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Niu Y, Chen X, Chen Y, Yao Z, Chen X, Liu Z, Meng X, Liu Y, Zhao Z, Fan H. A gender recognition method based on EEG microstates. Comput Biol Med 2024; 173:108366. [PMID: 38554661 DOI: 10.1016/j.compbiomed.2024.108366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Revised: 03/19/2024] [Accepted: 03/21/2024] [Indexed: 04/02/2024]
Abstract
BACKGROUND Gender carries important information related to male and female characteristics, and a large number of studies have attempted to use physiological measurement methods for gender classification. Although previous studies have shown that there exist statistical differences in some Electroencephalographic (EEG) microstate parameters between males and females, it is still unknown that whether these microstate parameters can be used as potential biomarkers for gender classification based on machine learning. METHODS We used two independent resting-state EEG datasets: the first dataset included 74 females and matched 74 males, and the second one included 42 males and matched 42 females. EEG microstate analysis based on modified k-means clustering method was applied, and temporal parameter and nonlinear characteristics (sample entropy and Lempel-Ziv complexity) of EEG microstate sequences were extracted to compare between males and females. More importantly, these microstate temporal parameters and complexity were tried to train six machine learning methods for gender classification. RESULTS We obtained five common microstates for each dataset and each group. Compared with the male group, the female group has significantly higher temporal parameters of microstate B, C, E and lower temporal parameters of microstate A and D, and higher complexity of microstate sequence. When using combination of microstate temporal parameters and complexity or only microstate temporal parameters as classification features in an independent test set (the second dataset), we achieved 95.2% classification accuracy. CONCLUSION Our research findings indicate that the dynamics of microstate have considerable Gender-specific alteration. EEG microstates can be used as neurophysiological biomarkers for gender classification.
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Affiliation(s)
- Yanxiang Niu
- Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin, China; Wenzhou Safety (Emergency) Institute, Tianjin University, 325000, Wenzhou, China
| | - Xin Chen
- Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin, China; Wenzhou Safety (Emergency) Institute, Tianjin University, 325000, Wenzhou, China
| | - Yuansen Chen
- Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin, China; Wenzhou Safety (Emergency) Institute, Tianjin University, 325000, Wenzhou, China
| | - Zixuan Yao
- Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin, China; Wenzhou Safety (Emergency) Institute, Tianjin University, 325000, Wenzhou, China
| | - Xuemei Chen
- Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin, China; Wenzhou Safety (Emergency) Institute, Tianjin University, 325000, Wenzhou, China
| | - Ziquan Liu
- Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin, China; Wenzhou Safety (Emergency) Institute, Tianjin University, 325000, Wenzhou, China
| | - Xiangyan Meng
- Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin, China; Wenzhou Safety (Emergency) Institute, Tianjin University, 325000, Wenzhou, China
| | - Yanqing Liu
- Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin, China; Wenzhou Safety (Emergency) Institute, Tianjin University, 325000, Wenzhou, China.
| | - Zongya Zhao
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China.
| | - Haojun Fan
- Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin, China; Wenzhou Safety (Emergency) Institute, Tianjin University, 325000, Wenzhou, China.
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Smolders L, De Baene W, van der Hofstad R, Florack L, Rutten GJ. Working memory performance in glioma patients is associated with functional connectivity between the right dorsolateral prefrontal cortex and default mode network. J Neurosci Res 2023; 101:1826-1839. [PMID: 37694505 DOI: 10.1002/jnr.25242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 04/26/2023] [Accepted: 08/21/2023] [Indexed: 09/12/2023]
Abstract
In healthy subjects, activity in the default mode network (DMN) and the frontoparietal network (FPN) has consistently been associated with working memory (WM). In particular, the dorsolateral prefrontal cortex (DLPFC) is important for WM. The functional-anatomical basis of WM impairment in glioma patients is, however, still poorly understood. We investigated whether WM performance of glioma patients is reflected in resting-state functional connectivity (FC) between the DMN and FPN, additionally focusing on the DLPFC. Resting-state functional MRI data were acquired from 45 glioma patients prior to surgery. WM performance was derived from a pre-operative N-back task. Scans were parcellated into ROIs using both the Gordon and Yeo atlas. FC was calculated as the average Pearson correlation between functional time series. The FC between right DLPFC and DMN was inversely related to WM performance for both the Gordon and Yeo atlas (p = .010). No association was found for FC between left DLPFC and DMN, nor between the whole FPN and DMN. The results are robust and not dependent on atlas choice or tumor location, as they hold for both the Gordon and Yeo atlases, and independently of location variables. Our findings show that WM performance of glioma patients can be quantified in terms of interactions between regions and large-scale networks that can be measured with resting-state fMRI. These group-based results are a necessary step toward development of biomarkers for clinical management of glioma patients, and provide additional evidence that global disruption of the DMN relates to cognitive impairment in glioma patients.
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Affiliation(s)
- Lars Smolders
- Department of Neurosurgery, Elisabeth-TweeSteden Hospital, Tilburg, The Netherlands
- Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Wouter De Baene
- Department of Cognitive Neuropsychology, Tilburg University, Tilburg, The Netherlands
| | - Remco van der Hofstad
- Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Luc Florack
- Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Geert-Jan Rutten
- Department of Neurosurgery, Elisabeth-TweeSteden Hospital, Tilburg, The Netherlands
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Yang J, Zhang X, Gao X, Wu H, Li X, Yang L, Zhang N. Fiber Density and Structural Brain Connectome in Glioblastoma Are Correlated With Glioma Cell Infiltration. Neurosurgery 2023; 92:1234-1242. [PMID: 36744904 DOI: 10.1227/neu.0000000000002356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 11/08/2022] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Glioblastoma (GBM) preferred to infiltrate into white matter (WM) beyond the recognizable tumor margin. OBJECTIVE To investigate whether fiber density (FD) and structural brain connectome can provide meaningful information about WM destruction and glioma cell infiltration. METHODS GBM cases were collected based on inclusion criteria, and baseline information and preoperative MRI results were obtained. GBM lesions were automatically segmented into necrosis, contrast-enhanced tumor, and edema areas. We obtained the FD map to compute the FD and lnFD values in each subarea and reconstructed the structural brain connectome to obtain the topological metrics in each subarea. We also divided the edema area into a nonenhanced tumor (NET) area and a normal WM area based on the contralesional lnFD value in the edema area, and computed the NET ratio. RESULTS Twenty-five GBM cases were included in this retrospective study. The FD/lnFD value and topological metrics (aCp, aLp, aEg, aEloc, and ar) were significantly correlated with GBM subareas, which represented the extent of WM destruction and glioma cell infiltration. The FD/lnFD values and topological parameters were correlated with the NET ratio. In particular, the lnFD value in the edema area was correlated with the NET ratio (coefficient, 0.92). Therefore, a larger lnFD value indicates more severe glioma infiltration in the edema area and suggests an extended resection for better clinical outcomes. CONCLUSION The FD and structural brain connectome in this study provide a new insight into glioma infiltration and a different consideration of their clinical application in neuro-oncology.
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Affiliation(s)
- Jia Yang
- Department of Neurosurgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
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Sahrizan NSA, Manan HA, Abdul Hamid H, Abdullah JM, Yahya N. Functional Alteration in the Brain Due to Tumour Invasion in Paediatric Patients: A Systematic Review. Cancers (Basel) 2023; 15:cancers15072168. [PMID: 37046828 PMCID: PMC10093754 DOI: 10.3390/cancers15072168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 03/29/2023] [Accepted: 03/29/2023] [Indexed: 04/14/2023] Open
Abstract
Working memory, language and speech abilities, motor skills, and visual abilities are often impaired in children with brain tumours. This is because tumours can invade the brain's functional areas and cause alterations to the neuronal networks. However, it is unclear what the mechanism of tumour invasion is and how various treatments can cause cognitive impairment. Therefore, this study aims to systematically evaluate the effects of tumour invasion on the cognitive, language, motor, and visual abilities of paediatric patients, as well as discuss the alterations and modifications in neuronal networks and anatomy. The electronic database, PubMed, was used to find relevant studies. The studies were systematically reviewed based on the type and location of brain tumours, cognitive assessment, and pre- and post-operative deficits experienced by patients. Sixteen studies were selected based on the inclusion and exclusion criteria following the guidelines from PRISMA. Most studies agree that tumour invasion in the brain causes cognitive dysfunction and alteration in patients. The effects of a tumour on cognition, language, motor, and visual abilities depend on the type of tumour and its location in the brain. The alteration to the neuronal networks is also dependent on the type and location of the tumour. However, the default mode network (DMN) is the most affected network, regardless of the tumour type and location.Furthermore, our findings suggest that different treatment types can also contribute to patients' cognitive function to improve or deteriorate. Deficits that persisted or were acquired after surgery could result from surgical manipulation or the progression of the tumour's growth. Meanwhile, recovery from the deficits indicated that the brain has the ability to recover and reorganise itself.
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Affiliation(s)
- Nur Shaheera Aidilla Sahrizan
- Department of Radiology, Makmal Pemprosesan Imej Kefungsian (Functional Image Processing Laboratory), University Kebangsaan Malaysia Medical Centre, Kuala Lumpur 56000, Malaysia
- Department of Radiology and Intervency, Hospital Pakar Kanak-Kanak (Children Specialist Hospital), Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia
| | - Hanani Abdul Manan
- Department of Radiology, Makmal Pemprosesan Imej Kefungsian (Functional Image Processing Laboratory), University Kebangsaan Malaysia Medical Centre, Kuala Lumpur 56000, Malaysia
- Department of Radiology and Intervency, Hospital Pakar Kanak-Kanak (Children Specialist Hospital), Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia
| | - Hamzaini Abdul Hamid
- Department of Radiology, Makmal Pemprosesan Imej Kefungsian (Functional Image Processing Laboratory), University Kebangsaan Malaysia Medical Centre, Kuala Lumpur 56000, Malaysia
- Department of Radiology and Intervency, Hospital Pakar Kanak-Kanak (Children Specialist Hospital), Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia
| | - Jafri Malin Abdullah
- Jabatan Neurosains, Pusat Pengajian Sains Perubatan, Jalan Hospital USM, Kampus Kesihatan, Universiti Sains Malaysia, Kota Bharu 16150, Malaysia
- Brain and Behaviour Cluster, Pusat Pengajian Sains Perubatan, Kampus Kesihatan, Universiti Sains Malaysia, Kota Bharu 16150, Malaysia
- Department of Neurosciences & Brain Behaviour Cluster, Hospital Universiti Sains Malaysia, Kampus Kesihatan, Universiti Sains Malaysia, Kota Bharu 16150, Malaysia
| | - Noorazrul Yahya
- Diagnostic Imaging & Radiotherapy Program, School of Diagnostic & Applied Health Sciences, Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur 50300, Malaysia
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Pasquini L, Jenabi M, Peck KK, Holodny AI. Language reorganization in patients with left-hemispheric gliomas is associated with increased cortical volume in language-related areas and in the default mode network. Cortex 2022; 157:245-255. [PMID: 36356409 PMCID: PMC10201933 DOI: 10.1016/j.cortex.2022.09.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 08/02/2022] [Accepted: 09/19/2022] [Indexed: 12/15/2022]
Abstract
BACKGROUND Language function may reorganize to overcome focal impairment; however, the relation between functional and structural changes in patients with brain tumors remains unclear. We investigated the cortical volume of atypical language dominant (AD) patients with left frontal-insular high-grade (HGG) and low-grade glioma (LGG). We hypothesized atypical language being associated with areas of increased cortical volume in the right hemisphere, including language areas homologues. METHODS Patient were recruited following the criteria: left frontal-insular glioma; functional MRI and 3DT1-weighted images; no artifacts. We calculated an hemispheric language laterality index (LI), defined as: AD if LI < .2; left-dominant (LD) if LI ≥ .2. We measured cortical volume in three voxel-based morphometry (VBM) analyses: total AD vs. LD patients; AD vs. LD in HGG; AD vs. LD in LGG. We repeated the analysis in AD vs. LD healthy controls (HC). A minimum threshold of t > 2 and corrected p < .025 (Bonferroni) was employed. RESULTS We recruited 119 patients (44LGG, 75HGG). Hemispheric LI demonstrated 64/119AD and 55/119LD patients. The first VBM analysis demonstrated significantly increased cortical volume in AD patients in the right inferior frontal gyrus (IFG), right superior temporal gyrus (STG), right insula, right fusiform gyrus (FG), right precentral gyrus, right temporal-parietal junction, right posterior cingulate cortex (PCC), right hippocampus, right- and left cerebellum. AD patients with HGG showed the same areas of significantly increased cortical volume. AD patients with LGG displayed significantly increased cortical volume in right IFG, right STG, right insula, right FG, right anterior cingulate cortex, right PCC, right dorsal-lateral prefrontal cortex. HC showed no significant results. CONCLUSION Right-sided (atypical) language activations in patients with left-hemispheric gliomas are associated with areas of increased cortical volume. Additionally, default mode network nodes showed greater cortical volume in AD patients regardless of the tumor grade, supporting the idea of these cortices participating in the development of language plasticity.
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Affiliation(s)
- Luca Pasquini
- Neuroradiology Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Neuroradiology Unit, NESMOS Department, Sant'Andrea Hospital, La Sapienza University, Rome 00189, Italy.
| | - Mehrnaz Jenabi
- Neuroradiology Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Kyung K Peck
- Neuroradiology Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Andrei I Holodny
- Neuroradiology Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Department of Radiology, Weill Medical College of Cornell University, New York, NY 10065, USA; Department of Neuroscience, Weill-Cornell Graduate School of the Medical Sciences, New York, NY 10065, USA
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Chen Z, Ye N, Teng C, Li X. Alternations and Applications of the Structural and Functional Connectome in Gliomas: A Mini-Review. Front Neurosci 2022; 16:856808. [PMID: 35478847 PMCID: PMC9035851 DOI: 10.3389/fnins.2022.856808] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 02/28/2022] [Indexed: 12/12/2022] Open
Abstract
In the central nervous system, gliomas are the most common, but complex primary tumors. Genome-based molecular and clinical studies have revealed different classifications and subtypes of gliomas. Neuroradiological approaches have non-invasively provided a macroscopic view for surgical resection and therapeutic effects. The connectome is a structural map of a physical object, the brain, which raises issues of spatial scale and definition, and it is calculated through diffusion magnetic resonance imaging (MRI) and functional MRI. In this study, we reviewed the basic principles and attributes of the structural and functional connectome, followed by the alternations of connectomes and their influences on glioma. To extend the applications of connectome, we demonstrated that a series of multi-center projects still need to be conducted to systemically investigate the connectome and the structural–functional coupling of glioma. Additionally, the brain–computer interface based on accurate connectome could provide more precise structural and functional data, which are significant for surgery and postoperative recovery. Besides, integrating the data from different sources, including connectome and other omics information, and their processing with artificial intelligence, together with validated biological and clinical findings will be significant for the development of a personalized surgical strategy.
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Affiliation(s)
- Ziyan Chen
- Department of Neurosurgery, Xiangya Hospital, Central South University, Hunan, China
- Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China
| | - Ningrong Ye
- Department of Neurosurgery, Xiangya Hospital, Central South University, Hunan, China
- Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China
| | - Chubei Teng
- Department of Neurosurgery, Xiangya Hospital, Central South University, Hunan, China
- Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China
- Department of Neurosurgery, The First Affiliated Hospital, University of South China, Hengyang, China
| | - Xuejun Li
- Department of Neurosurgery, Xiangya Hospital, Central South University, Hunan, China
- Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China
- *Correspondence: Xuejun Li,
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What Can Resting-State fMRI Data Analysis Explain about the Functional Brain Connectivity in Glioma Patients? Tomography 2022; 8:267-280. [PMID: 35202187 PMCID: PMC8878995 DOI: 10.3390/tomography8010021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 01/12/2022] [Accepted: 01/14/2022] [Indexed: 11/24/2022] Open
Abstract
Resting-state functional MRI has been increasingly implemented in imaging protocols for the study of functional connectivity in glioma patients as a sequence able to capture the activity of brain networks and to investigate their properties without requiring the patients’ cooperation. The present review aims at describing the most recent results obtained through the analysis of resting-state fMRI data in different contexts of interest for brain gliomas: the identification and localization of functional networks, the characterization of altered functional connectivity, and the evaluation of functional plasticity in relation to the resection of the glioma. An analysis of the literature showed that significant and promising results could be achieved through this technique in all the aspects under investigation. Nevertheless, there is room for improvement, especially in terms of stability and generalizability of the outcomes. Further research should be conducted on homogeneous samples of glioma patients and at fixed time points to reduce the considerable variability in the results obtained across and within studies. Future works should also aim at establishing robust metrics for the assessment of the disruption of functional connectivity and its recovery at the single-subject level.
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Dong T, Huang Q, Huang S, Xin J, Jia Q, Gao Y, Shen H, Tang Y, Zhang H. Identification of Methamphetamine Abstainers by Resting-State Functional Magnetic Resonance Imaging. Front Psychol 2021; 12:717519. [PMID: 34526937 PMCID: PMC8435858 DOI: 10.3389/fpsyg.2021.717519] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 08/09/2021] [Indexed: 11/19/2022] Open
Abstract
Methamphetamine (MA) can cause brain structural and functional impairment, but there are few studies on whether this difference will sustain on MA abstainers. The purpose of this study is to investigate the correlation of brain networks in MA abstainers. In this study, 47 people detoxified for at least 14 months and 44 normal people took a resting-state functional magnetic resonance imaging (RS-fMRI) scan. A dynamic (i.e., time-varying) functional connectivity (FC) is obtained by applying sliding windows in the time courses on the independent components (ICs). The windowed correlation data for each IC were then clustered by k-means. The number of subjects in each cluster was used as a new feature for individual identification. The results show that the classifier achieved satisfactory performance (82.3% accuracy, 77.7% specificity, and 85.7% sensitivity). We find that there are significant differences in the brain networks of MA abstainers and normal people in the time domain, but the spatial differences are not obvious. Most of the altered functional connections (time-varying) are identified to be located at dorsal default mode network. These results have shown that changes in the correlation of the time domain may play an important role in identifying MA abstainers. Therefore, our findings provide valuable insights in the identification of MA and elucidate the pathological mechanism of MA from a resting-state functional integration point of view.
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Affiliation(s)
- Tingting Dong
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Qiuping Huang
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
- Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Institute of Mental Health of Central South University, Chinese National Technology Institute on Mental Disorders, Changsha, China
| | - Shucai Huang
- The Fourth People’s Hospital of Wuhu, Wuhu, China
| | - Jiang Xin
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Qiaolan Jia
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Yang Gao
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Hongxian Shen
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
- Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Institute of Mental Health of Central South University, Chinese National Technology Institute on Mental Disorders, Changsha, China
| | - Yan Tang
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Hao Zhang
- School of Computer Science and Engineering, Central South University, Changsha, China
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