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Cai J, Xie M, Liang S, Gong J, Deng W, Guo W, Ma X, Sham PC, Wang Q, Li T. Dysfunction of thalamocortical circuits in early-onset schizophrenia. Cereb Cortex 2024; 34:bhae313. [PMID: 39106176 DOI: 10.1093/cercor/bhae313] [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: 03/27/2024] [Revised: 06/30/2024] [Accepted: 07/21/2024] [Indexed: 08/09/2024] Open
Abstract
Previous studies have demonstrated that the thalamus is involved in multiple functional circuits in participants with schizophrenia. However, less is known about the thalamocortical circuit in the rare subtype of early-onset schizophrenia. A total of 110 participants with early-onset schizophrenia (47 antipsychotic-naive patients) and 70 matched healthy controls were recruited and underwent resting-state functional and diffusion-weighted magnetic resonance imaging scans. A data-driven parcellation method that combined the high spatial resolution of diffusion magnetic resonance imaging and the high sensitivity of functional magnetic resonance imaging was used to divide the thalamus. Next, the functional connectivity between each thalamic subdivision and the cortex/cerebellum was investigated. Compared to healthy controls, individuals with early-onset schizophrenia exhibited hypoconnectivity between subdivisions of the thalamus and the frontoparietal network, visual network, ventral attention network, somatomotor network and cerebellum, and hyperconnectivity between subdivisions of thalamus and the parahippocampal and temporal gyrus, which were included in limbic network. The functional connectivity between the right posterior cingulate cortex and 1 subdivision of the thalamus (region of interest 1) was positively correlated with the general psychopathology scale score. This study showed that the specific thalamocortical dysconnection in individuals with early-onset schizophrenia involves the prefrontal, auditory and visual cortices, and cerebellum. This study identified thalamocortical connectivity as a potential biomarker and treatment target for early-onset schizophrenia.
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Affiliation(s)
- Jia Cai
- Mental Health Center, West China Hospital of Sichuan University, No. 28th Dianxin Nan Str. Chengdu, Sichuan, 610041, China
| | - Min Xie
- Mental Health Center, West China Hospital of Sichuan University, No. 28th Dianxin Nan Str. Chengdu, Sichuan, 610041, China
| | - Sugai Liang
- Affiliated Mental Health Centre and Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, No. 305th Tianmushan Road, Xihu District, Hangzhou, Zhejiang 310013, China
| | - Jinnan Gong
- School of Computer Science, Chengdu University of Information Technology, No. 2006th, Xiyuan Road, Pidu District, Chengdu, Sichuan 611700, China
| | - Wei Deng
- Affiliated Mental Health Centre and Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, No. 305th Tianmushan Road, Xihu District, Hangzhou, Zhejiang 310013, China
| | - Wanjun Guo
- Affiliated Mental Health Centre and Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, No. 305th Tianmushan Road, Xihu District, Hangzhou, Zhejiang 310013, China
| | - Xiaohong Ma
- Mental Health Center, West China Hospital of Sichuan University, No. 28th Dianxin Nan Str. Chengdu, Sichuan, 610041, China
| | - Pak C Sham
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Central and Western District, Hong Kong, Special Administrative Region, 999077, China
- Centre for PanorOmic Sciences, The University of Hong Kong, Pokfulam, Central and Western District, Hong Kong, Special Administrative Region, 999077, China
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Pokfulam, Central and Western District, Hong Kong, Special Administrative Region, 999077, China
| | - Qiang Wang
- Mental Health Center, West China Hospital of Sichuan University, No. 28th Dianxin Nan Str. Chengdu, Sichuan, 610041, China
| | - Tao Li
- Affiliated Mental Health Centre and Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, No. 305th Tianmushan Road, Xihu District, Hangzhou, Zhejiang 310013, China
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Zhao C, Jiang R, Bustillo J, Kochunov P, Turner JA, Liang C, Fu Z, Zhang D, Qi S, Calhoun VD. Cross-cohort replicable resting-state functional connectivity in predicting symptoms and cognition of schizophrenia. Hum Brain Mapp 2024; 45:e26694. [PMID: 38727014 PMCID: PMC11083889 DOI: 10.1002/hbm.26694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 03/24/2024] [Accepted: 04/10/2024] [Indexed: 05/13/2024] Open
Abstract
Schizophrenia (SZ) is a debilitating mental illness characterized by adolescence or early adulthood onset of psychosis, positive and negative symptoms, as well as cognitive impairments. Despite a plethora of studies leveraging functional connectivity (FC) from functional magnetic resonance imaging (fMRI) to predict symptoms and cognitive impairments of SZ, the findings have exhibited great heterogeneity. We aimed to identify congruous and replicable connectivity patterns capable of predicting positive and negative symptoms as well as cognitive impairments in SZ. Predictable functional connections (FCs) were identified by employing an individualized prediction model, whose replicability was further evaluated across three independent cohorts (BSNIP, SZ = 174; COBRE, SZ = 100; FBIRN, SZ = 161). Across cohorts, we observed that altered FCs in frontal-temporal-cingulate-thalamic network were replicable in prediction of positive symptoms, while sensorimotor network was predictive of negative symptoms. Temporal-parahippocampal network was consistently identified to be associated with reduced cognitive function. These replicable 23 FCs effectively distinguished SZ from healthy controls (HC) across three cohorts (82.7%, 90.2%, and 86.1%). Furthermore, models built using these replicable FCs showed comparable accuracies to those built using the whole-brain features in predicting symptoms/cognition of SZ across the three cohorts (r = .17-.33, p < .05). Overall, our findings provide new insights into the neural underpinnings of SZ symptoms/cognition and offer potential targets for further research and possible clinical interventions.
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Affiliation(s)
- Chunzhi Zhao
- College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina
- Key Laboratory of Brain‐Machine Intelligence Technology, Ministry of EducationNanjing University of Aeronautics and AstronauticsNanjingChina
| | - Rongtao Jiang
- Department of Radiology and Biomedical ImagingYale School of MedicineNew HavenConnecticutUSA
| | - Juan Bustillo
- Department of Psychiatry and Behavioral SciencesUniversity of New MexicoAlbuquerqueNew MexicoUSA
| | - Peter Kochunov
- Department of Psychiatry and Behavioral SciencesUniversity of Texas Health Science Center HoustonHoustonTexasUSA
| | - Jessica A. Turner
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
| | - Chuang Liang
- College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina
- Key Laboratory of Brain‐Machine Intelligence Technology, Ministry of EducationNanjing University of Aeronautics and AstronauticsNanjingChina
| | - Zening Fu
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
| | - Daoqiang Zhang
- College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina
- Key Laboratory of Brain‐Machine Intelligence Technology, Ministry of EducationNanjing University of Aeronautics and AstronauticsNanjingChina
| | - Shile Qi
- College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina
- Key Laboratory of Brain‐Machine Intelligence Technology, Ministry of EducationNanjing University of Aeronautics and AstronauticsNanjingChina
| | - Vince D. Calhoun
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
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Guo L, Ma J, Cai M, Zhang M, Xu Q, Wang H, Zhang Y, Yao J, Sun Z, Chen Y, Xue H, Zhang Y, Wang S, Xue K, Zhu D, Liu F. Transcriptional signatures of the whole-brain voxel-wise resting-state functional network centrality alterations in schizophrenia. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2023; 9:87. [PMID: 38104130 PMCID: PMC10725456 DOI: 10.1038/s41537-023-00422-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 12/05/2023] [Indexed: 12/19/2023]
Abstract
Neuroimaging studies have revealed that patients with schizophrenia exhibit disrupted resting-state functional connectivity. However, the inconsistent findings across these studies have hindered our comprehensive understanding of the functional connectivity changes associated with schizophrenia, and the molecular mechanisms associated with these alterations remain largely unclear. A quantitative meta-analysis was first conducted on 21 datasets, involving 1057 patients and 1186 healthy controls, to examine disrupted resting-state functional connectivity in schizophrenia, as measured by whole-brain voxel-wise functional network centrality (FNC). Subsequently, partial least squares regression analysis was employed to investigate the relationship between FNC changes and gene expression profiles obtained from the Allen Human Brain Atlas database. Finally, gene enrichment analysis was performed to unveil the biological significance of the altered FNC-related genes. Compared with healthy controls, patients with schizophrenia show consistently increased FNC in the right inferior parietal cortex extending to the supramarginal gyrus, angular gyrus, bilateral medial prefrontal cortex, and right dorsolateral prefrontal cortex, while decreased FNC in the bilateral insula, bilateral postcentral gyrus, and right inferior temporal gyrus. Meta-regression analysis revealed that increased FNC in the right inferior parietal cortex was positively correlated with clinical score. In addition, these observed functional connectivity changes were found to be spatially associated with the brain-wide expression of specific genes, which were enriched in diverse biological pathways and cell types. These findings highlight the aberrant functional connectivity observed in schizophrenia and its potential molecular underpinnings, providing valuable insights into the neuropathology of dysconnectivity associated with this disorder.
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Affiliation(s)
- Lining Guo
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Juanwei Ma
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Mengjing Cai
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Minghui Zhang
- Department of Ultrasound, Tianjin Medical University General Hospital Airport Hospital, Tianjin, China
| | - Qiang Xu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - He Wang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yijing Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Jia Yao
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Zuhao Sun
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yayuan Chen
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Hui Xue
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yujie Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Shaoying Wang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Kaizhong Xue
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China.
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China.
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China.
| | - Dan Zhu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China.
- Department of Radiology, Tianjin Medical University General Hospital Airport Hospital, Tianjin, China.
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China.
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Orlichenko A, Qu G, Zhang G, Patel B, Wilson TW, Stephen JM, Calhoun VD, Wang YP. Latent Similarity Identifies Important Functional Connections for Phenotype Prediction. IEEE Trans Biomed Eng 2023; 70:1979-1989. [PMID: 37015625 PMCID: PMC10284019 DOI: 10.1109/tbme.2022.3232964] [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] [Indexed: 12/31/2022]
Abstract
OBJECTIVE Endophenotypes such as brain age and fluid intelligence are important biomarkers of disease status. However, brain imaging studies to identify these biomarkers often encounter limited numbers of subjects but high dimensional imaging features, hindering reproducibility. Therefore, we develop an interpretable, multivariate classification/regression algorithm, called Latent Similarity (LatSim), suitable for small sample size but high feature dimension datasets. METHODS LatSim combines metric learning with a kernel similarity function and softmax aggregation to identify task-related similarities between subjects. Inter-subject similarity is utilized to improve performance on three prediction tasks using multi-paradigm fMRI data. A greedy selection algorithm, made possible by LatSim's computational efficiency, is developed as an interpretability method. RESULTS LatSim achieved significantly higher predictive accuracy at small sample sizes on the Philadelphia Neurodevelopmental Cohort (PNC) dataset. Connections identified by LatSim gave superior discriminative power compared to those identified by other methods. We identified 4 functional brain networks enriched in connections for predicting brain age, sex, and intelligence. CONCLUSION We find that most information for a predictive task comes from only a few (1-5) connections. Additionally, we find that the default mode network is over-represented in the top connections of all predictive tasks. SIGNIFICANCE We propose a novel prediction algorithm for small sample, high feature dimension datasets and use it to identify connections in task fMRI data. Our work can lead to new insights in both algorithm design and neuroscience research.
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Messaritaki E, Foley S, Barawi K, Ettinger U, Jones DK. Increased structural connectivity in high schizotypy. Netw Neurosci 2023; 7:213-233. [PMID: 37334008 PMCID: PMC10270715 DOI: 10.1162/netn_a_00279] [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: 07/08/2022] [Accepted: 09/14/2022] [Indexed: 09/23/2023] Open
Abstract
The link between brain structural connectivity and schizotypy was explored in two healthy participant cohorts, collected at two different neuroimaging centres, comprising 140 and 115 participants, respectively. The participants completed the Schizotypal Personality Questionnaire (SPQ), through which their schizotypy scores were calculated. Diffusion-MRI data were used to perform tractography and to generate the structural brain networks of the participants. The edges of the networks were weighted with the inverse radial diffusivity. Graph theoretical metrics of the default mode, sensorimotor, visual, and auditory subnetworks were derived and their correlation coefficients with the schizotypy scores were calculated. To the best of our knowledge, this is the first time that graph theoretical measures of structural brain networks are investigated in relation to schizotypy. A positive correlation was found between the schizotypy score and the mean node degree and mean clustering coefficient of the sensorimotor and the default mode subnetworks. The nodes driving these correlations were the right postcentral gyrus, the left paracentral lobule, the right superior frontal gyrus, the left parahippocampal gyrus, and the bilateral precuneus, that is, nodes that exhibit compromised functional connectivity in schizophrenia. Implications for schizophrenia and schizotypy are discussed.
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Affiliation(s)
- Eirini Messaritaki
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | - Sonya Foley
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | - Kali Barawi
- School of Medicine, Cardiff University, Cardiff, UK
| | | | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
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Kong M, Chen T, Gao S, Ni S, Ming Y, Chai X, Ling C, Xu X. Abnormal network homogeneity of default-mode network and its relationships with clinical symptoms in antipsychotic-naïve first-diagnosis schizophrenia. Front Neurosci 2022; 16:921547. [PMID: 35968384 PMCID: PMC9369006 DOI: 10.3389/fnins.2022.921547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Accepted: 07/06/2022] [Indexed: 11/17/2022] Open
Abstract
Schizophrenia is a severe mental disorder affecting around 0.5–1% of the global population. A few studies have shown the functional disconnection in the default-mode network (DMN) of schizophrenia patients. However, the findings remain discrepant. In the current study, we compared the intrinsic network organization of DMN of 57 first-diagnosis drug-naïve schizophrenia patients with 50 healthy controls (HCs) using a homogeneity network (NH) and explored the relationships of DMN with clinical characteristics of schizophrenia patients. Receiver operating characteristic (ROC) curves analysis and support vector machine (SVM) analysis were applied to calculate the accuracy of distinguishing schizophrenia patients from HCs. Our results showed that the NH values of patients were significantly higher in the left superior medial frontal gyrus (SMFG) and right cerebellum Crus I/Crus II and significantly lower in the right inferior temporal gyrus (ITG) and bilateral posterior cingulate cortex (PCC) compared to those of HCs. Additionally, negative correlations were shown between aberrant NH values in the right cerebellum Crus I/Crus II and general psychopathology scores, between NH values in the left SMFG and negative symptom scores, and between the NH values in the right ITG and speed of processing. Also, patients’ age and the NH values in the right cerebellum Crus I/Crus II and the right ITG were the predictors of performance in the social cognition test. ROC curves analysis and SVM analysis showed that a combination of NH values in the left SMFG, right ITG, and right cerebellum Crus I/Crus II could distinguish schizophrenia patients from HCs with high accuracy. The results emphasized the vital role of DMN in the neuropathological mechanisms underlying schizophrenia.
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Affiliation(s)
- Mingjun Kong
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing, China
| | - Tian Chen
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing, China
| | - Shuzhan Gao
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing, China
| | - Sulin Ni
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing, China
| | - Yidan Ming
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing, China
| | - Xintong Chai
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing, China
| | - Chenxi Ling
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing, China
| | - Xijia Xu
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing, China
- Department of Psychiatry, Nanjing Brain Hospital, Medical School, Nanjing University, Nanjing, China
- *Correspondence: Xijia Xu,
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Zhu T, Wang Z, Zhou C, Fang X, Huang C, Xie C, Ge H, Yan Z, Zhang X, Chen J. Meta-analysis of structural and functional brain abnormalities in schizophrenia with persistent negative symptoms using activation likelihood estimation. Front Psychiatry 2022; 13:957685. [PMID: 36238945 PMCID: PMC9552970 DOI: 10.3389/fpsyt.2022.957685] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 09/05/2022] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Persistent negative symptoms (PNS) include both primary and secondary negative symptoms that persist after adequate treatment, and represent an unmet therapeutic need. Published magnetic resonance imaging (MRI) evidence of structural and resting-state functional brain abnormalities in schizophrenia with PNS has been inconsistent. Thus, the purpose of this meta-analysis is to identify abnormalities in structural and functional brain regions in patients with PNS compared to healthy controls. METHODS We systematically searched PubMed, Web of Science, and Embase for structural and functional imaging studies based on five research methods, including voxel-based morphometry (VBM), diffusion tensor imaging (DTI), functional connectivity (FC), the amplitude of low-frequency fluctuation or fractional amplitude of low-frequency fluctuation (ALFF/fALFF), and regional homogeneity (ReHo). Afterward, we conducted a coordinate-based meta-analysis by using the activation likelihood estimation algorithm. RESULTS Twenty-five structural MRI studies and thirty-two functional MRI studies were included in the meta-analyses. Our analysis revealed the presence of structural alterations in patients with PNS in some brain regions including the bilateral insula, medial frontal gyrus, anterior cingulate gyrus, left amygdala, superior temporal gyrus, inferior frontal gyrus, cingulate gyrus and middle temporal gyrus, as well as functional differences in some brain regions including the bilateral precuneus, thalamus, left lentiform nucleus, posterior cingulate gyrus, medial frontal gyrus, and superior frontal gyrus. CONCLUSION Our study suggests that structural brain abnormalities are consistently located in the prefrontal, temporal, limbic and subcortical regions, and functional alterations are concentrated in the thalamo-cortical circuits and the default mode network (DMN). This study provides new insights for targeted treatment and intervention to delay further progression of negative symptoms. SYSTEMATIC REVIEW REGISTRATION [https://www.crd.york.ac.uk/prospero/], identifier [CRD42022338669].
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Affiliation(s)
- Tingting Zhu
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Zixu Wang
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Chao Zhou
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xinyu Fang
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Chengbing Huang
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Department of Psychiatry, The Third People's Hospital of Huai'an, Huaian, China
| | - Chunming Xie
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine Southeast University, Nanjing, China
| | - Honglin Ge
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Zheng Yan
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xiangrong Zhang
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,The Affiliated Xuzhou Oriental Hospital of Xuzhou Medical University, Xuzhou, China
| | - Jiu Chen
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
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