1. Banire B, Al Thani D, Qaraqe M, Mansoor B. Face-Based Attention Recognition Model for Children with Autism Spectrum Disorder. Journal of healthcare informatics research. 2021; 5(4): 420-45.

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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s41666-021-00101-y.

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2. de Araujo CA. Autism: an ‘epidemic’ of contemporary times?. The Journal of analytical psychology. 2022; 67(1): 5-20.

The text discusses the growing incidence of autism in the world, presents an understanding of autism from the point of view of analytical psychology, and reflects on the treatment of autistic patients. Today, it is understood that autism is part of a continuum of characteristics on a spectrum with biological and congenital causes. It is a non-specific picture resulting from multiple causations of non-linear factors. Autism is a neuro-developmental disorder characterized by a triad of symptoms: persistent deficits in social communication and social interaction, and restricted and repetitive patterns of behaviours, interests, or activities. Autism spectrum disorder must be considered as a clinical entity, with current clearly defined characteristics. It is an extremely complex condition, which requires multidisciplinary approaches aiming at the possibility of prognosis and effective therapeutic approaches. This paper explores how a disturbance may occur from the intra-uterine phase, in which matriarchal experiences do not constellate. The structuring function of the patriarchal organization can then become dominant, and people with autism need understanding and help to organize their world and learn to live in it. As they don’t have the capacity to structure consciousness through the matriarchal archetype, they rely entirely on the structuring and organizing capacity of the Father archetype.

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3. Guerrero MGB, Sobotka SA. Understanding the Barriers to Receiving Autism Diagnoses for Hispanic and Latinx Families. Pediatric annals. 2022; 51(4): e167-e71.

Significant disparities exist in early identification of autism spectrum disorder (ASD) for Hispanic and Latinx children. ASD prevalence estimates are approximately identical for White and Black children but lower for Hispanic and Latinx children. Reasons for these racial and ethnic variations are likely multifactorial. This review sought to understand previously described barriers and limitations to accessing ASD diagnostic services in the Latinx and Hispanic communities. Three main categories of existing barriers were identified: (1) parent/family, (2) community, and (3) systemic. These barriers are complex and multifactorial in nature, including circumstantial limitations such as limited English proficiency, noncitizenship, and low-income status. These can limit health care access, and can lead to family and community cultural barriers, poor knowledge about ASD, and social stigma related to disabilities. Understanding and mitigating barriers is essential to reduce disparities to ASD diagnosis in the Hispanic and Latinx community. [Pediatr Ann. 2022;51(4):e167-e171.].

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4. Hand CJ, Kennedy A, Filik R, Pitchford M, Robus CM. Emoji Identification and Emoji Effects on Sentence Emotionality in ASD-Diagnosed Adults and Neurotypical Controls. Journal of autism and developmental disorders. 2022.

We investigated ASD-diagnosed adults’ and neurotypical (NT) controls’ processing of emoji and emoji influence on the emotionality of otherwise-neutral sentences. Study 1 participants categorised emoji representing the six basic emotions using a fixed-set of emotional adjectives. Results showed that ASD-diagnosed participants’ classifications of fearful, sad, and surprised emoji were more diverse and less ‘typical’ than NT controls’ responses. Study 2 participants read emotionally-neutral sentences; half paired with sentence-final happy emoji, half with sad emoji. Participants rated sentence + emoji stimuli for emotional valence. ASD-diagnosed and NT participants rated sentences + happy emoji as equally-positive, however, ASD-diagnosed participants rated sentences + sad emoji as more-negative than NT participants. We must acknowledge differential perceptions and effects of emoji, and emoji-text inter-relationships, when working with neurodiverse stakeholders.

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5. Lai MC. Clinical reflections on the intersections of autism and personality development. Autism : the international journal of research and practice. 2022; 26(4): 739-42.

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6. Manning C, Hassall CD, Hunt LT, Norcia AM, Wagenmakers EJ, Evans NJ, Scerif G. Behavioural and neural indices of perceptual decision-making in autistic children during visual motion tasks. Scientific reports. 2022; 12(1): 6072.

Many studies report atypical responses to sensory information in autistic individuals, yet it is not clear which stages of processing are affected, with little consideration given to decision-making processes. We combined diffusion modelling with high-density EEG to identify which processing stages differ between 50 autistic and 50 typically developing children aged 6-14 years during two visual motion tasks. Our pre-registered hypotheses were that autistic children would show task-dependent differences in sensory evidence accumulation, alongside a more cautious decision-making style and longer non-decision time across tasks. We tested these hypotheses using hierarchical Bayesian diffusion models with a rigorous blind modelling approach, finding no conclusive evidence for our hypotheses. Using a data-driven method, we identified a response-locked centro-parietal component previously linked to the decision-making process. The build-up in this component did not consistently relate to evidence accumulation in autistic children. This suggests that the relationship between the EEG measure and diffusion-modelling is not straightforward in autistic children. Compared to a related study of children with dyslexia, motion processing differences appear less pronounced in autistic children. Exploratory analyses also suggest weak evidence that ADHD symptoms moderate perceptual decision-making in autistic children.

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7. Mantzalas J, Richdale AL, Dissanayake C. A conceptual model of risk and protective factors for autistic burnout. Autism research : official journal of the International Society for Autism Research. 2022.

Early qualitative research indicates that autistic burnout is commonly experienced by autistic people and is associated with significant, negative consequences for their mental health, wellbeing, and quality of life, including suicidality. Findings to date suggest that factors associated with being autistic and the widespread lack of autism awareness and acceptance within society contribute to the onset and recurrence of autistic burnout. Based on autistic adults’ descriptions of their lived experiences, a Conceptual Model of Autistic Burnout (CMAB) is proposed, which describes a series of hypothesized relationships between identified risk and protective factors that may contribute to, or buffer against, autistic burnout. The theoretical framework for the CMAB is based on the Social-Relational model of disability and neurodiversity paradigm, and the Job Demands-Resources model of burnout, and Conservation of Resources theory. The CMAB offers a holistic perspective for understanding individual, social, and environmental factors that can influence autistic burnout via various direct and indirect pathways. Autistic burnout research is in its infancy and the CMAB provides a foundation for future investigations about this condition. LAY SUMMARY: Although many autistic people describe experiencing autistic burnout, there has been little research on this topic. Based on descriptions of autistic peoples’ lived experiences, we developed a conceptual model to explore how various risk and protective factors may interact to contribute to, or prevent, autistic burnout.

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8. Visnovcova Z, Ferencova N, Grendar M, Ondrejka I, Bona Olexova L, Bujnakova I, Tonhajzerova I. Electrodermal activity spectral and nonlinear analysis – potential biomarkers for sympathetic dysregulation in autism. General physiology and biophysics. 2022; 41(2): 123-31.

Autism spectrum disorder (ASD) is a neurodevelopmental disease characterized by emotional and social deficits, which can be associated with sympathetic dysregulation. Thus, we aimed to analyze the electrodermal activity (EDA) using time, and novel spectral and nonlinear indices in ASD. The cohort consisted of 45 ASD boys and 45 age-matched controls. EDA was continuously recorded at rest. The EDA indices were evaluated by time-, spectral-, and nonlinear-domain analysis. Our results revealed increased non-specific skin conductance responses, spectral parameters in high and very-high frequency bands, approximate and symbolic information entropy indicating sympathetic overactivity in ASD vs. controls (p < 0.05, for all). Surprisingly, the nonlinear index from detrended fluctuation analysis α1 was lower in ASD vs. controls (p = 0.024) providing thus distinct information about qualitative features of complex sympathetic regulation. Concluding, the complex time, spectral, and nonlinear EDA indices revealed discrete abnormalities in sympathetic cholinergic regulation as one of the potential pathomechanisms contributing to cardiovascular complications in ASD.

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9. Xie J, Wang L, Webster P, Yao Y, Sun J, Wang S, Zhou H. Identifying Visual Attention Features Accurately Discerning Between Autism and Typically Developing: a Deep Learning Framework. Interdisciplinary sciences, computational life sciences. 2022.

Atypical visual attention is a hallmark of autism spectrum disorder (ASD). Identifying the attention features accurately discerning between people with ASD and typically developing (TD) at the individual level remains a challenge. In this study, we developed a new systematic framework combining high accuracy deep learning classification, deep learning segmentation, image ablation and a direct measurement of classification ability to identify the discriminative features for autism identification. Our two-stream model achieved the state-of-the-art performance with a classification accuracy of 0.95. Using this framework, two new categories of features, Food & drink and Outdoor-objects, were identified as discriminative attention features, in addition to the previously reported features including Center-object and Human-faces, etc. Altered attention to the new categories helps to understand related atypical behaviors in ASD. Importantly, the area under curve (AUC) based on the combined top-9 features identified in this study was 0.92, allowing an accurate classification at the individual level. We also obtained a small but informative dataset of 12 images with an AUC of 0.86, suggesting a potentially efficient approach for the clinical diagnosis of ASD. Together, our deep learning framework based on VGG-16 provides a novel and powerful tool to recognize and understand abnormal visual attention in ASD, which will, in turn, facilitate the identification of biomarkers for ASD.

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