Age-Stratified Differences in Morphological Connectivity Patterns in ASD: An sMRI and Machine Learning Approach.

Researchers used brain scans to look at structural differences in children with autism across different age groups. They found that brain connectivity patterns in younger children (ages 6-11) were better for identifying autism using computer analysis, achieving about 76% accuracy. This suggests that autism may be easier to detect from brain scans in younger children compared to older ones.

This study examined age-related differences in brain morphological connectivity patterns for autism spectrum disorder (ASD) diagnosis using structural MRI data from ABIDE databases. Researchers analyzed 592 morphological features and 10,878 morphological connectivity features across three age groups (6-11, 11-18, and 6-18 years). Machine learning classification using random forest achieved best performance in the 6-11 age group, with 75.8% accuracy, 83.1% F1 score, 86% recall, and 80.4% precision for morphological connectivity features. The study suggests that age-specific morphological connectivity approaches may be promising for ASD diagnosis, with younger children showing more distinctive brain connectivity patterns that facilitate automated detection.

Age-stratified neuroimaging approaches may enhance autism diagnostic accuracy, particularly in younger children. Morphological connectivity analysis shows promise as a complementary diagnostic tool. Further validation needed before clinical implementation.

Sample size not reported. Single study without replication. Conference abstract with limited methodological details. Machine learning performance, while promising, shows room for improvement. Lacks comparison to clinical diagnostic standards.

Autism spectrum disorder (ASD) is one of the most common neurological disorders, and its early detection is extremely difficult. Researchers use different physiological and medical imaging signals to diagnose ASD based on the severity level and the age of the patient. In this study, morphological features (MF) and morphological connectivity features (MCF) are used to investigate the influence of age on the diagnosis of autism spectrum disorders (ASD). In this work, we have utilized structural magnetic resonance imaging (sMRI) data from ABIDE-I and ABIDE-II databases, divided into 6-11, 11-18, and 6-18 age groups, were pre processed and yielded 592 MF and 10,878 MCF per subject.

As a result, the 6-11 age group outperformed the others in both feature types, especially in MCF, with a random forest (RF) classifier achieving 75.8% accuracy, 83.1% F1 score, 86% recall, and 80.4% precision, respectively. Based on this, it can be concluded that an age-specific morphological connectivity approach holds promise for effective diagnosis of autism spectrum disorders.