Using graph convolutional network to characterize individuals with major depressive disorder across multiple imaging sites

Qin, Kun; Lei, Du; Pinaya, Walter Hugo Lopez; Pan, Nanfang; Li, Wenbin; Zhu, Ziyu; Sweeney, John A.; Mechelli, Andrea; Gong, Qiyong

doi:10.1016/j.ebiom.2022.103977

Cited by 44 publications

(22 citation statements)

References 51 publications

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“…Some limitations should be taken into account when evaluating the present findings. We were not able to reach the classification performance in the case of MDD with HC categorization when compared with the previous approach (36). However, our methods achieve higher performance in subgroup classification.…”

Section: Limitationscontrasting

confidence: 72%

“…The link between nodes are represented by a weighted adjacency matrix (A). Each node is linked to its nearest neighbors using a knearest neighbors (KNN) technique to establish edges (36). In order…”

Section: Methodsmentioning

confidence: 99%

“…There is a 77.78% accuracy rate when upsampling FEDN against REC and a 71.96% rate when downsampling. When we compared the existing approach (36) to the subgroup analysis, we found that the classification performance…”

Section: Fedn Vs Rec Classificationmentioning

confidence: 99%

“…Further a spatio-temporal GCN framework was created to train discriminative features from FC measures for the automated identification and treatment response prediction of MDD ( 35 ). The GCN model was developed to every participant's whole-brain functional network in order to differentiate MDD patients from HCs, recognize the most important regions making a contribution to classifying, and investigate the association between structural features of salient regions and clinical features ( 36 ).…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Limitationscontrasting

confidence: 72%

Section: Methodsmentioning

confidence: 99%

Section: Fedn Vs Rec Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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“…However, there is a dramatic divergence in classification accuracies because of the demographic and clinical heterogeneity across MDD studies. Qin et al (2022) noted that while there has been an increasing number of publications, the results are inconsistent with their reported classification accuracies varied from 61.7 to 98.4%. In addition, the optimization of machine learning models typically requires adequate training data to mount generalizability across different samples.…”

Section: Introductionmentioning

confidence: 90%

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

Zhu

Yu²,

He³

2023

Front. Hum. Neurosci.

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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.

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Graph Convolutional Network with Morphometric Similarity Networks for Schizophrenia Classification

Park,

Kim,

Lee

2023

Lecture Notes in Computer Science

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Using graph convolutional network to characterize individuals with major depressive disorder across multiple imaging sites

Cited by 44 publications

References 51 publications

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

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

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

Graph Convolutional Network with Morphometric Similarity Networks for Schizophrenia Classification

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