Classification of First-Episode Schizophrenia, Chronic Schizophrenia and Healthy Control Based on Brain Network of Mismatch Negativity by Graph Neural Network

Chang, Qi; Li, Cancheng; Tian, Qing; Bo, Qijing; Zhang, Jicong; Xiong, Yongqian; Wang, Chuanyue

doi:10.1109/tnsre.2021.3105669

Cited by 31 publications

(26 citation statements)

References 73 publications

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“…For example, Chang et al found that, compared to support vector machine (SVM), a GNN model of electroencephalography-based brain networks showed better performance in distinguishing among first-episode, chronic schizophrenia patients and controls. 28 Likewise, by using GNN combined with functional connectome data, Oh et al achieved a classification accuracy of 83.13%, which outperformed alternative machine-learning methods. 29 …”

Section: Introductionmentioning

confidence: 99%

Graph Convolutional Networks Reveal Network-Level Functional Dysconnectivity in Schizophrenia

Lei

Donohoe

Lui

et al. 2022

Schizophrenia Bulletin

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Background and Hypothesis Schizophrenia is increasingly understood as a disorder of brain dysconnectivity. Recently, graph-based approaches such as graph convolutional network (GCN) have been leveraged to explore complex pairwise similarities in imaging features among brain regions, which can reveal abstract and complex relationships within brain networks. Study Design We used GCN to investigate topological abnormalities of functional brain networks in schizophrenia. Resting-state functional magnetic resonance imaging data were acquired from 505 individuals with schizophrenia and 907 controls across 6 sites. Whole-brain functional connectivity matrix was extracted for each individual. We examined the performance of GCN relative to support vector machine (SVM), extracted the most salient regions contributing to both classification models, investigated the topological profiles of identified salient regions, and explored correlation between nodal topological properties of each salient region and severity of symptom. Study Results GCN enabled nominally higher classification accuracy (85.8%) compared with SVM (80.9%). Based on the saliency map, the most discriminative brain regions were located in a distributed network including striatal areas (ie, putamen, pallidum, and caudate) and the amygdala. Significant differences in the nodal efficiency of bilateral putamen and pallidum between patients and controls and its correlations with negative symptoms were detected in post hoc analysis. Conclusions The present study demonstrates that GCN allows classification of schizophrenia at the individual level with high accuracy, indicating a promising direction for detection of individual patients with schizophrenia. Functional topological deficits of striatal areas may represent a focal neural deficit of negative symptomatology in schizophrenia.

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Section: Introductionmentioning

confidence: 99%

Graph Convolutional Networks Reveal Network-Level Functional Dysconnectivity in Schizophrenia

Lei

Donohoe

Lui

et al. 2022

Schizophrenia Bulletin

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“…Şizofreni teşhisi, psikiyatristler tarafından kapsamlı bir şekilde yapılır. Ancak öznel bir bileşeni olan herhangi bir insan temelli karar olarak EEG ve makine öğrenimi yöntemleri hekimlere şizofreni teşhisinde yardımcı olmak için nesnel bir tamamlayıcı olarak görev sağlar [3,22].…”

Section: şIzofreniunclassified

“…Yapılan literatür taraması sonucunda MÖ kullanılarak EEG işareti işleme konuları 18 başlık altında toplanabilir. Bunlar: epilepsi [7,8], duygu durumu [9,10], beyin-bilgisayar arayüzleri [11,12], uyku evreleri [13,14], devinimsel görselleştirme [15,16], zihinsel iş yükü [17,18], Alzheimer [19,20], Sürücü dikkati [1,21], şizofreni [3,22], robotik [23,24], dikkat [2,25], Alkol [26,27], göz kırpmak [4,28], algı [6,29], niyet [30,31], nesnelerin interneti [32,33], kişi kimliği [5,34], beyin hastalığı [35].…”

Section: Introductionunclassified

EEG Sinyallerini İşlemek İçin Makine Öğreniminin Kullanıldığı Konular Üzerine Bir İnceleme

OMAR

Tepe

2022

Bayburt Üniversitesi Fen Bilimleri Dergisi

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The last decade has seen an increase in the use of artificial intelligence (AI) and machine learning (ML). Recent advances in the field of BC have led to renewed interest in the use of electroencephalography (EEG) for different fields. EEG is used in medical and biomedical applications such as analyzing mental workload and fatigue, diagnosing brain tumors and rehabilitation of central nervous system disorders; EEG-based motion analysis and classification is widely used in many areas from clinical applications to brain-machine interface and robotic applications. This article reviews the applications of many ML algorithms used in EEG signal processing, introduces commonly used algorithms, typical application scenarios, important advances and current problems. The study explored current applications of ML in EEG, including brain-computer interfaces, cognitive neuroscience, diagnosis of brain disorders, and more. First, the basic principles of ML algorithms used in EEG signal processing, including convolutional neural network, support vector machines, K-nearest neighbor and multidirectional convolutional neural network, are briefly explained. In addition, a general research on ML applications used in EEG analysis is presented. As a result, it was determined that the most SVM and CNN methods were used in the studies, and the study titles were mainly on epilepsy, BCI and Emotion, and the least on Alcohol, Sleeping States, Perception.

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“…Support vector machine (SVM), which is one of the most commonly used machine learning (ML) methods in pattern recognition ( Lei et al, 2020 ), has been widely utilized as a powerful computational approach to classify schizophrenic patients from healthy controls and predict outcomes based on neuroimaging data ( Wang et al, 2018 ; Chang et al, 2021 ). There are also very few studies that use SVM to classify and predict DS and NDS.…”

Section: Introductionmentioning

confidence: 99%

Using support vector machine to explore the difference of function connection between deficit and non-deficit schizophrenia based on gray matter volume

Zhu

Wang

et al. 2023

Front. Neurosci.

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ObjectiveSchizophrenia can be divided into deficient schizophrenia (DS) and non-deficient schizophrenia (NDS) according to the presence of primary and persistent negative symptoms. So far, there are few studies that have explored the differences in functional connectivity (FC) between the different subtypes based on the region of interest (ROI) from GMV (Gray matter volume), especially since the characteristics of brain networks are still unknown. This study aimed to investigate the alterations of functional connectivity between DS and NDS based on the ROI obtained by machine learning algorithms and differential GMV. Then, the relationships between the alterations and the clinical symptoms were analyzed. In addition, the thalamic functional connection imbalance in the two groups was further explored.MethodsA total of 16 DS, 31 NDS, and 38 health controls (HC) underwent resting-state fMRI scans, patient group will further be evaluated by clinical scales including the Brief Psychiatric Rating Scale (BPRS), the Scale for the Assessment of Negative Symptoms (SANS), and the Scale for the Assessment of Positive Symptoms (SAPS). Based on GMV image data, a support vector machine (SVM) is used to classify DS and NDS. Brain regions with high weight in the classification were used as seed points in whole-brain FC analysis and thalamic FC imbalance analysis. Finally, partial correlation analysis explored the relationships between altered FC and clinical scale in the two subtypes.ResultsThe relatively high classification accuracy is obtained based on the SVM. Compared to HC, the FC increased between the right inferior parietal lobule (IPL.R) bilateral thalamus, and lingual gyrus, and between the right inferior temporal gyrus (ITG.R) and the Salience Network (SN) in NDS. The FC between the right thalamus (THA.R) and Visual network (VN), between ITG.R and right superior occipital gyrus in the DS group was higher than that in HC. Furthermore, compared with NDS, the FC between the ITG.R and the left superior and middle frontal gyrus decreased in the DS group. The thalamic FC imbalance, which is characterized by frontotemporal-THA.R hypoconnectivity and sensory motor network (SMN)-THA.R hyperconnectivity was found in both subtypes. The FC value of THA.R and SMN was negatively correlated with the SANS score in the DS group but positively correlated with the SAPS score in the NDS group.ConclusionUsing an SVM classification method and based on an ROI from GMV, we highlighted the difference in functional connectivity between DS and NDS from the local to the brain network, which provides new information for exploring the neural physiopathology of the two subtypes of schizophrenic.

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Classification of First-Episode Schizophrenia, Chronic Schizophrenia and Healthy Control Based on Brain Network of Mismatch Negativity by Graph Neural Network

Cited by 31 publications

References 73 publications

Graph Convolutional Networks Reveal Network-Level Functional Dysconnectivity in Schizophrenia

Graph Convolutional Networks Reveal Network-Level Functional Dysconnectivity in Schizophrenia

EEG Sinyallerini İşlemek İçin Makine Öğreniminin Kullanıldığı Konular Üzerine Bir İnceleme

Using support vector machine to explore the difference of function connection between deficit and non-deficit schizophrenia based on gray matter volume

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