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144 results on '"Xi, Yi Bin"'

18. The correlation between dynamic functional architecture and response to electroconvulsive therapy combined with antipsychotics in schizophrenia

Author

Gong, Jie, primary, Cui, Long‐Biao, additional, Zhao, Ying‐Song, additional, Liu, Zhao‐Wen, additional, Yang, Xue‐Juan, additional, Xi, Yi‐Bin, additional, Liu, Lin, additional, Liu, Peng, additional, Sun, Jin‐Bo, additional, Zhao, Shu‐Wan, additional, Liu, Xiao‐Fan, additional, Jia, Jie, additional, Li, Ping, additional, Yin, Hong, additional, and Qin, Wei, additional

Published
2022
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22. Neuroimaging-based brain-age prediction of first-episode schizophrenia and the alteration of brain age after early medication.

Author

Xi, Yi-Bin, Wu, Xu-Sha, Cui, Long-Biao, Bai, Li-Jun, Gan, Shuo-Qiu, Jia, Xiao-Yan, Li, Xuan, Xu, Yong-Qiang, Kang, Xiao-Wei, Guo, Fan, and Yin, Hong

Abstract

Background: Neuroimaging- and machine-learning-based brain-age prediction of schizophrenia is well established. However, the diagnostic significance and the effect of early medication on first-episode schizophrenia remains unclear. Aims: To explore whether predicted brain age can be used as a biomarker for schizophrenia diagnosis, and the relationship between clinical characteristics and brain-predicted age difference (PAD), and the effects of early medication on predicted brain age. Method: The predicted model was built on 523 diffusion tensor imaging magnetic resonance imaging scans from healthy controls. First, the brain-PAD of 60 patients with first-episode schizophrenia, 60 healthy controls and 21 follow-up patients from the principal data-set and 40 pairs of individuals in the replication data-set were calculated. Next, the brain-PAD between groups were compared and the correlations between brain-PAD and clinical measurements were analysed. Results: The patients showed a significant increase in brain-PAD compared with healthy controls. After early medication, the brain-PAD of patients decreased significantly compared with baseline (P < 0.001). The fractional anisotropy value of 31/33 white matter tract features, which related to the brain-PAD scores, had significantly statistical differences before and after measurements (P < 0.05, false discovery rate corrected). Correlation analysis showed that the age gap was negatively associated with the positive score on the Positive and Negative Syndrome Scale in the principal data-set (r = −0.326, P = 0.014). Conclusions: The brain age of patients with first-episode schizophrenia may be older than their chronological age. Early medication holds promise for improving the patient's brain ageing. Neuroimaging-based brain-age prediction can provide novel insights into the understanding of schizophrenia. [ABSTRACT FROM AUTHOR]

Published
2022
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23. Synergistic effects of simultaneous transcranial direct current stimulation (tDCS) and transcutaneous auricular vagus nerve stimulation (taVNS) on the brain responses

Author

Sun, Jin-Bo, primary, Tian, Qian-Qian, additional, Yang, Xue-Juan, additional, Deng, Hui, additional, Li, Nan, additional, Meng, Ling-Xia, additional, Zhao, Zi-Xuan, additional, Zhu, Yuan-Qiang, additional, Xi, Yi-Bin, additional, Yang, Qun, additional, and Qin, Wei, additional

Published
2021
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25. Neuroanatomical Features That Predict Response to Electroconvulsive Therapy Combined With Antipsychotics in Schizophrenia: A Magnetic Resonance Imaging Study Using Radiomics Strategy

Author

Xi, Yi-Bin, primary, Cui, Long-Biao, additional, Gong, Jie, additional, Fu, Yu-Fei, additional, Wu, Xu-Sha, additional, Guo, Fan, additional, Yang, Xuejuan, additional, Li, Chen, additional, Wang, Xing-Rui, additional, Li, Ping, additional, Qin, Wei, additional, and Yin, Hong, additional

Published
2020
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27. Cortical abnormalities and identification for first-episode schizophrenia via high-resolution magnetic resonance imaging

Author

Liu, Lin, primary, Cui, Long-Biao, additional, Wu, Xu-Sha, additional, Fei, Ning-Bo, additional, Xu, Zi-Liang, additional, Wu, Di, additional, Xi, Yi-Bin, additional, Huang, Peng, additional, von Deneen, Karen M., additional, Qi, Shun, additional, Zhang, Ya-Hong, additional, Wang, Hua-Ning, additional, Yin, Hong, additional, and Qin, Wei, additional

Published
2020
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30. Connectome-Based Patterns of First-Episode Medication-Naïve Patients With Schizophrenia

Author

Cui, Long-Biao, Wei, Yongbin, Xi, Yi-Bin, Griffa, Alessandra, De Lange, Siemon C, Kahn, René S, Yin, Hong, Van den Heuvel, Martijn P, Cui, Long-Biao, Wei, Yongbin, Xi, Yi-Bin, Griffa, Alessandra, De Lange, Siemon C, Kahn, René S, Yin, Hong, and Van den Heuvel, Martijn P

Abstract

Emerging evidence indicates that a disruption in brain network organization may play an important role in the pathophysiology of schizophrenia. The neuroimaging fingerprint reflecting the pathophysiology of first-episode schizophrenia remains to be identified. Here, we aimed at characterizing the connectome organization of first-episode medication-naïve patients with schizophrenia. A cross-sectional structural and functional neuroimaging study using two independent samples (principal dataset including 42 medication-naïve, previously untreated patients and 48 healthy controls; replication dataset including 39 first-episode patients [10 untreated patients] and 66 healthy controls) was performed. Brain network architecture was assessed by means of white matter fiber integrity measures derived from diffusion-weighted imaging (DWI) and by means of structural-functional (SC-FC) coupling measured by combining DWI and resting-state functional magnetic resonance imaging. Connectome rich club organization was found to be significantly disrupted in medication-naïve patients as compared with healthy controls (P = .012, uncorrected), with rich club connection strength (P = .032, uncorrected) and SC-FC coupling (P < .001, corrected for false discovery rate) decreased in patients. Similar results were found in the replication dataset. Our findings suggest that a disruption of rich club organization and functional dynamics may reflect an early feature of schizophrenia pathophysiology. These findings add to our understanding of the neuropathological mechanisms of schizophrenia and provide new insights into the early stages of the disorder.

Published
2019
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31. Connectome-Based Patterns of First-Episode Medication-Naive Patients With Schizophrenia

Author

Onderzoeksgroep 6, MS Neonatologie, Onderzoeksgroep 11, Brain, Cui, Long Biao, Wei, Yongbin, Xi, Yi Bin, Griffa, Alessandra, De Lange, Siemon C., Kahn, René S., Yin, Hong, Van Den Heuvel, Martijn P., Onderzoeksgroep 6, MS Neonatologie, Onderzoeksgroep 11, Brain, Cui, Long Biao, Wei, Yongbin, Xi, Yi Bin, Griffa, Alessandra, De Lange, Siemon C., Kahn, René S., Yin, Hong, and Van Den Heuvel, Martijn P.

Published
2019

34. Motor Cortex Mapping in Patients With Hepatic Myelopathy After Transjugular Intrahepatic Portosystemic Shunt

Author

Cui, Long-Biao, primary, Ren, Shuyao, additional, Xi, Yi-Bin, additional, Zeng, Ling-Li, additional, Chen, Gang, additional, Liu, Kang, additional, Yu, Tianlei, additional, He, Chuangye, additional, Guo, Wengang, additional, Yin, Zhanxin, additional, Wang, Zhengyu, additional, Niu, Jing, additional, Luo, Bohan, additional, Hu, Dewen, additional, Han, Guohong, additional, and Yin, Hong, additional

Published
2019
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38. Regional gray matter abnormality in hepatic myelopathy patients after transjugular intrahepatic portosystemic shunt: a voxel-based morphometry study

Author

Yin, Hong, primary, Liu, Kang, additional, Chen, Gang, additional, Ren, Shu-Yao, additional, Zhu, Yuan-Qiang, additional, Yu, Tian-Lei, additional, Tian, Ping, additional, Li, Chen, additional, Xi, Yi-Bin, additional, Wang, Zheng-Yu, additional, Ye, Jian-Jun, additional, and Han, Guo-Hong, additional

Published
2019
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39. Baseline structural and functional magnetic resonance imaging predicts early treatment response in schizophrenia with radiomics strategy.

Author

Cui, Long‐Biao, Fu, Yu‐Fei, Liu, Lin, Wu, Xu‐Sha, Xi, Yi‐Bin, Wang, Hua‐Ning, Qin, Wei, and Yin, Hong

Subjects
FUNCTIONAL magnetic resonance imaging, RADIOMICS, MAGNETIC resonance imaging, SUPPORT vector machines, SCHIZOPHRENIA
Abstract

Multimodal neuroimaging features provide opportunities for accurate classification and personalized treatment options in the psychiatric domain. This study aimed to investigate whether brain features predict responses to the overall treatment of schizophrenia at the end of the first or a single hospitalization. Structural and functional magnetic resonance imaging (MRI) data from two independent samples (N = 85 and 63, separately) of schizophrenia patients at baseline were included. After treatment, patients were classified as responders and non‐responders. Radiomics features of gray matter morphology and functional connectivity were extracted using Least Absolute Shrinkage and Selection Operator. Support vector machine was used to explore the predictive performance. Prediction models were based on structural features (cortical thickness, surface area, gray matter regional volume, mean curvature, metric distortion, and sulcal depth), functional features (functional connectivity), and combined features. There were 12 features after dimensionality reduction. The structural features involved the right precuneus, cuneus, and inferior parietal lobule. The functional features predominately included inter‐hemispheric connectivity. We observed a prediction accuracy of 80.38% (sensitivity: 87.28%; specificity 82.47%) for the model using functional features, and 69.68% (sensitivity: 83.96%; specificity: 72.41%) for the one using structural features. Our model combining both structural and functional features achieved a higher accuracy of 85.03%, with 92.04% responder and 80.23% non‐responders to the overall treatment to be correctly predicted. These results highlight the power of structural and functional MRI‐derived radiomics features to predict early response to treatment in schizophrenia. Prediction models of the very early treatment response in schizophrenia could augment effective therapeutic strategies. [ABSTRACT FROM AUTHOR]

Published
2021
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40. Radiomics signature: A potential biomarker for the prediction of MGMT promoter methylation in glioblastoma.

Author

Xi, Yi‐bin, Guo, Fan, Xu, Zi‐liang, Li, Chen, Wei, Wei, Tian, Ping, Liu, Ting‐ting, Liu, Lin, Chen, Gang, Ye, Jing, Cheng, Guang, Cui, Long‐biao, Zhang, Hong‐juan, Qin, Wei, Yin, Hong, Xi, Yi-Bin, Xu, Zi-Liang, Liu, Ting-Ting, Cui, Long-Biao, and Zhang, Hong-Juan

Abstract

Background: In glioblastoma (GBM), promoter methylation of the DNA repair gene O-methylguanine-DNA methyltransferase (MGMT) is associated with beneficial chemotherapy.Purpose/hypothesis: To analyze radiomics features for utilizing the full potential of medical imaging as biomarkers of MGMT promoter methylation.Study Type: Retrospective.Population/subjects: In all, 98 GBM patients with known MGMT (48 methylated and 50 unmethylated tumors).Field Strength/sequence: 3.0T magnetic resonance (MR) images, containing T1 -weighted image (T1 WI), T2 -weighted image (T2 WI), and enhanced T1 WI.Assessment: A region of interest (ROI) of the tumor was delineated. A total of 1665 radiomics features were extracted and quantized, and were reduced using least absolute shrinkage and selection operator (LASSO) regularization.Statistical Testing: After the support vector machine construction, accuracy, sensitivity, and specificity were computed for different sequences. An independent validation cohort containing 20 GBM patients was utilized to further evaluate the radiomics model performance.Results: Radiomics features of T1 WI reached an accuracy of 67.54%. Enhanced T1 WI features reached an accuracy of 82.01%, while T2 WI reached an accuracy of 69.25%. The best classification system for predicting MGMT promoter methylation status originated from the combination of 36 T1 WI, T2 WI, and enhanced T1 WI images features, with an accuracy of 86.59%. Further validation on the independent cohort of 20 patients produced similar results, with an accuracy of 80%.Data Conclusion: Our results provide further evidence that radiomics MR features could predict MGMT methylation status in preoperative GBM. Multiple imaging modalities together can yield putative noninvasive biomarkers for the identification of MGMT.Level Of Evidence: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1380-1387. [ABSTRACT FROM AUTHOR]

Published
2018
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44. Aberrant perfusion and its connectivity within default mode network of first-episode drug-naïve schizophrenia patients and their unaffected first-degree relatives

Author

Cui, Long-Biao, primary, Wang, Liu-Xian, additional, Tian, Ping, additional, Wang, Hua-Ning, additional, Cai, Min, additional, Guo, Fan, additional, Li, Chen, additional, Wu, Yu-Jing, additional, Qiao, Peng-Gang, additional, Xu, Zi-Liang, additional, Liu, Lin, additional, He, Hong, additional, Wu, Wen-Jun, additional, Xi, Yi-Bin, additional, and Yin, Hong, additional

Published
2017
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45. Radiomics signature: A potential biomarker for the prediction of MGMT promoter methylation in glioblastoma

Author

Xi, Yi-bin, primary, Guo, Fan, additional, Xu, Zi-liang, additional, Li, Chen, additional, Wei, Wei, additional, Tian, Ping, additional, Liu, Ting-ting, additional, Liu, Lin, additional, Chen, Gang, additional, Ye, Jing, additional, Cheng, Guang, additional, Cui, Long-biao, additional, Zhang, Hong-juan, additional, Qin, Wei, additional, and Yin, Hong, additional

Published
2017
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47. Disturbed Brain Activity in Resting-State Networks of Patients with First-Episode Schizophrenia with Auditory Verbal Hallucinations: A Cross-sectional Functional MR Imaging Study

Author

Cui, Long-Biao, primary, Liu, Lin, additional, Guo, Fan, additional, Chen, Yun-Chun, additional, Chen, Gang, additional, Xi, Min, additional, Qin, Wei, additional, Sun, Jin-Bo, additional, Li, Chen, additional, Xi, Yi-Bin, additional, Wang, Hua-Ning, additional, and Yin, Hong, additional

Published
2017
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50. Anterior cingulate cortex-related connectivity in first-episode schizophrenia: a spectral dynamic causal modeling study with functional magnetic resonance imaging

Author

Cui, Long-Biao, primary, Liu, Jian, additional, Wang, Liu-Xian, additional, Li, Chen, additional, Xi, Yi-Bin, additional, Guo, Fan, additional, Wang, Hua-Ning, additional, Zhang, Lin-Chuan, additional, Liu, Wen-Ming, additional, He, Hong, additional, Tian, Ping, additional, Yin, Hong, additional, and Lu, Hongbing, additional

Published
2015
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