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Bao W, Gao Y, Feng R, Cao L, Zhou Z, Zhuo L, Li H, Ouyang X, Hu X, Li H, Huang G, Huang X. Negative family and interpersonal relationship are associated with centromedial amygdala functional connectivity alterations in adolescent depression. Eur Child Adolesc Psychiatry 2024:10.1007/s00787-024-02456-0. [PMID: 38743107 DOI: 10.1007/s00787-024-02456-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 04/27/2024] [Indexed: 05/16/2024]
Abstract
The amygdala, known for its functional heterogeneity, plays a critical role in the neural mechanism of adolescent major depressive disorder (aMDD). However, changes in its subregional functional networks in relation to stressful factors remain unclear. We recruited 78 comorbidity-free, medication-naive aMDD patients and 40 matched healthy controls (HC) to explore changes in resting-state functional connectivity (FC) across four amygdala subregions: the centromedial nucleus (CM), the basolateral nucleus (LB), the superficial nucleus (SF), and the amygdalostriatal transition area (Astr). Then, we performed partial correlation analysis to investigate the relationship between amygdala subregional FC and stressful factors as measured by the Chinese Version of Family Environment Scale (FES-CV) and the Adolescent Self-Rated Life Events Scale (ASLEC). Compared to HC, aMDD patients demonstrated significantly decreased functional connectivity between the left CM and left precentral gyrus, as well as between left SF and left precentral gyrus, and between left LB and posterior cingulate gyrus (PCC)/precuneus. In aMDD group, left CM-precentral gyrus FC exhibited negative correlation with interpersonal relationship and punishment, and positive correlation with family cohesion and expressiveness. This study reveals distinct patterns of abnormal functional connectivity among amygdala subregions in aMDD. Our findings suggest that the CM network, in particular, may be involved in stress-related factors in aMDD, which provide a potential target for the prevention and treatment of adolescent depression.
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Affiliation(s)
- Weijie Bao
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No.37 Guo Xue Xiang, Chengdu, 610041, China
| | - Yingxue Gao
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No.37 Guo Xue Xiang, Chengdu, 610041, China
| | - Ruohan Feng
- Department of Radiology, Sichuan Mental Health Center, The Third Hospital of Mianyang, Mianyang, China
| | - Lingxiao Cao
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No.37 Guo Xue Xiang, Chengdu, 610041, China
| | - Zilin Zhou
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No.37 Guo Xue Xiang, Chengdu, 610041, China
| | - Lihua Zhuo
- Department of Radiology, Sichuan Mental Health Center, The Third Hospital of Mianyang, Mianyang, China
| | - Hongwei Li
- Department of Radiology, Sichuan Mental Health Center, The Third Hospital of Mianyang, Mianyang, China
| | - Xinqin Ouyang
- Department of Radiology, Sichuan Mental Health Center, The Third Hospital of Mianyang, Mianyang, China
| | - Xinyue Hu
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No.37 Guo Xue Xiang, Chengdu, 610041, China
| | - Hailong Li
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No.37 Guo Xue Xiang, Chengdu, 610041, China
| | - Guoping Huang
- Department of Psychiatry, Sichuan Mental Health Center, The Third Hospital of Mianyang, Mianyang, China
| | - Xiaoqi Huang
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No.37 Guo Xue Xiang, Chengdu, 610041, China.
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China.
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Zhan L, Gao Y, Huang L, Zhang H, Huang G, Wang Y, Sun J, Xie Z, Li M, Jia X, Cheng L, Yu Y. Brain functional connectivity alterations of Wernicke's area in individuals with autism spectrum conditions in multi-frequency bands: A mega-analysis. Heliyon 2024; 10:e26198. [PMID: 38404781 PMCID: PMC10884452 DOI: 10.1016/j.heliyon.2024.e26198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 02/05/2024] [Accepted: 02/08/2024] [Indexed: 02/27/2024] Open
Abstract
Characterized by severe deficits in communication, most individuals with autism spectrum conditions (ASC) experience significant language dysfunctions, thereby impacting their overall quality of life. Wernicke's area, a classical and traditional brain region associated with language processing, plays a substantial role in the manifestation of language impairments. The current study carried out a mega-analysis to attain a comprehensive understanding of the neural mechanisms underpinning ASC, particularly in the context of language processing. The study employed the Autism Brain Image Data Exchange (ABIDE) dataset, which encompasses data from 443 typically developing (TD) individuals and 362 individuals with ASC. The objective was to detect abnormal functional connectivity (FC) between Wernicke's area and other language-related functional regions, and identify frequency-specific altered FC using Wernicke's area as the seed region in ASC. The findings revealed that increased FC in individuals with ASC has frequency-specific characteristics. Further, in the conventional frequency band (0.01-0.08 Hz), individuals with ASC exhibited increased FC between Wernicke's area and the right thalamus compared with TD individuals. In the slow-5 frequency band (0.01-0.027 Hz), increased FC values were observed in the left cerebellum Crus II and the right lenticular nucleus, pallidum. These results provide novel insights into the potential neural mechanisms underlying communication deficits in ASC from the perspective of language impairments.
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Affiliation(s)
- Linlin Zhan
- School of Western Studies, Heilongjiang University, Harbin, China
| | - Yanyan Gao
- College of Teacher Education, Zhejiang Normal University, Jinhua, China
| | - Lina Huang
- Department of Radiology, Changshu No. 2 People's Hospital, The Affiliated Changshu Hospital of Xuzhou Medical University, Changshu, Jiangsu, China
| | - Hongqiang Zhang
- Department of Radiology, Changshu No. 2 People's Hospital, The Affiliated Changshu Hospital of Xuzhou Medical University, Changshu, Jiangsu, China
| | - Guofeng Huang
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, China
| | - Yadan Wang
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, China
| | - Jiawei Sun
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, China
| | - Zhou Xie
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, China
| | - Mengting Li
- College of Teacher Education, Zhejiang Normal University, Jinhua, China
| | - Xize Jia
- College of Teacher Education, Zhejiang Normal University, Jinhua, China
| | - Lulu Cheng
- School of Foreign Studies, China University of Petroleum (East China), Qingdao, China
- Shanghai Center for Research in English Language Education, Shanghai International Studies University, Shanghai, China
| | - Yang Yu
- Psychiatry Department, The Second Affiliated Hospital Zhejiang University School of Medicine, Zhejiang, Hangzhou, China
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Huang Y, Zhang J, He K, Mo X, Yu R, Min J, Zhu T, Ma Y, He X, Lv F, Lei D, Liu M. Innovative Neuroimaging Biomarker Distinction of Major Depressive Disorder and Bipolar Disorder through Structural Connectome Analysis and Machine Learning Models. Diagnostics (Basel) 2024; 14:389. [PMID: 38396428 PMCID: PMC10888009 DOI: 10.3390/diagnostics14040389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 02/03/2024] [Accepted: 02/08/2024] [Indexed: 02/25/2024] Open
Abstract
Major depressive disorder (MDD) and bipolar disorder (BD) share clinical features, which complicates their differentiation in clinical settings. This study proposes an innovative approach that integrates structural connectome analysis with machine learning models to discern individuals with MDD from individuals with BD. High-resolution MRI images were obtained from individuals diagnosed with MDD or BD and from HCs. Structural connectomes were constructed to represent the complex interplay of brain regions using advanced graph theory techniques. Machine learning models were employed to discern unique connectivity patterns associated with MDD and BD. At the global level, both BD and MDD patients exhibited increased small-worldness compared to the HC group. At the nodal level, patients with BD and MDD showed common differences in nodal parameters primarily in the right amygdala and the right parahippocampal gyrus when compared with HCs. Distinctive differences were found mainly in prefrontal regions for BD, whereas MDD was characterized by abnormalities in the left thalamus and default mode network. Additionally, the BD group demonstrated altered nodal parameters predominantly in the fronto-limbic network when compared with the MDD group. Moreover, the application of machine learning models utilizing structural brain parameters demonstrated an impressive 90.3% accuracy in distinguishing individuals with BD from individuals with MDD. These findings demonstrate that combined structural connectome and machine learning enhance diagnostic accuracy and may contribute valuable insights to the understanding of the distinctive neurobiological signatures of these psychiatric disorders.
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Affiliation(s)
- Yang Huang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Jingbo Zhang
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China (J.M.)
| | - Kewei He
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China (J.M.)
| | - Xue Mo
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Renqiang Yu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Jing Min
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China (J.M.)
| | - Tong Zhu
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China (J.M.)
| | - Yunfeng Ma
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China (J.M.)
| | - Xiangqian He
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China (J.M.)
| | - Fajin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Du Lei
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China (J.M.)
| | - Mengqi Liu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
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Ravan M, Noroozi A, Sanchez MM, Borden L, Alam N, Flor-Henry P, Colic S, Khodayari-Rostamabad A, Minuzzi L, Hasey G. Diagnostic deep learning algorithms that use resting EEG to distinguish major depressive disorder, bipolar disorder, and schizophrenia from each other and from healthy volunteers. J Affect Disord 2024; 346:285-298. [PMID: 37963517 DOI: 10.1016/j.jad.2023.11.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 11/02/2023] [Accepted: 11/07/2023] [Indexed: 11/16/2023]
Abstract
BACKGROUND Mood disorders and schizophrenia affect millions worldwide. Currently, diagnosis is primarily determined by reported symptomatology. As symptoms may overlap, misdiagnosis is common, potentially leading to ineffective or destabilizing treatment. Diagnostic biomarkers could significantly improve clinical care by reducing dependence on symptomatic presentation. METHODS We used deep learning analysis (DLA) of resting electroencephalograph (EEG) to differentiate healthy control (HC) subjects (N = 239), from those with major depressive disorder (MDD) (N = 105), MDD-atypical (MDD-A) (N = 27), MDD-psychotic (MDD-P) (N = 35), bipolar disorder-depressed episode (BD-DE) (N = 71), BD-manic episode (BD-ME) (N = 49), and schizophrenia (SCZ) (N = 122) and also differentiate subjects with mental disorders on a pair-wise basis. DSM-III-R diagnoses were determined and supplemented by computerized Quick Diagnostic Interview Schedule. After EEG preprocessing, robust exact low-resolution electromagnetic tomography (ReLORETA) computed EEG sources for 82 brain regions. 20 % of all subjects were then set aside for independent testing. Feature selection methods were then used for the remaining subjects to identify brain source regions that are discriminating between diagnostic categories. RESULTS Pair-wise classification accuracies between 90 % and 100 % were obtained using independent test subjects whose data were not used for training purposes. The most frequently selected features across various pairs are in the postcentral, supramarginal, and fusiform gyri, the hypothalamus, and the left cuneus. Brain sites discriminating SCZ from HC were mainly in the left hemisphere while those separating BD-ME from HC were on the right. LIMITATIONS The use of superseded DSM-III-R diagnostic system and relatively small sample size in some disorder categories that may increase the risk of overestimation. CONCLUSIONS DLA of EEG could be trained to autonomously classify psychiatric disorders with over 90 % accuracy compared to an expert clinical team using standardized operational methods.
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Affiliation(s)
- Maryam Ravan
- Department of Electrical and Computer Engineering, New York Institute of Technology, New York, NY, USA.
| | - Amin Noroozi
- Department of Digital, Technologies, and Arts, Staffordshire University, Staffordshire, England, UK
| | - Mary Margarette Sanchez
- Department of Electrical and Computer Engineering, New York Institute of Technology, New York, NY, USA
| | - Lee Borden
- Department of Electrical and Computer Engineering, New York Institute of Technology, New York, NY, USA
| | - Nafia Alam
- Department of Electrical and Computer Engineering, New York Institute of Technology, New York, NY, USA
| | | | - Sinisa Colic
- Department of Electrical Engineering, University of Toronto, Canada
| | | | - Luciano Minuzzi
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
| | - Gary Hasey
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
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Chou PH, Liu WC, Lin WH, Hsu CW, Wang SC, Su KP. NIRS-aided differential diagnosis among patients with major depressive disorder, bipolar disorder, and schizophrenia. J Affect Disord 2023; 341:366-373. [PMID: 37634818 DOI: 10.1016/j.jad.2023.08.101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 08/19/2023] [Accepted: 08/21/2023] [Indexed: 08/29/2023]
Abstract
BACKGROUND To establish a clinically applicable neuroimaging-guided diagnostic support system that uses near-infrared spectroscopy (NIRS) for differential diagnosis at the individual level among major depressive disorder (MDD), bipolar disorder (BPD), and schizophrenia (SZ). METHODS A total of 192 participants were recruited, including 40 patients with MDD, 38 patients with BPD, 65 patients with SZ, and 49 healthy individuals. We analyzed the spatiotemporal characteristics of hemodynamic responses in the frontotemporal cortex during a verbal fluency test (VFT) measured by NIRS to assess the accuracy of single-subject classification for differential diagnosis among the three psychiatric disorders. The optimal threshold of the frontal centroid value (54 seconds) was utilized on the basis of the findings of the Japanese study. RESULTS The application of the optimal threshold of the frontal centroid value (54 seconds) allowed for the accurate differentiation of patients with unipolar MDD (72.5%) from BPD (78.9%) or SZ (84.6%). CONCLUSION These results suggest that the NIRS-aided differential diagnosis of major psychiatric disorders can be a promising biomarker in Taiwan. Future multi-site studies are needed to validate our findings.
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Affiliation(s)
- Po-Han Chou
- Department of Psychiatry, China Medical University Hsinchu Hospital, Hsinchu, Taiwan; Dr. Chou's Mental Health Clinic, Hsinchu, Taiwan
| | - Wen-Chun Liu
- An-Nan Hospital, China Medical University, Tainan, Taiwan
| | - Wei-Hao Lin
- Department of Psychiatry, Puli branch, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Chih-Wei Hsu
- Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan
| | - Shao-Cheng Wang
- Department of Psychiatry, Taoyuan General Hospital, Ministry of Health and Welfare, Taoyuan, Taiwan; Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States; Department of Medical Laboratory Science and Biotechnology, Chung Hwa University of Medical Technology, Tainan, Taiwan.
| | - Kuan-Pin Su
- An-Nan Hospital, China Medical University, Tainan, Taiwan; Mind-Body Interface Research Center (MBI-Lab), China Medical University Hospital, Taichung, Taiwan; College of Medicine, China Medical University, Taichung, Taiwan.
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Zhong J, Xu J, Wang Z, Yang H, Li J, Yu H, Huang W, Wan C, Ma H, Zhang N. Changes in brain functional networks in remitted major depressive disorder: a six-month follow-up study. BMC Psychiatry 2023; 23:628. [PMID: 37641013 PMCID: PMC10464087 DOI: 10.1186/s12888-023-05082-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 08/06/2023] [Indexed: 08/31/2023] Open
Abstract
BACKGROUND Patients with remitted major depressive disorder (rMDD) show abnormal functional connectivity of the central executive network (CEN), salience networks (SN) and default mode network (DMN). It is unclear how these change during remission, or whether changes are related to function. METHODS Three spatial networks in 17 patients with rMDD were compared between baseline and the six-month follow-up, and to 22 healthy controls. Correlations between these changes and psychosocial functioning were also assessed. RESULTS In the CEN, patients at baseline had abnormal functional connectivity in the right anterior cingulate, right dorsolateral prefrontal cortex (DLPFC) and inferior parietal lobule (IPL) compare with HCs. There were functional connection differences in the right DLPFC and left IPL at baseline during follow-up. Abnormal connectivity in the right DLPFC and medial prefrontal cortex (mPFC) were found at follow-up. In the SN, patients at baseline had abnormal functional connectivity in the insula, left anterior cingulate, left IPL, and right precuneus; compared with baseline, patients had higher connectivity in the right DLPFC at follow-up. In the DMN, patients at baseline had abnormal functional connectivity in the right mPFC. Resting-state functional connectivity of the IPL and DLPFC in the CEN correlated with psychosocial functioning. CONCLUSIONS At six-month follow-up, the CEN still showed abnormal functional connectivity in those with rMDD, while anomalies in the SN and DMN has disappeared. Resting-state functional connectivity of the CEN during early rMDD is associated with psychosocial function. CLINICAL TRIALS REGISTRATION Pharmacotherapy and Psychotherapy for MDD after Remission on Psychology and Neuroimaging. https://www. CLINICALTRIALS gov/ , registration number: NCT01831440 (15/4/2013).
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Affiliation(s)
- Jiaqi Zhong
- Affiliated Nanjing Brain Hospital of Nanjing Medical University, No.264 Guangzhou Street, Gulou District, Nanjing, 210029, Jiangsu, China
- Cognitive Behavioral Therapy Institute of Nanjing Medical University, Nanjing, 210029, Jiangsu, China
| | - Jingren Xu
- Affiliated Nanjing Brain Hospital of Nanjing Medical University, No.264 Guangzhou Street, Gulou District, Nanjing, 210029, Jiangsu, China
| | - Zhenzhen Wang
- Affiliated Nanjing Brain Hospital of Nanjing Medical University, No.264 Guangzhou Street, Gulou District, Nanjing, 210029, Jiangsu, China
- School of psychological and cognitive sciences, Peking University, Beijing, 100871, China
| | - Hao Yang
- Affiliated Nanjing Brain Hospital of Nanjing Medical University, No.264 Guangzhou Street, Gulou District, Nanjing, 210029, Jiangsu, China
| | - Jiawei Li
- Affiliated Nanjing Brain Hospital of Nanjing Medical University, No.264 Guangzhou Street, Gulou District, Nanjing, 210029, Jiangsu, China
| | - Haoran Yu
- Affiliated Nanjing Brain Hospital of Nanjing Medical University, No.264 Guangzhou Street, Gulou District, Nanjing, 210029, Jiangsu, China
| | - Wenyan Huang
- Affiliated Nanjing Brain Hospital of Nanjing Medical University, No.264 Guangzhou Street, Gulou District, Nanjing, 210029, Jiangsu, China
| | - Cheng Wan
- Department of Medical Informatic, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, 210029, Jiangsu, China
| | - Hui Ma
- Affiliated Nanjing Brain Hospital of Nanjing Medical University, No.264 Guangzhou Street, Gulou District, Nanjing, 210029, Jiangsu, China.
| | - Ning Zhang
- Affiliated Nanjing Brain Hospital of Nanjing Medical University, No.264 Guangzhou Street, Gulou District, Nanjing, 210029, Jiangsu, China.
- Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, 210029, Jiangsu, China.
- Cognitive Behavioral Therapy Institute of Nanjing Medical University, Nanjing, 210029, Jiangsu, China.
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Shunkai L, Chen P, Zhong S, Chen G, Zhang Y, Zhao H, He J, Su T, Yan S, Luo Y, Ran H, Jia Y, Wang Y. Alterations of insular dynamic functional connectivity and psychological characteristics in unmedicated bipolar depression patients with a recent suicide attempt. Psychol Med 2023; 53:3837-3848. [PMID: 35257645 DOI: 10.1017/s0033291722000484] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
BACKGROUND Mounting evidence showed that insula contributed to the neurobiological mechanism of suicidal behaviors in bipolar disorder (BD). However, no studies have analyzed the dynamic functional connectivity (dFC) of insular Mubregions and its association with personality traits in BD with suicidal behaviors. Therefore, we investigated the alterations of dFC variability in insular subregions and personality characteristics in BD patients with a recent suicide attempt (SA). METHODS Thirty unmedicated BD patients with SA, 38 patients without SA (NSA) and 35 demographically matched healthy controls (HCs) were included. The sliding-window analysis was used to evaluate whole-brain dFC for each insular subregion seed. We assessed between-group differences of psychological characteristics on the Minnesota Multiphasic Personality Inventory-2. Finally, a multivariate regression model was adopted to predict the severity of suicidality. RESULTS Compared to NSA and HCs, the SA group exhibited decreased dFC variability values between the left dorsal anterior insula and the left anterior cerebellum. These dFC variability values could also be utilized to predict the severity of suicidality (r = 0.456, p = 0.031), while static functional connectivity values were not appropriate for this prediction. Besides, the SA group scored significantly higher on the schizophrenia clinical scales (p < 0.001) compared with the NSA group. CONCLUSIONS Our findings indicated that the dysfunction of insula-cerebellum connectivity may underlie the neural basis of SA in BD patients, and highlighted the dFC variability values could be considered a neuromarker for predictive models of the severity of suicidality. Moreover, the psychiatric features may increase the vulnerability of suicidal behavior.
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Affiliation(s)
- Lai Shunkai
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, China
- Department of Psychiatry, First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Pan Chen
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, China
- Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, China
| | - Shuming Zhong
- Department of Psychiatry, First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Guanmao Chen
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, China
- Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, China
| | - Yiliang Zhang
- Department of Psychiatry, First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Hui Zhao
- Department of Psychiatry, First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Jiali He
- Department of Psychiatry, First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Ting Su
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, China
- Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, China
| | - Shuya Yan
- School of Management, Jinan University, Guangzhou, China
| | - Yange Luo
- School of Management, Jinan University, Guangzhou, China
| | - Hanglin Ran
- School of Management, Jinan University, Guangzhou, China
| | - Yanbin Jia
- Department of Psychiatry, First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Ying Wang
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, China
- Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, China
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Todeva-Radneva A, Kandilarova S, Paunova R, Stoyanov D, Zdravkova T, Sladky R. Functional Connectivity of the Anterior Cingulate Cortex and the Right Anterior Insula Differentiates between Major Depressive Disorder, Bipolar Disorder and Healthy Controls. Biomedicines 2023; 11:1608. [PMID: 37371703 DOI: 10.3390/biomedicines11061608] [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: 03/20/2023] [Revised: 05/28/2023] [Accepted: 05/30/2023] [Indexed: 06/29/2023] Open
Abstract
Background: This study aimed to explore possible differences of the whole-brain functional connectivity of the anterior cingulate cortex (ACC) and anterior insula (AI), in a sample of depressed patients with major depressive disorder (MDD), bipolar disorder (BD) and healthy controls (HC). Methods: A hundred and three subjects (nMDD = 35, nBD = 25, and nHC = 43) between the ages of eighteen and sixty-five years old underwent functional magnetic resonance imaging. The CONN Toolbox was used to process and analyze the functional connectivity of the ACC and AI. Results: The comparison between the patients (MDD/BD) and HC yielded increased resting-state functional connectivity (rsFC) between the ACC and the motor and somatosensory cortices (SSC), superior parietal lobule (SPL), precuneus, and lateral occipital cortex, which was driven by the BD group. In addition, hyperconnectivity between the right AI and the motor and SSC was found in BD, as compared to HC. In MDD, as compared to HC, hyperconnectivity between ACC and SPL and the lateral occipital cortex was found, with no statistical rsFC differences for the AI seed. Compared to BD, the MDD group showed ACC-cerebellum hyperconnectivity and a trend for increased rsFC between the right AI and the bilateral superior frontal cortex. Conclusions: Considering the observed hyperconnectivity between the ACC/somatosensory cortex in the patient group, we suggest depression may be related to an impairment of the sensory-discriminative function of the SSC, which results in the phenomenological signature of mental pain in both MDD and BD. These findings suggest that future research should investigate this particular network with respect to motor functions and executive control, as a potential differential diagnostic biomarker for MDD and BD.
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Affiliation(s)
- Anna Todeva-Radneva
- Department of Psychiatry and Medical Psychology, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria
- Research Institute, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria
| | - Sevdalina Kandilarova
- Department of Psychiatry and Medical Psychology, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria
- Research Institute, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria
| | - Rositsa Paunova
- Department of Psychiatry and Medical Psychology, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria
| | - Drozdstoy Stoyanov
- Department of Psychiatry and Medical Psychology, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria
- Research Institute, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria
| | - Tina Zdravkova
- Research Institute, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria
| | - Ronald Sladky
- Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, 1010 Vienna, Austria
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9
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Li MT, Sun JW, Zhan LL, Antwi CO, Lv YT, Jia XZ, Ren J. The effect of seed location on functional connectivity: evidence from an image-based meta-analysis. Front Neurosci 2023; 17:1120741. [PMID: 37325032 PMCID: PMC10264592 DOI: 10.3389/fnins.2023.1120741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Accepted: 05/15/2023] [Indexed: 06/17/2023] Open
Abstract
Introduction Default mode network (DMN) is the most involved network in the study of brain development and brain diseases. Resting-state functional connectivity (rsFC) is the most used method to study DMN, but different studies are inconsistent in the selection of seed. To evaluate the effect of different seed selection on rsFC, we conducted an image-based meta-analysis (IBMA). Methods We identified 59 coordinates of seed regions of interest (ROIs) within the default mode network (DMN) from 11 studies (retrieved from Web of Science and Pubmed) to calculate the functional connectivity; then, the uncorrected t maps were obtained from the statistical analyses. The IBMA was performed with the t maps. Results We demonstrate that the overlap of meta-analytic maps across different seeds' ROIs within DMN is relatively low, which cautions us to be cautious with seeds' selection. Discussion Future studies using the seed-based functional connectivity method should take the reproducibility of different seeds into account. The choice of seed may significantly affect the connectivity results.
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Affiliation(s)
- Meng-Ting Li
- School of Psychology, Zhejiang Normal University, Jinhua, China
| | - Jia-Wei Sun
- Department of Clinical Neuroscience, Division of Neuro, Karolinska Institutet, Stockholm, Sweden
| | - Lin-Lin Zhan
- School of Western Studies, Heilongjiang University, Harbin, China
| | | | - Ya-Ting Lv
- Center for Cognition and Brain Disorders, The Affiliated Hospital, Hangzhou Normal University, Hangzhou, China
| | - Xi-Ze Jia
- School of Psychology, Zhejiang Normal University, Jinhua, China
| | - Jun Ren
- School of Psychology, Zhejiang Normal University, Jinhua, China
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10
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Mao Y, Zhang P, Sun R, Zhang X, He Y, Li S, Yin T, Zeng F. Altered resting-state brain activity in functional dyspepsia patients: a coordinate-based meta-analysis. Front Neurosci 2023; 17:1174287. [PMID: 37250423 PMCID: PMC10213416 DOI: 10.3389/fnins.2023.1174287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Accepted: 04/14/2023] [Indexed: 05/31/2023] Open
Abstract
Background Neuroimaging studies have identified aberrant activity patterns in multiple brain regions in functional dyspepsia (FD) patients. However, due to the differences in study design, these previous findings are inconsistent, and the underlying neuropathological characteristics of FD remain unclear. Methods Eight databases were systematically searched for literature from inception to October 2022 with the keywords "Functional dyspepsia" and "Neuroimaging." Thereafter, the anisotropic effect size signed the differential mapping (AES-SDM) approach that was applied to meta-analyze the aberrant brain activity pattern of FD patients. Results A total of 11 articles with 260 FD patients and 202 healthy controls (HCs) were included. The AES-SDM meta-analysis demonstrated that FD patients manifested increased activity in the bilateral insula, left anterior cingulate gyrus, bilateral thalamus, right precentral gyrus, left supplementary motor area, right putamen, and left rectus gyrus and decreased functional activity in the right cerebellum compared to the HCs. Sensitivity analysis showed that all these above regions were highly reproducible, and no significant publication bias was detected. Conclusion The current study demonstrated that FD patients had significantly abnormal activity patterns in several brain regions involved in visceral sensation perception, pain modulation, and emotion regulation, which provided an integrated insight into the neuropathological characteristics of FD.
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Affiliation(s)
- Yangke Mao
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Pan Zhang
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Ruirui Sun
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Xinyue Zhang
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Yuqi He
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Siyang Li
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Tao Yin
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Fang Zeng
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Key Laboratory of Sichuan Province for Acupuncture and Chronobiology, Chengdu, China
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11
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Huang MH, Fan SY, Lin IM. EEG coherences of the fronto-limbic circuit between patients with major depressive disorder and healthy controls. J Affect Disord 2023; 331:112-120. [PMID: 36958482 DOI: 10.1016/j.jad.2023.03.055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 03/07/2023] [Accepted: 03/18/2023] [Indexed: 03/25/2023]
Abstract
BACKGROUND Imaging studies found that patients with major depressive disorder (MDD) showed abnormal functional connectivity in the fronto-limbic circuit, including the prefrontal cortex (PFC), anterior cingulate cortex (ACC), and limbic system (amygdala). This study used electroencephalography (EEG) coherence as an indicator of functional connectivity in the fronto-limbic circuit and examined the group differences between the MDD group and healthy controls (HC group), and the associations between EEG coherence and depressive symptoms. METHODS 125 and 132 participants in the MDD and HC groups have measured the symptoms of depression and anxiety, and delta, theta, alpha, and beta1-beta4 EEG coherences in the fronto-limbic circuit and examined the differences between the two groups, and the associations between the EEG coherence and depressive symptoms were examined. RESULTS Lower theta, alpha, beta1, beta3, and beta4 coherence in the fronto-limbic circuit and higher beta2 coherence between the PFC and limbic system in the MDD group than in the HC group. Negative correlations between delta, theta, beta1, beta3, and beta4 coherence and total depression, cognitive depression, and somatic depression; positive correlations between beta2 coherences in the PFC and limbic system, and total depression and cognitive depression scores in the MDD group. LIMITATIONS Whether low EEG coherence in the fronto-limbic circuit is applicable to other subtypes of MDD requires further study. CONCLUSIONS Low EEG coherences in the fronto-limbic circuit were related to depressive symptoms, and increased functional connectivity in the fronto-limbic circuit can be applied by neurofeedback in future studies.
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Affiliation(s)
- Min-Han Huang
- Department of Psychology, College of Humanities and Social Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan; Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan
| | - Sheng-Yu Fan
- Institute of Gerontology, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - I-Mei Lin
- Department of Psychology, College of Humanities and Social Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan; Institute of Gerontology, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
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12
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Discriminating between bipolar and major depressive disorder using a machine learning approach and resting-state EEG data. Clin Neurophysiol 2023; 146:30-39. [PMID: 36525893 DOI: 10.1016/j.clinph.2022.11.014] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 09/28/2022] [Accepted: 11/27/2022] [Indexed: 12/12/2022]
Abstract
OBJECTIVE Distinguishing major depressive disorder (MDD) from bipolar disorder (BD) is a crucial clinical challenge as effective treatment is quite different for each condition. In this study electroencephalography (EEG) was explored as an objective biomarker for distinguishing MDD from BD using an efficient machine learning algorithm (MLA) trained by a relatively large and balanced dataset. METHODS A 3 step MLA was applied: (1) a multi-step preprocessing method was used to improve the quality of the EEG signal, (2) symbolic transfer entropy (STE), an effective connectivity measure, was applied to the resultant EEG and (3) the MLA used the extracted STE features to distinguish MDD (N = 71) from BD (N = 71) subjects. RESULTS 14 connectivity features were selected by the proposed algorithm. Most of the selected features were related to the frontal, parietal, and temporal lobe electrodes. The major involved regions were the Broca region in the frontal lobe and the somatosensory association cortex in the parietal lobe. These regions are near electrodes FC5 and CPz and are involved in processing language and sensory information, respectively. The resulting classifier delivered an evaluation accuracy of 88.5% and a test accuracy of 89.3%, using 80% of the data for training and evaluation and the remaining 20% for testing, respectively. CONCLUSIONS The high evaluation and test accuracies of our algorithm, derived from a large balanced training sample suggests that this method may hold significant promise as a clinical tool. SIGNIFICANCE The proposed MLA may provide an inexpensive and readily available tool that clinicians may use to enhance diagnostic accuracy and shorten time to effective treatment.
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13
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Alterations in regional homogeneity and functional connectivity associated with cognitive impairment in patients with hypertension: a resting-state functional magnetic resonance imaging study. Hypertens Res 2023; 46:1311-1325. [PMID: 36690806 DOI: 10.1038/s41440-023-01168-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 11/09/2022] [Accepted: 12/22/2022] [Indexed: 01/24/2023]
Abstract
Our study aims to investigate the alterations and diagnostic efficiency of regional homogeneity (ReHo) and functional connectivity (FC) in hypertension patients with cognitive impairment. A total of 62 hypertension patients with cognitive impairment (HTN-CI), 59 hypertension patients with normal cognition (HTN-NC), and 58 healthy controls (HCs) with rs-fMRI data were enrolled in this study. Univariate analysis (based on whole-brain ReHo and seed-based FC maps) was performed to observe brain regions with significant differences among the three groups. Multiple voxel pattern analysis (MVPA) was applied to evaluate the diagnostic accuracy in classifying HTN-CI from HTN-NC and HCs. Compared with the HCs and HTN-NC, HTN-CI exhibited decreased ReHo in the right caudate, left postcentral gyrus, posterior cingulate gyrus, insula, while increased ReHo in the left superior occipital gyrus and superior parietal gyrus. HTN-CI showed increased FC between seed regions (left posterior cingulate gyrus, insula, postcentral gyrus) with many specific brain regions. MVPA analysis (based on whole-brain ReHo and seed-based FC maps) displayed high classification ability in distinguishing HTN-CI from HTN-NC and HCs. The ReHo values (right caudate) and the FC values (left postcentral gyrus seed to left posterior cingulate gyrus) were positively correlated with the MoCA scores in HTN-CI. HTN-CI was associated with decreased ReHo and increased FC mainly in the left posterior cingulate gyrus, postcentral gyrus, insula compared to HTN-NC and HC. Besides, MVPA analysis yields excellent diagnostic accuracy in classifying HTN-CI from HTN-NC and HCs. The findings may contribute to unveiling the underlying neuropathological mechanism of HTN-CI.
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14
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Hu S, Hao Z, Li M, Zhao M, Wen J, Gao Y, Wang Q, Xi H, Antwi CO, Jia X, Ren J. Resting-state abnormalities in functional connectivity of the default mode network in migraine: A meta-analysis. Front Neurosci 2023; 17:1136790. [PMID: 36937687 PMCID: PMC10014826 DOI: 10.3389/fnins.2023.1136790] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 02/15/2023] [Indexed: 03/05/2023] Open
Abstract
Migraine-a disabling neurological disorder, imposes a tremendous burden on societies. To reduce the economic and health toll of the disease, insight into its pathophysiological mechanism is key to improving treatment and prevention. Resting-state functional magnetic resonance imaging (rs-fMRI) studies suggest abnormal functional connectivity (FC) within the default mode network (DMN) in migraine patients. This implies that DMN connectivity change may represent a biomarker for migraine. However, the FC abnormalities appear inconsistent which hinders our understanding of the potential neuropathology. Therefore, we performed a meta-analysis of the FC within the DMN in migraine patients in the resting state to identify the common FC abnormalities. With efficient search and selection strategies, nine studies (published before July, 2022) were retrieved, containing 204 migraine patients and 199 healthy subjects. We meta-analyzed the data using the Anisotropic Effect Size version of Signed Differential Mapping (AES-SDM) method. Compared with healthy subjects, migraine patients showed increased connectivity in the right calcarine gyrus, left inferior occipital gyrus, left postcentral gyrus, right cerebellum, right parahippocampal gyrus, and right posterior cingulate gyrus, while decreased connectivity in the right postcentral gyrus, left superior frontal gyrus, right superior occipital gyrus, right orbital inferior frontal gyrus, left middle occipital gyrus, left middle frontal gyrus and left inferior frontal gyrus. These results provide a new perspective for the study of the pathophysiology of migraine and facilitate a more targeted treatment of migraine in the future.
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Affiliation(s)
- Su Hu
- School of Psychology, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China
| | - Zeqi Hao
- School of Psychology, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China
| | - Mengting Li
- School of Psychology, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China
| | - Mengqi Zhao
- School of Psychology, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China
| | - Jianjie Wen
- School of Psychology, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China
| | - Yanyan Gao
- School of Psychology, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China
| | - Qing Wang
- Department of Radiology, Changshu No.2 People’s Hospital, The Affiliated Changshu Hospital of Xuzhou Medical University, Changshu, Jiangsu, China
| | - Hongyu Xi
- School of Western Languages, Heilongjiang University, Harbin, China
| | - Collins Opoku Antwi
- School of Psychology, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China
| | - Xize Jia
- School of Psychology, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China
| | - Jun Ren
- School of Psychology, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China
- *Correspondence: Jun Ren,
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15
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A systematic review on the potential use of machine learning to classify major depressive disorder from healthy controls using resting state fMRI measures. Neurosci Biobehav Rev 2023; 144:104972. [PMID: 36436736 DOI: 10.1016/j.neubiorev.2022.104972] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 09/08/2022] [Accepted: 11/21/2022] [Indexed: 11/27/2022]
Abstract
BACKGROUND Major Depressive Disorder (MDD) is a psychiatric disorder characterized by functional brain deficits, as documented by resting-state functional magnetic resonance imaging (rs-fMRI) studies. AIMS In recent years, some studies used machine learning (ML) approaches, based on rs-fMRI features, for classifying MDD from healthy controls (HC). In this context, this review aims to provide a comprehensive overview of the results of these studies. DESIGN The studies research was performed on 3 online databases, examining English-written articles published before August 5, 2022, that performed a two-class ML classification using rs-fMRI features. The search resulted in 20 eligible studies. RESULTS The reviewed studies showed good performance metrics, with better performance achieved when the dataset was restricted to a more homogeneous group in terms of disease severity. Regions within the default mode network, salience network, and central executive network were reported as the most important features in the classification algorithms. LIMITATIONS The small sample size together with the methodological and clinical heterogeneity limited the generalizability of the findings. CONCLUSIONS In conclusion, ML applied to rs-fMRI features can be a valid approach to classify MDD and HC subjects and to discover features that can be used for additional investigation of the pathophysiology of the disease.
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Resting state functional connectivity as a marker of internalizing disorder onset in high-risk youth. Sci Rep 2022; 12:21337. [PMID: 36494495 PMCID: PMC9734132 DOI: 10.1038/s41598-022-25805-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 12/05/2022] [Indexed: 12/13/2022] Open
Abstract
While research has linked alterations in functional connectivity of the default mode (DMN), cognitive control (CCN), and salience networks (SN) to depression and anxiety, little research has examined whether these alterations may be premorbid vulnerabilities. This study examined resting state functional connectivity (RSFC) of the CCN, DMN, and SN as markers of risk for developing an onset of a depressive or anxiety disorder in adolescents at high familial risk for these disorders. At baseline, 135 participants aged 11-17 completed resting-state functional magnetic resonance imaging, measures of internalizing symptoms, and diagnostic interviews to assess history of depressive and anxiety disorders. Diagnostic assessments were completed again at 9- or 18-month follow-up for 112 participants. At baseline, increased CCN connectivity to areas of the visual network, and decreased connectivity between the left SN and the precentral gyrus, predicted an increased likelihood of a new onset at follow-up. Increased connectivity between the right SN and postcentral gyrus at baseline predicted first episode onsets at follow-up. Altered connectivity between these regions may represent a risk factor for developing a clinically significant onset of an internalizing disorder. Results may have implications for understanding the neural bases of internalizing disorders for early identification and prevention efforts.
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17
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Wang R, Mo F, Shen Y, Song Y, Cai H, Zhu J. Functional connectivity gradients of the insula to different cerebral systems. Hum Brain Mapp 2022; 44:790-800. [PMID: 36206289 PMCID: PMC9842882 DOI: 10.1002/hbm.26099] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 09/16/2022] [Accepted: 09/24/2022] [Indexed: 01/25/2023] Open
Abstract
The diverse functional roles of the insula may emerge from its heavy connectivity to an extensive network of cortical and subcortical areas. Despite several previous attempts to investigate the hierarchical organization of the insula by applying the recently developed gradient approach to insula-to-whole brain connectivity data, little is known about whether and how there is variability across connectivity gradients of the insula to different cerebral systems. Resting-state functional MRI data from 793 healthy subjects were used to discover and validate functional connectivity gradients of the insula, which were computed based on its voxel-wise functional connectivity profiles to distinct cerebral systems. We identified three primary patterns of functional connectivity gradients of the insula to distinct cerebral systems. The connectivity gradients to the higher-order transmodal associative systems, including the prefrontal, posterior parietal, temporal cortices, and limbic lobule, showed a ventroanterior-dorsal axis across the insula; those to the lower-order unimodal primary systems, including the motor, somatosensory, and occipital cortices, displayed radiating transitions from dorsoanterior toward both ventroanterior and dorsoposterior parts of the insula; the connectivity gradient to the subcortical nuclei exhibited an organization along the anterior-posterior axis of the insula. Apart from complementing and extending previous literature on the heterogeneous connectivity patterns of insula subregions, the presented framework may offer ample opportunities to refine our understanding of the role of the insula in many brain disorders.
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Affiliation(s)
- Rui Wang
- Department of RadiologyThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina,Research Center of Clinical Medical Imaging, Anhui ProvinceHefeiChina,Anhui Provincial Institute of Translational MedicineHefeiChina
| | - Fan Mo
- Department of RadiologyThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina,Research Center of Clinical Medical Imaging, Anhui ProvinceHefeiChina,Anhui Provincial Institute of Translational MedicineHefeiChina
| | - Yuhao Shen
- Department of RadiologyThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina,Research Center of Clinical Medical Imaging, Anhui ProvinceHefeiChina,Anhui Provincial Institute of Translational MedicineHefeiChina
| | - Yu Song
- Department of RadiologyThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina,Research Center of Clinical Medical Imaging, Anhui ProvinceHefeiChina,Anhui Provincial Institute of Translational MedicineHefeiChina
| | - Huanhuan Cai
- Department of RadiologyThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina,Research Center of Clinical Medical Imaging, Anhui ProvinceHefeiChina,Anhui Provincial Institute of Translational MedicineHefeiChina
| | - Jiajia Zhu
- Department of RadiologyThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina,Research Center of Clinical Medical Imaging, Anhui ProvinceHefeiChina,Anhui Provincial Institute of Translational MedicineHefeiChina
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18
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Yang Y, Wang F, Andrade-Machado R, De Vito A, Wang J, Zhang T, Liu H. Disrupted functional connectivity patterns of the left inferior frontal gyrus subregions in benign childhood epilepsy with centrotemporal spikes. Transl Pediatr 2022; 11:1552-1561. [PMID: 36247884 PMCID: PMC9561512 DOI: 10.21037/tp-22-270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 08/26/2022] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Benign epilepsy with centrotemporal spikes (BECTS) is one of the most common pediatric epileptic syndromes. Recent studies have shown that BECTS can lead to significant language dysfunction. Although research supports the role of the left inferior frontal gyrus (LIFG) in BECTS, it is unclear whether the subregions of the LIFG show different change patterns in patients with this syndrome. METHODS Using resting-state functional magnetic resonance imaging (fMRI) data in a group of 49 BECTS patients and 49 healthy controls, we investigated whether the BECTS patients show abnormal connectivity patterns of the LIFG subregions. RESULTS Compared with healthy controls, the BECTS patients exhibited higher connectivity between the following: the inferior frontal sulcus (IFS) and the right anterior cingulate cortex (ACC), and the ventral area 44 (A44v) region and the left hippocampus/parahippocampus. Also, a decreased connectivity was found between the IFS and the left inferior temporal gyrus (ITG). No other significant differences in functional connectivity were found in the other 4 functional subregions of the LIFG in the BECTS. CONCLUSIONS These findings provide evidence for BECTS-related functional connectivity patterns of the LIFG subregions and suggest that different subregions may be involved in different neural circuits associated with language function in the BECTS.
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Affiliation(s)
- Yang Yang
- Department of Radiology, Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, China.,Department of Radiology, Suining Central Hospital, Suining, China
| | - Fuqin Wang
- Department of Radiology, Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, China
| | - René Andrade-Machado
- Epilepsy Fellow at Children Hospital of Michigan, Detroit Medical Center, Detroit, MI, USA
| | - Andrea De Vito
- Department of Neuroradiology, H. S. Gerardo Monza, Monza, Italy
| | - Jiaojian Wang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,Center for Language and Brain, Shenzhen Institute of Neuroscience, Shenzhen, China
| | - Tijiang Zhang
- Department of Radiology, Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, China
| | - Heng Liu
- Department of Radiology, Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, China
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19
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Siegel-Ramsay JE, Bertocci MA, Wu B, Phillips ML, Strakowski SM, Almeida JRC. Distinguishing between depression in bipolar disorder and unipolar depression using magnetic resonance imaging: a systematic review. Bipolar Disord 2022; 24:474-498. [PMID: 35060259 DOI: 10.1111/bdi.13176] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
OBJECTIVES Magnetic resonance imaging (MRI) studies comparing bipolar and unipolar depression characterize pathophysiological differences between these conditions. However, it is difficult to interpret the current literature due to differences in MRI modalities, analysis methods, and study designs. METHODS We conducted a systematic review of publications using MRI to compare individuals with bipolar and unipolar depression. We grouped studies according to MRI modality and task design. Within the discussion, we critically evaluated and summarized the functional MRI research and then further complemented these findings by reviewing the structural MRI literature. RESULTS We identified 88 MRI publications comparing participants with bipolar depression and unipolar depressive disorder. Compared to individuals with unipolar depression, participants with bipolar disorder exhibited heightened function, increased within network connectivity, and reduced grey matter volume in salience and central executive network brain regions. Group differences in default mode network function were less consistent but more closely associated with depressive symptoms in participants with unipolar depression but distractibility in bipolar depression. CONCLUSIONS When comparing mood disorder groups, the neuroimaging evidence suggests that individuals with bipolar disorder are more influenced by emotional and sensory processing when responding to their environment. In contrast, depressive symptoms and neurofunctional response to emotional stimuli were more closely associated with reduced central executive function and less adaptive cognitive control of emotionally oriented brain regions in unipolar depression. Researchers now need to replicate and refine network-level trends in these heterogeneous mood disorders and further characterize MRI markers associated with early disease onset, progression, and recovery.
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Affiliation(s)
- Jennifer E Siegel-Ramsay
- Department of Psychiatry and Behavioral Sciences, Dell Medical School, University of Texas, Austin, Texas, USA
| | - Michele A Bertocci
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Bryan Wu
- Department of Psychiatry and Behavioral Sciences, Dell Medical School, University of Texas, Austin, Texas, USA
| | - Mary L Phillips
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Stephen M Strakowski
- Department of Psychiatry and Behavioral Sciences, Dell Medical School, University of Texas, Austin, Texas, USA
| | - Jorge R C Almeida
- Department of Psychiatry and Behavioral Sciences, Dell Medical School, University of Texas, Austin, Texas, USA
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Abstract
BACKGROUND In this modern era, depression is one of the most prevalent mental disorders from which millions of individuals are affected today. The symptoms of depression are heterogeneous and often coincide with other disorders such as bipolar disorder, Parkinson's, schizophrenia, etc. It is a serious mental illness that may lead to other health problems if left untreated. Currently, identifying individuals with depression is totally based on the expertise of the clinician's experience. In order to assist clinicians in identifying the characteristics and classifying depressed people, different types of data modalities and machine learning techniques have been incorporated by researchers in this field. This study aims to find the answers to some important questions related to the trend of publications, data modality, machine learning models, dataset usage, pre-processing techniques and feature extraction and selection techniques that are prevalent and guide the direction of future research on depression diagnosis. METHODS This systematic review was conducted using a broad range of articles from two major databases: IEEE Xplore and PubMed. Studies ranging from the years 2011 to April 2021 were retrieved from the databases resulting in a total of 590 articles (53 articles from the IEEE Xplore database and 537 articles from the PubMed database). Out of those, the articles which satisfied the defined inclusion criteria were investigated for further analysis. RESULTS A total of 135 articles were identified and analysed for this review. High growth in the number of publications has been observed in recent years. Furthermore, significant diversity in the use of data modalities and machine learning classifiers has also been noted in this study. fMRI data with an SVM classifier was found to be the most popular choice among researchers. In most of the studies, data scarcity and small sample size, particularly for neuroimaging data are major concerns. The use of identical data pre-processing tools for similar data modalities can be seen. This study also provides statistical analysis of the current framework with respect to the modality, machine learning classifier, sample size and accuracy by applying one-way ANOVA and the Tukey - Kramer test. CONCLUSION The results indicate that an effective fusion of machine learning techniques with a potential data modality has a promising future for assisting clinicians in automatic depression diagnosis.
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Affiliation(s)
- Sweta Bhadra
- Department of CS & IT, Cotton University, Guwahati, India
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21
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Luo L, Yang T, Zheng X, Zhang X, Gao S, Li Y, Stamatakis EA, Sahakian B, Becker B, Lin Q, Kendrick KM. Altered centromedial amygdala functional connectivity in adults is associated with childhood emotional abuse and predicts levels of depression and anxiety. J Affect Disord 2022; 303:148-154. [PMID: 35157948 DOI: 10.1016/j.jad.2022.02.023] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 02/08/2022] [Accepted: 02/10/2022] [Indexed: 10/19/2022]
Abstract
BACKGROUND Childhood maltreatment is significantly associated with greater occurrence of mental disorders in adulthood such as depression and anxiety. As a key node of the limbic system, the amygdala is engaged in emotional processing and regulation and is dysfunctional in many psychiatric disorders. The present study aimed at exploring the association between childhood maltreatment and amygdala-based functional networks and their potential contributions to depression and anxiety. METHODS Totally 90 Chinese healthy volunteers participated in a resting-state fMRI experiment. Levels of childhood maltreatment experience were assessed using the Childhood Trauma Questionnaire (CTQ-SF) as well as levels of depression and anxiety. Associations between CTQ-SF scores and bilateral amygdala gray matter volume and resting-state functional connectivity (RSFC) of the amygdala and selected regions of interest were analyzed using multiple regression analyses with sex and age as covariates. A subsequent moderation analysis was performed to identify whether associations were predictive of depression and anxiety levels. RESULTS Childhood maltreatment was significantly negatively associated with RSFC between left amygdala and anterior insula. Further sub-region analyses revealed that this negative association only occurred for the left centromedial amygdala subregion, which subsequently moderated the relationship between levels of childhood emotional abuse and depression / anxiety. LIMITATIONS No psychiatric patients were involved and specific neural associations with different childhood maltreatment subtypes need to be examined in future studies. CONCLUSION The present findings provide evidence for altered RSFC of centromedial amygdala and the anterior insula associated with childhood maltreatment and which moderate levels of depression and anxiety in adulthood.
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Affiliation(s)
- Lizhu Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, China; Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu 610066, China
| | - Ting Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Xiaoxiao Zheng
- The Brain Cognition and Brain Disease Institute (BCBDI), Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Xindi Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Shan Gao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Yunge Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, University of Cambridge, Cambridge, CB2 0QQ, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Barbara Sahakian
- Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK
| | - Benjamin Becker
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Qiyuan Lin
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, China.
| | - Keith M Kendrick
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, China.
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22
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Machine learning approaches for prediction of bipolar disorder based on biological, clinical and neuropsychological markers: a systematic review and meta-analysis. Neurosci Biobehav Rev 2022; 135:104552. [PMID: 35120970 DOI: 10.1016/j.neubiorev.2022.104552] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 01/11/2022] [Accepted: 01/30/2022] [Indexed: 01/10/2023]
Abstract
Applying machine learning (ML) to objective markers may overcome prognosis uncertainty due to the subjective nature of the diagnosis of bipolar disorder (BD). This PRISMA-compliant meta-analysis provides new systematic evidence of the BD classification accuracy reached by different markers and ML algorithms. We focused on neuroimaging, electrophysiological techniques, peripheral biomarkers, genetic data, neuropsychological or clinical measures, and multimodal approaches. PubMed, Embase and Scopus were searched through 3rd December 2020. Meta-analyses were performed using random-effect models. Overall, 81 studies were included in this systematic review and 65 in the meta-analysis (11,336 participants, 3,903 BD). The overall pooled classification accuracy was 0.77 (95%CI[0.75;0.80]). Despite subgroup analyses for diagnostic comparison group, psychiatric disorders, marker, ML algorithm, and validation procedure were not significant, linear discriminant analysis significantly outperformed support vector machine for peripheral biomarkers (p=0.03). Sample size was inversely related to accuracy. Evidence of publication bias was detected. Ultimately, although ML reached a high accuracy in differentiating BD from other psychiatric disorders, best practices in methodology are needed for the advancement of future studies.
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23
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Liu J, Cao L, Li H, Gao Y, Bu X, Liang K, Bao W, Zhang S, Qiu H, Li X, Hu X, Lu L, Zhang L, Hu X, Huang X, Gong Q. Abnormal resting-state functional connectivity in patients with obsessive-compulsive disorder: A systematic review and meta-analysis. Neurosci Biobehav Rev 2022; 135:104574. [DOI: 10.1016/j.neubiorev.2022.104574] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 12/12/2021] [Accepted: 02/07/2022] [Indexed: 12/31/2022]
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24
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Zhao Z, Zhang Y, Chen N, Li Y, Guo H, Guo M, Yao Z, Hu B. Altered temporal reachability highlights the role of sensory perception systems in major depressive disorder. Prog Neuropsychopharmacol Biol Psychiatry 2022; 112:110426. [PMID: 34389436 DOI: 10.1016/j.pnpbp.2021.110426] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 07/11/2021] [Accepted: 08/05/2021] [Indexed: 01/10/2023]
Abstract
BACKGROUND The latest studies have considered the time-dependent structures in dynamic brain networks. However, the effect of periphery structures on the temporal flow of information remains unexplored in patients with major depressive disorder (MDD). In this work, we aimed to explore the pattern of interactions between brain regions in MDD across space and time. METHODS We concentrated on the temporal reachability of nodes in temporal brain networks derived from the resting-state functional magnetic resonance imaging (rs-fMRI) of 55 MDD patients and 62 sex-, age-matched healthy controls. Specifically, temporal connectedness and temporal efficiency (TEF) were estimated based on the length of temporal paths between node pairs. Subsequently, the temporal clustering coefficient (TCC) and temporal distance were jointly employed to explore the patterns in which a node's periphery structure affects its reachability. RESULTS Significantly higher TEF and lower TCC were found in temporal brain networks in MDD. Besides, significant between-group differences of nodal TCC were detected in regions of sensory perception systems. Considering the temporal paths that begin or end at these regions, MDD patients showed several altered temporal distances. CONCLUSION Our results showed that the temporal reachability of specific brain regions in MDD could be affected as their periphery structures evolve, which may explain the dysfunction of sensory perception systems in the spatiotemporal domain.
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Affiliation(s)
- Ziyang Zhao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Yinghui Zhang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China; Guangyuan Mental Health Center, Guangyuan, China
| | - Nan Chen
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Yongchao Li
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Hanning Guo
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Man Guo
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Zhijun Yao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China.
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China; Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University and Institute of Semiconductors, Chinese Academy of Sciences, Lanzhou, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China; Ministry of Education, Engineering Research Center of Open Source Software and Real-Time System (Lanzhou University), Lanzhou, China.
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25
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Kandilarova S, Stoyanov DS, Paunova R, Todeva-Radneva A, Aryutova K, Maes M. Effective Connectivity between Major Nodes of the Limbic System, Salience and Frontoparietal Networks Differentiates Schizophrenia and Mood Disorders from Healthy Controls. J Pers Med 2021; 11:1110. [PMID: 34834462 PMCID: PMC8623155 DOI: 10.3390/jpm11111110] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 10/24/2021] [Accepted: 10/26/2021] [Indexed: 12/18/2022] Open
Abstract
This study was conducted to examine whether there are quantitative or qualitative differences in the connectome between psychiatric patients and healthy controls and to delineate the connectome features of major depressive disorder (MDD), schizophrenia (SCZ) and bipolar disorder (BD), as well as the severity of these disorders. Toward this end, we performed an effective connectivity analysis of resting state functional MRI data in these three patient groups and healthy controls. We used spectral Dynamic Causal Modeling (spDCM), and the derived connectome features were further subjected to machine learning. The results outlined a model of five connections, which discriminated patients from controls, comprising major nodes of the limbic system (amygdala (AMY), hippocampus (HPC) and anterior cingulate cortex (ACC)), the salience network (anterior insula (AI), and the frontoparietal and dorsal attention network (middle frontal gyrus (MFG), corresponding to the dorsolateral prefrontal cortex, and frontal eye field (FEF)). Notably, the alterations in the self-inhibitory connection of the anterior insula emerged as a feature of both mood disorders and SCZ. Moreover, four out of the five connectome features that discriminate mental illness from controls are features of mood disorders (both MDD and BD), namely the MFG→FEF, HPC→FEF, AI→AMY, and MFG→AMY connections, whereas one connection is a feature of SCZ, namely the AMY→SPL connectivity. A large part of the variance in the severity of depression (31.6%) and SCZ (40.6%) was explained by connectivity features. In conclusion, dysfunctions in the self-regulation of the salience network may underpin major mental disorders, while other key connectome features shape differences between mood disorders and SCZ, and can be used as potential imaging biomarkers.
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Affiliation(s)
- Sevdalina Kandilarova
- Department of Psychiatry and Medical Psychology and Research Institute, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria; (D.S.S.); (R.P.); (A.T.-R.); (K.A.); (M.M.)
| | - Drozdstoy St. Stoyanov
- Department of Psychiatry and Medical Psychology and Research Institute, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria; (D.S.S.); (R.P.); (A.T.-R.); (K.A.); (M.M.)
| | - Rositsa Paunova
- Department of Psychiatry and Medical Psychology and Research Institute, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria; (D.S.S.); (R.P.); (A.T.-R.); (K.A.); (M.M.)
| | - Anna Todeva-Radneva
- Department of Psychiatry and Medical Psychology and Research Institute, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria; (D.S.S.); (R.P.); (A.T.-R.); (K.A.); (M.M.)
| | - Katrin Aryutova
- Department of Psychiatry and Medical Psychology and Research Institute, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria; (D.S.S.); (R.P.); (A.T.-R.); (K.A.); (M.M.)
| | - Michael Maes
- Department of Psychiatry and Medical Psychology and Research Institute, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria; (D.S.S.); (R.P.); (A.T.-R.); (K.A.); (M.M.)
- Department of Psychiatry, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand
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26
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Li MT, Zhang SX, Li X, Antwi CO, Sun JW, Wang C, Sun XH, Jia XZ, Ren J. Amplitude of Low-Frequency Fluctuation in Multiple Frequency Bands in Tension-Type Headache Patients: A Resting-State Functional Magnetic Resonance Imaging Study. Front Neurosci 2021; 15:742973. [PMID: 34759792 PMCID: PMC8573136 DOI: 10.3389/fnins.2021.742973] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Accepted: 09/15/2021] [Indexed: 11/13/2022] Open
Abstract
Purpose: Tension-type headache (TTH), the most prevalent primary headache disorder, imposes an enormous burden on the people of the world. The quest to ease suffering from this neurological disorder has sustained research interest. The present study aimed at evaluating the amplitude of low-frequency oscillations (LFOs) of the brain in multiple frequency bands in patients with TTH. Methods: To address this question, 63 participants were enrolled in the study, including 32 TTH patients and 31 healthy controls (HCs). For all the participants, amplitude of low-frequency fluctuation (ALFF) was measured in six frequency bands (conventional frequency bands, 0.01-0.08 Hz; slow-2, 0.198-0.25 Hz; slow-3, 0.073-0.198 Hz; slow-4, 0.027-0.073 Hz; slow-5, 0.01-0.027 Hz; and slow-6, 0-0.01 Hz), and the differences between TTH patients and HCs were examined. To explore the relationship between the altered ALFF brain regions in the six frequency bands and the Visual Analog Scale (VAS) score in the TTH patients, Pearson's correlation analysis was performed. Results: In all the six frequency bands, a decreased ALFF value was detected, and regions showing reduced ALFF values were mostly located in the middle frontal gyrus and superior gyrus. A frequency-dependent alternating characterization of intrinsic brain activity was found in the left caudate nucleus in the slow-2 band of 0.198-0.25 Hz and in the right inferior frontal orbital gyrus in the slow-5 band of 0.01-0.027 Hz. For the correlation results, both the left anterior cingulate and paracingulate gyri and right superior parietal gyrus showed a positive correlation with the VAS score in the slow-4 frequency band of 0.027-0.073 Hz. Conclusion: The ALFF alterations in the brain regions of TTH patients are involved in pain processing. The altered LFOs in the multiple regions may help promote the understanding of the pathophysiology of TTH. These observations could also allow the future treatment of TTH to be more directional and targeted and could promote the development of TTH treatment.
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Affiliation(s)
- Meng-Ting Li
- School of Teacher Education, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, jinhua, China
| | - Shu-Xian Zhang
- Department of Medical Imaging, Affiliated Hospital of Weifang Medical University, Weifang, China
| | - Xue Li
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, China
| | - Collins Opoku Antwi
- Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, jinhua, China
| | - Jia-Wei Sun
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, China
| | - Chao Wang
- Department of Medical Imaging, Affiliated Hospital of Weifang Medical University, Weifang, China
| | - Xi-He Sun
- Department of Medical Imaging, Affiliated Hospital of Weifang Medical University, Weifang, China
| | - Xi-Ze Jia
- School of Teacher Education, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, jinhua, China
| | - Jun Ren
- School of Teacher Education, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, jinhua, China
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27
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Wang H, Yu K, Yang T, Zeng L, Li J, Dai C, Peng Z, Shao Y, Fu W, Qi J. Altered Functional Connectivity in the Resting State Neostriatum After Complete Sleep Deprivation: Impairment of Motor Control and Regulatory Network. Front Neurosci 2021; 15:665687. [PMID: 34483817 PMCID: PMC8416068 DOI: 10.3389/fnins.2021.665687] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 07/26/2021] [Indexed: 11/17/2022] Open
Abstract
Sleep loss not only compromises individual physiological functions but also induces a psychocognitive decline and even impairs the motor control and regulatory network. In this study, we analyzed whole-brain functional connectivity changes in the putamen and caudate nucleus as seed points in the neostriatum after 36 h of complete sleep deprivation in 30 healthy adult men by resting state functional magnetic resonance imaging to investigate the physiological mechanisms involved in impaired motor control and regulatory network in individuals in the sleep-deprived state. The functional connectivity between the putamen and the bilateral precentral, postcentral, superior temporal, and middle temporal gyrus, and the left caudate nucleus and the postcentral and inferior temporal gyrus were significantly reduced after 36 h of total sleep deprivation. This may contribute to impaired motor perception, fine motor control, and speech motor control in individuals. It may also provide some evidence for neurophysiological changes in the brain in the sleep-deprived state and shed new light on the study of the neostriatum in the basal ganglia.
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Affiliation(s)
- Haiteng Wang
- School of Psychology, Beijing Sport University, Beijing, China
| | - Ke Yu
- Department of Neurology, The General Hospital of Western Theater Command, Chengdu, China
| | - Tianyi Yang
- School of Psychology, Beijing Sport University, Beijing, China
| | - Lingjing Zeng
- School of Psychology, Beijing Sport University, Beijing, China
| | - Jialu Li
- School of Psychology, Beijing Sport University, Beijing, China
| | - Cimin Dai
- School of Psychology, Beijing Sport University, Beijing, China
| | - Ziyi Peng
- School of Psychology, Beijing Sport University, Beijing, China
| | - Yongcong Shao
- School of Psychology, Beijing Sport University, Beijing, China
| | - Weiwei Fu
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Jianlin Qi
- Air Force Medical Center, Beijing, China
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28
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Insula activity in resting-state differentiates bipolar from unipolar depression: a systematic review and meta-analysis. Sci Rep 2021; 11:16930. [PMID: 34417487 PMCID: PMC8379217 DOI: 10.1038/s41598-021-96319-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Accepted: 07/26/2021] [Indexed: 02/07/2023] Open
Abstract
Symptomatic overlap of depressive episodes in bipolar disorder (BD) and major depressive disorder (MDD) is a major diagnostic and therapeutic problem. Mania in medical history remains the only reliable distinguishing marker which is problematic given that episodes of depression compared to episodes of mania are more frequent and predominantly present at the beginning of BD. Resting-state functional magnetic resonance imaging (rs-fMRI) is a non-invasive, task-free, and well-tolerated method that may provide diagnostic markers acquired from spontaneous neural activity. Previous rs-fMRI studies focused on differentiating BD from MDD depression were inconsistent in their findings due to low sample power, heterogeneity of compared samples, and diversity of analytical methods. This meta-analysis investigated resting-state activity differences in BD and MDD depression using activation likelihood estimation. PubMed, Web of Science, Scopus and Google Scholar databases were searched for whole-brain rs-fMRI studies which compared MDD and BD currently depressed patients between Jan 2000 and August 2020. Ten studies were included, representing 234 BD and 296 MDD patients. The meta-analysis found increased activity in the left insula and adjacent area in MDD compared to BD. The finding suggests that the insula is involved in neural activity patterns during resting-state that can be potentially used as a biomarker differentiating both disorders.
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29
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Zhang D, Wang M, Gao J, Huang Y, Qi F, Lei Y, Ai K, Yan X, Cheng M, Su Y, Lei X, Zhang X. Altered Functional Connectivity of Insular Subregions in Type 2 Diabetes Mellitus. Front Neurosci 2021; 15:676624. [PMID: 34220433 PMCID: PMC8242202 DOI: 10.3389/fnins.2021.676624] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 05/06/2021] [Indexed: 01/09/2023] Open
Abstract
Diabetes-related brain damage can lead to cognitive decline and increase the risk of depression, but the neuropathological mechanism of this phenomenon remains unclear. Different insular subregions have obvious functional heterogeneity, which is related to many aspects of type 2 diabetes mellitus (T2DM)-related brain damage. However, little is known about changes in functional connectivity (FC) in insular subregions in patients with T2DM. Therefore, we aimed to investigate FC between different insular subregions and clinical/cognitive variables in patients with T2DM. Fifty-seven patients with T2DM and 55 healthy controls (HCs) underwent a neuropsychological assessment and resting-state FC examination. We defined three insular subregions, including the bilateral dorsal anterior insula (dAI), bilateral ventral anterior insula (vAI), and bilateral posterior insula (PI). We examined differences in FC between insular subregions and the whole brain in patients with T2DM compared with HCs. A correlation analysis was performed to examine the relationship between FC and clinical/cognitive variables. Compared with HCs, patients with T2DM showed significantly decreased FC between the dAI and the right inferior frontal gyrus, right superior/middle temporal gyrus, right hippocampus, and right precentral gyrus. FC between the vAI and the right supramarginal gyrus, as well as the PI and the right precentral/postcentral gyrus, was reduced in the T2DM group compared with the control group. In the T2DM group, we showed a significant negative correlation between glycated hemoglobin concentration and FC in the dAI and right hippocampus (r = −0.428, P = 0.001) after Bonferroni correction. We conclude that different insular subregions present distinct FC patterns with functional regions and that abnormal FC in these insular subregions may affect cognitive, emotional, and sensorimotor functions in patients with T2DM.
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Affiliation(s)
- Dongsheng Zhang
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Man Wang
- Department of Graduate, Xi'an Medical University, Xi'an, China
| | - Jie Gao
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Yang Huang
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Fei Qi
- Department of Graduate, Xi'an Medical University, Xi'an, China
| | - Yumeng Lei
- Department of Graduate, Xi'an Medical University, Xi'an, China
| | - Kai Ai
- Department of Clinical Science, Philips Healthcare, Xi'an, China
| | - Xuejiao Yan
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Miao Cheng
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Yu Su
- Department of Graduate, Xi'an Medical University, Xi'an, China
| | - Xiaoyan Lei
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Xiaoling Zhang
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, China
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30
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Reviewing applications of structural and functional MRI for bipolar disorder. Jpn J Radiol 2021; 39:414-423. [PMID: 33389525 DOI: 10.1007/s11604-020-01074-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 11/19/2020] [Indexed: 02/07/2023]
Abstract
Bipolar disorders (BDs) represent one of the leading causes of disability and morbidity globally. The use of functional magnetic resonance imaging (fMRI) is being increasingly studied as a tool to improve the diagnosis and treatment of BDs. While morphological biomarkers can be identified through the use of structural magnetic resonance imaging (sMRI), recent studies have demonstrated that varying degrees of both structural and functional impairments indicate differing bipolar subtypes. Within fMRI, resting-state fMRI has specifically drawn increased interest for its capability to detect different neuronal activation patterns compared to task-based fMRI. This study aims to review recently published literature regarding the use of fMRI to investigate structural-functional relationships in BD diagnosis and specifically resting-state fMRI to provide an opinion on fMRI's modern clinical application. All sources in this literature review were collected through searches on both PubMed and Google Scholar databases for terms such as 'resting-state fMRI' and 'functional neuroimaging biomarkers of bipolar disorder'. While there are promising results supporting the use of fMRI for improving differential accuracy and establishing clinically relevant biomarkers, additional evidence will be required before fMRI is considered a dependable component of the overall BD diagnostic process.
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31
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Fournier JC, Roberts NJ, Ford KL. Personality and psychopathology: In defense of a practical path toward integrating psychometric and biological approaches to advance a comprehensive model. J Pers 2020; 90:61-74. [PMID: 33135156 DOI: 10.1111/jopy.12605] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 10/27/2020] [Indexed: 12/14/2022]
Abstract
Personality and psychopathology each reflect patterns of internal experience and outward behavior that differ between people and affect functioning. Drawing strict distinctions between the two concepts is not only difficult, but it may prove unnecessary for advancing an integrated model of psychological experiences associated with mental illness. We argue that developing such a model will be critical for improving treatment outcomes, and we discuss a practical path forward. Proponents of psychometric approaches to developing models of psychological experience focus on observable phenotypes and utilize statistical methods to describe patterns of covariation among a broad range of symptoms and dispositions. Advocates of biologically based approaches emphasize neuroscientific tools for identifying abnormalities in brain function that give rise to an individual's experience. There is substantial evidence that measures of personality and measures of symptoms capture nonoverlapping, clinically important information for understanding how and for whom treatments for mental illness work. In this article, we highlight the importance of combining psychometric and neurobiological approaches in order to understand which features of an individual those measures reflect, which aspects of neurobiology generate and maintain those features, how they relate to each other, and critically, how best to alter them to reduce distress and dysfunction.
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Affiliation(s)
- Jay C Fournier
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Nicole J Roberts
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Katy Lauren Ford
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
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