Autism detection based on eye movement sequences on the web: a scanpath trend analysis approach

This is an accepted manuscript of an article published by ACM in W4A '20: Proceedings of the 17th International Web for All Conference on 20/04/2020, available online: https://doi.org/10.1145/3371300.3383340 The accepted version of the publication may differ from the final published version.Autism diagnostic procedure is a subjective, challenging and expensive procedure and relies on behavioral, historical and parental report information. In our previous, we proposed a machine learning classifier to be used as a potential screening tool or used in conjunction with other diagnostic methods, thus aiding established diagnostic methods. The classifier uses eye movements of people on web pages but it only considers non-sequential data. It achieves the best accuracy by combining data from several web pages and it has varying levels of accuracy on different web pages. In this present paper, we investigate whether it is possible to detect autism based on eye-movement sequences and achieve stable accuracy across different web pages to be not dependent on specific web pages. We used Scanpath Trend Analysis (STA) which is designed for identifying a trending path of a group of users on a web page based on their eye movements. We first identify trending paths of people with autism and neurotypical people. To detect whether or not a person has autism, we calculate the similarity of his/her path to the trending paths of people with autism and neurotypical people. If the path is more similar to the trending path of neurotypical people, we classify the person as a neurotypical person. Otherwise, we classify her/him as a person with autism. We systematically evaluate our approach with an eye-tracking dataset of 15 verbal and highly-independent people with autism and 15 neurotypical people on six web pages. Our evaluation shows that the STA approach performs better on individual web pages and provides more stable accuracy across different pages

Adults with High-functioning Autism Process Web Pages With Similar Accuracy but Higher Cognitive Effort Compared to Controls

To accommodate the needs of web users with high-functioning autism, a designer's only option at present is to rely on guidelines that: i) have not been empirically evaluated and ii) do not account for the di erent levels of autism severity. Before designing effective interventions, we need to obtain an empirical understanding of the aspects that speci c user groups need support with. This has not yet been done for web users at the high ends of the autism spectrum, as often they appear to execute tasks effortlessly, without facing barriers related to their neurodiverse processing style. This paper investigates the accuracy and efficiency with which high-functioning web users with autism and a control group of neurotypical participants obtain information from web pages. Measures include answer correctness and a number of eye-tracking features. The results indicate similar levels of accuracy for the two groups at the expense of efficiency for the autism group, showing that the autism group invests more cognitive effort in order to achieve the same results as their neurotypical counterparts

DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning

Genome-wide association studies (GWAS) identify genetic variants associated with traits or diseases. GWAS never directly link variants to regulatory mechanisms. Instead, the functional annotation of variants is typically inferred by post hoc analyses. A specific class of deep learning-based methods allows for the prediction of regulatory effects per variant on several cell type-specific chromatin features. We here describe \textquotedblDeepWAS\textquotedbl, a new approach that integrates these regulatory effect predictions of single variants into a multivariate GWAS setting. Thereby, single variants associated with a trait or disease are directly coupled to their impact on a chromatin feature in a cell type. Up to 61 regulatory SNPs, called dSNPs, were associated with multiple sclerosis (MS, 4,888 cases and 10,395 controls), major depressive disorder (MDD, 1,475 cases and 2,144 controls), and height (5,974 individuals). These variants were mainly non-coding and reached at least nominal significance in classical GWAS. The prediction accuracy was higher for DeepWAS than for classical GWAS models for 91% of the genome-wide significant, MS-specific dSNPs. DSNPs were enriched in public or cohort-matched expression and methylation quantitative trait loci and we demonstrated the potential of DeepWAS to generate testable functional hypotheses based on genotype data alone. DeepWAS is available at https://github.com/cellmapslab/DeepWAS

Clinical spectrum of early onset “Mediterranean” (homozygous p.P131L mutation) mitochondrial neurogastrointestinal encephalomyopathy

Mitochondrial neurogastrointestinal encephalomyopathy (MNGIE) is an autosomal recessive mitochondrial disorder characterized by cumulative and progressive gastrointestinal and neurological findings. This retrospective observational study, aimed to explore the time of presentation, diagnosis and clinical follow-up of 13 patients with a confirmed MNGIE disease of Mediterranean origin. The mean age of symptom onset was 7 years (6 months−21 years) and the average diagnosis age was 15.4 years ±8.4. Four of 13 patients (30%) died before 30 years at the mean age of 19.7 years ±6.8. Cachexia and gastrointestinal symptoms were observed in all patients (100%). The mean body mass index standard deviation score at diagnosis was 4.8 ± 2.8. At least three subocclusive episodes were presented in patients who died in last year of their life. The main neurological symptom found in most patients was peripheral neuropathy (92%). Ten patients (77%) had leukoencephalopathy and the remaining three patients without were under 10 years of age. The new homozygous “Mediterranean” TYMP mutation, p.P131L (c.392 C > T) was associated with an early presentation and poor prognosis in nine patients (69%) from five separates families. Based on the observations from this Mediterranean MNGIE cohort, we propose that the unexplained abdominal pain combined with cachexia is an indicator of MNGIE. High-platelet counts and nerve conduction studies may be supportive laboratory findings and the frequent subocclusive episodes could be a negative prognostic factor for mortality. Finally, the homozygous p.P131L (c.392 C > T) mutation could be associated with rapid progressive disease with poor prognosis

DeepWAS: multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning

Genome-wide association studies (GWAS) identify genetic variants associated with traits or diseases. GWAS never directly link variants to regulatory mechanisms. Instead, the functional annotation of variants is typically inferred by post hoc analyses. A specific class of deep learning-based methods allows for the prediction of regulatory effects per variant on several cell type-specific chromatin features. We here describe "DeepWAS", a new approach that integrates these regulatory effect predictions of single variants into a multivariate GWAS setting. Thereby, single variants associated with a trait or disease are directly coupled to their impact on a chromatin feature in a cell type. Up to 61 regulatory SNPs, called dSNPs, were associated with multiple sclerosis (MS, 4,888 cases and 10,395 controls), major depressive disorder (MDD, 1,475 cases and 2,144 controls), and height (5,974 individuals). These variants were mainly non-coding and reached at least nominal significance in classical GWAS. The prediction accuracy was higher for DeepWAS than for classical GWAS models for 91% of the genome-wide significant, MS-specific dSNPs. DSNPs were enriched in public or cohort-matched expression and methylation quantitative trait loci and we demonstrated the potential of DeepWAS to generate testable functional hypotheses based on genotype data alone. DeepWAS is available at https://github.com/cellmapslab/DeepWAS