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Shared and Unshared Feature Extraction in Major Depression During Music Listening Using Constrained Tensor

Xiulin Wang1,2, Wenya Liu2,3, Xiaoyu Wang2

  • 1Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China.

Frontiers in Human Neuroscience
|January 3, 2022
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Summary

This study analyzed electroencephalography (EEG) signals from major depression disorder (MDD) patients and healthy controls listening to music. Findings reveal shared brain dynamics but also unique oscillatory network changes in MDD patients during music perception.

Keywords:
CANDECOMP/PARAFACEEGconstrained tensor factorizationmajor depressive disordernaturalistic music stimuli

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Area of Science:

  • Neuroscience
  • Data Science
  • Biomedical Engineering

Background:

  • Electroencephalography (EEG) signals are complex mixtures of brain activity and noise.
  • Analyzing group-level EEG data with common and individual dynamics presents significant challenges.
  • Understanding brain responses to music in major depression disorder (MDD) is crucial for diagnosis and treatment.

Purpose of the Study:

  • To develop a data-driven framework for extracting shared and unshared features from EEG signals.
  • To investigate group-level brain activity differences and similarities between MDD patients and healthy controls (HC) during music listening.
  • To identify spatio-temporal-spectral patterns associated with music perception in different groups.

Main Methods:

  • Proposed a nonnegative and coupled tensor factorization framework for feature extraction.
  • Applied constrained tensor factorization to preserve data structure and identify common/individual components.
  • Integrated music information retrieval, correlation analysis, and hierarchical clustering for comprehensive analysis.

Main Results:

  • Successfully extracted two shared spatio-temporal-spectral feature patterns between MDD and HC groups.
  • Identified three distinct individual feature patterns within the MDD and HC groups.
  • Demonstrated similar overall brain dynamics in both groups during music listening, with notable changes in MDD oscillatory networks.

Conclusions:

  • MDD patients exhibit similar general brain responses to music as HC, but with altered oscillatory network characteristics.
  • These identified brain dynamics and network changes may serve as potential biomarkers for MDD diagnosis.
  • The findings offer insights into music perception in MDD and potential avenues for therapeutic interventions.