Analysis of Functional Brain Network in MDD Based on Improved Empirical Mode Decomposition With Resting State EEG Data

Shao, Xuexiao; Sun, Shuting; Li, Jianxiu; Kong, Wenwen; Zhu, Jing; Li, Xiaowei; Hu, Bin

doi:10.1109/tnsre.2021.3092140

Cited by 33 publications

(17 citation statements)

References 44 publications

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“…Although the Ho-BFN proposed in this study can be helpful in the diagnosis of patients with MDD, several limitations could be discussed. As in many previous studies ( Shao et al, 2021 ; Zhu et al, 2021 ), the first limitation relates to the small sample size. In the future, further validation of the reliability of our method with larger sample sizes (e.g., using other publicly available datasets) should be considered to avoid possible overfitting issues.…”

Section: Discussionmentioning

confidence: 99%

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

Zhao

Gao²,

Zhi³

et al. 2022

Front. Comput. Neurosci.

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Brain function networks (BFN) are widely used in the diagnosis of electroencephalography (EEG)-based major depressive disorder (MDD). Typically, a BFN is constructed by calculating the functional connectivity (FC) between each pair of channels. However, it ignores high-order relationships (e.g., relationships among multiple channels), making it a low-order network. To address this issue, a novel classification framework, based on matrix variate normal distribution (MVND), is proposed in this study. The framework can simultaneously generate high-and low-order BFN and has a distinct mathematical interpretation. Specifically, the entire time series is first divided into multiple epochs. For each epoch, a BFN is constructed by calculating the phase lag index (PLI) between different EEG channels. The BFNs are then used as samples, maximizing the likelihood of MVND to simultaneously estimate its low-order BFN (Lo-BFN) and high-order BFN (Ho-BFN). In addition, to solve the problem of the excessively high dimensionality of Ho-BFN, Kronecker product decomposition is used for dimensionality reduction while retaining the original high-order information. The experimental results verified the effectiveness of Ho-BFN for MDD diagnosis in 24 patients and 24 normal controls. We further investigated the selected discriminative Lo-BFN and Ho-BFN features and revealed that those extracted from different networks can provide complementary information, which is beneficial for MDD diagnosis.

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Section: Discussionmentioning

confidence: 99%

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

Zhao

Gao²,

Zhi³

et al. 2022

Front. Comput. Neurosci.

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“…In recent years, research on MDD has focused on brain structure and function using morphological or neurobiological features. Functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG), electroencephalography (EEG), and positron emission tomography (PET) are the common physiological methods employed in comparing people with MDD to healthy controls (HCs) (5). Researchers have found that patients with MDD have abnormal communication among the functional brain networks using functional connectivity (FC) of resting-state fMRI (R-fMRI), which detects synchronized and desynchronized spontaneous activity within anatomically diverse networks (6)(7)(8).…”

Section: Introductionmentioning

confidence: 99%

Ensemble graph neural network model for classification of major depressive disorder using whole-brain functional connectivity

et al. 2023

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Major depressive disorder (MDD) is characterized by impairments in mood and cognitive functioning, and it is a prominent source of global disability and stress. A functional magnetic resonance imaging (fMRI) can aid clinicians in their assessments of individuals for the identification of MDD. Herein, we employ a deep learning approach to the issue of MDD classification. Resting-state fMRI data from 821 individuals with MDD and 765 healthy controls (HCs) is employed for investigation. An ensemble model based on graph neural network (GNN) has been created with the goal of identifying patients with MDD among HCs as well as differentiation between first-episode and recurrent MDDs. The graph convolutional network (GCN), graph attention network (GAT), and GraphSAGE models serve as a base models for the ensemble model that was developed with individual whole-brain functional networks. The ensemble's performance is evaluated using upsampling and downsampling, along with 10-fold cross-validation. The ensemble model achieved an upsampling accuracy of 71.18% and a downsampling accuracy of 70.24% for MDD and HC classification. While comparing first-episode patients with recurrent patients, the upsampling accuracy is 77.78% and the downsampling accuracy is 71.96%. According to the findings of this study, the proposed GNN-based ensemble model achieves a higher level of accuracy and suggests that our model produces can assist healthcare professionals in identifying MDD.

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“…Li et al [ 20 ] extracted linear and nonlinear EEG features, selected features closely related to depression from different frequency bands using five feature selection algorithms, and then realized effective depression detection. Literature [ 21 – 23 ] has successively realized MDD detection based on BFN from different aspects such as graph theory analysis, minimum spanning tree, and improved empirical mode decomposition.…”

Section: Introductionmentioning

confidence: 99%

Spatial–Temporal EEG Fusion Based on Neural Network for Major Depressive Disorder Detection

Zhang

Wei

Yan

et al. 2023

Interdiscip Sci Comput Life Sci

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In view of the major depressive disorder characteristics such as high mortality as well as high recurrence, it is important to explore an objective and effective detection method for major depressive disorder. Considering the advantages complementary of different machine learning algorithms in information mining process, as well as the fusion complementary of different information, in this study, the spatial–temporal electroencephalography fusion framework using neural network is proposed for major depressive disorder detection. Since electroencephalography is a typical time series signal, we introduce recurrent neural network embedded in long short-term memory unit for extract temporal domain features to solve the problem of long-distance information dependence. To reduce the volume conductor effect, the temporal electroencephalography data are mapping into a spatial brain functional network using phase lag index, then the spatial domain features were extracted from brain functional network using 2D convolutional neural networks. Considering the complementarity between different types of features, the spatial–temporal electroencephalography features are fused to achieve data diversity. The experimental results show that spatial–temporal features fusion can improve the detection accuracy of major depressive disorder with a highest of 96.33%. In addition, our research also found that theta, alpha, and full frequency band in brain regions of left frontal, left central, right temporal are closely related to MDD detection, especially theta frequency band in left frontal region. Graphical Abstract Only using single-dimension EEG data as decision basis, it is difficult to fully explore the valuable information hidden in the data, which affects the overall detection performance of MDD. Meanwhile, different algorithms have their own advantages for different application scenarios. Ideally, different algorithms should use their respective advantages to jointly address complex problems in engineering fields. To this end, we propose a computer-aided MDD detection framework based on spatial–temporal EEG fusion using neural network, as shown in Fig. 1 . The simplified process is as follows: (1) Raw EEG data acquisition and preprocessing. (2) The time series EEG data of each channel are input as recurrent neural network (RNN), and RNN is used to process and extract temporal domain (TD) features. (3) The BFN among different EEG channels is constructed, and CNN is used to process and extract the spatial domain (SD) features of the BFN. (4) Based on the theory of information complementarity, the spatial–temporal information is fused to realize efficient MDD detection. Fig. 1 MDD detection framework based on spatial–temporal EEG fusion

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Analysis of Functional Brain Network in MDD Based on Improved Empirical Mode Decomposition With Resting State EEG Data

Cited by 33 publications

References 44 publications

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

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

Ensemble graph neural network model for classification of major depressive disorder using whole-brain functional connectivity

Spatial–Temporal EEG Fusion Based on Neural Network for Major Depressive Disorder Detection

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