Previous studies have found that Autism Spectrum Disorder (ASD) children scored lower during a Go/No-Go task and faced difficulty focusing their gaze on the speaker's face during a conversation. To date, however, there has not been an adequate study examining children's response and gaze during the Go/No-Go task to distinguish ASD from typical children. We investigated typical and ASD children's gaze modulation when they played a version of the Go/No-Go game. The proposed system represents the Go and the No-Go stimuli as chicken and cat characters, respectively. It tracks children's gaze using an eye tracker mounted on the monitor. Statistically significant between-group differences in spatial and auto-regressive temporal gaze-related features for 21 ASD and 31 typical children suggest that ASD children had more unstable gaze modulation during the test. Using the features that differ significantly as inputs, the AdaBoost meta-learning algorithm attained an accuracy rate of 88.6% in differentiating the ASD subjects from the typical ones.
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http://dx.doi.org/10.1038/s41598-021-01050-7 | DOI Listing |
Front Neurosci
January 2025
Department of Experimental Pharmacology, Mossakowski Medical Research Institute, Polish Academy of Sciences, Warsaw, Poland.
Over the last three decades, dynamically evolving research using novel technologies, including virtual environments (VEs), has presented promising solutions for neuroscience and neuropsychology. This article explores the known and potential benefits and drawbacks of employing modern technologies for diagnosing and treating developmental disorders, exemplified by autism spectrum disorder (ASD). ASD's complex nature is ideal for illustrating the advantages and disadvantages of the digital world.
View Article and Find Full Text PDFFront Psychiatry
January 2025
School of Education Sciences, Chongqing Normal University, Chongqing, China.
The current study employed network analysis to examine the relationship between symptoms from factor level about autism traits and problematic mobile phone use (PMPU) and to explore their associations with depression. We measured the above three variables in 949 college students in China with Autism Spectrum Quotient (AQ), Smartphone Addiction Scale (SAS), Center for Epidemiological Studies Depression Scale (CES-D). Central and bridge symptoms were pinpointed through the examination of centrality index.
View Article and Find Full Text PDFBMC Pediatr
January 2025
Institute of Neurodevelopment, Cognition, and Inclusive Education (INCEI), Ribeirão das Neves, Belo Horizonte, MG, Brazil.
Background: Understanding the priorities of parents of children and adolescents with autism spectrum disorder (ASD) is crucial for implementing evidence-based programs. This study aims to identify the functional priorities of parents of Brazilian children and adolescents with ASD, analyze variations in priorities according to the levels of support and age groups of the participants, and categorize the goals according to the categories of the International Classification of Functioning, Disability, and Health (ICF). Additionally, this study aimed to evaluate changes in parents' performance and satisfaction with functional priorities after intervention with the Global Integration Method (Métodode Integração Global - MIG).
View Article and Find Full Text PDFBMC Pediatr
January 2025
Nutrition & Health Innovation Research Institute, Edith Cowan University, Perth, WA, Australia.
Background: Growing evidence shows that dysregulated metabolic intrauterine environments can affect offspring's neurodevelopment and behaviour. However, the results of individual cohort studies have been inconsistent. We aimed to investigate the association between maternal diabetes before pregnancy and gestational diabetes mellitus (GDM) with neurodevelopmental, cognitive and behavioural outcomes in children.
View Article and Find Full Text PDFJ Neural Eng
January 2025
Department of Bioengineering, Lehigh University, Department of Bioengineering, Lehigh University, Bethlehem, PA 18015, Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA 18015, Bethlehem, 18015-3027, UNITED STATES.
Functional magnetic resonance imaging (fMRI) is often modeled as networks of Regions of Interest (ROIs) and their functional connectivity to study brain functions and mental disorders. Limited fMRI data due to high acquisition costs hampers recognition model performance. We aim to address this issue using generative diffusion models for data augmentation.
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