Supervised machine learning: A new method to predict the outcomes following exercise intervention in children with autism spectrum disorder

Effects of mini-basketball training program on social communication impairments and regional homogeneity of brain functions in preschool children with autism spectrum disorder

BACKGROUND

Social communication impairments (SCI) is a core symptom of autism spectrum disorder (ASD) and is marked by challenges in social interaction. Although physical exercise has been shown to improve SCI, this finding has not been supported by comprehensive scientific evidence. Existing research has established a strong link between the SCI in children with ASD and abnormalities in regional homogeneity (ReHo). Therefore, investigating the effects of physical exercise on SCI and Reho in patients with ASD may help to elucidate the neurological mechanisms involved.

METHODS

The present study included 30 preschool children diagnosed with ASD, with 15 participants in each group (experimental and control). The experimental group underwent a 12-week mini-basketball training program (MBTP) based on routine behavioral rehabilitation, while the control group only received routine behavioral rehabilitation. The Social Responsiveness Scale-Second Edition (SRS-2) was employed to assess SCI in both groups. Resting-state functional magnetic resonance imaging technology was used to evaluate ReHo in both groups.

RESULTS

After 12-week of MBTP, significant group × time interactions were observed between the experimental and control groups in total SRS-2 scores (F = 14.514, p < 0.001, ηp2 = 0.341), as well as in the domains of social cognition (F = 15.620, p < 0.001, ηp2 = 0.358), social communication (F = 12.460, p < 0.01, ηp2 = 0.308), and autistic mannerisms (F = 9.970, p < 0.01, ηp2 = 0.263). No statistical difference was found in the scores for the social awareness subscale and social motivation subscale in the group × time interaction (all p > 0.05). The experimental group exhibited increased ReHo in the right Cerebellum_Crus1 and right parahippocampal gyrus, coupled with decreased ReHo in the left middle frontal gyrus (orbital part), left superior frontal gyrus (dorsolateral), left postcentral gyrus, and right superior parietal gyrus. Furthermore, a decrease in ReHo in the left postcentral gyrus positively correlated with changes in social communication scores in SCI behaviors (p < 0.05).

CONCLUSIONS

Our study underscores the effectiveness of a 12-week MBTP in ameliorating SCI and abnormalities in ReHo among preschool children with ASD.

TRIAL REGISTRATION

The trial is retrospectively registered on the Chinese Clinical Trial Registry (ChiCTR1900024973; August 5, 2019).

Diagnosing autism severity associated with physical fitness and gray matter volume in children with autism spectrum disorder: Explainable machine learning method

PURPOSE

This study aimed to investigate the relationship between physical fitness, gray matter volume (GMV), and autism severity in children with autism spectrum disorder (ASD). Besides, we sought to diagnose autism severity associated with physical fitness and GMV using machine learning methods.

METHODS

Ninety children diagnosed with ASD underwent physical fitness tests, magnetic resonance imaging scans, and autism severity assessments. Diagnosis models were established using extreme gradient boosting (XGB), random forest (RF), support vector machine (SVM), and decision tree (DT) algorithms. Hyperparameters were optimized through the grid search cross-validation method. The shapley additive explanation (SHAP) method was employed to explain the diagnosis results.

RESULTS

Our study revealed associations between muscular strength in physical fitness and GMV in specific brain regions (left paracentral lobule, bilateral thalamus, left inferior temporal gyrus, and cerebellar vermis I-II) with autism severity in children with ASD. The accuracy (95 % confidence interval) of the XGB, RF, SVM, and DT models were 77.9 % (77.3, 78.6 %), 72.4 % (71.7, 73.2 %), 71.9 % (71.1, 72.6 %), and 66.9 % (66.2, 67.7 %), respectively. SHAP analysis revealed that muscular strength and thalamic GMV significantly influenced the decision-making process of the XGB model.

CONCLUSION

Machine learning methods can effectively diagnose autism severity associated with physical fitness and GMV in children with ASD. In this respect, the XGB model demonstrated excellent performance across various indicators, suggesting its potential for diagnosing autism severity.

Predicting academic achievement from the collaborative influences of executive function, physical fitness, and demographic factors among primary school students in China: ensemble learning methods

BACKGROUND

Elevated levels of executive function and physical fitness play a pivotal role in shaping future quality of life. However, few studies have examined the collaborative influences of physical and mental health on academic achievement. This study aims to investigate the key factors that collaboratively influence primary school students' academic achievement from executive function, physical fitness, and demographic factors. Additionally, ensemble learning methods are employed to predict academic achievement, and their predictive performance is compared with individual learners.

METHODS

A cluster sampling method was utilized to select 353 primary school students from Huai'an, China, who underwent assessments for executive function, physical fitness, and academic achievement. The recursive feature elimination cross-validation method was employed to identify key factors that collaboratively influence academic achievement. Ensemble learning models, utilizing eXtreme Gradient Boosting and Random Forest algorithms, were constructed based on Bagging and Boosting methods. Individual learners were developed using Support Vector Machine, Decision Tree, Logistic Regression, and Linear Discriminant Analysis algorithms, followed by the establishment of a Stacking ensemble learning model.

RESULTS

Our findings revealed that sex, body mass index, muscle strength, cardiorespiratory function, inhibition, working memory, and shifting were key factors influencing the academic achievement of primary school students. Moreover, ensemble learning models demonstrated superior predictive performance compared to individual learners in predicting academic achievement among primary school students.

CONCLUSIONS

Our results suggest that recognizing sex differences and emphasizing the simultaneous development of cognition and physical well-being can positively impact the academic development of primary school students. Ensemble learning methods warrant further attention, as they enable the establishment of an accurate academic early warning system for primary school students.