Clinical Report: Multimodal MRI and Machine Learning Approaches for Analyzing Brain Features Associated with Autism in Different Age Groups of Youth

Overview

This study explores the use of machine learning and multimodal neuroimaging to identify brain characteristics associated with autism across different age groups.

Background

Autism is a prevalent neurodevelopmental condition that presents diagnostic challenges due to behavioral variability and co-occurrence with other conditions. Current diagnostic methods rely heavily on subjective behavioral assessments. Advances in neuroimaging and machine learning provide tools for identifying autism-related brain features.

Data Highlights

Key Findings

• Classification accuracies for unimodal models ranged from 68.3% to 77.6% across modalities.
• dMRI achieved the highest accuracy of 77.6% in younger children (5–11 years).
• The multimodal approach improved classification performance in all age groups.
• Feature-importance analyses revealed different informative brain regions for children and adolescents.
• Several diffusion-derived features correlated significantly with social responsiveness scores.

Clinical Implications

The study presents multimodal neuroimaging and machine learning as tools for identifying autism-related brain features.

Conclusion

The findings support the use of multimodal neuroimaging in identifying autism-related brain features, emphasizing the importance of age-specific analyses.

Related Resources & Content

1. DIGITAL HEALTH, SAGE Journals, 2026 -- From brain scans to classifiers: A systematic review of ML-based autism diagnostic frameworks
2. Frontiers in Pediatrics, 2026 -- Research on the severity of symptoms in children with ASD based on integrated machine learning and structural equation modeling: age-specific predictive features and mediation effect path analysis
3. Frontiers in Psychiatry, 2026 -- A naturalistic, non-invasive method for capturing biometric data during autism evaluations
4. Clinical Screening for Autism Spectrum Disorder | Autism Spectrum Disorder (ASD) | CDC
5. Frontiers in Psychiatry — Multimodal non-invasive approaches for early Alzheimer’s disease detection: a review of neuroelectrophysiological and neuroimaging techniques
6. Human Brain Mapping | Neuroimaging Journal | Wiley Online Library
7. Functional near-infrared spectroscopy-based machine learning techniques for autism spectrum disorder diagnosis: a systematic review and meta-analysis - PubMed
8. Clinical Screening for Autism Spectrum Disorder | Autism Spectrum Disorder (ASD) | CDC