Cognitive flexibility in autism: Evidence from young autistic children

Autism Detection in Children: Integrating Machine Learning and Natural Language Processing in Narrative Analysis

Despite the consensus that early identification leads to better outcomes for individuals with autism spectrum disorder (ASD), recent research reveals that the average age of diagnosis in the Greek population is approximately six years. However, this age of diagnosis is delayed by an additional two years for families from lower-income or minority backgrounds. These disparities result in adverse impacts on intervention outcomes, which are further burdened by the often time-consuming and labor-intensive language assessments for children with ASD. There is a crucial need for tools that increase access to early assessment and diagnosis that will be rigorous and objective. The current study leverages the capabilities of artificial intelligence to develop a reliable and practical model for distinguishing children with ASD from typically-developing peers based on their narrative and vocabulary skills. We applied natural language processing-based extraction techniques to automatically acquire language features (narrative and vocabulary skills) from storytelling in 68 children with ASD and 52 typically-developing children, and then trained machine learning models on the children's combined narrative and expressive vocabulary data to generate behavioral targets that effectively differentiate ASD from typically-developing children. According to the findings, the model could distinguish ASD from typically-developing children, achieving an accuracy of 96%. Specifically, out of the models used, hist gradient boosting and XGBoost showed slightly superior performance compared to the decision trees and gradient boosting models, particularly regarding accuracy and F1 score. These results bode well for the deployment of machine learning technology for children with ASD, especially those with limited access to early identification services.

Reading comprehension differences between children with Autism Spectrum Disorder and low cognitive abilities and children with Autism Spectrum Disorder and intact cognitive skills: the roles of decoding, fluency and morphosyntax

Introduction

Reading comprehension is one of the most important skills learned in school and it has an important contribution to the academic success of children with Autism Spectrum Disorder (ASD). Though previous studies have investigated reading comprehension difficulties in ASD and highlighted factors that contribute to these difficulties, this evidence has mainly stemmed from children with ASD and intact cognitive skills. Also, much emphasis has been placed on the relation between reading comprehension and word recognition skills, while the role of other skills, including fluency and morphosyntax, remains underexplored. This study addresses these gaps by investigating reading comprehension in two groups of school-aged children with ASD, one with intact and one with low cognitive abilities, also exploring the roles of word decoding, fluency and morphosyntax in each group's reading comprehension performance.

Methods

The study recruited 16 children with ASD and low cognitive abilities, and 22 age-matched children with ASD and intact cognitive skills. The children were assessed on four reading subdomains, namely, decoding, fluency, morphosyntax, and reading comprehension.

Results

The children with ASD and low cognitive abilities scored significantly lower than their peers with intact cognitive abilities in all reading subdomains, except for decoding, verb production and compound word formation. Regression analyses showed that reading comprehension in the group with ASD and intact cognitive abilities was independently driven by their decoding and fluency skills, and to a lesser extent, by morphosyntax. On the other hand, the children with ASD and low cognitive abilities mainly drew on their decoding, and to a lesser extent, their morphosyntactic skills to perform in reading comprehension.

Discussion

The results suggest that reading comprehension was more strongly affected in the children with ASD and low cognitive abilities as compared to those with intact cognitive skills. About half of the children with ASD and intact cognitive skills also exhibited mild-to-moderate reading comprehension difficulties, further implying that ASD may influence reading comprehension regardless of cognitive functioning. Finally, strengths in decoding seemed to predominantly drive cognitively-impaired children's reading performance, while the group with ASD and intact cognitive skills mainly recruited fluency and metalinguistic lexical skills to cope with reading comprehension demands, further suggesting that metalinguistic awareness may be a viable way to enhance reading comprehension in ASD.

Intellectual development in young children with autism spectrum disorders: A longitudinal study

Intelligence profiles in autism have been characterized by great variability. The questions of how autistic children's intelligence changes over time, and what factors influence these changes deserve study as part of efforts to document child autism profiles, but also because the relationship between intellectual functioning and children's background characteristics is poorly understood, particularly in a longitudinal context. A total of 39 autistic children and 39 age-matched neurotypical children (5-9 years old) completed two IQ assessments at preschool age and up to 4 years later. Repeated-measures analyses assessed longitudinal changes in the children's verbal (VIQ), performance (PIQ), and full-scale IQ (FSIQ) at group level. We further sought to identify clusters with distinct profiles in each group by adopting an unsupervised K-means clustering approach, and detect possible between-subgroup differences in terms of children's socioeconomic status and autism severity. The largest cluster in the autistic group was composed of children whose PIQ significantly dropped at follow-up, while the second largest cluster improved in all quotients; the smallest cluster, wherein children had more highly educated mothers than the rest of the clusters, was characterized by large improvement in VIQ. For the neurotypical children, there was a two-cluster division; the majority of them improved in the three quotients, while very few dropped in PIQ at follow-up. The relation between socioeconomic status and IQ changes was significant for both groups. The findings demonstrate both the complexity of intelligence changes in autism and the need to view this complexity through the lens of the children's socioeconomic diversity.

The Physiology of Cognition in Autism Spectrum Disorder: Current and Future Challenges

Cognitive impairment is among the most challenging characteristics of autism spectrum disorder (ASD). Although ASD is one of the common neurodevelopmental disorders, we are still behind in diagnosing and treating cognitive impairment in ASD. Cognitive impairment in ASD varies, meaning it could be at the sensory perception level to cognitive processing, learning, and memory. There are no diagnostic criteria for cognitive impairment that are specific to ASD. The leading causes of cognitive impairment in ASD could be neurological, immune, and gastrointestinal dysfunction. Immune dysfunction might lead to neuroinflammation, affecting neural connectivity, glutamate/gamma-aminobutyric acid (GABA) balance, and plasticity. The gut-brain axes are essential in the developing brain. Special retinal changes have recently been detected in ASD, which need clinical investigation to find their possible role in early diagnosis. Early intervention is crucial for ASD cognitive dysfunction. Due to the heterogeneity of the disease, the clinical manifestation of ASD makes it difficult for clinicians to develop gold-standard diagnostic and therapeutic criteria. We suggest a triad for diagnosis, which includes clinical tests for immune and gastrointestinal dysfunction biomarkers, clinical examination for the retina, and an objective neurocognitive evaluation for ASD, and to develop a treatment strategy involving these three aspects. Developing clear treatment criteria for cognitive impairment for ASD would improve the quality of life of ASD people and their caregivers and would delay or prevent dementia-related disorders in ASD people.

Relational memory weakness in autism despite the use of a controlled encoding task

Introduction

Recent work challenged past findings that documented relational memory impairments in autism. Previous studies often relied solely on explicit behavioral responses to assess relational memory integrity, but successful performance on behavioral tasks may rely on other cognitive abilities (e.g., executive functioning) that are impacted in some autistic individuals. Eye-tracking tasks do not require explicit behavioral responses, and, further, eye movements provide an indirect measure of memory. The current study examined whether memory-specific viewing patterns toward scenes differ between autistic and non-autistic individuals.

Methods

Using a long-term memory paradigm that equated for complexity between item and relational memory tasks, participants studied a series of scenes. Following the initial study phase, scenes were re-presented, accompanied by an orienting question that directed participants to attend to either features of an item (i.e., in the item condition) or spatial relationships between items (i.e., in the relational condition) that might be subsequently modified during test. At test, participants viewed scenes that were unchanged (i.e., repeated from study), scenes that underwent an "item" modification (an exemplar switch) or a "relational" modification (a location switch), and scenes that had not been presented before. Eye movements were recorded throughout.

Results

During study, there were no significant group differences in viewing directed to regions of scenes that might be manipulated at test, suggesting comparable processing of scene details during encoding. However, there was a group difference in explicit recognition accuracy for scenes that underwent a relational change. Marginal group differences in the expression of memory-based viewing effects during test for relational scenes were consistent with this behavioral outcome, particularly when analyses were limited to scenes recognized correctly with high confidence. Group differences were also evident in correlational analyses that examined the association between study phase viewing and recognition accuracy and between performance on the Picture Sequence Memory Test and recognition accuracy.

Discussion

Together, our findings suggest differences in the integrity of relational memory representations and/or in the relationships between subcomponents of memory in autism.