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A Convolutional Neural Network and Graph Convolutional Network Based Framework for AD Classification.

Lan Lin1, Min Xiong1, Ge Zhang1

  • 1Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China.

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|February 28, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel framework combining CNNs and GCNs for early Alzheimer's disease (AD) detection. The research highlights how graph structure and input data significantly impact diagnostic accuracy for AD, mild cognitive impairment (MCI), and cognitive normal (CN) classifications.

Keywords:
Alzheimer’s diseasedeep learninggraph convolutional networksneuroimaging

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Medical Imaging Analysis

Background:

  • Convolutional Neural Networks (CNNs) are widely used for early Alzheimer's disease (AD) detection.
  • Population graphs, representing subject relationships, integrate diverse data but their impact on Graph Convolutional Networks (GCNs) for AD staging is understudied.
  • GCNs process non-Euclidean data, extracting topological information from population graphs for disease classification.

Purpose of the Study:

  • To investigate how GCN input properties, specifically edge-assigning functions, affect AD-staging performance.
  • To evaluate the impact of demographic and neuropsychological data on classification accuracy for AD, MCI, and CN.
  • To develop and validate a unified CNN-GCN framework for improved AD diagnosis.

Main Methods:

  • A novel framework integrating DenseNet (for feature extraction) and GCNs was developed.
  • Population graphs were constructed with nodes representing imaging features and edges weighted by imaging/non-imaging data combinations.
  • Three experiments systematically analyzed the effect of demographic information, neuropsychological tests, and edge assignment functions on classification.

Main Results:

  • The unified CNN-GCN framework achieved high accuracy: 91.6% (AD vs. CN), 91.2% (AD vs. MCI), 96.8% (MCI vs. CN), and 89.4% (multi-class).
  • Experiment 2, incorporating neuropsychological tests into the edge-assigning function, yielded the best multi-class classification results.
  • Both imaging features and edge-assigning functions significantly influenced the classification accuracy of the population graph.

Conclusions:

  • The proposed integrated CNN-GCN framework demonstrates superior performance in early AD detection and staging.
  • Careful selection of imaging features and edge-assigning functions is crucial for optimizing GCN performance in neurodegenerative disease classification.
  • This approach offers a promising avenue for leveraging complex population graph structures in clinical neuroscience.