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Related Concept Videos

Autism Spectrum Disorder01:19

Autism Spectrum Disorder

89
Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by persistent deficits in social communication and interaction alongside restrictive and repetitive behaviors or interests. ASD is sometimes accompanied by intellectual impairment.
These core symptoms manifest differently among individuals, ranging from mild to severe. The disorder's complexity extends beyond its clinical presentation, encompassing a diverse range of biological, cognitive, and sociocultural influences.
89

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Related Experiment Video

Updated: Jun 30, 2025

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
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Multipattern graph convolutional network-based autism spectrum disorder identification.

Wenhao Zhou1,2, Mingxiang Sun3, Xiaowen Xu4,5

  • 1College of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing 400074, China.

Cerebral Cortex (New York, N.Y. : 1991)
|March 18, 2024
PubMed
Summary
This summary is machine-generated.

Early autism spectrum disorder (ASD) diagnosis is improved using a novel multipattern graph convolution network (MPGCN) that analyzes functional brain networks from resting-state fMRI data, achieving high diagnostic accuracy.

Keywords:
autism spectrum disorderbrain connectivity networksgraph convolution networkmultipatternresting-state fMRI

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Area of Science:

  • Neuroimaging
  • Machine Learning
  • Developmental Neuroscience

Background:

  • Resting-state fMRI (rs-fMRI) aids early autism spectrum disorder (ASD) diagnosis by analyzing functional brain networks (FBNs).
  • Graph convolutional networks (GCNs) offer automated feature extraction from FBNs for ASD identification.
  • Prior GCN methods focused on single connection patterns, limiting diagnostic performance by not utilizing complementary information.

Purpose of the Study:

  • To introduce a multipattern graph convolution network (MPGCN) for enhanced ASD diagnosis.
  • To integrate multiple FBN connection patterns for improved diagnostic accuracy.
  • To overcome limitations of single-pattern GCNs in ASD identification.

Main Methods:

  • Developed and implemented a novel MPGCN model incorporating multiple graph convolution modules.
  • Integrated diverse FBN connection patterns within the GCN framework.
  • Utilized rs-fMRI data from 92 subjects from the Autism Brain Imaging Data Exchange (ABIDE) database.

Main Results:

  • The MPGCN model achieved a diagnostic accuracy of 91.1%.
  • The model obtained an area under the ROC curve (AUC) score of 0.9742.
  • Demonstrated superior performance compared to previous single-pattern GCN approaches.

Conclusions:

  • The proposed MPGCN effectively integrates multiple FBN connection patterns for accurate ASD diagnosis.
  • MPGCN represents a significant advancement in leveraging rs-fMRI data for early ASD identification.
  • The findings suggest MPGCN's potential for clinical application in diagnosing autism spectrum disorder.