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Schizophrenia Identification Using Multi-View Graph Measures of Functional Brain Networks.

Yizhen Xiang1, Jianxin Wang1,2, Guanxin Tan1

  • 1School of Computer Science and Engineering, Central South University, Changsha, China.

Frontiers in Bioengineering and Biotechnology
|February 4, 2020
PubMed
Summary

This study introduces an improved method for identifying schizophrenia (SZ) using multi-view graph measures of functional brain networks, achieving high diagnostic accuracy. The novel approach enhances schizophrenia identification compared to existing techniques.

Keywords:
SVMSchizophrenia identificationfMRIfunctional brain networksmulti-view graph measures

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Area of Science:

  • Neuroscience
  • Psychiatry
  • Medical Imaging

Background:

  • Schizophrenia (SZ) significantly impacts social functioning, necessitating accurate diagnostic methods.
  • Resting-state functional magnetic resonance imaging (rs-fMRI) and brain network analysis show promise for SZ identification.
  • Existing brain network analysis methods have limitations in effectively identifying schizophrenia.

Purpose of the Study:

  • To propose an improved method for schizophrenia identification using multi-view graph measures of functional brain networks.
  • To enhance the accuracy and effectiveness of schizophrenia diagnosis through advanced feature selection and classification techniques.

Main Methods:

  • Construction of individual functional connectivity networks using the Brainnetome atlas.
  • Calculation of multi-view graph measures for feature representation.
  • Application of Sparse Group Lasso for feature selection, considering regional relationships.
  • Classification using a Support Vector Machine (SVM) classifier.
  • Validation using a leave-one-out cross-validation (LOOCV) scheme on 145 subjects.

Main Results:

  • The proposed method achieved a diagnostic accuracy of 93.10% for schizophrenia identification.
  • The multi-view graph measure approach demonstrated superior effectiveness compared to existing methods.
  • Feature selection using Sparse Group Lasso effectively identified discriminative features based on regional grouping.

Conclusions:

  • The developed method offers a more effective approach for schizophrenia identification.
  • Multi-view graph measures combined with advanced feature selection significantly improve diagnostic accuracy.
  • This technique holds potential for clinical application in schizophrenia diagnosis.