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Related Experiment Video

Updated: Jan 22, 2026

Testing Sensory and Multisensory Function in Children with Autism Spectrum Disorder

Testing Sensory and Multisensory Function in Children with Autism Spectrum Disorder

Published on: April 22, 2015

Characterizing and Predicting Autism Spectrum Disorder by Performing Resting-State Functional Network Community

Yuqing Song¹, Thomas Martial Epalle¹, Hu Lu¹

¹School of Computer Science and Telecommunication Engineering, Jiangsu University, Zhenjiang, China.

Frontiers in Human Neuroscience

|July 2, 2019

Summary

Autism spectrum disorder (ASD) shows disturbed brain network modularity. Novel analysis of these network patterns can predict ASD with high accuracy, supporting the dysconnectivity theory.

Keywords:

autism spectrum disorder community detection linear discriminant analysis machine learning resting-state connectivity analysis

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Published on: April 12, 2018

Area of Science:

Neuroscience
Network Science
Biomarker Discovery

Background:

Autism spectrum disorder (ASD) is increasingly viewed as a neuropsychological disconnection syndrome.
Complex network and graph theories offer tools to analyze brain network alterations in neurological conditions.
The predictive potential of modular organization patterns in resting-state functional magnetic resonance imaging (rs-fMRI) for brain pathology remains unexplored.

Purpose of the Study:

To introduce a novel analysis technique for identifying community pattern alterations in functional brain networks associated with ASD.
To develop machine learning classifiers utilizing community pattern quality metrics for predicting ASD clinical classification.

Main Methods:

Analysis of resting-state functional magnetic resonance imaging (rs-fMRI) data from 235 subjects across six public datasets.
Application of community detection algorithms to assess modular organization in functional brain networks.
Development and evaluation of machine learning classifiers using five community pattern quality metrics as predictive features.

Main Results:

Significant disturbances in the modular structure of functional brain networks were observed in individuals with ASD compared to controls.
Machine learning models achieved high predictive accuracy, with peak performance around 85.16% for in-site data and 75.00% for multisite data.
The identified community pattern quality metrics demonstrated significant predictive power for classifying ASD.

Conclusions:

The modular organization of functional brain networks is significantly altered in autism spectrum disorder.
Novel network analysis metrics derived from community patterns show promise as biomarkers for ASD.
These findings support the dysconnectivity theory of autism spectrum disorder and highlight the potential of machine learning for diagnostic applications.