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133 results on '"Zhao, Youjin"'

7. Impaired visual-motor functional connectivity in first-episode medication-naïve patients with major depressive disorder

Author

Hu, Yongbo, primary, Li, Shiyi, additional, Li, Jin, additional, Zhao, Youjin, additional, Li, Meiling, additional, Cui, Weigang, additional, Peng, Xiaolong, additional, Dong, Zaiquan, additional, Zhang, Lianqing, additional, Xu, Haizhen, additional, Gao, Li, additional, Huang, Xiaoqi, additional, Kuang, Weihong, additional, Gong, Qiyong, additional, and Liu, Hesheng, additional

Published
2023
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11. The acute effects of repetitive transcranial magnetic stimulation on laminar diffusion anisotropy of neocortical gray matter

Author

Zhang, Wenjing, primary, Liu, Naici, additional, Zhao, Youjin, additional, Yao, Chenyang, additional, Yang, Dan, additional, Yang, Chengmin, additional, Sun, Hui, additional, Wei, Xia, additional, Sweeney, John A., additional, Liang, Huilou, additional, Zhang, Miaoqi, additional, Gong, Qiyong, additional, and Lui, Su, additional

Published
2023
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12. Shared and Disorder-Specific Alterations of Brain Temporal Dynamics in Obsessive-Compulsive Disorder and Schizophrenia

Author

Luo, Lekai, primary, Li, Qian, additional, Wang, Yaxuan, additional, He, Ning, additional, Wang, Yuxia, additional, You, Wanfang, additional, Zhang, Qian, additional, Long, Fenghua, additional, Chen, Lizhou, additional, Zhao, Youjin, additional, Yao, Li, additional, Sweeney, John A, additional, Gong, Qiyong, additional, and Li, Fei, additional

Published
2023
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13. Predicting treatment outcomes in major depressive disorder using brain magnetic resonance imaging: a meta-analysis

Author

Long, Fenghua, Chen, Yufei, Zhang, Qian, Li, Qian, Wang, Yaxuan, Wang, Yitian, Li, Haoran, Zhao, Youjin, McNamara, Robert K., DelBello, Melissa P., Sweeney, John A., Gong, Qiyong, and Li, Fei

Abstract

Recent studies have provided promising evidence that neuroimaging data can predict treatment outcomes for patients with major depressive disorder (MDD). As most of these studies had small sample sizes, a meta-analysis is warranted to identify the most robust findings and imaging modalities, and to compare predictive outcomes obtained in magnetic resonance imaging (MRI) and studies using clinical and demographic features. We conducted a literature search from database inception to July 22, 2023, to identify studies using pretreatment clinical or brain MRI features to predict treatment outcomes in patients with MDD. Two meta-analyses were conducted on clinical and MRI studies, respectively. The meta-regression was employed to explore the effects of covariates and compare the predictive performance between clinical and MRI groups, as well as across MRI modalities and intervention subgroups. Meta-analysis of 13 clinical studies yielded an area under the curve (AUC) of 0.73, while in 44 MRI studies, the AUC was 0.89. MRI studies showed a higher sensitivity than clinical studies (0.78 vs. 0.62, Z = 3.42, P= 0.001). In MRI studies, resting-state functional MRI (rsfMRI) exhibited a higher specificity than task-based fMRI (tbfMRI) (0.79 vs. 0.69, Z = −2.86, P= 0.004). No significant differences in predictive performance were found between structural and functional MRI, nor between different interventions. Of note, predictive MRI features for treatment outcomes in studies using antidepressants were predominantly located in the limbic and default mode networks, while studies of electroconvulsive therapy (ECT) were restricted mainly to the limbic network. Our findings suggest a promise for pretreatment brain MRI features to predict MDD treatment outcomes, outperforming clinical features. While tasks in tbfMRI studies differed, those studies overall had less predictive utility than rsfMRI data. Overlapping but distinct network-level measures predicted antidepressants and ECT outcomes. Future studies are needed to predict outcomes using multiple MRI features, and to clarify whether imaging features predict outcomes generally or differ depending on treatments.

Published
2024
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19. Controllability of Functional Brain Networks and Its Clinical Significance in First-Episode Schizophrenia

Author

Li, Qian, primary, Yao, Li, additional, You, Wanfang, additional, Liu, Jiang, additional, Deng, Shikuang, additional, Li, Bin, additional, Luo, Lekai, additional, Zhao, Youjin, additional, Wang, Yuxia, additional, Wang, Yaxuan, additional, Zhang, Qian, additional, Long, Fenghua, additional, Sweeney, John A, additional, Gu, Shi, additional, Li, Fei, additional, and Gong, Qiyong, additional

Published
2022
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20. Altered controllability of white matter networks and related brain function changes in first-episode drug-naive schizophrenia

Author

Tang, Biqiu, primary, Zhang, Wenjing, additional, Liu, Jiang, additional, Deng, Shikuang, additional, Hu, Na, additional, Li, Siyi, additional, Zhao, Youjin, additional, Liu, Nian, additional, Zeng, Jiaxin, additional, Cao, Hengyi, additional, Sweeney, John A, additional, Gong, Qiyong, additional, Gu, Shi, additional, and Lui, Su, additional

Published
2022
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22. Controllability of Functional Brain Networks and Its Clinical Significance in First-Episode Schizophrenia.

Author

Li, Qian, Yao, Li, You, Wanfang, Liu, Jiang, Deng, Shikuang, Li, Bin, Luo, Lekai, Zhao, Youjin, Wang, Yuxia, Wang, Yaxuan, Zhang, Qian, Long, Fenghua, Sweeney, John A, Gu, Shi, Li, Fei, and Gong, Qiyong

Subjects
RESEARCH, NEURAL pathways, LARGE-scale brain networks, SCHIZOPHRENIA, BRAIN mapping, HEALTH status indicators, AGE factors in disease, PATHOLOGICAL psychology, DISABILITIES, STATISTICAL correlation
Abstract

Background and Hypothesis Disrupted control of brain state transitions may contribute to the diverse dysfunctions of cognition, emotion, and behavior that are fundamental to schizophrenia. Control theory provides the rationale for evaluating brain state transitions from a controllability perspective, which may help reveal the brain mechanism for clinical features such as cognitive control deficits associated with schizophrenia. We hypothesized that brain controllability would be altered in patients with schizophrenia, and that controllability of brain networks would be related to clinical symptomatology. Study Design Controllability measurements of functional brain networks, including average controllability and modal controllability, were calculated and compared between 125 first-episode never-treated patients with schizophrenia and 133 healthy controls (HCs). Associations between controllability metrics and clinical symptoms were evaluated using sparse canonical correlation analysis. Study Results Compared to HCs, patients showed significantly increased average controllability (P FDR =.023) and decreased modal controllability (P FDR =.023) in dorsal anterior cingulate cortex (dACC). General psychopathology symptoms and positive symptoms were positively correlated with average controllability in regions of default mode network and negatively associated with average controllability in regions of sensorimotor, dorsal attention, and frontoparietal networks. Conclusions Our findings suggest that altered controllability of functional activity in dACC may play a critical role in the pathophysiology of schizophrenia, consistent with the importance of this region in cognitive and brain state control operations. The demonstration of associations of functional controllability with psychosis symptoms suggests that the identified alterations in average controllability of brain function may contribute to the severity of acute psychotic illness in schizophrenia. [ABSTRACT FROM AUTHOR]

Published
2023
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27. Cortical thickness abnormalities in patients with first episode psychosis: a meta-analysis of psychoradiologic studies and replication in an independent sample

Author

Wen, Keren, primary, Zhao, Youjin, additional, Gong, Qiyong, additional, Zhu, Ziyu, additional, Li, Qian, additional, Pan, Nanfang, additional, Fu, Shiqin, additional, Radua, Joaquim, additional, Vieta, Eduard, additional, Kumar, Poornima, additional, Kemp, Graham J, additional, and Biswal, Bharat B, additional

Published
2021
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34. White Matter Abnormalities in Anorexia Nervosa: Psychoradiologic Evidence From Meta-Analysis of Diffusion Tensor Imaging Studies Using Tract Based Spatial Statistics

Author

Zhang, Simin, primary, Wang, Weina, additional, Su, Xiaorui, additional, Li, Lei, additional, Yang, Xibiao, additional, Su, Jingkai, additional, Tan, Qiaoyue, additional, Zhao, Youjin, additional, Sun, Huaiqiang, additional, Kemp, Graham J., additional, Gong, Qiyong, additional, and Yue, Qiang, additional

Published
2020
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35. Correction: Meta-analysis of cortical thickness abnormalities in medication-free patients with major depressive disorder

Author

Li, Qian, primary, Zhao, Youjin, additional, Chen, Ziqi, additional, Long, Jingyi, additional, Dai, Jing, additional, Huang, Xiaoqi, additional, Lui, Su, additional, Radua, Joaquim, additional, Vieta, Eduard, additional, Kemp, Graham J., additional, Sweeney, John A., additional, Li, Fei, additional, and Gong, Qiyong, additional

Published
2019
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36. Association of peripheral cytokine levels with cerebral structural abnormalities in schizophrenia

Author

Wu, Dongsheng, primary, Lv, Peilin, additional, Li, Fei, additional, Zhang, Wenjing, additional, Fu, Gui, additional, Dai, Jing, additional, Hu, Na, additional, Liu, Jieke, additional, Xiao, Yuan, additional, Li, Siyi, additional, Shah, Chandan, additional, Tao, Bo, additional, Zhao, Youjin, additional, Gong, Qiyong, additional, and Lui, Su, additional

Published
2019
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38. Disrupted single-subject grey matter networks in non-comorbid medication-naive patients with major depressive disorder or social anxiety disorder

Author

Zhao, Youjin

Subjects
Comparative studies, genetic structures, Neuroradiology brain, CNS, MR, Experimental investigations
Abstract

Aims and objectives Methods and materials Results Conclusion Personal information References, Aims and objectives: To psychoradiologically investigate the topological organization of single-subject gray matter (GM) networks in non-comorbid medication-naive patients with major depressive disorder (MDD) or social anxiety disorder...

Published
2019
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39. Video Recognition of Human Fall Based on Spatiotemporal Features

Author

Zhao Youjin, Min Fan, Min Gao, Kai Wang, Qingyu Xiong, and Xiling Shen

Subjects
business.industry, Computer science, 0206 medical engineering, Frame (networking), Supervised learning, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Pattern recognition, 02 engineering and technology, 020601 biomedical engineering, Theoretical Computer Science, Computational Theory and Mathematics, Hyperplane, Artificial Intelligence, Margin (machine learning), Principal component analysis, Radial basis function kernel, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Computer vision, Artificial intelligence, business, Software, Image gradient, Linear filter
Abstract

A systematic framework for recognizing human fall from video is presented in this work. For the foreground extraction, instead of remodeling background of every video frame, we directly extract cuboids that are composed of spatiotemporal interest points detected by separable linear filter from video sequences. We then represent these video patches as local image gradient descriptors with greatly reduced dimensions by principle component analysis (PCA). From labeled video patches, a supervised learning method based on Gaussian RBF kernel is proposed to determine the maximum margin between fall and normal activity, and then a novel video sequence can be categorize into fall or normal activity by an optimal hyperplane. We tested the above method on datasets set up based on the LPO-CV testing paradigm, which verified the proposed method and demonstrated its advantage over other state-of-the-art approaches for fall recognition.

Published
2015

42. Decoupling of Gray and White Matter Functional Networks in Medication‐Naïve Patients With Major Depressive Disorder.

Author

Zhao, Youjin, Zhang, Feifei, Zhang, Wenjing, Chen, Lizhou, Chen, Ziqi, Lui, Su, and Gong, Qiyong

Abstract

Background: Major depressive disorder (MDD) has been increasingly conceptualized as a disconnection syndrome. However, most studies have only focused on functional connectivity (FC) alterations in gray matter (GM), and the functional alterations in white matter (WM) remain largely unknown in MDD. Purpose: To investigate WM functional alterations and the functional interaction between GM and WM networks in medication‐naïve MDD. Study Type: Prospective. Subjects: Sixty‐eight patients with MDD and 66 age‐ and sex‐matched healthy controls (HCs). Field Strength/Sequence: Resting state‐functional MRI (fMRI) using a gradient‐echo imaging sequence and T1‐weighted images were acquired at 3.0T. Assessment: Functional GM and WM networks, based on resting‐state blood oxygenation level‐dependent (BOLD) signals, were identified by the K‐means clustering algorithm, and FC matrices were obtained for each subject. Statistical Tests: Two‐sample t‐tests, Pearson chi‐square test, and Pearson correlation analysis. Results: Both the GM and WM of the visual network (GM1 and WM11) showed reduced FC with the sensorimotor network (WM5 and GM8), lateral temporal network (GM5 and WM6), cingulo‐opercular network (GM9), and dorsal attention network (GM7) in MDD patients compared to controls (P < 0.05, false discovery rate [FDR]‐corrected). Reduced FC between the anterior cingulum network (WM3) and the lateral temporal network (GM5 and WM6) and temporal pole network (GM13) and between GM13 and the medial temporal network (GM4) and medial prefrontal‐subcortical network (GM10) were also observed in MDD patients (P < 0.05, FDR‐corrected). In addition, the WM BOLD signal in the sensorimotor network was negatively correlated with illness duration (r = –0.286, P = 0.018). Data Conclusion: Disconnectivity between the GM and WM networks in the perception‐motor system may be the foundation of extensively disrupted connections in MDD. Furthermore, the observed decoupling between subsystems of the default mode network may help explain previous findings of persistent negative rumination and theory of mind deficits in depression. Level of Evidence: 3. Technical Efficacy: Stage 3. [ABSTRACT FROM AUTHOR]

Published
2021
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43. Microstructural white matter abnormalities in pediatric and adult obsessive‐compulsive disorder: A systematic review and meta‐analysis.

Author

Li, Qian, Zhao, Youjin, Huang, Zixuan, Guo, Yi, Long, Jingyi, Luo, Lekai, You, Wanfang, Sweeney, John A., Li, Fei, and Gong, Qiyong

Subjects
*OBSESSIVE-compulsive disorder, *WHITE matter (Nerve tissue), *CORPUS callosum, *ADULTS, *AGE of onset
Abstract

Objective: To identify the most prominent and replicable fractional anisotropy (FA) alterations of white matter associated with obsessive‐compulsive disorder (OCD) in tract‐based spatial statistics (TBSS) studies. Methods: We reviewed previous TBSS studies (n = 20) in OCD and performed a meta‐analysis (n = 16) of FA differences. Results: No between‐group differences in FA were detected in the pooled meta‐analysis. However, reduced FA was identified in the genu and anterior body of corpus callosum (CC) in adult OCD. FA reductions in the anterior body of CC were associated with a later age of onset in adult patients with OCD. For pediatric OCD, decreased FA in earlier adolescence and increased FA in later adolescence were seemingly related to an altered trajectory of brain maturation. Conclusions: Absent in the pooled sample but robust in adults, disrupted microstructural organization in the anterior part of CC indicates a bias of deficits toward connections in interhemispheric connections of rostral neocortical regions, which could lead to deficits of interhemispheric communication and thus contribute to cognitive and emotional deficits in adult OCD. The correlation between FA in the anterior body of CC and older illness onset suggests that patients with later adult onset of illness may represent a biologically distinct subgroup. For pediatric OCD, alterations in neurodevelopmental maturation may contribute to inconsistent patterns of FA alteration relative to controls during adolescence. While most studies of OCD have emphasized alterations of within hemisphere fronto‐striatal circuits, these results indicate that between hemisphere connectivity of this circuitry may also represent important pathophysiology of the illness. [ABSTRACT FROM AUTHOR]

Published
2021
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45. Research on Healthy Anomaly Detection Model Based on Deep Learning from Multiple Time-Series Physiological Signals

Author

Ma Longkun, Min Fan, Sun Guotan, Qingyu Xiong, Zhao Youjin, Tong Liu, and Kai Wang

Subjects
Article Subject, Computer science, 02 engineering and technology, Machine learning, computer.software_genre, Convolutional neural network, 03 medical and health sciences, QA76.75-76.765, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, Multiple time, Computer software, Series (mathematics), business.industry, Deep learning, Anomaly (natural sciences), Health technology, Computer Science Applications, A priori and a posteriori, 020201 artificial intelligence & image processing, Anomaly detection, Artificial intelligence, Data mining, business, computer, 030217 neurology & neurosurgery, Software
Abstract

Health is vital to every human being. To further improve its already respectable medical technology, the medical community is transitioning towards a proactive approach which anticipates and mitigates risks before getting ill. This approach requires measuring the physiological signals of human and analyzes these data at regular intervals. In this paper, we present a novel approach to apply deep learning in physiological signals analysis that allows doctor to identify latent risks. However, extracting high level information from physiological time-series data is a hard problem faced by the machine learning communities. Therefore, in this approach, we apply model based on convolutional neural network that can automatically learn features from raw physiological signals in an unsupervised manner and then based on the learned features use multivariate Gauss distribution anomaly detection method to detect anomaly data. Our experiment is shown to have a significant performance in physiological signals anomaly detection. So it is a promising tool for doctor to identify early signs of illness even if the criteria are unknown a priori.

Published
2016
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47. Gray Matter Abnormalities in Non-comorbid Medication-naive Patients with Major Depressive Disorder or Social Anxiety Disorder

Author

Zhao, Youjin, primary, Chen, Lizhou, additional, Zhang, Wenjing, additional, Xiao, Yuan, additional, Shah, Chandan, additional, Zhu, Hongru, additional, Yuan, Minlan, additional, Sun, Huaiqiang, additional, Yue, Qiang, additional, Jia, Zhiyun, additional, Zhang, Wei, additional, Kuang, Weihong, additional, Gong, Qiyong, additional, and Lui, Su, additional

Published
2017
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