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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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Age-Specific Diagnostic Classification of ASD Using Deep Learning Approaches.

Vaibhav Jain¹, Sandeep Singh Sengar², Jac Fredo Agastinose Ronickom¹

¹Indian Institute of Technology (Banaras Hindu University), Varanasi, India.

Studies in Health Technology and Informatics

|October 23, 2023

Summary

This summary is machine-generated.

This study used deep learning to analyze brain functional connectivity in Autism Spectrum Disorder (ASD). Age-specific models showed better diagnostic accuracy for ASD, highlighting the importance of age in brain connectivity patterns.

Keywords:

Autism Spectrum Disorder Deep Learning Functional Connectivity fMRI

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Area of Science:

Neuroscience
Artificial Intelligence
Biomedical Engineering

Background:

Autism Spectrum Disorder (ASD) is highly heterogeneous, lacking universal biomarkers due to variations in etiology, genetics, and brain functional connectivity (FC).
Existing diagnostic approaches struggle with ASD's complexity, necessitating novel methods for accurate identification.

Purpose of the Study:

To investigate the role of age and multivariate patterns in brain FC for diagnosing ASD using deep learning.
To develop and evaluate age-specific deep learning models for discriminating between ASD and typically developing individuals.

Main Methods:

Utilized functional magnetic resonance imaging (fMRI) data from ABIDE-I and ABIDE-II databases across three age groups (6-11, 11-18, 6-18 years).
Extracted blood-oxygen-level dependent (BOLD) time series to compute 236x236 FC matrices using Pearson correlations.
Employed convolutional neural networks (MobileNetV2, DenseNet201) with FC heat maps as input for age-specific diagnostic models.

Main Results:

DenseNet201 demonstrated superior feature extraction and accuracy compared to MobileNetV2.
Age-specific models achieved the highest accuracy: 72.19% for 6-11 years, 71.88% for 11-18 years, and 69.74% for 6-18 years.
The 6-11 years age group dataset yielded the best diagnostic performance.

Conclusions:

Age-specific deep learning models can effectively address the heterogeneity in ASD.
Analyzing age-related patterns in brain functional connectivity improves diagnostic discrimination for Autism Spectrum Disorder.