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This chapter reviews deep learning models previously used to identify neuro-biomarkers related to major depressive disorder in both adults and children, detailing the outcomes of prior research and current advances in this field. In addition, this chapter explores the use of convolutional neural networks to classify and detect neuro-biomarkers associated with major depressive disorder compared to age-matched healthy individuals. Specifically, convolutional neural networks, utilizing the visual geometry group (VGG16) and DeprNet models, were applied to analyze resting-state, eyes-closed electroencephalography (EEG) data. The EEG data undergoes thorough pre-processing, and deep learning modules are employed to facilitate the understanding and analysis of the data. These techniques aim to extract neuro-biomarkers of interest within each frequency band and across various regions of interest, striving to develop a robust and generic method for interpreting the model’s “black box.” The approach used in this research incorporates several robust scoring methods, addresses data imbalance, manages the limited data availability, and optimizes the hyperparameters of these deep learning models.

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Deep Learning Analysis Approach for Major Depressive Disorder in Children and Adolescents

  • 章节

  • 发表日期：

    2025年

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  • Amir Jahanian Najafabadi,
  • Khaled Bagh

摘要

This chapter reviews deep learning models previously used to identify neuro-biomarkers related to major depressive disorder in both adults and children, detailing the outcomes of prior research and current advances in this field. In addition, this chapter explores the use of convolutional neural networks to classify and detect neuro-biomarkers associated with major depressive disorder compared to age-matched healthy individuals. Specifically, convolutional neural networks, utilizing the visual geometry group (VGG16) and DeprNet models, were applied to analyze resting-state, eyes-closed electroencephalography (EEG) data. The EEG data undergoes thorough pre-processing, and deep learning modules are employed to facilitate the understanding and analysis of the data. These techniques aim to extract neuro-biomarkers of interest within each frequency band and across various regions of interest, striving to develop a robust and generic method for interpreting the model’s “black box.” The approach used in this research incorporates several robust scoring methods, addresses data imbalance, manages the limited data availability, and optimizes the hyperparameters of these deep learning models.