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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

A deep connectome learning network using graph convolution for connectome-disease association study.

Yanwu Yang¹, Chenfei Ye², Ting Ma³

¹Department of Electronic and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, China; Peng Cheng Laboratory, Shenzhen, China.

Neural Networks : the Official Journal of the International Neural Network Society

|May 6, 2023

Summary

This summary is machine-generated.

This study introduces a new deep learning method, the multivariate distance-based connectome network (MDCN), to better identify brain connectome differences in autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD). MDCN improves disease classification and interpretation by focusing on region-specific brain features.

Keywords:

Connectome-wide association study Deep connectome learning Distance-based connectome network Graph neural network

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Area of Science:

Neuroscience
Machine Learning
Brain Imaging Analysis

Background:

Multivariate analysis and deep learning (CNN, GNN) are used in connectome-wide association studies (CWAS).
Existing methods may overlook crucial region-specific brain features essential for differentiating disorders like autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD).

Purpose of the Study:

To propose a novel deep learning framework, the multivariate distance-based connectome network (MDCN), to address the limitations of existing CWAS methods.
To enhance the identification of individual differences and associations between brain connectome features and neurological disorders.
To incorporate an explainable artificial intelligence (XAI) method for improved interpretation of results.

Main Methods:

Developed a multivariate distance-based connectome network (MDCN) utilizing efficient parcellation-wise learning.
Integrated an explainable method, parcellation-wise gradient and class activation map (p-GradCAM), for pattern identification.
Validated the MDCN framework on two large, multicenter public datasets for classifying ASD and ADHD against healthy controls.

Main Results:

MDCN demonstrated superior performance in classifying ASD and ADHD compared to state-of-the-art methods.
The method effectively identified individual patterns and pinpointed connectome associations relevant to the diseases.
Results showed a high degree of overlap with established findings in the field, validating the approach.

Conclusions:

The proposed MDCN framework offers a powerful tool for connectome-wide association studies, bridging deep learning and CWAS.
MDCN enhances the understanding of brain disorders by effectively leveraging region-specific connectome features.
This approach provides new insights into the neurobiological underpinnings of ASD and ADHD and their individual variations.