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Ma LH, Li S, Jiao XH, Li ZY, Zhou Y, Zhou CR, Zhou CH, Zheng H, Wu YQ. BLA-involved circuits in neuropsychiatric disorders. Ageing Res Rev 2024; 99:102363. [PMID: 38838785 DOI: 10.1016/j.arr.2024.102363] [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: 11/04/2023] [Revised: 05/04/2024] [Accepted: 05/30/2024] [Indexed: 06/07/2024]
Abstract
The basolateral amygdala (BLA) is the subregion of the amygdala located in the medial of the temporal lobe, which is connected with a wide range of brain regions to achieve diverse functions. Recently, an increasing number of studies have focused on the participation of the BLA in many neuropsychiatric disorders from the neural circuit perspective, aided by the rapid development of viral tracing methods and increasingly specific neural modulation technologies. However, how to translate this circuit-level preclinical intervention into clinical treatment using noninvasive or minor invasive manipulations to benefit patients struggling with neuropsychiatric disorders is still an inevitable question to be considered. In this review, we summarized the role of BLA-involved circuits in neuropsychiatric disorders including Alzheimer's disease, perioperative neurocognitive disorders, schizophrenia, anxiety disorders, depressive disorders, posttraumatic stress disorders, autism spectrum disorders, and pain-associative affective states and cognitive dysfunctions. Additionally, we provide insights into future directions and challenges for clinical translation.
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Affiliation(s)
- Lin-Hui Ma
- Department of Anesthesiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Shuai Li
- Department of Anesthesiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Xin-Hao Jiao
- Jiangsu Province Key Laboratory of Anesthesiology, NMPA Key Laboratory for Research and Evaluation of Narcotic and Psychotropic Drugs, Xuzhou Medical University, Xuzhou 221004, China
| | - Zi-Yi Li
- Jiangsu Province Key Laboratory of Anesthesiology, NMPA Key Laboratory for Research and Evaluation of Narcotic and Psychotropic Drugs, Xuzhou Medical University, Xuzhou 221004, China
| | - Yue Zhou
- Jiangsu Province Key Laboratory of Anesthesiology, NMPA Key Laboratory for Research and Evaluation of Narcotic and Psychotropic Drugs, Xuzhou Medical University, Xuzhou 221004, China
| | - Chen-Rui Zhou
- Jiangsu Province Key Laboratory of Anesthesiology, NMPA Key Laboratory for Research and Evaluation of Narcotic and Psychotropic Drugs, Xuzhou Medical University, Xuzhou 221004, China
| | - Cheng-Hua Zhou
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Xuzhou Medical University, Xuzhou 221004, China.
| | - Hui Zheng
- Department of Anesthesiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
| | - Yu-Qing Wu
- Jiangsu Province Key Laboratory of Anesthesiology, NMPA Key Laboratory for Research and Evaluation of Narcotic and Psychotropic Drugs, Xuzhou Medical University, Xuzhou 221004, China.
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2
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Gao J, Qian M, Wang Z, Li Y, Luo N, Xie S, Shi W, Li P, Chen J, Chen Y, Wang H, Liu W, Li Z, Yang Y, Guo H, Wan P, Lv L, Lu L, Yan J, Song Y, Wang H, Zhang H, Wu H, Ning Y, Du Y, Cheng Y, Xu J, Xu X, Zhang D, Jiang T. Exploring Schizophrenia Classification Through Multimodal MRI and Deep Graph Neural Networks: Unveiling Brain Region-Specific Weight Discrepancies and Their Association With Cell-Type Specific Transcriptomic Features. Schizophr Bull 2024:sbae069. [PMID: 38754993 DOI: 10.1093/schbul/sbae069] [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] [Indexed: 05/18/2024]
Abstract
BACKGROUND AND HYPOTHESIS Schizophrenia (SZ) is a prevalent mental disorder that imposes significant health burdens. Diagnostic accuracy remains challenging due to clinical subjectivity. To address this issue, we explore magnetic resonance imaging (MRI) as a tool to enhance SZ diagnosis and provide objective references and biomarkers. Using deep learning with graph convolution, we represent MRI data as graphs, aligning with brain structure, and improving feature extraction, and classification. Integration of multiple modalities is expected to enhance classification. STUDY DESIGN Our study enrolled 683 SZ patients and 606 healthy controls from 7 hospitals, collecting structural MRI and functional MRI data. Both data types were represented as graphs, processed by 2 graph attention networks, and fused for classification. Grad-CAM with graph convolution ensured interpretability, and partial least squares analyzed gene expression in brain regions. STUDY RESULTS Our method excelled in the classification task, achieving 83.32% accuracy, 83.41% sensitivity, and 83.20% specificity in 10-fold cross-validation, surpassing traditional methods. And our multimodal approach outperformed unimodal methods. Grad-CAM identified potential brain biomarkers consistent with gene analysis and prior research. CONCLUSIONS Our study demonstrates the effectiveness of deep learning with graph attention networks, surpassing previous SZ diagnostic methods. Multimodal MRI's superiority over unimodal MRI confirms our initial hypothesis. Identifying potential brain biomarkers alongside gene biomarkers holds promise for advancing objective SZ diagnosis and research in SZ.
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Affiliation(s)
- Jingjing Gao
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Maomin Qian
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhengning Wang
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Yanling Li
- School of Electrical Engineering and Electronic Information, Xihua University, Chengdu, China
| | - Na Luo
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Sangma Xie
- Institute of Biomedical Engineering and Instrumentation, School of Automation, Hangzhou Dianzi University, Hangzhou, China
| | - Weiyang Shi
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Peng Li
- Institute of Mental Health, Peking University Sixth Hospital, Beijing, China
- Key Laboratory of Mental Health, Ministry of Health, and National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Jun Chen
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yunchun Chen
- Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, Xi'an, China
| | - Huaning Wang
- Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, Xi'an, China
| | - Wenming Liu
- Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, Xi'an, China
| | - Zhigang Li
- Zhumadian Psychiatric Hospital, Zhumadian, China
| | - Yongfeng Yang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry of Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Hua Guo
- Zhumadian Psychiatric Hospital, Zhumadian, China
| | - Ping Wan
- Zhumadian Psychiatric Hospital, Zhumadian, China
| | - Luxian Lv
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry of Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Lin Lu
- Institute of Mental Health, Peking University Sixth Hospital, Beijing, China
- Key Laboratory of Mental Health, Ministry of Health, and National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Jun Yan
- Institute of Mental Health, Peking University Sixth Hospital, Beijing, China
- Key Laboratory of Mental Health, Ministry of Health, and National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Yuqing Song
- Institute of Mental Health, Peking University Sixth Hospital, Beijing, China
- Key Laboratory of Mental Health, Ministry of Health, and National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Huiling Wang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
| | - Hongxing Zhang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry of Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
- Department of Psychology, Xinxiang Medical University, Xinxiang, China
| | - Huawang Wu
- The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, China
| | - Yuping Ning
- The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, China
| | - Yuhui Du
- School of Computer and Information Technology, Shanxi University, Taiyuan, China
| | - Yuqi Cheng
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Jian Xu
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Xiufeng Xu
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Dai Zhang
- Institute of Mental Health, Peking University Sixth Hospital, Beijing, China
- Key Laboratory of Mental Health, Ministry of Health, and National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
- Center for Life Sciences/PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
| | - Tianzai Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou, China
- Xiaoxiang Institute for Brain Health and Yongzhou Central Hospital, Yongzhou, China
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3
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Chung CF, Dugré JR, Potvin S. Dysconnectivity of the Nucleus Accumbens and Amygdala in Youths with Thought Problems: A Dimensional Approach. Brain Connect 2024; 14:226-238. [PMID: 38526373 DOI: 10.1089/brain.2023.0082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/26/2024] Open
Abstract
Background: Youths with thought problems (TP) are at risk to develop psychosis and obsessive-compulsive disorder (OCD). Yet, the pathophysiological mechanisms underpinning TP are still unclear. Functional magnetic resonance imaging (fMRI) studies have shown that striatal and limbic alterations are associated with psychosis-like and obsessive-like symptoms in individuals at clinical risk for psychosis, schizophrenia, and OCD. More specifically, nucleus accumbens (NAcc) and amygdala are mainly involved in these associations. The current study aims to investigate the neural correlates of TP in youth populations using a dimensional approach and explore potential cognitive functions and neurotransmitters associated with it. Methods: Seed-to-voxels functional connectivity analyses using NAcc and amygdala as regions-of-interest were conducted with resting-state fMRI data obtained from 1360 young individuals, and potential confounders related to TP such as anxiety and cognitive functions were included as covariates in multiple regression analyses. Replicability was tested in using an adult cohort. In addition, functional decoding and neurochemical correlation analyses were performed to identify the associated cognitive functions and neurotransmitters. Results: The altered functional connectivities between the right NAcc and posterior parahippocampal gyrus, between the right amygdala and lateral prefrontal cortex, and between the left amygdala and the secondary visual area were the best predictors of TP in multiple regression model. These functional connections are mainly involved in social cognition and reward processing. Conclusions: The results show that alterations in the functional connectivity of the NAcc and the amygdala in neural pathways involved in social cognition and reward processing are associated with severity of TP in youths.
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Affiliation(s)
- Chen-Fang Chung
- Centre de Recherche de l'Institut, Universitaire en Santé Mentale de Montréal, Montreal, Canada
- Department of Psychiatry and Addiction, Faculty of medicine, University of Montreal, Montreal, Canada
| | - Jules R Dugré
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, United Kingdom
| | - Stéphane Potvin
- Centre de Recherche de l'Institut, Universitaire en Santé Mentale de Montréal, Montreal, Canada
- Department of Psychiatry and Addiction, Faculty of medicine, University of Montreal, Montreal, Canada
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4
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Chang Z, Liu L, Lin L, Wang G, Zhang C, Tian H, Liu W, Wang L, Zhang B, Ren J, Zhang Y, Xie Y, Du X, Wei X, Wei L, Luo Y, Dong H, Li X, Zhao Z, Liang M, Zhang C, Wang X, Yu C, Qin W, Liu H. Selective disrupted gray matter volume covariance of amygdala subregions in schizophrenia. Front Psychiatry 2024; 15:1349989. [PMID: 38742128 PMCID: PMC11090100 DOI: 10.3389/fpsyt.2024.1349989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 04/11/2024] [Indexed: 05/16/2024] Open
Abstract
Objective Although extensive structural and functional abnormalities have been reported in schizophrenia, the gray matter volume (GMV) covariance of the amygdala remain unknown. The amygdala contains several subregions with different connection patterns and functions, but it is unclear whether the GMV covariance of these subregions are selectively affected in schizophrenia. Methods To address this issue, we compared the GMV covariance of each amygdala subregion between 807 schizophrenia patients and 845 healthy controls from 11 centers. The amygdala was segmented into nine subregions using FreeSurfer (v7.1.1), including the lateral (La), basal (Ba), accessory-basal (AB), anterior-amygdaloid-area (AAA), central (Ce), medial (Me), cortical (Co), corticoamygdaloid-transition (CAT), and paralaminar (PL) nucleus. We developed an operational combat harmonization model for 11 centers, subsequently employing a voxel-wise general linear model to investigate the differences in GMV covariance between schizophrenia patients and healthy controls across these subregions and the entire brain, while adjusting for age, sex and TIV. Results Our findings revealed that five amygdala subregions of schizophrenia patients, including bilateral AAA, CAT, and right Ba, demonstrated significantly increased GMV covariance with the hippocampus, striatum, orbitofrontal cortex, and so on (permutation test, P< 0.05, corrected). These findings could be replicated in most centers. Rigorous correlation analysis failed to identify relationships between the altered GMV covariance with positive and negative symptom scale, duration of illness, and antipsychotic medication measure. Conclusion Our research is the first to discover selectively impaired GMV covariance patterns of amygdala subregion in a large multicenter sample size of patients with schizophrenia.
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Affiliation(s)
- Zhongyu Chang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Liping Liu
- Department of Psychiatry, The First Psychiatric Hospital of Harbin, Harbin, Heilongjiang, China
| | - Liyuan Lin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Gang Wang
- Wuhan Mental Health Center, The Ninth Clinical School, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chen Zhang
- Department of Biochemistry and Psychopharmacology, Shanghai Mental Health Center, Shanghai, China
| | - Hongjun Tian
- Department of Psychiatry, Tianjin Fourth Center Hospital, The Fourth Central Clinical College, Tianjin Medical University, Tianjin, China
| | - Wei Liu
- Department of Psychiatry, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Lina Wang
- Department of Psychiatry, Tianjin Fourth Center Hospital, The Fourth Central Clinical College, Tianjin Medical University, Tianjin, China
| | - Bin Zhang
- Department of Psychiatry, Tianjin Fourth Center Hospital, The Fourth Central Clinical College, Tianjin Medical University, Tianjin, China
| | - Juanjuan Ren
- Department of Biochemistry and Psychopharmacology, Shanghai Mental Health Center, Shanghai, China
| | - Yu Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yingying Xie
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Xiaotong Du
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Xiaotong Wei
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Luli Wei
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yun Luo
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Haoyang Dong
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Xin Li
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Zhen Zhao
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Meng Liang
- School of Medical Imaging, Tianjin Medical University, Tianjin, China
| | - Congpei Zhang
- Department of Psychiatry, The First Psychiatric Hospital of Harbin, Harbin, Heilongjiang, China
| | - Xijin Wang
- Department of Psychiatry, The First Psychiatric Hospital of Harbin, Harbin, Heilongjiang, China
| | - Chunshui Yu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
- School of Medical Imaging, Tianjin Medical University, Tianjin, China
- State Key Laboratory of Experimental Hematology, Tianjin Medical University General Hospital, Tianjin, China
| | - Wen Qin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Huaigui Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
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5
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Foster SL, Breukelaar IA, Ekanayake K, Lewis S, Korgaonkar MS. Functional Magnetic Resonance Imaging of the Amygdala and Subregions at 3 Tesla: A Scoping Review. J Magn Reson Imaging 2024; 59:361-375. [PMID: 37352130 DOI: 10.1002/jmri.28836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Revised: 05/18/2023] [Accepted: 05/18/2023] [Indexed: 06/25/2023] Open
Abstract
The amygdalae are a pair of small brain structures, each of which is composed of three main subregions and whose function is implicated in neuropsychiatric conditions. Functional Magnetic Resonance Imaging (fMRI) has been utilized extensively in investigation of amygdala activation and functional connectivity (FC) with most clinical research sites now utilizing 3 Tesla (3T) MR systems. However, accurate imaging and analysis remains challenging not just due to the small size of the amygdala, but also its location deep in the temporal lobe. Selection of imaging parameters can significantly impact data quality with implications for the accuracy of study results and validity of conclusions. Wide variation exists in acquisition protocols with spatial resolution of some protocols suboptimal for accurate assessment of the amygdala as a whole, and for measuring activation and FC of the three main subregions, each of which contains multiple nuclei with specialized roles. The primary objective of this scoping review is to provide a broad overview of 3T fMRI protocols in use to image the activation and FC of the amygdala with particular reference to spatial resolution. The secondary objective is to provide context for a discussion culminating in recommendations for a standardized protocol for imaging activation of the amygdala and its subregions. As the advantages of big data and protocol harmonization in imaging become more apparent so, too, do the disadvantages of data heterogeneity. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Sheryl L Foster
- Sydney School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
- Department of Radiology, Westmead Hospital, Westmead, New South Wales, Australia
| | - Isabella A Breukelaar
- Brain Dynamics Centre, The Westmead Institute for Medical Research, Westmead, New South Wales, Australia
| | - Kanchana Ekanayake
- University Library, The University of Sydney, Sydney, New South Wales, Australia
| | - Sarah Lewis
- Sydney School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
| | - Mayuresh S Korgaonkar
- Brain Dynamics Centre, The Westmead Institute for Medical Research, Westmead, New South Wales, Australia
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6
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Kuang LD, He ZM, Zhang J, Li F. Coupled canonical polyadic decomposition of multi-group fMRI data with spatial reference and orthonormality constraints. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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7
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Hu H, Liu F, Liu L, Mei Y, Xie B, Shao Y, Qiao Y. Smaller amygdala subnuclei volume in schizophrenia patients with violent behaviors. Brain Imaging Behav 2023; 17:11-17. [PMID: 36565399 DOI: 10.1007/s11682-022-00736-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/11/2022] [Indexed: 01/01/2023]
Abstract
To investigate the association between the volume of amygdala subnuclei and violent behaviors in patients with schizophrenia (SCZ). In the present study, we recruited 40 SCZ patients with violent behaviors (VS), 26 SCZ patients without violent behaviors (NVS), and 28 matched healthy controls (HC) who completed T1-weighted magnetic resonance imaging. Both the total amygdala and amygdala subnuclei volumes were estimated with FreeSurfer. When comparing the SCZ patients with HC, SCZ patients had a smaller volume of the left basal nucleus (P < 0.05, uncorrected). Further, the VS patients had a smaller volume of the left amygdala central nucleus than the NVS group (P < 0.05, Bonferroni corrected). Our study suggests that a smaller volume of the left amygdala basal nucleus may be a biomarker for SCZ and that a smaller volume of the left central nucleus may be relevant to violence risk in patients with schizophrenia.
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Affiliation(s)
- Hao Hu
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Fengju Liu
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Li Liu
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Yi Mei
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China.
| | - Bin Xie
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China.
| | - Yang Shao
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China.
| | - Yi Qiao
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China.
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Xuan FL, Yan L, Li Y, Fan F, Deng H, Gou M, Chithanathan K, Heinla I, Yuan L, Seppa K, Zharkovsky A, Kalda A, Hong LE, Hu GF, Tan Y, Tian L. Glial receptor PLXNB2 regulates schizophrenia-related stress perception via the amygdala. Front Immunol 2022; 13:1005067. [PMID: 36325348 PMCID: PMC9619215 DOI: 10.3389/fimmu.2022.1005067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 09/30/2022] [Indexed: 11/30/2022] Open
Abstract
Stress is a trigger for the development of psychiatric disorders. However, how stress trait differs in schizophrenia patients is still unclear. Stress also induces and exacerbates immune activation in psychiatric disorders. Plexins (Plxn) and its ligands semaphorins (Sema) are important cellular receptors with plural functions in both the brain and the immune system. Recently, the role of Plxn/Sema in regulation of neuroinflammation was also noticed. Here, when investigating immune mechanisms underlying stress susceptibility in schizophrenia, we discovered the role of Plxnb2 in stress response. Patients of first-episode schizophrenia (FES) with high stress (FES-hs, n=51) and low stress (FES-ls, n=50) perception and healthy controls (HCs) (n=49) were first recruited for neuroimaging and blood bulk RNA sequencing (RNA-seq). A mouse model of chronic unpredictable stress (CUS) and intra-amygdaloid functional blocking of Plxnb2 were further explored to depict target gene functions. Compared to HCs, FES-hs patients had bigger caudate and thalamus (FDR=0.02&0.001, respectively) whereas FES-ls patients had smaller amygdala (FDR=0.002). Blood RNA-seq showed differentially expressed PLXNB2 and its ligands among patient groups and HCs (FDR<0.05~0.01). Amygdaloid size and PLXNB2 level were both negatively correlated with stress perception (p<0.01&0.05, respectively), which fully mediated the amygdaloid positive association with PLXNB2 expression (β=0.9318, 95% CI: 0.058~1.886) in FES-hs patients. In mice, Plxnb2 was enriched in astrocytes and microglia and CUS reduced its expression in astrocytes (p<0.05). Inhibition of amygdaloid Plxnb2 by its functional blocking monoclonal antibody (mAb)-102 induced mice anxiety (p<0.05), amygdaloid enlargement (p<0.05), and microglial ramification (p<0.001) compared to saline. These data suggest that PLXNB2 regulates amygdala-dependent stress responses.
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Affiliation(s)
- Fang-Ling Xuan
- Institute of Biomedicine and Translational Medicine, Faculty of Medicine, University of Tartu, Tartu, Estonia
| | - Ling Yan
- Institute of Biomedicine and Translational Medicine, Faculty of Medicine, University of Tartu, Tartu, Estonia
| | - Yanli Li
- Psychiatry Research Centre, Beijing Huilongguan Hospital, Peking University Health Science Center, Beijing, China
| | - Fengmei Fan
- Psychiatry Research Centre, Beijing Huilongguan Hospital, Peking University Health Science Center, Beijing, China
| | - Hu Deng
- Psychiatry Research Centre, Beijing Huilongguan Hospital, Peking University Health Science Center, Beijing, China
| | - Mengzhuang Gou
- Psychiatry Research Centre, Beijing Huilongguan Hospital, Peking University Health Science Center, Beijing, China
| | - Keerthana Chithanathan
- Institute of Biomedicine and Translational Medicine, Faculty of Medicine, University of Tartu, Tartu, Estonia
| | - Indrek Heinla
- Institute of Biomedicine and Translational Medicine, Faculty of Medicine, University of Tartu, Tartu, Estonia
| | - Liang Yuan
- Department of Medicine, Tufts Medical Center, Boston, MA, United States
| | - Kadri Seppa
- Institute of Biomedicine and Translational Medicine, Faculty of Medicine, University of Tartu, Tartu, Estonia
| | - Alexander Zharkovsky
- Institute of Biomedicine and Translational Medicine, Faculty of Medicine, University of Tartu, Tartu, Estonia
| | - Anti Kalda
- Institute of Biomedicine and Translational Medicine, Faculty of Medicine, University of Tartu, Tartu, Estonia
| | - L. Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland, School of Medicine, Baltimore, MD, United States
| | - Guo-Fu Hu
- Department of Medicine, Tufts Medical Center, Boston, MA, United States
| | - Yunlong Tan
- Psychiatry Research Centre, Beijing Huilongguan Hospital, Peking University Health Science Center, Beijing, China
- *Correspondence: Li Tian, ; Yunlong Tan,
| | - Li Tian
- Institute of Biomedicine and Translational Medicine, Faculty of Medicine, University of Tartu, Tartu, Estonia
- *Correspondence: Li Tian, ; Yunlong Tan,
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Frequency-Specific Analysis of the Dynamic Reconfiguration of the Brain in Patients with Schizophrenia. Brain Sci 2022; 12:brainsci12060727. [PMID: 35741612 PMCID: PMC9221032 DOI: 10.3390/brainsci12060727] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 05/01/2022] [Accepted: 05/28/2022] [Indexed: 12/10/2022] Open
Abstract
The analysis of resting-state fMRI signals usually focuses on the low-frequency range/band (0.01−0.1 Hz), which does not cover all aspects of brain activity. Studies have shown that distinct frequency bands can capture unique fluctuations in brain activity, with high-frequency signals (>0.1 Hz) providing valuable information for the diagnosis of schizophrenia. We hypothesized that it is meaningful to study the dynamic reconfiguration of schizophrenia through different frequencies. Therefore, this study used resting-state functional magnetic resonance (RS-fMRI) data from 42 schizophrenia and 40 normal controls to investigate dynamic network reconfiguration in multiple frequency bands (0.01−0.25 Hz, 0.01−0.027 Hz, 0.027−0.073 Hz, 0.073−0.198 Hz, 0.198−0.25 Hz). Based on the time-varying dynamic network constructed for each frequency band, we compared the dynamic reconfiguration of schizophrenia and normal controls by calculating the recruitment and integration. The experimental results showed that the differences between schizophrenia and normal controls are observed in the full frequency, which is more significant in slow3. In addition, as visual network, attention network, and default mode network differ a lot from each other, they can show a high degree of connectivity, which indicates that the functional network of schizophrenia is affected by the abnormal brain state in these areas. These shreds of evidence provide a new perspective and promote the current understanding of the characteristics of dynamic brain networks in schizophrenia.
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Yang W, Xu X, Wang C, Cheng Y, Li Y, Xu S, Li J. Alterations of dynamic functional connectivity between visual and executive-control networks in schizophrenia. Brain Imaging Behav 2022; 16:1294-1302. [PMID: 34997915 DOI: 10.1007/s11682-021-00592-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 10/20/2021] [Indexed: 01/28/2023]
Abstract
Schizophrenia is a chronic mental disorder characterized by continuous or relapsing episodes of psychosis. While previous studies have detected functional network connectivity alterations in patients with schizophrenia, and most have focused on static functional connectivity. However, brain activity is believed to change dynamically over time. Therefore, we computed dynamic functional network connectivity using the sliding window method in 38 patients with schizophrenia and 31 healthy controls. We found that patients with schizophrenia exhibited higher occurrences in the weakly and sparsely connected state (state 3) than healthy controls, positively correlated with negative symptoms. In addition, patients exhibited fewer occurrences in a strongly connected state (state 4) than healthy controls. Lastly, the dynamic functional network connectivity between the right executive-control network and the medial visual network was decreased in schizophrenia patients compared to healthy controls. Our results further prove that brain activity is dynamic, and that alterations of dynamic functional network connectivity features might be a fundamental neural mechanism in schizophrenia.
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Affiliation(s)
- Weiliang Yang
- Laboratory of Biological Psychiatry, Institute of Mental Health, Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, Tianjin, 300222, China
| | - Xuexin Xu
- Department of Radiology, MRI Center, Tianjin Children Hospital, Tianjin Medical University Affiliated Tianjin Children Hospital, Tianjin, China
| | - Chunxiang Wang
- Department of Radiology, MRI Center, Tianjin Children Hospital, Tianjin Medical University Affiliated Tianjin Children Hospital, Tianjin, China
| | - Yongying Cheng
- Laboratory of Biological Psychiatry, Institute of Mental Health, Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, Tianjin, 300222, China
| | - Yan Li
- Laboratory of Biological Psychiatry, Institute of Mental Health, Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, Tianjin, 300222, China
| | - Shuli Xu
- Laboratory of Biological Psychiatry, Institute of Mental Health, Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, Tianjin, 300222, China
| | - Jie Li
- Laboratory of Biological Psychiatry, Institute of Mental Health, Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, Tianjin, 300222, China.
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11
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Walther S, Lefebvre S, Conring F, Gangl N, Nadesalingam N, Alexaki D, Wüthrich F, Rüter M, Viher PV, Federspiel A, Wiest R, Stegmayer K. Limbic links to paranoia: increased resting-state functional connectivity between amygdala, hippocampus and orbitofrontal cortex in schizophrenia patients with paranoia. Eur Arch Psychiatry Clin Neurosci 2022; 272:1021-1032. [PMID: 34636951 PMCID: PMC9388427 DOI: 10.1007/s00406-021-01337-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 09/22/2021] [Indexed: 11/24/2022]
Abstract
Paranoia is a frequent and highly distressing experience in psychosis. Models of paranoia suggest limbic circuit pathology. Here, we tested whether resting-state functional connectivity (rs-fc) in the limbic circuit was altered in schizophrenia patients with current paranoia. We collected MRI scans in 165 subjects including 89 patients with schizophrenia spectrum disorders (schizophrenia, schizoaffective disorder, brief psychotic disorder, schizophreniform disorder) and 76 healthy controls. Paranoia was assessed using a Positive And Negative Syndrome Scale composite score. We tested rs-fc between bilateral nucleus accumbens, hippocampus, amygdala and orbitofrontal cortex between groups and as a function of paranoia severity. Patients with paranoia had increased connectivity between hippocampus and amygdala compared to patients without paranoia. Likewise, paranoia severity was linked to increased connectivity between hippocampus and amygdala. Furthermore, paranoia was associated with increased connectivity between orbitofrontal and medial prefrontal cortex. In addition, patients with paranoia had increased functional connectivity within the frontal hubs of the default mode network compared to healthy controls. These results demonstrate that current paranoia is linked to aberrant connectivity within the core limbic circuit and prefrontal cortex reflecting amplified threat processing and impaired emotion regulation. Future studies will need to explore the association between limbic hyperactivity, paranoid ideation and perceived stress.
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Affiliation(s)
- Sebastian Walther
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Stephanie Lefebvre
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland.
| | - Frauke Conring
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Nicole Gangl
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Niluja Nadesalingam
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Danai Alexaki
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Florian Wüthrich
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Maximilian Rüter
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Petra V. Viher
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Andrea Federspiel
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Roland Wiest
- Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University of Bern, Bern, Switzerland
| | - Katharina Stegmayer
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
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12
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Identifying brain regions supporting amygdalar functionality: Application of a novel graph theory technique. Neuroimage 2021; 244:118614. [PMID: 34571162 DOI: 10.1016/j.neuroimage.2021.118614] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 09/21/2021] [Indexed: 11/22/2022] Open
Abstract
Effective amygdalar functionality depends on the concerted activity of a complex network of regions. Thus, the role of the amygdala cannot be fully understood without identifying the set of brain structures that allow the processes performed by the amygdala to emerge. However, this identification has yet to occur, hampering our ability to understand both normative and pathological processes that rely on the amygdala. We developed and applied novel graph theory methods to diffusion-based anatomical networks in a large sample (n = 1,052, 54.28% female, mean age=28.75) to identify nodes that critically support amygdalar interactions with the larger brain network. We examined three graph properties, each indexing a different emergent aspect of amygdalar network communication: current-flow betweenness centrality (amygdalar influence on information flowing between other pairs of nodes), node communicability (clarity of communication between the amygdala and other nodes), and subgraph centrality (amygdalar influence over local network processing). Findings demonstrate that each of these aspects of amygdalar communication is associated with separable sets of regions and, in some cases, these sets map onto previously identified sub-circuits. For example, betweenness and communicability were each associated with different sub-circuits that have been identified in previous work as supporting distinct aspects of memory-guided behavior. Other regions identified span basic (e.g., visual cortex) to higher-order (e.g., insula) sensory processing and executive functions (e.g., dorsolateral prefrontal cortex). Present findings expand our current understanding of amygdalar function by showing that there is no single 'amygdala network', but rather multiple networks, each supporting different modes of amygdalar interaction with the larger brain network. Additionally, our novel method allowed for the identification of how such regions support the amygdala, which has not been previously explored.
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13
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Vafadari B. Stress and the Role of the Gut-Brain Axis in the Pathogenesis of Schizophrenia: A Literature Review. Int J Mol Sci 2021; 22:ijms22189747. [PMID: 34575911 PMCID: PMC8471971 DOI: 10.3390/ijms22189747] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 08/19/2021] [Accepted: 08/31/2021] [Indexed: 12/21/2022] Open
Abstract
Schizophrenia is a severe neuropsychiatric disorder, and its etiology remains largely unknown. Environmental factors have been reported to play roles in the pathogenesis of schizophrenia, and one of the major environmental factors identified for this disorder is psychosocial stress. Several studies have suggested that stressful life events, as well as the chronic social stress associated with city life, may lead to the development of schizophrenia. The other factor is the gut–brain axis. The composition of the gut microbiome and alterations thereof may affect the brain and may lead to schizophrenia. The main interest of this review article is in overviewing the major recent findings on the effects of stress and the gut–brain axis, as well as their possible bidirectional effects, in the pathogenesis of schizophrenia.
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Affiliation(s)
- Behnam Vafadari
- Clinic for Anesthesiology, University Medical Center Göttingen, Georg-August-University, 37073 Göttingen, Germany
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14
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Fu Z, Iraji A, Sui J, Calhoun VD. Whole-Brain Functional Network Connectivity Abnormalities in Affective and Non-Affective Early Phase Psychosis. Front Neurosci 2021; 15:682110. [PMID: 34220438 PMCID: PMC8250435 DOI: 10.3389/fnins.2021.682110] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 04/27/2021] [Indexed: 11/13/2022] Open
Abstract
Psychosis disorders share overlapping symptoms and are characterized by a wide-spread breakdown in functional brain integration. Although neuroimaging studies have identified numerous connectivity abnormalities in affective and non-affective psychoses, whether they have specific or unique connectivity abnormalities, especially within the early stage is still poorly understood. The early phase of psychosis is a critical period with fewer chronic confounds and when treatment intervention may be most effective. In this work, we examined whole-brain functional network connectivity (FNC) from both static and dynamic perspectives in patients with affective psychosis (PAP) or with non-affective psychosis (PnAP) and healthy controls (HCs). A fully automated independent component analysis (ICA) pipeline called "Neuromark" was applied to high-quality functional magnetic resonance imaging (fMRI) data with 113 early-phase psychosis patients (32 PAP and 81 PnAP) and 52 HCs. Relative to the HCs, both psychosis groups showed common abnormalities in static FNC (sFNC) between the thalamus and sensorimotor domain, and between subcortical regions and the cerebellum. PAP had specifically decreased sFNC between the superior temporal gyrus and the paracentral lobule, and between the cerebellum and the middle temporal gyrus/inferior parietal lobule. On the other hand, PnAP showed increased sFNC between the fusiform gyrus and the superior medial frontal gyrus. Dynamic FNC (dFNC) was investigated using a combination of a sliding window approach, clustering analysis, and graph analysis. Three reoccurring brain states were identified, among which both psychosis groups had fewer occurrences in one antagonism state (state 2) and showed decreased network efficiency within an intermediate state (state 1). Compared with HCs and PnAP, PAP also showed a significantly increased number of state transitions, indicating more unstable brain connections in affective psychosis. We further found that the identified connectivity features were associated with the overall positive and negative syndrome scale, an assessment instrument for general psychopathology and positive symptoms. Our findings support the view that subcortical-cortical information processing is disrupted within five years of the initial onset of psychosis and provide new evidence that abnormalities in both static and dynamic connectivity consist of shared and unique features for the early affective and non-affective psychoses.
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Affiliation(s)
- Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Armin Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Jing Sui
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
- Chinese Academy of Sciences (CAS) Centre for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
- Department of Psychiatry, School of Medicine, Yale University, New Haven, CT, United States
- Department of Psychology and Computer Science, Neuroscience Institute and Physics, Georgia State University, Atlanta, GA, United States
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
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15
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Kim WS, Shen G, Liu C, Kang NI, Lee KH, Sui J, Chung YC. Altered amygdala-based functional connectivity in individuals with attenuated psychosis syndrome and first-episode schizophrenia. Sci Rep 2020; 10:17711. [PMID: 33077769 PMCID: PMC7573592 DOI: 10.1038/s41598-020-74771-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 10/06/2020] [Indexed: 11/26/2022] Open
Abstract
Altered resting-state functional connectivity (FC) of the amygdala (AMY) has been demonstrated to be implicated in schizophrenia (SZ) and attenuated psychosis syndrome (APS). Specifically, no prior work has investigated FC in individuals with APS using subregions of the AMY as seed regions of interest. The present study examined AMY subregion-based FC in individuals with APS and first-episode schizophrenia (FES) and healthy controls (HCs). The resting state FC maps of the three AMY subregions were computed and compared across the three groups. Correlation analysis was also performed to examine the relationship between the Z-values of regions showing significant group differences and symptom rating scores. Individuals with APS showed hyperconnectivity between the right centromedial AMY (CMA) and left frontal pole cortex (FPC) and between the laterobasal AMY and brain stem and right inferior lateral occipital cortex compared to HCs. Patients with FES showed hyperconnectivity between the right superficial AMY and left occipital pole cortex and between the left CMA and left thalamus compared to the APS and HCs respectively. A negative relationship was observed between the connectivity strength of the CMA with the FPC and negative-others score of the Brief Core Schema Scales in the APS group. We observed different altered FC with subregions of the AMY in individuals with APS and FES compared to HCs. These results shed light on the pathogenetic mechanisms underpinning the development of APS and SZ.
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Affiliation(s)
- Woo-Sung Kim
- Department of Psychiatry, Medical School, Jeonbuk National University, Geonjiro 20, Jeonju, Korea
| | - Guangfan Shen
- Department of Psychiatry, Medical School, Jeonbuk National University, Geonjiro 20, Jeonju, Korea
| | - Congcong Liu
- Department of Psychiatry, Medical School, Jeonbuk National University, Geonjiro 20, Jeonju, Korea
| | - Nam-In Kang
- Department of Psychiatry, Maeumsarang Hospital, Wanju, Jeollabuk-do, Korea
| | - Keon-Hak Lee
- Department of Psychiatry, Maeumsarang Hospital, Wanju, Jeollabuk-do, Korea
| | - Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,University of Chinese Academy of Sciences, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing, 100049, China
| | - Young-Chul Chung
- Department of Psychiatry, Medical School, Jeonbuk National University, Geonjiro 20, Jeonju, Korea. .,Department of Psychiatry, Jeonbuk National University Hospital, Jeonju, Korea. .,Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea.
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