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Deep Canonical Correlation Fusion Algorithm Based on Denoising Autoencoder for ASD Diagnosis and Pathogenic Brain

Huilian Zhang1,2, Jie Chen1,2, Bo Liao1,2

  • 1Key Laboratory of Data Science and Intelligence Education, Ministry of Education, Hainan Normal University, Haikou, 571126, China.

Interdisciplinary Sciences, Computational Life Sciences
|April 4, 2024
PubMed
Summary

A new Deep Canonical Correlation Fusion algorithm based on Denoising Autoencoder (DCCF-DAE) effectively diagnoses Autism Spectrum Disorder (ASD). This advanced framework integrates structural and functional brain imaging data for improved early detection and monitoring.

Keywords:
Autism spectrum disorderAutoencoderDeep canonical correlation fusionRs-fMRIsMRI

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Machine Learning

Background:

  • Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by atypical neural activity.
  • Early intervention is critical for managing ASD progression.
  • Current diagnostic approaches often utilize structural (sMRI) and resting-state functional MRI (rs-fMRI), but autoencoder applications remain underexplored.

Purpose of the Study:

  • To introduce and evaluate a novel framework, Deep Canonical Correlation Fusion algorithm based on Denoising Autoencoder (DCCF-DAE), for Autism Spectrum Disorder classification.
  • To explore the efficacy of autoencoders in disease classification using high-dimensional neuroimaging data.
  • To integrate functional and structural MRI data for enhanced ASD diagnosis and identification of critical brain regions.

Main Methods:

  • Development of the DCCF-DAE framework utilizing advanced autoencoders for efficient feature extraction from multimodal neuroimaging data.
  • Application of the Deep Canonical Correlation Fusion (DCCF) model to integrate extracted features.
  • Utilizing fused features for Autism Spectrum Disorder classification and identification of Regions of Interest (ROIs).

Main Results:

  • The DCCF-DAE framework demonstrated effective handling of high-dimensional data.
  • Integration of functional and structural data through DCCF improved ASD diagnostic accuracy.
  • The proposed framework achieved outstanding performance in ASD diagnosis when compared against other methods using the ABIDE database.

Conclusions:

  • The DCCF-DAE framework shows significant potential as a tool for accurate early diagnosis of Autism Spectrum Disorder.
  • The method effectively integrates multimodal neuroimaging data, offering insights into ASD mechanisms.
  • This approach highlights the value of advanced autoencoders and fusion techniques in neurodevelopmental disorder research.