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Cheng H, Gao L, Jing R, Hou B, Guo X, Yao Y, Feng M, Xing B, Feng F, Fan Y. Reversibility of Impaired Large-Scale Functional Brain Networks in Cushing's Disease after Surgery Treatment: A Longitudinal Study. Neuroendocrinology 2023; 114:250-262. [PMID: 37913760 PMCID: PMC10911171 DOI: 10.1159/000534789] [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: 05/20/2023] [Accepted: 10/13/2023] [Indexed: 11/03/2023]
Abstract
INTRODUCTION Chronic exposure to excessive endogenous cortisol leads to brain changes in Cushing's disease (CD). However, it remains unclear how CD affects large-scale functional networks (FNs) and whether these effects are reversible after treatment. This study aimed to investigate functional network changes of CD patients and their reversibility in a longitudinal cohort. METHODS Active CD patients (N = 37) were treated by transsphenoidal pituitary surgery and reexamined 3 months later. FNs were computed from resting-state fMRI data of the CD patients and matched normal controls (NCs, N = 37). A pattern classifier was built on the FNs to distinguish active CD patients from controls and applied to FNs of the CD patients at the 3-month follow-up. Two subgroups of endocrine-remitted CD patients were identified according to their classification scores, referred to as image-based phenotypically (IBP) recovered and unrecovered CD patients, respectively. The informative FNs identified by the classification model were compared between NCs, active CD patients, and endocrine-remitted patients as well as between IBP recovered and unrecovered CD patients to explore their functional network reversibility. RESULTS All 37 CD patients reached endocrine remission after treatment. The classification model identified three informative FNs, including cerebellar network (CerebN), fronto-parietal network (FPN), and default mode network. Among them, CerebN and FPN partially recovered toward normal at 3 months after treatment. Moreover, the informative FNs were correlated with 24-h urinary-free cortisol and emotion scales in CD patients. CONCLUSION These findings suggest that CD patients have aberrant FNs that are partially reversible toward normal after treatment.
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Affiliation(s)
- Hewei Cheng
- Research Center of Biomedical Engineering, Chongqing University of Posts and Telecommunications, Chongqing, PR China
- Chongqing Engineering Laboratory of Digital Medical Equipment and Systems, Chongqing University of Posts and Telecommunications, Chongqing, PR China
- Chongqing Engineering Research Center of Medical Electronics and Information Technology, Chongqing University of Posts and Telecommunications, Chongqing, PR China
| | - Lu Gao
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, PR China
- China Pituitary Disease Registry Center, China Pituitary Adenoma Specialist Council, Beijing, PR China
- Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, PR China
| | - Rixing Jing
- School of Instrument Science and Opto-Electronic Engineering, Beijing Information Science and Technology University, Beijing, PR China
| | - Bo Hou
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, PR China
| | - Xiaopeng Guo
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, PR China
- China Pituitary Disease Registry Center, China Pituitary Adenoma Specialist Council, Beijing, PR China
| | - Yong Yao
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, PR China
- China Pituitary Disease Registry Center, China Pituitary Adenoma Specialist Council, Beijing, PR China
| | - Ming Feng
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, PR China
- China Pituitary Disease Registry Center, China Pituitary Adenoma Specialist Council, Beijing, PR China
| | - Bing Xing
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, PR China
- China Pituitary Disease Registry Center, China Pituitary Adenoma Specialist Council, Beijing, PR China
| | - Feng Feng
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, PR China
| | - Yong Fan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Jing R, Chen P, Wei Y, Si J, Zhou Y, Wang D, Song C, Yang H, Zhang Z, Yao H, Kang X, Fan L, Han T, Qin W, Zhou B, Jiang T, Lu J, Han Y, Zhang X, Liu B, Yu C, Wang P, Liu Y. Altered large-scale dynamic connectivity patterns in Alzheimer's disease and mild cognitive impairment patients: A machine learning study. Hum Brain Mapp 2023; 44:3467-3480. [PMID: 36988434 PMCID: PMC10203807 DOI: 10.1002/hbm.26291] [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: 12/03/2022] [Revised: 02/27/2023] [Accepted: 03/15/2023] [Indexed: 03/30/2023] Open
Abstract
Alzheimer's disease (AD) is a common neurodegeneration disease associated with substantial disruptions in the brain network. However, most studies investigated static resting-state functional connections, while the alteration of dynamic functional connectivity in AD remains largely unknown. This study used group independent component analysis and the sliding-window method to estimate the subject-specific dynamic connectivity states in 1704 individuals from three data sets. Informative inherent states were identified by the multivariate pattern classification method, and classifiers were built to distinguish ADs from normal controls (NCs) and to classify mild cognitive impairment (MCI) patients with informative inherent states similar to ADs or not. In addition, MCI subgroups with heterogeneous functional states were examined in the context of different cognition decline trajectories. Five informative states were identified by feature selection, mainly involving functional connectivity belonging to the default mode network and working memory network. The classifiers discriminating AD and NC achieved the mean area under the receiver operating characteristic curve of 0.87 with leave-one-site-out cross-validation. Alterations in connectivity strength, fluctuation, and inter-synchronization were found in AD and MCIs. Moreover, individuals with MCI were clustered into two subgroups, which had different degrees of atrophy and different trajectories of cognition decline progression. The present study uncovered the alteration of dynamic functional connectivity in AD and highlighted that the dynamic states could be powerful features to discriminate patients from NCs. Furthermore, it demonstrated that these states help to identify MCIs with faster cognition decline and might contribute to the early prevention of AD.
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Affiliation(s)
- Rixing Jing
- School of Instrument Science and Opto‐Electronics EngineeringBeijing Information Science and Technology UniversityBeijingChina
| | - Pindong Chen
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijingChina
| | - Yongbin Wei
- School of Artificial IntelligenceBeijing University of Posts and TelecommunicationsBeijingChina
| | - Juanning Si
- School of Instrument Science and Opto‐Electronics EngineeringBeijing Information Science and Technology UniversityBeijingChina
| | - Yuying Zhou
- Department of NeurologyTianjin Huanhu Hospital, Tianjin UniversityTianjinChina
| | - Dawei Wang
- Department of RadiologyQilu Hospital of Shandong UniversityJi'nanChina
| | - Chengyuan Song
- Department of NeurologyQilu Hospital of Shandong UniversityJi'nanChina
| | - Hongwei Yang
- Department of RadiologyXuanwu Hospital of Capital Medical UniversityBeijingChina
| | | | - Hongxiang Yao
- Department of Radiology, the Second Medical CentreNational Clinical Research Centre for Geriatric Diseases, Chinese PLA General HospitalBeijingChina
| | - Xiaopeng Kang
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijingChina
| | - Lingzhong Fan
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
| | - Tong Han
- Department of RadiologyTianjin Huanhu HospitalTianjinChina
| | - Wen Qin
- Department of RadiologyTianjin Medical University General HospitalTianjinChina
| | - Bo Zhou
- Department of Neurologythe Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General HospitalBeijingChina
| | - Tianzi Jiang
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijingChina
| | - Jie Lu
- Department of RadiologyXuanwu Hospital of Capital Medical UniversityBeijingChina
| | - Ying Han
- Department of NeurologyXuanwu Hospital of Capital Medical UniversityBeijingChina
- Beijing Institute of GeriatricsBeijingChina
- National Clinical Research Center for Geriatric DisordersBeijingChina
| | - Xi Zhang
- Department of Neurologythe Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General HospitalBeijingChina
| | - Bing Liu
- State Key Laboratory of Cognition Neuroscience & LearningBeijing Normal UniversityBeijingChina
| | - Chunshui Yu
- Department of RadiologyTianjin Medical University General HospitalTianjinChina
| | - Pan Wang
- Department of NeurologyTianjin Huanhu Hospital, Tianjin UniversityTianjinChina
| | - Yong Liu
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijingChina
- School of Artificial IntelligenceBeijing University of Posts and TelecommunicationsBeijingChina
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Fu Z, Abbott CC, Sui J, Calhoun VD. Predictive signature of static and dynamic functional connectivity for ECT clinical outcomes. Front Pharmacol 2023; 14:1102413. [PMID: 36755955 PMCID: PMC9899999 DOI: 10.3389/fphar.2023.1102413] [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: 11/18/2022] [Accepted: 01/11/2023] [Indexed: 01/24/2023] Open
Abstract
Introduction: Electroconvulsive therapy (ECT) remains one of the most effective approaches for treatment-resistant depressive episodes, despite the potential cognitive impairment associated with this treatment. As a potent stimulator of neuroplasticity, ECT might normalize aberrant depression-related brain function via the brain's reconstruction by forming new neural connections. Multiple lines of evidence have demonstrated that functional connectivity (FC) changes are reliable indicators of antidepressant efficacy and cognitive changes from static and dynamic perspectives. However, no previous studies have directly ascertained whether and how different aspects of FC provide complementary information in terms of neuroimaging-based prediction of clinical outcomes. Methods: In this study, we implemented a fully automated independent component analysis framework to an ECT dataset with subjects (n = 50, age = 65.54 ± 8.92) randomized to three treatment amplitudes (600, 700, or 800 milliamperes [mA]). We extracted the static functional network connectivity (sFNC) and dynamic FNC (dFNC) features and employed a partial least square regression to build predictive models for antidepressant outcomes and cognitive changes. Results: We found that both antidepressant outcomes and memory changes can be robustly predicted by the changes in sFNC (permutation test p < 5.0 × 10-3). More interestingly, by adding dFNC information, the model achieved higher accuracy for predicting changes in the Hamilton Depression Rating Scale 24-item (HDRS24, t = 9.6434, p = 1.5 × 10-21). The predictive maps of clinical outcomes show a weakly negative correlation, indicating that the ECT-induced antidepressant outcomes and cognitive changes might be associated with different functional brain neuroplasticity. Discussion: The overall results reveal that dynamic FC is not redundant but reflects mechanisms of ECT that cannot be captured by its static counterpart, especially for the prediction of antidepressant efficacy. Tracking the predictive signatures of static and dynamic FC will help maximize antidepressant outcomes and cognitive safety with individualized ECT dosing.
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Affiliation(s)
- Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States,*Correspondence: Christopher C. Abbott, ; Zening Fu,
| | - Christopher C. Abbott
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, United States,*Correspondence: Christopher C. Abbott, ; Zening Fu,
| | - Jing Sui
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States,State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States,Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
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Jiang Y, Duan M, He H, Yao D, Luo C. Structural and Functional MRI Brain Changes in Patients with Schizophrenia Following Electroconvulsive Therapy: A Systematic Review. Curr Neuropharmacol 2022; 20:1241-1252. [PMID: 34370638 PMCID: PMC9886826 DOI: 10.2174/1570159x19666210809101248] [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: 03/23/2021] [Revised: 07/17/2021] [Accepted: 07/31/2021] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Schizophrenia (SZ) is a severe psychiatric disorder typically characterized by multidimensional psychotic syndromes. Electroconvulsive therapy (ECT) is a treatment option for medication-resistant patients with SZ or treating acute symptoms. Although the efficacy of ECT has been demonstrated in clinical use, its therapeutic mechanisms in the brain remain elusive. OBJECTIVE This study aimed to summarize brain changes on structural magnetic resonance imaging (sMRI) and functional MRI (fMRI) after ECT. METHODS According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic review was carried out. The PubMed and Medline databases were systematically searched using the following medical subject headings (MeSH): (electroconvulsive therapy OR ECT) AND (schizophrenia) AND (MRI OR fMRI OR DTI OR DWI). RESULTS This review yielded 12 MRI studies, including 4 with sMRI, 5 with fMRI and 3 with multimodal MRI. Increases in volumes of the hippocampus and its adjacent regions (parahippocampal gyrus and amygdala), as well as the insula and frontotemporal regions, were noted after ECT. fMRI studies found ECT-induced changes in different brain regions/networks, including the hippocampus, amygdala, default model network, salience network and other regions/networks that are thought to highly correlate with the pathophysiologic characteristics of SZ. The results of the correlation between brain changes and symptom remissions are inconsistent. CONCLUSION Our review provides evidence supporting ECT-induced brain changes on sMRI and fMRI in SZ and explores the relationship between these changes and symptom remission.
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Affiliation(s)
- Yuchao Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P.R. China; ,High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, P.R. China;
| | - Mingjun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P.R. China; ,Address correspondence to these authors at the The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Second North Jianshe Road, Chengdu 610054, China; Tel: 86-28-83201018; Fax: 86-28-83208238; E-mails: (C. Luo) and (M. Duan)
| | - Hui He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P.R. China; ,High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, P.R. China;
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P.R. China; ,High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, P.R. China; ,Research Unit of NeuroInformation (2019RU035), Chinese Academy of Medical Sciences, Chengdu, P.R. China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P.R. China; ,High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, P.R. China; ,Research Unit of NeuroInformation (2019RU035), Chinese Academy of Medical Sciences, Chengdu, P.R. China,Address correspondence to these authors at the The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Second North Jianshe Road, Chengdu 610054, China; Tel: 86-28-83201018; Fax: 86-28-83208238; E-mails: (C. Luo) and (M. Duan)
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Gong J, Cui LB, Zhao YS, Liu ZW, Yang XJ, Xi YB, Liu L, Liu P, Sun JB, Zhao SW, Liu XF, Jia J, Li P, Yin H, Qin W. The correlation between dynamic functional architecture and response to electroconvulsive therapy combined with antipsychotics in schizophrenia. Eur J Neurosci 2022; 55:2024-2036. [PMID: 35388553 DOI: 10.1111/ejn.15664] [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: 05/06/2021] [Revised: 03/29/2022] [Accepted: 03/31/2022] [Indexed: 11/26/2022]
Abstract
Attempts to determine why some patients respond to electroconvulsive therapy (ECT) are valuable in schizophrenia. Schizophrenia is associated with aberrant dynamic functional architecture, which might impact the efficacy of ECT. We aimed to explore the relationship between pre-treatment temporal variability and ECT acute efficacy. Forty-eight patients with schizophrenia and thirty healthy controls underwent functional magnetic resonance imaging to examine whether patterns of temporary variability of functional architecture differ between high responders (HR) and low responders (LR) at baseline. Compared with LR, HR exhibited significantly abnormal temporal variability in right inferior front gyrus (IFGtriang.R), left temporal pole (TPOsup.L) and right middle temporal gyrus (MTG.R). In the pooled patient group, ∆PANSS was correlated with the temporal variability of these regions. Patients with schizophrenia with a distinct dynamic functional architecture appear to reveal differential response to ECT. Our findings provide not only an understanding of the neural functional architecture patterns that are found in schizophrenia but also the possibility of using these measures as moderators for ECT selection.
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Affiliation(s)
- Jie Gong
- Engineering Research Center of Molecular and Neuroimaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China
| | - Long-Biao Cui
- Department of Radiology, The Second Medical Center, Chinese PLA General Hospital, Beijing, China.,Department of Clinical Psychology, School of Medical Psychology, Fourth Military Medical University, Xi'an, China
| | - Ying-Song Zhao
- Engineering Research Center of Molecular and Neuroimaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China
| | - Zhao-Wen Liu
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Xue-Juan Yang
- Engineering Research Center of Molecular and Neuroimaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China
| | - Yi-Bin Xi
- Department of Radiology, Xi'an People's Hospital, Xi'an, China
| | - Lin Liu
- Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Peng Liu
- Engineering Research Center of Molecular and Neuroimaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China
| | - Jin-Bo Sun
- Engineering Research Center of Molecular and Neuroimaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China
| | - Shu-Wan Zhao
- Department of Clinical Psychology, School of Medical Psychology, Fourth Military Medical University, Xi'an, China.,Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Xiao-Fan Liu
- Department of Clinical Psychology, School of Medical Psychology, Fourth Military Medical University, Xi'an, China.,Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Jie Jia
- Department of Early Intervention, Xi'an Mental Health Center, Xi'an, Shaanxi, China
| | - Ping Li
- Department of Medical Imaging, Xi'an Mental Health Center, Xi'an, Shaanxi, China
| | - Hong Yin
- Department of Radiology, Xi'an People's Hospital, Xi'an, China.,Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Wei Qin
- Engineering Research Center of Molecular and Neuroimaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China
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Jiang J, Li J, Xu Y, Zhang B, Sheng J, Liu D, Wang W, Yang F, Guo X, Li Q, Zhang T, Tang Y, Jia Y, Daskalakis ZJ, Wang J, Li C. Magnetic Seizure Therapy Compared to Electroconvulsive Therapy for Schizophrenia: A Randomized Controlled Trial. Front Psychiatry 2021; 12:770647. [PMID: 34899429 PMCID: PMC8656219 DOI: 10.3389/fpsyt.2021.770647] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Accepted: 10/29/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Magnetic seizure therapy (MST) is a potential alternative to electroconvulsive therapy (ECT). However, reports on the use of MST for patients with schizophrenia, particularly in developing countries, which is a main indication for ECT, are limited. Methods: From February 2017 to July 2018, 79 inpatients who met the DSM-5 criteria for schizophrenia were randomized to receive 10 sessions of MST (43 inpatients) or ECT (36 inpatients) over the course of 4 weeks. At baseline and 4-week follow-up, the Positive and Negative Syndrome Scale (PANSS) and the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) were used to assess symptom severity and cognitive functions, respectively. Results: Seventy-one patients who completed at least half of the treatment protocol were included in the per-protocol analysis. MST generated a non-significant larger antipsychotic effect in terms of a reduction in PANSS total score [g = 0.17, 95% confidence interval (CI) = -0.30, 0.63] and response rate [relative risk (RR) = 1.41, 95% CI = 0.83-2.39]. Twenty-four participants failed to complete the cognitive assessment as a result of severe psychotic symptoms. MST showed significant less cognitive impairment over ECT in terms of immediate memory (g = 1.26, 95% CI = 0.63-1.89), language function (g =1.14, 95% CI = 0.52-1.76), delayed memory (g = 0.75, 95% CI = 0.16-1.35), and global cognitive function (g = 1.07, 95% CI = 0.45-1.68). The intention-to-treat analysis generated similar results except for the differences in delayed memory became statistically insignificant. Better baseline cognitive performance predicted MST and ECT response. Conclusions: Compared to bitemporal ECT with brief pulses and age-dose method, MST had similar antipsychotic efficacy with fewer cognitive impairments, indicating that MST is a promising alternative to ECT as an add-on treatment for schizophrenia. Clinical Trial Registration: ClinicalTrials.gov, identifier: NCT02746965.
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Affiliation(s)
- Jiangling Jiang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jin Li
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yuanhong Xu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Bin Zhang
- Psychological and Psychiatric Neuroimage Lab, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Jianhua Sheng
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Dengtang Liu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wenzheng Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fuzhong Yang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaoyun Guo
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qingwei Li
- Department of Psychiatry, Tongji Hospital of Tongji University, Shanghai, China
| | - Tianhong Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yingying Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yuping Jia
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zafiris J. Daskalakis
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States
| | - Jijun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai, China
- Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Science, Shanghai, China
- Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China
| | - Chunbo Li
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai, China
- Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Science, Shanghai, China
- Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China
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Increased Homotopic Connectivity in the Prefrontal Cortex Modulated by Olanzapine Predicts Therapeutic Efficacy in Patients with Schizophrenia. Neural Plast 2021; 2021:9954547. [PMID: 34512748 PMCID: PMC8429031 DOI: 10.1155/2021/9954547] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 08/08/2021] [Accepted: 08/18/2021] [Indexed: 11/18/2022] Open
Abstract
Background Previous studies have revealed the abnormalities in homotopic connectivity in schizophrenia. However, the relationship of these deficits to antipsychotic treatment in schizophrenia remains unclear. This study explored the effects of antipsychotic therapy on brain homotopic connectivity and whether the homotopic connectivity of these regions might predict individual treatment response in schizophrenic patients. Methods A total of 21 schizophrenic patients and 20 healthy controls were scanned by the resting-state functional magnetic resonance imaging. The patients received olanzapine treatment and were scanned at two time points. Voxel-mirrored homotopic connectivity (VMHC) and pattern classification techniques were applied to analyze the imaging data. Results Schizophrenic patients presented significantly decreased VMHC in the temporal and inferior frontal gyri, medial prefrontal cortex (MPFC), and motor and low-level sensory processing regions (including the fusiform gyrus and cerebellum lobule VI) relative to healthy controls. The VMHC in the superior/middle MPFC was significantly increased in the patients after eight weeks of treatment. Support vector regression (SVR) analyses revealed that VMHC in the superior/middle MPFC at baseline can predict the symptomatic improvement of the positive and negative syndrome scale after eight weeks of treatment. Conclusions This study demonstrated that olanzapine treatment may normalize decreased homotopic connectivity in the superior/middle MPFC in schizophrenic patients. The VMHC in the superior/middle MPFC may predict individual response for antipsychotic therapy. The findings of this study conduce to the comprehension of the therapy effects of antipsychotic medications on homotopic connectivity in schizophrenia.
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Moon SY, Kim M, Lho SK, Oh S, Kim SH, Kwon JS. Systematic Review of the Neural Effect of Electroconvulsive Therapy in Patients with Schizophrenia: Hippocampus and Insula as the Key Regions of Modulation. Psychiatry Investig 2021; 18:486-499. [PMID: 34218638 PMCID: PMC8256139 DOI: 10.30773/pi.2020.0438] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 03/03/2021] [Indexed: 12/22/2022] Open
Abstract
OBJECTIVE Electroconvulsive therapy (ECT) has been the most potent treatment option for treatment-resistant schizophrenia (TRS). However, the underlying neural mechanisms of ECT in schizophrenia remain largely unclear. This paper examines studies that investigated structural and functional changes after ECT in patients with schizophrenia. METHODS We carried out a systematic review with following terms: 'ECT', 'schizophrenia', and the terms of various neuroimaging modalities. RESULTS Among the 325 records available from the initial search in May 2020, 17 studies were included. Cerebral blood flow in the frontal, temporal, and striatal structures was shown to be modulated (n=3), although the results were divergent. Magnetic resonance spectroscopy (MRS) studies suggested that the ratio of N-acetyl-aspartate/creatinine was increased in the left prefrontal cortex (PFC; n=2) and left thalamus (n=1). The hippocampus and insula (n=6, respectively) were the most common regions of structural/functional modulation, which also showed symptom associations. Functional connectivity of the default mode network (DMN; n=5), PFC (n=4), and thalamostriatal system (n=2) were also commonly modulated. CONCLUSION Despite proven effectiveness, there has been a dearth of studies investigating the neurobiological mechanisms underlying ECT. There is preliminary evidence of structural and functional modulation of the hippocampus and insula, functional changes in the DMN, PFC, and thalamostriatal system after ECT in patients with schizophrenia. We discuss the rationale and implications of these findings and the potential mechanism of action of ECT. More studies evaluating the mechanisms of ECT are needed, which could provide a unique window into what leads to treatment response in the otherwise refractory TRS population.
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Affiliation(s)
- Sun-Young Moon
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea.,Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
| | - Minah Kim
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea.,Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
| | - Silvia Kyungjin Lho
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea.,Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
| | - Sanghoon Oh
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea.,Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
| | - Se Hyun Kim
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea.,Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
| | - Jun Soo Kwon
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea.,Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea.,Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea.,Institute of Human Behavioral Medicine, SNU-MRC, Seoul, Republic of Korea
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9
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Abstract
Wilson's disease patients with neurological symptoms have motor symptoms and cognitive deficits, including frontal executive, visuospatial processing, and memory impairments. Although the brain structural abnormalities associated with Wilson's disease have been documented, it remains largely unknown how Wilson's disease affects large-scale functional brain networks. In this study, we investigated functional brain networks in Wilson's disease. Particularly, we analyzed resting state functional magnetic resonance images of 30 Wilson's disease patients and 26 healthy controls. First, functional brain networks for each participant were extracted using an independent component analysis method. Then, a computationally efficient pattern classification method was developed to identify discriminative brain functional networks associated with Wilson's disease. Experimental results indicated that Wilson's disease patients, compared with healthy controls, had altered large-scale functional brain networks, including the dorsal anterior cingulate cortex and basal ganglia network, the middle frontal gyrus, the dorsal striatum, the inferior parietal lobule, the precuneus, the temporal pole, and the posterior lobe of cerebellum. Classification models built upon these networks distinguished between neurological WD patients and HCs with accuracy up to 86.9% (specificity: 86.7%, sensitivity: 89.7%). The classification scores were correlated with the United Wilson's Disease Rating Scale measures and durations of disease of the patients. These results suggest that Wilson's disease patients have multiple aberrant brain functional networks, and classification scores derived from these networks are associated with severity of clinical symptoms.
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10
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Shan X, Liao R, Ou Y, Pan P, Ding Y, Liu F, Chen J, Zhao J, Guo W, He Y. Increased regional homogeneity modulated by metacognitive training predicts therapeutic efficacy in patients with schizophrenia. Eur Arch Psychiatry Clin Neurosci 2021; 271:783-798. [PMID: 32215727 PMCID: PMC8119286 DOI: 10.1007/s00406-020-01119-w] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Accepted: 03/11/2020] [Indexed: 02/07/2023]
Abstract
Previous studies have demonstrated the efficacy of metacognitive training (MCT) in schizophrenia. However, the underlying mechanisms related to therapeutic effect of MCT remain unknown. The present study explored the treatment effects of MCT on brain regional neural activity using regional homogeneity (ReHo) and whether these regions' activities could predict individual treatment response in schizophrenia. Forty-one patients with schizophrenia and 20 healthy controls were scanned using resting-state functional magnetic resonance imaging. Patients were randomly divided into drug therapy (DT) and drug plus psychotherapy (DPP) groups. The DT group received only olanzapine treatment, whereas the DPP group received olanzapine and MCT for 8 weeks. The results revealed that ReHo in the right precuneus, left superior medial prefrontal cortex (MPFC), right parahippocampal gyrus and left rectus was significantly increased in the DPP group after 8 weeks of treatment. Patients in the DT group showed significantly increased ReHo in the left ventral MPFC/anterior cingulate cortex (ACC), left superior MPFC/middle frontal gyrus (MFG), left precuneus, right rectus and left MFG, and significantly decreased ReHo in the bilateral cerebellum VIII and left inferior occipital gyrus (IOG) after treatment. Support vector regression analyses showed that high ReHo levels at baseline in the right precuneus and left superior MPFC could predict symptomatic improvement of Positive and Negative Syndrome Scale (PANSS) after 8 weeks of DPP treatment. Moreover, high ReHo levels at baseline and alterations of ReHo in the left ventral MPFC/ACC could predict symptomatic improvement of PANSS after 8 weeks of DT treatment. This study suggests that MCT is associated with the modulation of ReHo in schizophrenia. ReHo in the right precuneus and left superior MPFC may predict individual therapeutic response for MCT in patients with schizophrenia.
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Affiliation(s)
- Xiaoxiao Shan
- grid.452708.c0000 0004 1803 0208Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, 410011 Hunan China ,National Clinical Research Center on Mental Disorders, Changsha, 410011 Hunan China
| | - Rongyuan Liao
- grid.412990.70000 0004 1808 322XThe Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan China
| | - Yangpan Ou
- grid.452708.c0000 0004 1803 0208Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, 410011 Hunan China ,National Clinical Research Center on Mental Disorders, Changsha, 410011 Hunan China
| | - Pan Pan
- grid.452708.c0000 0004 1803 0208Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, 410011 Hunan China ,National Clinical Research Center on Mental Disorders, Changsha, 410011 Hunan China
| | - Yudan Ding
- grid.452708.c0000 0004 1803 0208Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, 410011 Hunan China ,National Clinical Research Center on Mental Disorders, Changsha, 410011 Hunan China
| | - Feng Liu
- grid.412645.00000 0004 1757 9434Department of Radiology, Tianjin Medical University General Hospital, Tianjin, 300000 China
| | - Jindong Chen
- grid.452708.c0000 0004 1803 0208Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, 410011 Hunan China ,National Clinical Research Center on Mental Disorders, Changsha, 410011 Hunan China
| | - Jingping Zhao
- grid.452708.c0000 0004 1803 0208Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, 410011 Hunan China ,National Clinical Research Center on Mental Disorders, Changsha, 410011 Hunan China
| | - Wenbin Guo
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China. .,National Clinical Research Center on Mental Disorders, Changsha, 410011, Hunan, China.
| | - Yiqun He
- The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, China.
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11
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Mi WF, Tabarak S, Wang L, Zhang SZ, Lin X, Du LT, Liu Z, Bao YP, Gao XJ, Zhang WH, Wang XQ, Fan TT, Li LZ, Hao XN, Fu Y, Shi Y, Guo LH, Sun HQ, Liu L, Si TM, Zhang HY, Lu L, Li SX. Effects of agomelatine and mirtazapine on sleep disturbances in major depressive disorder: evidence from polysomnographic and resting-state functional connectivity analyses. Sleep 2020; 43:5837058. [PMID: 32406918 DOI: 10.1093/sleep/zsaa092] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Revised: 05/04/2020] [Indexed: 11/14/2022] Open
Abstract
To investigate effects of agomelatine and mirtazapine on sleep disturbances in patients with major depressive disorder. A total of 30 depressed patients with sleep disturbances, 27 of which completed the study, took agomelatine or mirtazapine for 8 weeks. Subjective scales were administered, and polysomnography was performed at baseline and at the end of week 1 and 8. Functional magnetic resonance imaging was performed at baseline and at the end of week 8. Compared with baseline, scores on the Hamilton Depression Scale, Hamilton Anxiety Scale, Pittsburgh Sleep Quality Index, Sleep Dysfunction Rating Scale, and Insomnia Severity Index after 8 weeks of treatment significantly decreased in both groups, with no significant differences between groups, accompanied by significant increases in total sleep time, sleep efficiency, and rapid eye movement (REM) sleep and significant decrease in wake after sleep onset. Mirtazapine treatment increased N3 sleep at week 1 compared with agomelatine treatment, but this difference disappeared at week 8. The increases in the percentage and duration of N3 sleep were positively correlated with increases in connectivity between right dorsal lateral prefrontal cortex (dlPFC) and right precuneus and between left posterior cingulate cortex and right precuneus in both groups, respectively. Functional connectivity (FC) between right dlPFC and left precuneus in mirtazapine group was higher compared with agomelatine group after 8 weeks of treatment. These findings indicated that both agomelatine and mirtazapine improved sleep in depressed patients, and the effect of mirtazapine was greater than agomelatine with regard to rapidly increasing N3 sleep and gradually improving FC in the brain.
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Affiliation(s)
- Wei-Feng Mi
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Haidian, Beijing, China
| | - Serik Tabarak
- Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
| | - Li Wang
- Xuanwu Hospital Capital Medical University, Xicheng, Beijing, China
| | - Su-Zhen Zhang
- Department of Psychiatry, Huzhou 3rd Hospital, Huzhou, Zhejiang, China
| | - Xiao Lin
- Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
| | - Lan-Ting Du
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Haidian, Beijing, China
| | - Zhen Liu
- Beijing Key laboratory of Drug Dependence, National Institute on Drug Dependence, Peking University, Beijing, China.,Department of Pharmacology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China
| | - Yan-Ping Bao
- Beijing Key laboratory of Drug Dependence, National Institute on Drug Dependence, Peking University, Beijing, China.,Department of Pharmacology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China
| | - Xue-Jiao Gao
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Haidian, Beijing, China
| | - Wei-Hua Zhang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Haidian, Beijing, China
| | - Xue-Qin Wang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Haidian, Beijing, China
| | - Teng-Teng Fan
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Haidian, Beijing, China
| | - Ling-Zhi Li
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Haidian, Beijing, China
| | - Xiao-Nan Hao
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Haidian, Beijing, China
| | - Yi Fu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Haidian, Beijing, China
| | - Ying Shi
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Haidian, Beijing, China
| | - Li-Hua Guo
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Haidian, Beijing, China
| | - Hong-Qiang Sun
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Haidian, Beijing, China
| | - Lin Liu
- Beijing Key laboratory of Drug Dependence, National Institute on Drug Dependence, Peking University, Beijing, China.,Department of Pharmacology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China
| | - Tian-Mei Si
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Haidian, Beijing, China
| | - Hong-Yan Zhang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Haidian, Beijing, China
| | - Lin Lu
- Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China.,Beijing Key laboratory of Drug Dependence, National Institute on Drug Dependence, Peking University, Beijing, China
| | - Su-Xia Li
- Beijing Key laboratory of Drug Dependence, National Institute on Drug Dependence, Peking University, Beijing, China.,Department of Pharmacology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China
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12
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Li P, Jing RX, Zhao RJ, Shi L, Sun HQ, Ding Z, Lin X, Lu L, Fan Y. Association between functional and structural connectivity of the corticostriatal network in people with schizophrenia and unaffected first-degree relatives. J Psychiatry Neurosci 2020; 45:395-405. [PMID: 32436671 PMCID: PMC7595738 DOI: 10.1503/jpn.190015] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND Dysfunction of the corticostriatal network has been implicated in the pathophysiology of schizophrenia, but findings are inconsistent within and across imaging modalities. We used multimodal neuroimaging to analyze functional and structural connectivity in the corticostriatal network in people with schizophrenia and unaffected first-degree relatives. METHODS We collected resting-state functional magnetic resonance imaging and diffusion tensor imaging scans from people with schizophrenia (n = 47), relatives (n = 30) and controls (n = 49). We compared seed-based functional and structural connectivity across groups within striatal subdivisions defined a priori. RESULTS Compared with controls, people with schizophrenia had altered connectivity between the subdivisions and brain regions in the frontal and temporal cortices and thalamus; relatives showed different connectivity between the subdivisions and the right anterior cingulate cortex (ACC) and the left precuneus. Post-hoc t tests revealed that people with schizophrenia had decreased functional connectivity in the ventral loop (ventral striatum-right ACC) and dorsal loop (executive striatum-right ACC and sensorimotor striatum-right ACC), accompanied by decreased structural connectivity; relatives had reduced functional connectivity in the ventral loop and the dorsal loop (right executive striatum-right ACC) and no significant difference in structural connectivity compared with the other groups. Functional connectivity among people with schizophrenia in the bilateral ventral striatum-right ACC was correlated with positive symptom severity. LIMITATIONS The number of relatives included was moderate. Striatal subdivisions were defined based on a relatively low threshold, and structural connectivity was measured based on fractional anisotropy alone. CONCLUSION Our findings provide insight into the role of hypoconnectivity of the ventral corticostriatal system in people with schizophrenia.
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Affiliation(s)
- Peng Li
- From the Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), and National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China (Li, Shi, Sun, Lin, Lu); the National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China (Jing); the University of Chinese Academy of Sciences, Beijing, China (Jing); the Department of Alcohol and Drug Dependence, Beijing Hui-Long-Guan Hospital, Peking University, Beijing, China (Zhao); the National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence, Peking University, Beijing, China (Ding); the Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China (Lin, Lu); and the Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA (Fan)
| | - Ri-Xing Jing
- From the Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), and National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China (Li, Shi, Sun, Lin, Lu); the National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China (Jing); the University of Chinese Academy of Sciences, Beijing, China (Jing); the Department of Alcohol and Drug Dependence, Beijing Hui-Long-Guan Hospital, Peking University, Beijing, China (Zhao); the National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence, Peking University, Beijing, China (Ding); the Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China (Lin, Lu); and the Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA (Fan)
| | - Rong-Jiang Zhao
- From the Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), and National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China (Li, Shi, Sun, Lin, Lu); the National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China (Jing); the University of Chinese Academy of Sciences, Beijing, China (Jing); the Department of Alcohol and Drug Dependence, Beijing Hui-Long-Guan Hospital, Peking University, Beijing, China (Zhao); the National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence, Peking University, Beijing, China (Ding); the Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China (Lin, Lu); and the Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA (Fan)
| | - Le Shi
- From the Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), and National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China (Li, Shi, Sun, Lin, Lu); the National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China (Jing); the University of Chinese Academy of Sciences, Beijing, China (Jing); the Department of Alcohol and Drug Dependence, Beijing Hui-Long-Guan Hospital, Peking University, Beijing, China (Zhao); the National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence, Peking University, Beijing, China (Ding); the Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China (Lin, Lu); and the Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA (Fan)
| | - Hong-Qiang Sun
- From the Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), and National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China (Li, Shi, Sun, Lin, Lu); the National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China (Jing); the University of Chinese Academy of Sciences, Beijing, China (Jing); the Department of Alcohol and Drug Dependence, Beijing Hui-Long-Guan Hospital, Peking University, Beijing, China (Zhao); the National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence, Peking University, Beijing, China (Ding); the Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China (Lin, Lu); and the Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA (Fan)
| | - Zengbo Ding
- From the Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), and National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China (Li, Shi, Sun, Lin, Lu); the National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China (Jing); the University of Chinese Academy of Sciences, Beijing, China (Jing); the Department of Alcohol and Drug Dependence, Beijing Hui-Long-Guan Hospital, Peking University, Beijing, China (Zhao); the National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence, Peking University, Beijing, China (Ding); the Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China (Lin, Lu); and the Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA (Fan)
| | - Xiao Lin
- From the Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), and National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China (Li, Shi, Sun, Lin, Lu); the National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China (Jing); the University of Chinese Academy of Sciences, Beijing, China (Jing); the Department of Alcohol and Drug Dependence, Beijing Hui-Long-Guan Hospital, Peking University, Beijing, China (Zhao); the National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence, Peking University, Beijing, China (Ding); the Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China (Lin, Lu); and the Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA (Fan)
| | - Lin Lu
- From the Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), and National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China (Li, Shi, Sun, Lin, Lu); the National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China (Jing); the University of Chinese Academy of Sciences, Beijing, China (Jing); the Department of Alcohol and Drug Dependence, Beijing Hui-Long-Guan Hospital, Peking University, Beijing, China (Zhao); the National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence, Peking University, Beijing, China (Ding); the Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China (Lin, Lu); and the Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA (Fan)
| | - Yong Fan
- From the Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), and National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China (Li, Shi, Sun, Lin, Lu); the National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China (Jing); the University of Chinese Academy of Sciences, Beijing, China (Jing); the Department of Alcohol and Drug Dependence, Beijing Hui-Long-Guan Hospital, Peking University, Beijing, China (Zhao); the National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence, Peking University, Beijing, China (Ding); the Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China (Lin, Lu); and the Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA (Fan)
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13
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Yang X, Xu Z, Xi Y, Sun J, Liu P, Liu P, Li P, Jia J, Yin H, Qin W. Predicting responses to electroconvulsive therapy in schizophrenia patients undergoing antipsychotic treatment: Baseline functional connectivity among regions with strong electric field distributions. Psychiatry Res Neuroimaging 2020; 299:111059. [PMID: 32135406 DOI: 10.1016/j.pscychresns.2020.111059] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Revised: 02/16/2020] [Accepted: 02/21/2020] [Indexed: 01/15/2023]
Abstract
This study explored imaging predictors of electroconvulsive therapy (ECT) outcome in schizophrenia patients based on pre-treatment functional connectivity (FC) within regions with strong ECT electric fields distribution. Forty-seven patients received standard antipsychotic drugs combined with ECT as well as two brain imaging sessions. Regions of interest (ROI) with strong electric field distribution were determined by ECT simulation. Using baseline functional connectivity between ROIs, a model was constructed to predict the percentage reduction of Positive and Negative Syndrome Scale (PANSS) scores. The strong electric fields were distributed in the orbital prefrontal lobe, medial temporal lobe, and other parts of the temporal lobe. Ten functional connectivity features within the electric field distribution areas showed a predictive ability for ECT outcome. The correlation coefficient between the predictive and real values of cross-validation was 0.7165. Among the predictive features, ECT induced a significant decrease in functional connectivity between the right amygdala and the left hippocampus. These results suggest that pretreatment functional connectivity patterns in brain regions with strong electric field distributions during ECT could be potential predictors of the efficacy of ECT augmentation in schizophrenia. These findings may help to improve individualized clinical treatment in the future.
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Affiliation(s)
- Xuejuan Yang
- Engineering Research Center of Molecular & Neuroimaging, Ministry of Education, School of Life Science and Technology, Xidian University, 266 Xinglong Section of Xifeng Road, Xi'an, Shaanxi 710126, China
| | - Ziliang Xu
- Engineering Research Center of Molecular & Neuroimaging, Ministry of Education, School of Life Science and Technology, Xidian University, 266 Xinglong Section of Xifeng Road, Xi'an, Shaanxi 710126, China
| | - Yibin Xi
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, No. 127 West Changle Road, Xi'an, Shaanxi 710032, China
| | - Jinbo Sun
- Engineering Research Center of Molecular & Neuroimaging, Ministry of Education, School of Life Science and Technology, Xidian University, 266 Xinglong Section of Xifeng Road, Xi'an, Shaanxi 710126, China
| | - Peng Liu
- Engineering Research Center of Molecular & Neuroimaging, Ministry of Education, School of Life Science and Technology, Xidian University, 266 Xinglong Section of Xifeng Road, Xi'an, Shaanxi 710126, China
| | - Peng Liu
- Engineering Research Center of Molecular & Neuroimaging, Ministry of Education, School of Life Science and Technology, Xidian University, 266 Xinglong Section of Xifeng Road, Xi'an, Shaanxi 710126, China
| | - Ping Li
- Department of Medical Imaging, Xi'an Mental Health Center, Xi'an, Shaanxi 710061, China
| | - Jie Jia
- Department of early intervention, Xi'an Mental Health Center, Xi'an, Shaanxi 710061, China
| | - Hong Yin
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, No. 127 West Changle Road, Xi'an, Shaanxi 710032, China.
| | - Wei Qin
- Engineering Research Center of Molecular & Neuroimaging, Ministry of Education, School of Life Science and Technology, Xidian University, 266 Xinglong Section of Xifeng Road, Xi'an, Shaanxi 710126, China.
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14
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Predicting response to electroconvulsive therapy combined with antipsychotics in schizophrenia using multi-parametric magnetic resonance imaging. Schizophr Res 2020; 216:262-271. [PMID: 31826827 DOI: 10.1016/j.schres.2019.11.046] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Revised: 09/04/2019] [Accepted: 11/25/2019] [Indexed: 12/23/2022]
Abstract
Electroconvulsive therapy (ECT) has been shown to be effective in schizophrenia, particularly when rapid symptom reduction is needed or in cases of resistance to drug treatment. However, there are no markers available to predict response to ECT. Here, we examine whether multi-parametric magnetic resonance imaging (MRI)-based radiomic features can predict response to ECT for individual patients. A total of 57 treatment-resistant schizophrenia patients, or schizophrenia patients with an acute episode or suicide attempts were randomly divided into primary (42 patients) and test (15 patients) cohorts. We collected T1-weighted structural MRI and diffusion MRI for 57 patients before receiving ECT and extracted 600 radiomic features for feature selection and prediction. To predict a continuous improvement in symptoms (ΔPANSS), the prediction process was performed with a support vector regression model based on a leave-one-out cross-validation framework in primary cohort and was tested in test cohort. The multi-parametric MRI-based radiomic model, including four structural MRI feature from left inferior frontal gyrus, right insula, left middle temporal gyrus and right superior temporal gyrus respectively and six diffusion MRI features from tracts connecting frontal or temporal gyrus possessed a low root mean square error of 15.183 in primary cohort and 14.980 in test cohort. The Pearson's correlation coefficients between predicted and actual values were 0.671 and 0.777 respectively. These results demonstrate that multi-parametric MRI-based radiomic features may predict response to ECT for individual patients. Such features could serve as prognostic neuroimaging biomarkers that provide a critical step toward individualized treatment response prediction in schizophrenia.
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15
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Li H, Fan Y. Interpretable, highly accurate brain decoding of subtly distinct brain states from functional MRI using intrinsic functional networks and long short-term memory recurrent neural networks. Neuroimage 2019; 202:116059. [PMID: 31362049 PMCID: PMC6819260 DOI: 10.1016/j.neuroimage.2019.116059] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2019] [Revised: 07/25/2019] [Accepted: 07/26/2019] [Indexed: 11/17/2022] Open
Abstract
Decoding brain functional states underlying cognitive processes from functional MRI (fMRI) data using multivariate pattern analysis (MVPA) techniques has achieved promising performance for characterizing brain activation patterns and providing neurofeedback signals. However, it remains challenging to decode subtly distinct brain states for individual fMRI data points due to varying temporal durations and dependency among different cognitive processes. In this study, we develop a deep learning based framework for brain decoding by leveraging recent advances in intrinsic functional network modeling and sequence modeling using long short-term memory (LSTM) recurrent neural networks (RNNs). Particularly, subject-specific intrinsic functional networks (FNs) are computed from resting-state fMRI data and are used to characterize functional signals of task fMRI data with a compact representation for building brain decoding models, and LSTM RNNs are adopted to learn brain decoding mappings between functional profiles and brain states. Validation results on fMRI data from the HCP dataset have demonstrated that brain decoding models built on training data using the proposed method could learn discriminative latent feature representations and effectively distinguish subtly distinct working memory tasks of different subjects with significantly higher accuracy than conventional decoding models. Informative FNs of the brain decoding models identified as brain activation patterns of working memory tasks were largely consistent with the literature. The method also obtained promising decoding performance on motor and social cognition tasks. Our results suggest that LSTM RNNs in conjunction with FNs could build interpretable, highly accurate brain decoding models.
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Affiliation(s)
- Hongming Li
- Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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16
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Sambataro F, Thomann PA, Nolte HM, Hasenkamp JH, Hirjak D, Kubera KM, Hofer S, Seidl U, Depping MS, Stieltjes B, Maier-Hein K, Wolf RC. Transdiagnostic modulation of brain networks by electroconvulsive therapy in schizophrenia and major depression. Eur Neuropsychopharmacol 2019; 29:925-935. [PMID: 31279591 DOI: 10.1016/j.euroneuro.2019.06.002] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2019] [Revised: 05/14/2019] [Accepted: 06/10/2019] [Indexed: 12/30/2022]
Abstract
Major depressive disorder (MDD) and schizophrenia (SCZ) share neurobiological and clinical commonalities. Altered functional connectivity of large-scale brain networks has been associated with both disorders. Electroconvulsive therapy (ECT) has proven to be an effective treatment in severe forms of MDD and SCZ. However, the role of ECT on the modulation of the dynamics of brain networks is still unknown. In this study, we used resting state functional magnetic resonance imaging (rs-fMRI) to investigate functional connectivity in 16 pharmacoresistant patients with SCZ or MDD and a matched group of normal controls. Patients were scanned before and after right-sided unilateral ECT. Group spatial independent component analysis was carried out with a multiple analysis of covariance (MANCOVA) approach to estimate the effects of ECT treatment on intrinsic components (INs). Functional network connectivity (FNC) was calculated between pairs of INs. Patients had reduced connectivity within a striato-thalamic network in the thalamus as well as increased low frequency oscillations in a striatal network. ECT reduced low frequency oscillations (LFOs) on a striatal network along with increasing functional connectivity in the medial prefrontal cortex within the DMN. Following ECT treatment, the FNC of the executive network was reduced with the DMN and increased with the salience network, respectively. Our findings suggest transnosological effects of ECT on the connectivity of large-scale networks as well as at the level of their interplay. Furthermore, they support a transnosological approach for the investigation not only of the neural correlates of the disease but also of the brain mechanism of treatment of mental disorders.
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Affiliation(s)
- Fabio Sambataro
- Department of Neuroscience (DNS), University of Padova, Padua, Italy.
| | - Philipp Arthur Thomann
- Center for Psychosocial Medicine, Department of General Psychiatry, Heidelberg University, 69115 Heidelberg, Germany; Center for Mental Health, Odenwald District Healthcare Center, Erbach, Germany
| | - Henrike Maria Nolte
- Center for Psychosocial Medicine, Department of General Psychiatry, Heidelberg University, 69115 Heidelberg, Germany
| | - J H Hasenkamp
- Center for Psychosocial Medicine, Department of General Psychiatry, Heidelberg University, 69115 Heidelberg, Germany
| | - Dusan Hirjak
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, 68159 Mannheim, Germany
| | - Katharina M Kubera
- Center for Psychosocial Medicine, Department of General Psychiatry, Heidelberg University, 69115 Heidelberg, Germany
| | - Stefan Hofer
- Center for Psychosocial Medicine, Department of General Psychiatry, Heidelberg University, 69115 Heidelberg, Germany; Department of Anaesthesiology, Westpfalz-Klinikum GmbH, 67655 Kaiserslautern, Germany
| | - Ulrich Seidl
- Department of Anaesthesiology, Westpfalz-Klinikum GmbH, 67655 Kaiserslautern, Germany
| | - Malte Sebastian Depping
- Center for Psychosocial Medicine, Department of General Psychiatry, Heidelberg University, 69115 Heidelberg, Germany
| | - Bram Stieltjes
- Department of Radiology, Section Quantitative Imaging Based Disease Characterization, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Klaus Maier-Hein
- Medical Image Computing Group, Division of Medical and Biological Informatics, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Robert Christian Wolf
- Center for Psychosocial Medicine, Department of General Psychiatry, Heidelberg University, 69115 Heidelberg, Germany.
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Jing R, Li P, Ding Z, Lin X, Zhao R, Shi L, Yan H, Liao J, Zhuo C, Lu L, Fan Y. Machine learning identifies unaffected first-degree relatives with functional network patterns and cognitive impairment similar to those of schizophrenia patients. Hum Brain Mapp 2019; 40:3930-3939. [PMID: 31148311 DOI: 10.1002/hbm.24678] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Revised: 05/17/2019] [Accepted: 05/20/2019] [Indexed: 12/19/2022] Open
Abstract
Schizophrenia (SCZ) patients and their unaffected first-degree relatives (FDRs) share similar functional neuroanatomy. However, it remains largely unknown to what extent unaffected FDRs with functional neuroanatomy patterns similar to patients can be identified at an individual level. In this study, we used a multivariate pattern classification method to learn informative large-scale functional networks (FNs) and build classifiers to distinguish 32 patients from 30 healthy controls and to classify 34 FDRs as with or without FNs similar to patients. Four informative FNs-the cerebellum, default mode network (DMN), ventral frontotemporal network, and posterior DMN with parahippocampal gyrus-were identified based on a training cohort and pattern classifiers built upon these FNs achieved a correct classification rate of 83.9% (sensitivity 87.5%, specificity 80.0%, and area under the receiver operating characteristic curve [AUC] 0.914) estimated based on leave-one-out cross-validation for the training cohort and a correct classification rate of 77.5% (sensitivity 72.5%, specificity 82.5%, and AUC 0.811) for an independent validation cohort. The classification scores of the FDRs and patients were negatively correlated with their measures of cognitive function. FDRs identified by the classifiers as having SCZ patterns were similar to the patients, but significantly different from the controls and FDRs with normal patterns in terms of their cognitive measures. These results demonstrate that the pattern classifiers built upon the informative FNs can serve as biomarkers for quantifying brain alterations in SCZ and help to identify FDRs with FN patterns and cognitive impairment similar to those of SCZ patients.
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Affiliation(s)
- Rixing Jing
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Peng Li
- Institute of Mental Health, National Clinical Research Center for Mental Disorders, Key Laboratory of Mental Health and Peking University Sixth Hospital, Peking University, Beijing, China
| | - Zengbo Ding
- National Institute on Drug Dependence and Beijing Key laboratory of Drug Dependence, Peking University, Beijing, China
| | - Xiao Lin
- Institute of Mental Health, National Clinical Research Center for Mental Disorders, Key Laboratory of Mental Health and Peking University Sixth Hospital, Peking University, Beijing, China
- Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
| | - Rongjiang Zhao
- Department of Alcohol and Drug Dependence, Beijing Hui-Long-Guan Hospital, Peking University, Beijing, China
| | - Le Shi
- Institute of Mental Health, National Clinical Research Center for Mental Disorders, Key Laboratory of Mental Health and Peking University Sixth Hospital, Peking University, Beijing, China
| | - Hao Yan
- Institute of Mental Health, National Clinical Research Center for Mental Disorders, Key Laboratory of Mental Health and Peking University Sixth Hospital, Peking University, Beijing, China
| | - Jinmin Liao
- Institute of Mental Health, National Clinical Research Center for Mental Disorders, Key Laboratory of Mental Health and Peking University Sixth Hospital, Peking University, Beijing, China
| | - Chuanjun Zhuo
- Tianjin Mental Health Center, Nankai University Affiliated Tianjin Anding Hospital, Tianjin, China
- Department of Psychiatry, Tianjin Medical University, Tianjin, China
| | - Lin Lu
- Institute of Mental Health, National Clinical Research Center for Mental Disorders, Key Laboratory of Mental Health and Peking University Sixth Hospital, Peking University, Beijing, China
- National Institute on Drug Dependence and Beijing Key laboratory of Drug Dependence, Peking University, Beijing, China
- Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
| | - Yong Fan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
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18
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Cui LB, Cai M, Wang XR, Zhu YQ, Wang LX, Xi YB, Wang HN, Zhu X, Yin H. Prediction of early response to overall treatment for schizophrenia: A functional magnetic resonance imaging study. Brain Behav 2019; 9:e01211. [PMID: 30701701 PMCID: PMC6379641 DOI: 10.1002/brb3.1211] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Revised: 12/18/2018] [Accepted: 12/19/2018] [Indexed: 12/21/2022] Open
Abstract
INTRODUCTION Treatment response at an early stage of schizophrenia is of considerable value with regard to future management of the disorder; however, there are currently no biomarkers that can inform physicians about the likelihood of response. OBJECTS We aim to develop and validate regional brain activity derived from functional magnetic resonance imaging (fMRI) as a potential signature to predict early treatment response in schizophrenia. METHODS Amplitude of low-frequency fluctuation (ALFF) was measured at the start of the first/single episode resulting in hospitalization. Inpatients were included in a principal dataset (n = 79) and a replication dataset (n = 44). Two groups of healthy controls (n = 87; n = 106) were also recruited for each dataset. The clinical response was assessed at discharge from the hospital. The predictive capacity of normalized ALFF in patients by healthy controls, ALFFratio , was evaluated based on diagnostic tests and clinical correlates. RESULTS In the principal dataset, responders exhibited increased baseline ALFF in the left postcentral gyrus/inferior parietal lobule relative to non-responders. ALFFratio of responders before treatment was significantly higher than that of non-responders (p < 0.001). The area under the receiver operating characteristic curve was 0.746 for baseline ALFFratio to distinguish responders from non-responders, and the sensitivity, specificity, and accuracy were 72.7%, 68.6%, and 70.9%, respectively. Similar results were found in the independent replication dataset. CONCLUSIONS Baseline regional activity of the brain seems to be predictive of early response to treatment for schizophrenia. This study shows that psycho-neuroimaging holds promise for influencing the clinical treatment and management of schizophrenia.
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Affiliation(s)
- Long-Biao Cui
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China.,School of Medical Psychology, Fourth Military Medical University, Xi'an, China
| | - Min Cai
- Department of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Xing-Rui Wang
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Yuan-Qiang Zhu
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Liu-Xian Wang
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Yi-Bin Xi
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Hua-Ning Wang
- Department of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Xia Zhu
- School of Medical Psychology, Fourth Military Medical University, Xi'an, China
| | - Hong Yin
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
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19
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Li M, Deng W, Das T, Li Y, Zhao L, Ma X, Wang Y, Yu H, Li X, Meng YJ, Wang Q, Palaniyappan L, Li T. Neural substrate of unrelenting negative symptoms in schizophrenia: a longitudinal resting-state fMRI study. Eur Arch Psychiatry Clin Neurosci 2018; 268:641-651. [PMID: 29128871 DOI: 10.1007/s00406-017-0851-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2017] [Accepted: 11/02/2017] [Indexed: 02/05/2023]
Abstract
Developing a mechanistic insight into the specific brain processes that underpin improvement in negative symptoms can help us design novel chemical and physical treatments against these unrelenting symptoms. The aim of the present study is to explore the longitudinal changes in the brain's regional functional efficiency that accompany improvement in negative symptoms seen in first-episode patients with schizophrenia when treated with antipsychotic for 1 year. Forty-seven first-episode patients with schizophrenia were scanned at a drug-naive baseline state and followed up for 1 year to identify negative symptom responders (Rn) and non-responders (NRn). Fractional amplitude of low-frequency fluctuations (fALFF) and Granger analysis of effective connectivity (EC) were used to examine the different patterns of regional function and connectivity between Rn and NRn during the 1 year follow-up. Increase of fALFF in the left superior temporal gyrus (STG) and increase of EC from the left STG to the dorsolateral prefrontal cortex (DLPFC) was found in Rn compared to NRn. We further validated that the identified changes in fALFF/EC of STG occur specifically in relation to negative symptoms only (i.e., not pseudo-specific in relation to positive, extrapyramidal or depressive symptoms), and occur irrespective of arbitrary clinical categorization of treatment response. An increase in fALFF in the precuneus and the inferior parietal lobule, and a decrease in EC from the left STG to the occipital cortex, were also found at the 1 year follow-up irrespective of improvement in negative symptoms. Interventions that improve the functional efficiency of left STG and its prefrontal connectivity may show efficacy in alleviating negative symptoms in first-episode schizophrenia.
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Affiliation(s)
- Mingli Li
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.,West China Brain Research Center, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Wei Deng
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.,West China Brain Research Center, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Tushar Das
- Robarts Research Institute and The Brain and Mind Institute, University of Western Ontario, London, ON, Canada.,Department of Psychiatry, University of Western Ontario, London, ON, Canada.,Lawson Health Research Institute, London, ON, Canada
| | - Yinfei Li
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.,West China Brain Research Center, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Liansheng Zhao
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.,West China Brain Research Center, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Xiaohong Ma
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.,West China Brain Research Center, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Yingcheng Wang
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.,West China Brain Research Center, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Hua Yu
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.,West China Brain Research Center, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Xiaojing Li
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.,West China Brain Research Center, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Ya-Jing Meng
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.,West China Brain Research Center, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Qiang Wang
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.,West China Brain Research Center, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Lena Palaniyappan
- Robarts Research Institute and The Brain and Mind Institute, University of Western Ontario, London, ON, Canada. .,Department of Psychiatry, University of Western Ontario, London, ON, Canada. .,Lawson Health Research Institute, London, ON, Canada. .,Prevention and Early Intervention Program for Psychoses (PEPP), A2-636, LHSC-VH, 800 Commissioners Road, London, ON, N6A 5W9, Canada.
| | - Tao Li
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China. .,West China Brain Research Center, West China Hospital, Sichuan University, Chengdu, People's Republic of China.
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20
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Koutsouleris N, Wobrock T, Guse B, Langguth B, Landgrebe M, Eichhammer P, Frank E, Cordes J, Wölwer W, Musso F, Winterer G, Gaebel W, Hajak G, Ohmann C, Verde PE, Rietschel M, Ahmed R, Honer WG, Dwyer D, Ghaseminejad F, Dechent P, Malchow B, Kreuzer PM, Poeppl TB, Schneider-Axmann T, Falkai P, Hasan A. Predicting Response to Repetitive Transcranial Magnetic Stimulation in Patients With Schizophrenia Using Structural Magnetic Resonance Imaging: A Multisite Machine Learning Analysis. Schizophr Bull 2018; 44:1021-1034. [PMID: 28981875 PMCID: PMC6101524 DOI: 10.1093/schbul/sbx114] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
BACKGROUND The variability of responses to plasticity-inducing repetitive transcranial magnetic stimulation (rTMS) challenges its successful application in psychiatric care. No objective means currently exists to individually predict the patients' response to rTMS. METHODS We used machine learning to develop and validate such tools using the pre-treatment structural Magnetic Resonance Images (sMRI) of 92 patients with schizophrenia enrolled in the multisite RESIS trial (http://clinicaltrials.gov, NCT00783120): patients were randomized to either active (N = 45) or sham (N = 47) 10-Hz rTMS applied to the left dorsolateral prefrontal cortex 5 days per week for 21 days. The prediction target was nonresponse vs response defined by a ≥20% pre-post Positive and Negative Syndrome Scale (PANSS) negative score reduction. RESULTS Our models predicted this endpoint with a cross-validated balanced accuracy (BAC) of 85% (nonresponse/response: 79%/90%) in patients receiving active rTMS, but only with 51% (48%/55%) in the sham-treated sample. Leave-site-out cross-validation demonstrated cross-site generalizability of the active rTMS predictor despite smaller training samples (BAC: 71%). The predictive pre-treatment pattern involved gray matter density reductions in prefrontal, insular, medio-temporal, and cerebellar cortices, and increments in parietal and thalamic structures. The low BAC of 58% produced by the active rTMS predictor in sham-treated patients, as well as its poor performance in predicting positive symptom courses supported the therapeutic specificity of this brain pattern. CONCLUSIONS Individual responses to active rTMS in patients with predominant negative schizophrenia may be accurately predicted using structural neuromarkers. Further multisite studies are needed to externally validate the proposed treatment stratifier and develop more personalized and biologically informed rTMS interventions.
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Affiliation(s)
- Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Klinikum der Universität München, Ludwig-Maximilians-Universität, Munich,To whom correspondence should be addressed; Professor for Neurodiagnostic Applications in Psychiatry, Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Nussbaumstr. 7, D-80336 Munich, Germany; tel: 0049-(0)-89-4400-55885, fax: 0049-(0)-89-4400-55776, e-mail:
| | - Thomas Wobrock
- Department of Psychiatry and Psychotherapy, Georg-August-University Goettingen,County Hospitals Darmstadt-Dieburg, Groß-Umstadt
| | - Birgit Guse
- Department of Psychiatry and Psychotherapy, Georg-August-University Goettingen
| | - Berthold Langguth
- Department of Psychiatry and Psychotherapy, University of Regensburg
| | - Michael Landgrebe
- Department of Psychiatry and Psychotherapy, University of Regensburg,Department of Psychiatry, Psychosomatics and Psychotherapy, kbo-Lech-Mangfall-Klinik Agatharied, Germany
| | - Peter Eichhammer
- Department of Psychiatry and Psychotherapy, University of Regensburg
| | - Elmar Frank
- Department of Psychiatry and Psychotherapy, University of Regensburg
| | - Joachim Cordes
- Department of Psychiatry and Psychotherapy, Heinrich-Heine University, Düsseldorf
| | - Wolfgang Wölwer
- Department of Psychiatry and Psychotherapy, Heinrich-Heine University, Düsseldorf
| | - Francesco Musso
- Department of Psychiatry and Psychotherapy, Heinrich-Heine University, Düsseldorf
| | - Georg Winterer
- Experimental & Clinical Research Center (ECRC), Charite – University Medicine Berlin
| | - Wolfgang Gaebel
- Department of Psychiatry and Psychotherapy, Heinrich-Heine University, Düsseldorf
| | - Göran Hajak
- European Clinical Research Infrastructure Network (ECRIN), Düsseldorf, Germany,Coordination Centre for Clinical Trials, Heinrich-Heine-University, Düsseldorf
| | - Christian Ohmann
- Coordination Centre for Clinical Trials, Heinrich-Heine University, Düsseldorf
| | - Pablo E Verde
- Coordination Centre for Clinical Trials, Heinrich-Heine University, Düsseldorf
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Institute of Central Mental Health, Medical Faculty Mannheim, University of Heidelberg
| | - Raees Ahmed
- Referat Klinische Studien Management, Georg-August-University Goettingen
| | - William G Honer
- Institute of Mental Health, The University of British Columbia, Vancouver, Canada
| | - Dominic Dwyer
- Department of Psychiatry and Psychotherapy, Klinikum der Universität München, Ludwig-Maximilians-Universität, Munich
| | - Farhad Ghaseminejad
- Institute of Mental Health, The University of British Columbia, Vancouver, Canada
| | - Peter Dechent
- Department of Cognitive Neurology, Georg-August-University Goettingen
| | - Berend Malchow
- Department of Psychiatry and Psychotherapy, Klinikum der Universität München, Ludwig-Maximilians-Universität, Munich
| | - Peter M Kreuzer
- Department of Psychiatry and Psychotherapy, University of Regensburg
| | - Tim B Poeppl
- Department of Psychiatry and Psychotherapy, University of Regensburg
| | - Thomas Schneider-Axmann
- Department of Psychiatry and Psychotherapy, Klinikum der Universität München, Ludwig-Maximilians-Universität, Munich
| | - Peter Falkai
- Department of Psychiatry and Psychotherapy, Klinikum der Universität München, Ludwig-Maximilians-Universität, Munich
| | - Alkomiet Hasan
- Department of Psychiatry and Psychotherapy, Klinikum der Universität München, Ludwig-Maximilians-Universität, Munich
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21
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Abstract
PURPOSE OF REVIEW ECT remains an important, yet underutilized, treatment for schizophrenia. Recent research shows that medication-resistant patients with schizophrenia, including those resistant to clozapine, respond well to ECT augmentation. The purpose of this article is to review recent studies of the use of ECT in the treatment of schizophrenia. RECENT FINDINGS We performed an electronic database search for articles on ECT and schizophrenia, published in 2017. The main themes of these articles are: epidemiological data on ECT use from various countries; retrospective studies, prospective studies and meta-analyses focusing on efficacy and cognitive side-effects of ECT in schizophrenia; ECT technical parameters and potential biomarkers. SUMMARY There is growing evidence to support the use of ECT for augmentation of antipsychotic response in the treatment of schizophrenia. Cognitive side-effects are generally mild and transient. In fact, many studies show improvement in cognition, possibly related to the improvement in symptoms. There is wide variation among countries in the use of ECT for the treatment of schizophrenia. There are also variations in the choice of ECT electrode placement, parameters and schedules. These technical differences are likely minor and should not interfere with the treatment being offered to patients. Further, long-term studies are needed to optimize ECT treatment parameters, to examine the effect of maintenance ECT and to investigate neuroimaging/biomarkers to understand the mechanism of action and identify potential response predictors to ECT.
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