned from the study of both normative and atypical development can be mutually informative in the evolution of our understanding of these fundamental processes. A case in point is the study of social attention. This Autism 101 commentary provides an educational summary of research over the last few decades indicates that social attention is major construct in the study of human social-cognitive development, autism and other forms of psychopathology. The commentary also describes how this research can inform the Social Process dimension of the RDoC framework.

A randomized controlled trial into the effects of probiotics on electroencephalography in preschoolers with autism

LAY ABSTRACT

This study investigates the effects of a probiotic on preschoolers' brain electrical activity with autism spectrum disorder. Autism is a disorder with an increasing prevalence characterized by an enormous individual, family, and social cost. Although the etiology of autism spectrum disorder is unknown, an interaction between genetic and environmental factors is implicated, converging in altered brain synaptogenesis and, therefore, connectivity. Besides deepening the knowledge on the resting brain electrical activity that characterizes this disorder, this study allows analyzing the positive central effects of a 6-month therapy with a probiotic through a randomized, double-blind placebo-controlled study and the correlations between electroencephalography activity and biochemical and clinical parameters. In subjects treated with probiotics, we observed a decrease of power in frontopolar regions in beta and gamma bands, and increased coherence in the same bands together with a shift in frontal asymmetry, which suggests a modification toward a typical brain activity. Electroencephalography measures were significantly correlated with clinical and biochemical measures. These findings support the importance of further investigations on probiotics' benefits in autism spectrum disorder to better elucidate mechanistic links between probiotics supplementation and changes in brain activity.

Face-Based Attention Recognition Model for Children with Autism Spectrum Disorder

Attention recognition plays a vital role in providing learning support for children with autism spectrum disorders (ASD). The unobtrusiveness of face-tracking techniques makes it possible to build automatic systems to detect and classify attentional behaviors. However, constructing such systems is a challenging task due to the complexity of attentional behavior in ASD. This paper proposes a face-based attention recognition model using two methods. The first is based on geometric feature transformation using a support vector machine (SVM) classifier, and the second is based on the transformation of time-domain spatial features to 2D spatial images using a convolutional neural network (CNN) approach. We conducted an experimental study on different attentional tasks for 46 children (ASD n=20, typically developing children n=26) and explored the limits of the face-based attention recognition model for participant and task differences. Our results show that the geometric feature transformation using an SVM classifier outperforms the CNN approach. Also, attention detection is more generalizable within typically developing children than within ASD groups and within low-attention tasks than within high-attention tasks. This paper highlights the basis for future face-based attentional recognition for real-time learning and clinical attention interventions.

Children ASD Evaluation Through Joint Analysis of EEG and Eye-Tracking Recordings With Graph Convolution Network

Recent advances in neuroscience indicate that analysis of bio-signals such as rest state electroencephalogram (EEG) and eye-tracking data can provide more reliable evaluation of children autism spectrum disorder (ASD) than traditional methods of behavior measurement relying on scales do. However, the effectiveness of the new approaches still lags behind the increasing requirement in clinical or educational practices as the “bio-marker” information carried by the bio-signal of a single-modality is likely insufficient or distorted. This study proposes an approach to joint analysis of EEG and eye-tracking for children ASD evaluation. The approach focuses on deep fusion of the features in two modalities as no explicit correlations between the original bio-signals are available, which also limits the performance of existing methods along this direction. First, the synchronization measures, information entropy, and time-frequency features of the multi-channel EEG are derived. Then a random forest applies to the eye-tracking recordings of the same subjects to single out the most significant features. A graph convolutional network (GCN) model then naturally fuses the two group of features to differentiate the children with ASD from the typically developed (TD) subjects. Experiments have been carried out on the two types of the bio-signals collected from 42 children (21 ASD and 21 TD subjects, 3–6 years old). The results indicate that (1) the proposed approach can achieve an accuracy of 95% in ASD detection, and (2) strong correlations exist between the two bio-signals collected even asynchronously, in particular the EEG synchronization against the face related/joint attentions in terms of covariance.

Joint Analysis of Eye Blinks and Brain Activity to Investigate Attentional Demand during a Visual Search Task

In several fields, the need for a joint analysis of brain activity and eye activity to investigate the association between brain mechanisms and manifest behavior has been felt. In this work, two levels of attentional demand, elicited through a conjunction search task, have been modelled in terms of eye blinks, brain activity, and brain network features. Moreover, the association between endogenous neural mechanisms underlying attentional demand and eye blinks, without imposing a time-locked structure to the analysis, has been investigated. The analysis revealed statistically significant spatial and spectral modulations of the recorded brain activity according to the different levels of attentional demand, and a significant reduction in the number of eye blinks when a higher amount of attentional investment was required. Besides, the integration of information coming from high-density electroencephalography (EEG), brain source localization, and connectivity estimation allowed us to merge spectral and causal information between brain areas, characterizing a comprehensive model of neurophysiological processes behind attentional demand. The analysis of the association between eye and brain-related parameters revealed a statistically significant high correlation (R > 0.7) of eye blink rate with anterofrontal brain activity at 8 Hz, centroparietal brain activity at 12 Hz, and a significant moderate correlation with the participation of right Intra Parietal Sulcus in alpha band (R = -0.62). Due to these findings, this work suggests the possibility of using eye blinks measured from one sensor placed on the forehead as an unobtrusive measure correlating with neural mechanisms underpinning attentional demand.

A Simple and Effective Way to Study Executive Functions by Using 360° Videos

Executive dysfunctions constitute a significant public health problem due to their high impact on everyday life and personal independence. Therefore, the identification of early strategies to assess and rehabilitate these impairments appears to be a priority. The ecological limitations of traditional neuropsychological tests and the numerous difficulties in administering tests in real-life scenarios have led to the increasing use of virtual reality (VR) and 360° environment-based tools for assessing executive functions (EFs) in real life. This perspective aims at proposing the development and implementation of Executive-functions Innovative Tool 360° (EXIT 360°), an innovative, enjoyable, and ecologically valid tool for a multidimensional and multicomponent evaluation of executive dysfunctions. EXIT 360° allows a complete and integrated executive functioning assessment through an original task for EFs delivered via a mobile-powered VR headset combined with eye tracker (ET) and electroencephalograms (EEG). Our tool is born as a 360°-based instrument, easily accessible and clinically usable, that will radically transform clinicians’ and patient’s assessment experience. In EXIT 360°, patients are engaged in a “game for health,” where they must perform everyday subtasks in 360° daily life environments. In this way, the clinicians can obtain quickly more ecologically valid information about several aspects of EFs (e.g., planning, problem-solving). Moreover, the multimodal approach allows completing the assessment of EFs by integrating verbal responses, reaction times, and physiological data (eye movements and brain activation). Overall, EXIT 360° will allow obtaining simultaneously and in real time more information about executive dysfunction and its impact in real life, allowing clinicians to tailor the rehabilitation to the subject’s needs.

EYES Are The Window to the Mind: Eye-Tracking Technology as a Novel Approach to Study Clinical Characteristics of ADHD

In recent years, the study of eye movements has increasingly become a window into neurological function and processes. This paper reports data about applications of eye-tracking technology in the field of neurodevelopmental disorders, especially in studying the clinical characteristics of ADHD. These studies indicated that eye-tracking approach represents a non-invasive methodology to test specific domains, such as attention networks and inhibition control, in an objectively and reliable way in youths. Although the studies results are still not conclusive, eye-tracking represents a potential valid support for clinicians in identifying specific biomarkers to orient ADHD diagnosis and the best intervention strategies.

Increased Accuracy of Emotion Recognition in Individuals with Autism-Like Traits after Five Days of Magnetic Stimulations

Individuals with autism-like traits (ALT) belong to a subclinical group with similar social deficits as autism spectrum disorders (ASD). Their main social deficits include atypical eye contact and difficulty in understanding facial expressions, both of which are associated with an abnormality of the right posterior superior temporal sulcus (rpSTS). It is still undetermined whether it is possible to improve the social function of ALT individuals through noninvasive neural modulation. To this end, we randomly assigned ALT individuals into the real (n = 16) and sham (n = 16) stimulation groups. All subjects received five consecutive days of intermittent theta burst stimulation (iTBS) on the rpSTS. Eye tracking data and functional magnetic resonance imaging (fMRI) data were acquired on the first and sixth days. The real group showed significant improvement in emotion recognition accuracy after iTBS, but the change was not significantly larger than that in the sham group. Resting-state functional connectivity (rsFC) between the rpSTS and the left cerebellum significantly decreased in the real group than the sham group after iTBS. At baseline, rsFC in the left cerebellum was negatively correlated with emotion recognition accuracy. Our findings indicated that iTBS of the rpSTS could improve emotion perception of ALT individuals by modulating associated neural networks. This stimulation protocol could be a vital therapeutic strategy for the treatment of ASD.

Big data approaches to develop a comprehensive and accurate tool aimed at improving autism spectrum disorder diagnosis and subtype stratification

Purpose

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that is difficult to diagnose accurately due to its heterogeneous clinical manifestations. Comprehensive models combining different big data approaches (e.g. neuroimaging, genetics, eye tracking, etc.) may offer the opportunity to characterize ASD from multiple distinct perspectives. This paper aims to provide an overview of a novel diagnostic approach for ASD classification and stratification based on these big data approaches.

Design/methodology/approach

Multiple types of data were collected and recorded for three consecutive years, including clinical assessment, neuroimaging, gene mutation and expression and response signal data. The authors propose to establish a classification model for predicting ASD clinical diagnostic status by integrating the various data types. Furthermore, the authors suggest a data-driven approach to stratify ASD into subtypes based on genetic and genomic data.

Findings

By utilizing complementary information from different types of ASD patient data, the proposed integration model has the potential to achieve better prediction performance than models focusing on only one data type. The use of unsupervised clustering for the gene-based data-driven stratification will enable identification of more homogeneous subtypes. The authors anticipate that such stratification will facilitate a more consistent and personalized ASD diagnostic tool.

Originality/value

This study aims to utilize a more comprehensive investigation of ASD-related data types than prior investigations, including proposing longitudinal data collection and a storage scheme covering diverse populations. Furthermore, this study offers two novel diagnostic models that focus on case-control status prediction and ASD subtype stratification, which have been under-explored in the prior literature.

Mapping the Specific Pathways to Early-Onset Mental Health Disorders: The "Watch Me Grow for REAL" Study Protocol

BACKGROUND

From birth, the human propensity to selectively attend and respond to critical super-stimuli forms the basis of future socio-emotional development and health. In particular, the first super-stimuli to preferentially engage and elicit responses in the healthy newborn are the physical touch, voice and face/eyes of caregivers. From this grows selective attention and responsiveness to emotional expression, scaffolding the development of empathy, social cognition, and other higher human capacities. In this paper, the protocol for a longitudinal, prospective birth-cohort study is presented. The major aim of this study is to map the emergence of individual differences and disturbances in the system of social-Responsiveness, Emotional Attention, and Learning (REAL) through the first 3 years of life to predict the specific emergence of the major childhood mental health problems, as well as social adjustment and impairment more generally. A further aim of this study is to examine how the REAL variables interact with the quality of environment/caregiver interactions.

METHODS/DESIGN

A prospective, longitudinal birth-cohort study will be conducted. Data will be collected from four assessments and mothers' electronic medical records.

DISCUSSION

This study will be the first to test a clear developmental map of both the unique and specific causes of childhood psychopathology and will identify more precise early intervention targets for children with complex comorbid conditions.

Relative Average Look Duration and its Association with Neurophysiological Activity in Young Children with Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is characterized by early attentional differences that often precede the hallmark symptoms of social communication impairments. Development of novel measures of attentional behaviors may lead to earlier identification of children at risk for ASD. In this work, we first introduce a behavioral measure, Relative Average Look Duration (RALD), indicating attentional preference to different stimuli, such as social versus nonsocial stimuli; and then study its association with neurophysiological activity. We show that (1) ASD and typically developing (TD) children differ in both (absolute) Average Look Duration (ALD) and RALD to stimuli during an EEG experiment, with the most pronounced differences in looking at social stimuli; and (2) associations between looking behaviors and neurophysiological activity, as measured by EEG, are different for children with ASD versus TD. Even when ASD children show attentional engagement to social content, our results suggest that their underlying brain activity is different than TD children. This study therefore introduces a new measure of social/nonsocial attentional preference in ASD and demonstrates the value of incorporating attentional variables measured simultaneously with EEG into the analysis pipeline.

How Attention to Faces and Objects Changes Over Time in Toddlers with Autism Spectrum Disorders: Preliminary Evidence from An Eye Tracking Study

Further understanding of the longitudinal changes in visual pattern of toddlers with autism spectrum disorders (ASDs) is needed. We examined twelve 19 to 33-month-old toddlers at their first diagnosis (mean age: 25.1 months) and after six months (mean age: 31.7 months) during two initiating joint attention (IJA) tasks using eye tracking. Results were compared with the performance of age-matched typically developing (TD) toddlers evaluated at a single time-point. Autistic toddlers showed longitudinal changes in the visual sensory processing of the IJA tasks, approaching TD performance with an improvement in the ability to disengage and to explore the global space. Findings suggest the use of eye tracking technology as an objective, non-intrusive, adjunctive tool to measure outcomes in toddlers with ASD.

Diagnostic Assessment Techniques and Non-Invasive Biomarkers for Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a complex heterogeneous neurological disorder that has led to a spectrum of diagnosis techniques. The screening instruments, medical and technological tools initiate the diagnosis process. Clinicians and psychologists propose therapies depending on the examination done by these methodologies. The literature has accounted dozens of diagnostic methods and alternative and complementary therapies but still lack in highlighting the proper biomarker for early detection and intervention. The emerging multi-modal neuro-imaging techniques have correlated the brain's functional and structural measures and diagnosed ASD with more sensitivity than individual approaches. The purpose of this review article is: (i) to provide an overview of the emerging ASD diagnosis methods and different markers and; (ii) to present the idea of integrating all the individual methods in to a multi-modal diagnostic system to enhance detection sensitivity. This system possesses the potential to diagnose and predict ASD clinically, neurologically & objectively with high detection sensitivity.

Support for Employees with ASD in the Workplace Using a Bluetooth Skin Resistance Sensor⁻A Preliminary Study

The application of a Bluetooth skin resistance sensor in assisting people with Autism Spectrum Disorders (ASD), in their day-to-day work, is presented in this paper. The design and construction of the device are discussed. The authors have considered the best placement of the sensor, on the body, to gain the most accurate readings of user stress levels, under various conditions. Trial tests were performed on a group of sixteen people to verify the correct functioning of the device. Resistance levels were compared to those from the reference system. The placement of the sensor has also been determined, based on wearer convenience. With the Bluetooth Low Energy block, users can be notified immediately about their abnormal stress levels via a smartphone application. This can help people with ASD, and those who work with them, to facilitate stress control and make necessary adjustments to their work environment.

EEG Analytics for Early Detection of Autism Spectrum Disorder: A data-driven approach

Autism spectrum disorder (ASD) is a complex and heterogeneous disorder, diagnosed on the basis of behavioral symptoms during the second year of life or later. Finding scalable biomarkers for early detection is challenging because of the variability in presentation of the disorder and the need for simple measurements that could be implemented routinely during well-baby checkups. EEG is a relatively easy-to-use, low cost brain measurement tool that is being increasingly explored as a potential clinical tool for monitoring atypical brain development. EEG measurements were collected from 99 infants with an older sibling diagnosed with ASD, and 89 low risk controls, beginning at 3 months of age and continuing until 36 months of age. Nonlinear features were computed from EEG signals and used as input to statistical learning methods. Prediction of the clinical diagnostic outcome of ASD or not ASD was highly accurate when using EEG measurements from as early as 3 months of age. Specificity, sensitivity and PPV were high, exceeding 95% at some ages. Prediction of ADOS calibrated severity scores for all infants in the study using only EEG data taken as early as 3 months of age was strongly correlated with the actual measured scores. This suggests that useful digital biomarkers might be extracted from EEG measurements.

Neuropsychiatric effects of epilepsy in developmental disorders

PURPOSE OF REVIEW

The overlap of neuropsychiatric illness and developmental disability continues to be prominently recognized in clinical practice and in the academic literature. Theoretical and practical considerations may represent a frontier for understanding brain and behavior relationships. The purpose of this review is to explore this common relationship and report on recent literature that helps advance the larger fields of psychiatry and neurology.

RECENT FINDINGS

Overlap between developmental disability, epilepsy, and neuropsychiatric illness may be more common than originally thought. Excessive excitatory neurotransmitter activity may be present in epilepsy and in autism spectrum disorder. Specific seizure types may be associated with features of developmental disabilities and neuropsychiatric conditions.

SUMMARY

Neuropsychiatric illness is common in epilepsy and more frequent in developmental disabilities than generally recognized. Seizure foci in the temporal lobe may play a significant role. Brain connectivity and specific neurotransmitter systems are active areas of investigation. Antiepileptic drugs may improve neuropsychiatric symptoms in persons with developmental disability and epilepsy.