Early Treatment Of Autism Spectrum Disorders Research Articles

Diagnostic Biomarker Exploration of Autistic Patients With Different Ages and Different Verbal Intelligence Quotients Based on Random Forest Model

As a neurodevelopmental disorder with complex pathogenesis, the existing diagnostic methods of autism still only rely on the scale method. In recent years, there have been some methods using machine learning to classify Autism Spectrum Disorders (ASD), and achieved good accuracy. However, the generalization of these models is poor, and the conclusions are inconsistent. The main reason is that most of them use single site dataset or private dataset for analysis, which will lead to one-sided conclusion. They did not analyze the phenotypic features of the dataset, such as handedness, gender, and age. In order to make the obtained brain diagnostic biomarkers of ASD more universal and generalized, instead of analyzing the dataset from a single site, the whole dataset is divided into subgroups according to age and Verbal Intelligence Quotient (VIQ), and then each subgroup is classified and analyzed by Random Forest (RF) model. The experimental results show that if all male subjects are used for classification, the accuracy of classification can only reach about 55%. By using the proposed grouping method and RF model, the classification accuracy for different subgroups will be improved by 3% ~ 17%. Through the analysis of the importance and difference of the features in each subgroup, we can find that the features obtained in the above experiments are closely related to the functions of speech, emotion, auditory and visual information processing. This may partly explain why ASD patients have speech, social disorder, repetitive behavior and narrow interests. The classification methods proposed and diagnostic biomarkers obtained in this paper may provide some reference for the clinical diagnosis and early treatment of ASD.

Impaired disengagement of attention and its relationship to emotional distress in infants at high-risk for autism spectrum disorder

ABSTRACTIntroduction: We provide data on visual orienting and emotional distress in infants at high and low risk for autism spectrum disorder (ASD). Method: Participants included 83 high-risk (HR) infants with an older sibling with ASD and 53 low-risk (LR) control infants with no family history of ASD. Infants were assessed on the gap-overlap task and a parent-completed temperament questionnaire at 6 and 12 months of age. At 36 months of age, an independent, gold standard diagnostic assessment for ASD was conducted. Results: HR infants subsequently diagnosed with ASD were distinguished at 12 months by an asymmetric disengage impairment (for left- vs. right-sided stimuli) that was associated with an increase in latencies between 6 and 12 months. Across groups, prolonged left-directed disengage latencies at 12 months were associated with emotional distress (high irritability and difficult to soothe). Conclusions: The asymmetry in our findings raises the question of whether the disengage problem in ASD is at base one of orienting or alerting attention. Our findings also raise the question of whether attention training might be a critical ingredient in the early treatment of ASD.