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Identifying depression disorder using multi-view high-order brain function network derived from

Feng Zhao1, Tianyu Gao1, Zhi Cao1

  • 1School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China.

Frontiers in Computational Neuroscience
|November 17, 2022
PubMed
Summary

This study introduces a new framework using matrix variate normal distribution to analyze brain function networks (BFN) from electroencephalography (EEG) data for major depressive disorder (MDD) diagnosis. The method effectively identifies both low- and high-order brain network features for improved diagnostic accuracy.

Keywords:
EEGbrain function networkshigh-order brain function networksmajor depression disordermatrix variate normal distribution

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Area of Science:

  • Neuroscience
  • Computational Psychiatry
  • Medical Imaging Analysis

Background:

  • Brain function networks (BFN) derived from electroencephalography (EEG) are crucial for diagnosing major depressive disorder (MDD).
  • Traditional BFN analysis often relies on low-order functional connectivity (FC), potentially overlooking complex, high-order relationships within brain activity.

Purpose of the Study:

  • To develop a novel classification framework capable of simultaneously generating both low-order BFN (Lo-BFN) and high-order BFN (Ho-BFN).
  • To enhance the diagnostic accuracy of MDD by leveraging both low- and high-order network features.

Main Methods:

  • A novel framework based on matrix variate normal distribution (MVND) was proposed to construct BFNs.
  • EEG data were divided into epochs, and phase lag index (PLI) was used to calculate FC for BFN construction.
  • MVND likelihood maximization was employed to estimate Lo-BFN and Ho-BFN, with Kronecker product decomposition used for Ho-BFN dimensionality reduction.

Main Results:

  • The proposed framework successfully generated both Lo-BFN and Ho-BFN.
  • Experimental results demonstrated the effectiveness of Ho-BFN in diagnosing MDD, with 24 patients and 24 controls.
  • Selected discriminative Lo-BFN and Ho-BFN features provided complementary information, enhancing MDD diagnosis.

Conclusions:

  • The MVND-based framework offers a distinct mathematical interpretation for simultaneously generating high- and low-order BFNs.
  • High-order BFN features are effective for MDD diagnosis, complementing low-order network information.
  • This approach advances EEG-based analysis for neurological and psychiatric disorders.