The classification of brain network for major depressive disorder patients based on deep graph convolutional neural network

Abstract

IntroductionThe early diagnosis of major depressive disorder (MDD) is very important for patients that suffer from severe and irreversible consequences of depression. It has been indicated that functional connectivity (FC) analysis based on functional magnetic resonance imaging (fMRI) data can provide valuable biomarkers for clinical diagnosis. However, previous studies mainly focus on brain disease classification in small sample sizes, which may lead to dramatic divergences in classification accuracy.MethodsThis paper attempts to address this limitation by applying the deep graph convolutional neural network (DGCNN) method on a large multi-site MDD dataset. The resting-state fMRI data are acquired from 830 MDD patients and 771 normal controls (NC) shared by the REST-meta-MDD consortium.ResultsThe DGCNN model trained with the binary network after thresholding, identified MDD patients from normal controls and achieved an accuracy of 72.1% with 10-fold cross-validation, which is 12.4%, 9.8%, and 7.6% higher than SVM, RF, and GCN, respectively. Moreover, the process of dataset reading and model training is faster. Therefore, it demonstrates the advantages of the DGCNN model with low time complexity and sound classification performance.DiscussionBased on a large, multi-site dataset from MDD patients, the results expressed that DGCNN is not an extremely accurate method for MDD diagnosis. However, there is an improvement over previous methods with our goal of better understanding brain function and ultimately providing a biomarker or diagnostic capability for MDD diagnosis.

Automatic Diagnosis of Major Depressive Disorder Using a High- and Low-Frequency Feature Fusion Framework

“…We compared current approaches for diagnosing MDD based on deep learning models. Zhu et al [25] proposed the only deep graph convolutional neural network (DGCNN) method for brain network classification between 830 MDD patients and 771 normal controls (NC), with a final accuracy of 72.1%. Venkatapathy et al [26] proposed an ensemble model for the classification between 821 patients with MDD and 765 HCs, and the final model achieved 71.18% accuracy in upsampling and 70.24% accuracy in downsampling.…”

DepressionGraph: A Two-Channel Graph Neural Network for the Diagnosis of Major Depressive Disorders Using rs-fMRI

“…The overall performance of DepressionGraph is compared with the existing studies (Figure 8). The comparison experiment focuses on the models using the same dataset as in this study, including Gu et al [38], Li et al [39], Jie et al [40], Guo et al [41], Yao et al [27], Zhang et al [42], and Zhu et al [43]. Figure 8 shows that DepressionGraph outperforms all the other studies in both the two performance measurements, Acc and F1.…”

“…Zhang et al proposed a multi-view graph neural network to detect MDD across ten sites and achieved Acc = 65.61% and F1 = 64.55%, which were worse than DepressionGraph [42]. Zhu et al evaluated their model across 16 sites, and DepressionGraph outperformed their model by 0.78% in Acc and 6.78% in F1 on the same site [43]. The multi-site investigations referenced in [42,43] reported F1 scores around 65%, whereas the DepressionGraph model attained a more elevated F1 score of 71.13%.…”

Classification Study of Alzheimer’s Disease Based on Self-Attention Mechanism and DTI Imaging Using GCN

“…More and more studies have been conducted in recent years on using GCN networks to classify mental diseases. Besides Alzheimer's disease, GCN has also applied to classify other diseases such as Parkinson's disease [37][38][39], autism [40][41][42], major depression [43][44][45], schizophrenia [46][47][48], attention deficit hyperactivity disorder [49][50], and bipolar disorder [51][52].…”