The potential of eye-tracking as a sensitive measure of behavioural change in response to intervention

SARS-CoV-2 Infection Impairs Oculomotor Functions: A Longitudinal Eye-tracking Study

Although Severe Acute Respiratory Syndrome Coronavirus 2 infection (SARS-CoV-2) is primarily recognized as a respiratory disease, mounting evidence suggests that it may lead to neurological and cognitive impairments. The current study used three eye-tracking tasks (free-viewing, fixation, and smooth pursuit) to assess the oculomotor functions of mild infected cases over six months with symptomatic SARS-CoV-2 infected volunteers. Fifty symptomatic SARS-CoV-2 infected, and 24 self-reported healthy controls completed the eye-tracking tasks in an initial assessment. Then, 45, and 40 symptomatic SARS-CoV-2 infected completed the tasks at 2- and 6-months post-infection, respectively. In the initial assessment, symptomatic SARS-CoV-2 infected exhibited impairments in diverse eye movement metrics. Over the six months following infection, the infected reported overall improvement in health condition, except for self-perceived mental health. The eye movement patterns in the free-viewing task shifted toward a more focal processing mode and there was no significant improvement in fixation stability among the infected. A linear discriminant analysis shows that eye movement metrics could differentiate the infected from healthy controls with an accuracy of approximately 62%, even 6 months post-infection. These findings suggest that symptomatic SARSCoV- 2 infection may result in persistent impairments in oculomotor functions, and the employment of eye-tracking technology can offer valuable insights into both the immediate and long-term effects of SARS-CoV-2 infections. Future studies should employ a more balanced research design and leverage advanced machine-learning methods to comprehensively investigate the impact of SARSCoV- 2 infection on oculomotor functions.

Deep Convolutional Symmetric Encoder—Decoder Neural Networks to Predict Students’ Visual Attention

Prediction of visual attention is a new and challenging subject, and to the best of our knowledge, there are not many pieces of research devoted to the anticipation of students’ cognition when solving tests. The aim of this paper is to propose, implement, and evaluate a machine learning method that is capable of predicting saliency maps of students who participate in a learning task in the form of quizzes based on quiz questionnaire images. Our proposal utilizes several deep encoder–decoder symmetric schemas which are trained on a large set of saliency maps generated with eye tracking technology. Eye tracking data were acquired from students, who solved various tasks in the sciences and natural sciences (computer science, mathematics, physics, and biology). The proposed deep convolutional encoder–decoder network is capable of producing accurate predictions of students’ visual attention when solving quizzes. Our evaluation showed that predictions are moderately positively correlated with actual data with a coefficient of 0.547 ± 0.109. It achieved better results in terms of correlation with real saliency maps than state-of-the-art methods. Visual analyses of the saliency maps obtained also correspond with our experience and expectations in this field. Both source codes and data from our research can be downloaded in order to reproduce our results.

Me looking at you, looking at me: The stare-in-the-crowd effect and autism spectrum disorder

INTRODUCTION

The stare-in-the-crowd (SITC) effect describes the ability to detect self-directed gaze in a crowd. Given the importance of gaze detection in initiating and maintaining social interactions, there is a need to better characterize the SITC effect.

METHODS

Autistic and neurotypical young adults were presented with four SITC conditions. Eye tracking outcomes and arousal were compared by diagnosis and condition using repeated measures analysis of variance. Hierarchical regression was used to explore behavioral measures.

RESULTS

Significant interaction of diagnosis and condition was found for eye tracking outcomes. Overall, autistic participants exhibited less looking than neurotypical participants. Interest area dwell time, fixation count, and second fixation duration were significantly higher for conditions with shifting gaze, as well as conditions with more self-directed gaze across participants. Two hierarchical regression models of gaze behaviors with advanced theory of mind as a predictor were significant.

DISCUSSION

Autistic individuals respond to various gaze conditions in similar patterns to neurotypical individuals, but to a lesser extent. These findings offer important targets for social interventions.

Towards a Multivariate Biomarker-Based Diagnosis of Autism Spectrum Disorder: Review and Discussion of Recent Advancements

An ever-evolving understanding of autism spectrum disorder (ASD) pathophysiology necessitates that diagnostic standards also evolve from being observation-based to include quantifiable clinical measurements. The multisystem nature of ASD motivates the use of multivariate methods of statistical analysis over common univariate approaches for discovering clinical biomarkers relevant to this goal. In addition to characterization of important behavioral patterns for improving current diagnostic instruments, multivariate analyses to date have allowed for thorough investigation of neuroimaging-based, genetic, and metabolic abnormalities in individuals with ASD. This review highlights current research using multivariate statistical analyses to quantify the value of these behavioral and physiological markers for ASD diagnosis. A detailed discussion of a blood-based diagnostic test for ASD using specific metabolite concentrations is also provided. The advancement of ASD biomarker research promises to provide earlier and more accurate diagnoses of the disorder.

Atypical Social Attention and Emotional Face Processing in Autism Spectrum Disorder: Insights From Face Scanning and Pupillometry

Social attention deficits are a hallmark characteristic within autism spectrum disorder (ASD) and have been hypothesized to have cascading effects on emotion recognition. Eye-tracking methodology has emerged as a potentially reliable, feasible, and sensitive biomarker for examining core phenotypic features of ASD; however, these findings are mixed with regards to measuring treatment change in clinical trials. The present study aimed to assess the utility of an eye-tracking paradigm to discriminate between clinical groups in social attention and emotion recognition through face scanning and pupillometry. The present study also assessed the reliability of this paradigm within the ASD sample to further our understanding of the utility of eye-tracking for future clinical trials. Participants included 42 individuals with ASD, 29 developmental disability (DD) controls, and 62 typically developing (TD) controls between 3 and 25 years of age. An emotional faces eye-tracking paradigm was administered to all participants, with the ASD group completing the paradigm a second time approximately 2 months later. Participants' average proportion of looking and number of fixations to specific areas of interest (AOI) were examined along with changes in pupil reactivity while viewing different emotional faces. Results suggest atypical face-scanning through a reduced proportion of looking and the number of fixations toward the eyes in the ASD group regardless of the emotion that was presented. Further, pupillometry measures were able to detect increases in pupil dilation to happy faces in the ASD group. Lastly, test-retest reliability coefficients varied between the poor and excellent range based on the mechanism assessed, with the proportion of looking demonstrating the highest reliability coefficients. These findings build on the promise of eye-tracking as a feasible and reliable biomarker for identifying social attention and emotion recognition deficits in ASD. Detecting differences in emotion recognition explicitly through facial scanning was not as clear. Specific mechanisms within the eye-tracking paradigm may be viable options for assessing treatment-specific outcomes.

Multivariate Analysis of Plasma Metabolites in Children with Autism Spectrum Disorder and Gastrointestinal Symptoms Before and After Microbiota Transfer Therapy

Current diagnosis of autism spectrum disorder (ASD) is based on assessment of behavioral symptoms, although there is strong evidence that ASD affects multiple organ systems including the gastrointestinal (GI) tract. This study used Fisher discriminant analysis (FDA) to evaluate plasma metabolites from 18 children with ASD and chronic GI problems (ASD + GI cohort) and 20 typically developing (TD) children without GI problems (TD − GI cohort). Using three plasma metabolites that may represent three general groups of metabolic abnormalities, it was possible to distinguish the ASD + GI cohort from the TD − GI cohort with 94% sensitivity and 100% specificity after leave-one-out cross-validation. After the ASD + GI participants underwent Microbiota Transfer Therapy with significant improvement in GI and ASD-related symptoms, their metabolic profiles shifted significantly to become more similar to the TD − GI group, indicating potential utility of this combination of plasma metabolites as a biomarker for treatment efficacy. Two of the metabolites, sarcosine and inosine 5′-monophosphate, improved greatly after treatment. The third metabolite, tyramine O-sulfate, showed no change in median value, suggesting it and correlated metabolites to be a possible target for future therapies. Since it is unclear whether the observed differences are due to metabolic abnormalities associated with ASD or with GI symptoms (or contributions from both), future studies aiming to classify ASD should feature TD participants with GI symptoms and have larger sample sizes to improve confidence in the results.

Online comprehension across different semantic categories in preschool children with autism spectrum disorder

Background

Word comprehension across semantic categories is a key area of language development. Using online automated eye-tracking technology to reduce response demands during a word comprehension test may be advantageous in children with autism spectrum disorder (ASD).

Objectives

To measure online accuracy of word recognition across eleven semantic categories in preschool children with ASD and in typically developing (TD) children matched for gender and developmental age.

Methods

Using eye-tracker methodology we measured the relative number of fixations on a target image as compared to a foil of the same category shown simultaneously on screen. This online accuracy measure was considered a measure of word understanding. We tested the relationship between online accuracy and offline word recognition and the effects of clinical variables on online accuracy. Twenty-four children with ASD and 21 TD control children underwent the eye-tracking task.

Results

On average, children with ASD were significantly less accurate at fixating on the target image than the TD children. After multiple comparison correction, no significant differences were found across the eleven semantic categories of the experiment between preschool children with ASD and younger TD children matched for developmental age. The ASD group showed higher intragroup variability consistent with greater variation in vocabulary growth rates. Direct effects of non-verbal cognitive levels, vocabulary levels and gesture productions on online word recognition in both groups support a dimensional view of language abilities in ASD.

Conclusions

Online measures of word comprehension across different semantic categories show higher interindividual variability in children with ASD and may be useful for objectively monitor gains on targeted language interventions.