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Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging
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A Model for Diagnosing Autism Patients Using Spatial and Statistical Measures Using rs-fMRI and sMRI by Adopting

Kiruthigha Manikantan1, Suresh Jaganathan1

  • 1Department of Computer Science and Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai 603110, India.

Diagnostics (Basel, Switzerland)
|March 29, 2023
PubMed
Summary

This study introduces a novel autism diagnosis model using graphical neural networks. By integrating structural brain imaging and functional connectivity data, the model achieves improved diagnostic accuracy for autism spectrum disorder.

Keywords:
autism spectrum disorderdeep learninggraph convolution networksrs-fMRIsMRI

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Medical Imaging Analysis

Background:

  • Autism spectrum disorder (ASD) diagnosis relies on behavioral assessments, with neuroimaging offering complementary insights.
  • Current neuroimaging approaches for ASD often analyze structural MRI (sMRI) or resting-state fMRI (rs-fMRI) data independently.
  • Integrating multimodal neuroimaging data can potentially enhance diagnostic accuracy for ASD.

Purpose of the Study:

  • To propose a novel diagnostic model for autism spectrum disorder (ASD) utilizing graphical neural networks (GNNs).
  • To leverage both structural (sMRI) and functional (rs-fMRI) neuroimaging data within a unified GNN framework.
  • To improve the accuracy of ASD classification by incorporating brain structural similarities.

Main Methods:

  • A GNN model was developed where subjects are nodes and radiomic features from sMRI serve as edges.
  • Radiomic features, including first-order and texture features, were extracted from sMRI to define graph edges.
  • Spatial-temporal data from rs-fMRI were processed using 3D Convolutional Neural Networks (3DCNN) to represent node features.

Main Results:

  • The proposed GNN model demonstrated improved classification accuracy for ASD compared to methods using single imaging modalities.
  • Integrating sMRI-derived radiomic features (edges) and rs-fMRI-derived brain summaries (nodes) enhanced diagnostic performance.
  • The model's reliance on structural brain similarities outperformed approaches using phenotypic data or graph kernel functions.

Conclusions:

  • Graphical neural networks offer a powerful framework for integrating multimodal neuroimaging data in ASD diagnosis.
  • Combining structural and functional brain imaging features through GNNs significantly boosts diagnostic accuracy for autism.
  • This approach highlights the potential of GNNs in advancing neuroimaging-based diagnostics for neurological disorders.