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MAMF-GCN: Multi-scale adaptive multi-channel fusion deep graph convolutional network for predicting mental disorder.

Jiacheng Pan1, Haocai Lin1, Yihong Dong1

  • 1Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, China.

Computers in Biology and Medicine
|July 25, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel deep graph convolutional network (GCN) for objective mental disorder diagnosis using fMRI data. The proposed method enhances diagnostic accuracy by effectively integrating multi-modal and multi-scale brain imaging features.

Keywords:
Brain neuroscienceDeep learningDisease predictionGraph neural networkSemi-supervised classification

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Current mental disorder diagnoses lack objectivity, relying on subjective symptoms and scales.
  • Functional magnetic resonance imaging (fMRI) offers potential for objective diagnosis but presents challenges in data integration and analysis.
  • Graph neural networks (GNNs) show promise for processing complex relational fMRI data, yet deep integration of multi-modal and multi-scale features remains a challenge.

Purpose of the Study:

  • To develop an objective diagnostic method for mental disorders using fMRI data.
  • To address the limitations of existing Graph Convolutional Network (GCN) methods in integrating multi-modal and multi-scale features.
  • To improve the depth of feature learning and overcome the constraints of single-atlas approaches in GCN models.

Main Methods:

  • Proposed a multi-scale adaptive multi-channel fusion deep graph convolutional network (MAMF-GCN) with an attention mechanism.
  • Utilized an encoder to combine imaging and non-imaging data, generating similarity weights between subjects.
  • Employed multi-channel processing across different brain atlases to extract multi-scale imaging features and fused them using adaptive convolution within a deep GCN.

Main Results:

  • The MAMF-GCN method demonstrated superior node classification performance compared to state-of-the-art methods on the Autism Brain Imaging Data Exchange (ABIDE) and Major Depressive Disorder (MDD) datasets.
  • Achieved performance improvements of 3.37%-39.83% for MDD and 12.59%-32.92% for ABIDE dataset disease prediction tasks.
  • Showcased effective performance in real-life clinical diagnosis scenarios, validating its practical utility.

Conclusions:

  • The MAMF-GCN effectively extracts and fuses multi-scale embeddings from imaging features and phenotypic information using an attention mechanism.
  • The proposed method significantly enhances classification performance for brain disorder diagnosis.
  • This approach offers a robust and objective diagnostic tool for mental disorders by leveraging advanced GNN techniques on fMRI data.