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Autism Spectrum Disorder

Autism Spectrum Disorder

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.

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Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

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The Dynamical Biomarkers in Functional Connectivity of Autism Spectrum Disorder Based on Dynamic Graph Embedding.

Yanting Liu¹, Hao Wang¹, Yanrui Ding²

¹School of Science, Jiangnan University, Wuxi, 214122, China.

Interdisciplinary Sciences, Computational Life Sciences

|December 7, 2023

Summary

Researchers developed new models to diagnose Autism Spectrum Disorder (ASD) using dynamic brain networks (DBNs). These models identify altered brain connectivity patterns, offering potential biomarkers for early ASD identification.

Keywords:

ASD Biomarkers identification Dynamic brain network Graph embedding Rs-fMRI

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Area of Science:

Neuroscience
Developmental Disorders
Biomarker Discovery

Background:

Autism Spectrum Disorder (ASD) diagnosis is challenging due to its complex neurological and developmental nature.
Dynamic Brain Networks (DBNs) provide rich spatio-temporal information crucial for understanding brain function in ASD.
Identifying dynamic communication patterns across brain regions is key to discovering ASD diagnostic biomarkers.

Purpose of the Study:

To propose novel diagnostic models for ASD leveraging spatio-temporal characteristics of DBNs.
To investigate dynamic graph embedding for representing interactive information among brain regions.
To identify potential DBN-based biomarkers for ASD identification.

Main Methods:

Development of two diagnostic models: dgEmbed-KNN and Aggregation-SVM.
Utilizing spatio-temporal information from DBNs and dynamic graph embedding.
Analyzing aggregation brain network connections as features for classification.

Main Results:

The dgEmbed-KNN model showed slightly superior classification accuracy compared to traditional and deep learning methods.
The Aggregation-SVM model demonstrated strong capacity in diagnosing ASD using aggregated brain network connections.
Identified over- and under-connections in specific brain regions (postcentral gyrus, insula, cerebellum, caudate nucleus, temporal pole) and abnormal dynamic interactions within/between functional subnetworks (DMN, visual, auditory, saliency networks) in ASD.

Conclusions:

DBNs offer valuable insights for ASD diagnosis and treatment.
The proposed dgEmbed-KNN and Aggregation-SVM models effectively utilize DBN features for ASD identification.
Discovered dynamic connectivity alterations provide potential DBN biomarkers for ASD.