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An enhanced multi-modal brain graph network for classifying neuropsychiatric disorders.

Liangliang Liu1, Yu-Ping Wang2, Yi Wang1

  • 1College of Information and Management Science, Henan Agricultural University, Zhengzhou, Henan 450046, P.R. China.

Medical Image Analysis
|July 25, 2022
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Summary

This study introduces an enhanced multi-modal graph convolutional network (MME-GCN) for classifying neuropsychiatric disorders (NDs). The novel method effectively fuses structural and functional brain imaging data, achieving high classification accuracy.

Keywords:
Functional graphGraph convolutional networkMulti-modalNeuropsychiatric disorderStructural graph

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Neuropsychiatric disorders (NDs) are linked to alterations in brain structure and function.
  • Structural MRI (sMRI) and functional MRI (fMRI) offer complementary data for ND analysis.
  • Integrating sMRI and fMRI data for comprehensive analysis presents significant challenges.

Purpose of the Study:

  • To develop an enhanced multi-modal graph convolutional network (MME-GCN) for classifying NDs.
  • To efficiently extract and fuse structural and functional brain data for improved diagnostic accuracy.
  • To identify key brain connections associated with NDs through data fusion.

Main Methods:

  • Constructed structural and functional brain graphs from sMRI and fMRI data using a common brain atlas.
  • Employed machine learning to extract salient features from the structural graph.
  • Adjusted functional graph edge weights based on extracted structural features.
  • Trained a multi-layer GCN for binary classification of ND patients versus healthy controls.

Main Results:

  • Achieved 93.71% classification accuracy on an open dataset from the Consortium for Neuropsychiatric Phenomics.
  • Identified and verified important features from structural brain graphs within the functional graph.
  • Discovered specific brain connections crucial for understanding NDs using the MME-GCN approach.

Conclusions:

  • The MME-GCN model demonstrates high efficacy in classifying NDs by integrating structural and functional neuroimaging data.
  • Feature extraction from structural MRI and its application to functional MRI analysis is a viable strategy for ND research.
  • The study highlights specific brain network alterations associated with NDs, paving the way for further investigation.