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Gallucci J, Secara MT, Chen O, Oliver LD, Jones BDM, Marawi T, Foussias G, Voineskos AN, Hawco C. A systematic review of structural and functional magnetic resonance imaging studies on the neurobiology of depressive symptoms in schizophrenia spectrum disorders. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2024; 10:59. [PMID: 38961144 PMCID: PMC11222445 DOI: 10.1038/s41537-024-00478-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 06/10/2024] [Indexed: 07/05/2024]
Abstract
Depressive symptoms in Schizophrenia Spectrum Disorders (SSDs) negatively impact suicidality, prognosis, and quality of life. Despite this, efficacious treatments are limited, largely because the neural mechanisms underlying depressive symptoms in SSDs remain poorly understood. We conducted a systematic review to provide an overview of studies that investigated the neural correlates of depressive symptoms in SSDs using neuroimaging techniques. We searched MEDLINE, PsycINFO, EMBASE, Web of Science, and Cochrane Library databases from inception through June 19, 2023. Specifically, we focused on structural and functional magnetic resonance imaging (MRI), encompassing: (1) T1-weighted imaging measuring brain morphology; (2) diffusion-weighted imaging assessing white matter integrity; or (3) T2*-weighted imaging measures of brain function. Our search yielded 33 articles; 14 structural MRI studies, 18 functional (f)MRI studies, and 1 multimodal fMRI/MRI study. Reviewed studies indicate potential commonalities in the neurobiology of depressive symptoms between SSDs and major depressive disorders, particularly in subcortical and frontal brain regions, though confidence in this interpretation is limited. The review underscores a notable knowledge gap in our understanding of the neurobiology of depression in SSDs, marked by inconsistent approaches and few studies examining imaging metrics of depressive symptoms. Inconsistencies across studies' findings emphasize the necessity for more direct and comprehensive research focusing on the neurobiology of depression in SSDs. Future studies should go beyond "total score" depression metrics and adopt more nuanced assessment approaches considering distinct subdomains. This could reveal unique neurobiological profiles and inform investigations of targeted treatments for depression in SSDs.
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Affiliation(s)
- Julia Gallucci
- Campbell Family Mental Health Research Institute, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Maria T Secara
- Campbell Family Mental Health Research Institute, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Oliver Chen
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Lindsay D Oliver
- Campbell Family Mental Health Research Institute, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Brett D M Jones
- Campbell Family Mental Health Research Institute, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Tulip Marawi
- Campbell Family Mental Health Research Institute, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - George Foussias
- Campbell Family Mental Health Research Institute, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Aristotle N Voineskos
- Campbell Family Mental Health Research Institute, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Colin Hawco
- Campbell Family Mental Health Research Institute, Toronto, ON, Canada.
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada.
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
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Moghaddam HS, Parsaei M, Taghavizanjani F, Cattarinussi G, Aarabi MH, Sambataro F. White matter alterations in affective and non-affective early psychosis: A diffusion MRI study. J Affect Disord 2024; 351:615-623. [PMID: 38290585 DOI: 10.1016/j.jad.2024.01.238] [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: 06/07/2023] [Revised: 01/06/2024] [Accepted: 01/26/2024] [Indexed: 02/01/2024]
Abstract
BACKGROUND The early years after the onset of psychotic disorders, known as "early psychosis" (EP) are critical to determining the path of psychosis trajectory. We used a Diffusion Magnetic Resonance Imaging (DMRI) connectometry approach to assess the microstructural changes of white matter (WM) associated with EP. METHODS We used the Human Connectome Project in Early Psychosis (HCP-EP) dataset to collect DMRI data from patients with EP. The imaging data were processed in the Montreal Neuroimaging Initiative space and transformed into quantitative anisotropy (QA). The QA value was translated into the WM connectivity of each tract and used in the subsequent analysis. RESULTS 121 patients with EP (94 non-affective/27 affective) and 56 healthy controls were recruited. EP was associated with increased QA in the body and tapetum of corpus callosum (CC) and decreased QA in the bilateral cerebellum, and middle cerebellar peduncle. Compared to non-affective psychosis, affective psychosis showed increased QA in the bilateral cerebellum and vermis and decreased QA in the forceps minor, body of CC, right cingulum, and bilateral inferior fronto-occipital fasciculus. Furthermore, QA changes in several WM tracts were correlated with positive and negative symptom scale scores. LIMITATIONS DMRI intrinsic limitations, limited sample size, and neurobiological effects of psychotropic treatment. CONCLUSIONS EP is associated with alterations in WM connectivity primarily in the CC and cerebellar regions. Also, affective and non-affective psychosis have distinct alterations in WM connectivity. These results can be used for the early diagnosis and differentiation of psychotic disorders.
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Affiliation(s)
| | - Mohammadamin Parsaei
- Maternal, Fetal & Neonatal Research Center, Family Health Research Institute, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
| | - Fateme Taghavizanjani
- Psychiatric Research Center, Roozbeh Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Giulia Cattarinussi
- Department of Neuroscience (DNS), University of Padova, Padua, Italy; Padova Neuroscience Center, University of Padova, Padua, Italy; Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Mohammad Hadi Aarabi
- Department of Neuroscience (DNS), University of Padova, Padua, Italy; Padova Neuroscience Center, University of Padova, Padua, Italy
| | - Fabio Sambataro
- Department of Neuroscience (DNS), University of Padova, Padua, Italy; Padova Neuroscience Center, University of Padova, Padua, Italy.
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Videtta G, Squarcina L, Rossetti MG, Brambilla P, Delvecchio G, Bellani M. White matter modifications of corpus callosum in bipolar disorder: A DTI tractography review. J Affect Disord 2023; 338:220-227. [PMID: 37301293 DOI: 10.1016/j.jad.2023.06.012] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 05/08/2023] [Accepted: 06/04/2023] [Indexed: 06/12/2023]
Abstract
BACKGROUND The recent widespread use of diffusion tensor imaging (DTI) tractography allowed researchers to investigate the diffusivity modifications and neuroanatomical changes of white matter (WM) fascicles in major psychiatric disorders, including bipolar disorder (BD). In BD, corpus callosum (CC) seems to have a crucial role in explaining the pathophysiology and cognitive impairment of this psychiatric disorder. This review aims to provide an overview on the latest results emerging from studies that investigated neuroanatomical changes of CC in BD using DTI tractography. METHODS Bibliographic research was conducted on PubMed, Scopus and Web of Science datasets until March 2022. Ten studies fulfilled our inclusion criteria. RESULTS From the reviewed DTI tractography studies a significant decrease of fractional anisotropy emerged in the genu, body and splenium of CC of BD patients compared to controls. This finding is coupled with reduction of fiber density and modification in fiber tract length. Finally, an increase of radial and mean diffusivity in forceps minor and in the entire CC was also reported. LIMITATIONS Small sample size, heterogeneity in terms of methodological (diffusion gradient) and clinical (lifetime comorbidity, BD status, pharmacological treatments) characteristics. CONCLUSIONS Overall, these findings suggest the presence of structural modifications in CC in BD patients, which may in turn explain the cognitive impairments often observed in this psychiatric disorder, especially in executive processing, motor control and visual memory. Finally, structural modifications may suggest an impairment in the amount of functional information and a morphological impact within those brain regions connected by CC.
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Affiliation(s)
- Giovanni Videtta
- Department of Biomedical Sciences for Health, University of Milan, Milan, Italy
| | - Letizia Squarcina
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Maria Gloria Rossetti
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Paolo Brambilla
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy; Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Giuseppe Delvecchio
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy.
| | - Marcella Bellani
- Section of Psychiatry, Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
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Huang J, Ke P, Chen X, Li S, Zhou J, Xiong D, Huang Y, Li H, Ning Y, Duan X, Li X, Zhang W, Wu F, Wu K. Multimodal Magnetic Resonance Imaging Reveals Aberrant Brain Age Trajectory During Youth in Schizophrenia Patients. Front Aging Neurosci 2022; 14:823502. [PMID: 35309897 PMCID: PMC8929292 DOI: 10.3389/fnagi.2022.823502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Accepted: 01/18/2022] [Indexed: 11/13/2022] Open
Abstract
Accelerated brain aging had been widely reported in patients with schizophrenia (SZ). However, brain aging trajectories in SZ patients have not been well-documented using three-modal magnetic resonance imaging (MRI) data. In this study, 138 schizophrenia patients and 205 normal controls aged 20–60 were included and multimodal MRI data were acquired for each individual, including structural MRI, resting state-functional MRI and diffusion tensor imaging. The brain age of each participant was estimated by features extracted from multimodal MRI data using linear multiple regression. The correlation between the brain age gap and chronological age in SZ patients was best fitted by a positive quadratic curve with a peak chronological age of 47.33 years. We used the peak to divide the subjects into a youth group and a middle age group. In the normal controls, brain age matched chronological age well for both the youth and middle age groups, but this was not the case for schizophrenia patients. More importantly, schizophrenia patients exhibited increased brain age in the youth group but not in the middle age group. In this study, we aimed to investigate brain aging trajectories in SZ patients using multimodal MRI data and revealed an aberrant brain age trajectory in young schizophrenia patients, providing new insights into the pathophysiological mechanisms of schizophrenia.
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Affiliation(s)
- Jiayuan Huang
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou, China
| | - Pengfei Ke
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China
| | - Xiaoyi Chen
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou, China
| | - Shijia Li
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China
| | - Jing Zhou
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China
| | - Dongsheng Xiong
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China
| | - Yuanyuan Huang
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Hehua Li
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Yuping Ning
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Xujun Duan
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaobo Li
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States
| | - Wensheng Zhang
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Fengchun Wu
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, China
- *Correspondence: Fengchun Wu,
| | - Kai Wu
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou, China
- National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, China
- Guangdong Province Key Laboratory of Biomedical Engineering, South China University of Technology, Guangzhou, China
- Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, China
- Institute for Healthcare Artificial Intelligence Application, Guangdong Second Provincial General Hospital, Guangzhou, China
- Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
- Kai Wu,
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Pringle C, Kilday JP, Kamaly-Asl I, Stivaros SM. The role of artificial intelligence in paediatric neuroradiology. Pediatr Radiol 2022; 52:2159-2172. [PMID: 35347371 PMCID: PMC9537195 DOI: 10.1007/s00247-022-05322-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 08/22/2021] [Accepted: 02/11/2022] [Indexed: 01/17/2023]
Abstract
Imaging plays a fundamental role in the managing childhood neurologic, neurosurgical and neuro-oncological disease. Employing multi-parametric MRI techniques, such as spectroscopy and diffusion- and perfusion-weighted imaging, to the radiophenotyping of neuroradiologic conditions is becoming increasingly prevalent, particularly with radiogenomic analyses correlating imaging characteristics with molecular biomarkers of disease. However, integration into routine clinical practice remains elusive. With modern multi-parametric MRI now providing additional data beyond anatomy, informing on histology, biology and physiology, such metric-rich information can present as information overload to the treating radiologist and, as such, information relevant to an individual case can become lost. Artificial intelligence techniques are capable of modelling the vast radiologic, biological and clinical datasets that accompany childhood neurologic disease, such that this information can become incorporated in upfront prognostic modelling systems, with artificial intelligence techniques providing a plausible approach to this solution. This review examines machine learning approaches than can be used to underpin such artificial intelligence applications, with exemplars for each machine learning approach from the world literature. Then, within the specific use case of paediatric neuro-oncology, we examine the potential future contribution for such artificial intelligence machine learning techniques to offer solutions for patient care in the form of decision support systems, potentially enabling personalised medicine within this domain of paediatric radiologic practice.
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Affiliation(s)
- Catherine Pringle
- Children’s Brain Tumour Research Network (CBTRN), Royal Manchester Children’s Hospital, Manchester, UK ,Division of Informatics, Imaging, and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine, and Health, University of Manchester, Manchester, UK
| | - John-Paul Kilday
- Children’s Brain Tumour Research Network (CBTRN), Royal Manchester Children’s Hospital, Manchester, UK ,The Centre for Paediatric, Teenage and Young Adult Cancer, Institute of Cancer Sciences, University of Manchester, Manchester, UK
| | - Ian Kamaly-Asl
- Children’s Brain Tumour Research Network (CBTRN), Royal Manchester Children’s Hospital, Manchester, UK ,The Centre for Paediatric, Teenage and Young Adult Cancer, Institute of Cancer Sciences, University of Manchester, Manchester, UK
| | - Stavros Michael Stivaros
- Division of Informatics, Imaging, and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine, and Health, University of Manchester, Manchester, UK. .,Department of Paediatric Radiology, Royal Manchester Children's Hospital, Central Manchester University Hospitals NHS Foundation Trust, Oxford Road, Manchester, M13 9WL, UK. .,The Geoffrey Jefferson Brain Research Centre, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK.
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Zhu T, Wang Z, Zhou C, Fang X, Huang C, Xie C, Ge H, Yan Z, Zhang X, Chen J. Meta-analysis of structural and functional brain abnormalities in schizophrenia with persistent negative symptoms using activation likelihood estimation. Front Psychiatry 2022; 13:957685. [PMID: 36238945 PMCID: PMC9552970 DOI: 10.3389/fpsyt.2022.957685] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 09/05/2022] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Persistent negative symptoms (PNS) include both primary and secondary negative symptoms that persist after adequate treatment, and represent an unmet therapeutic need. Published magnetic resonance imaging (MRI) evidence of structural and resting-state functional brain abnormalities in schizophrenia with PNS has been inconsistent. Thus, the purpose of this meta-analysis is to identify abnormalities in structural and functional brain regions in patients with PNS compared to healthy controls. METHODS We systematically searched PubMed, Web of Science, and Embase for structural and functional imaging studies based on five research methods, including voxel-based morphometry (VBM), diffusion tensor imaging (DTI), functional connectivity (FC), the amplitude of low-frequency fluctuation or fractional amplitude of low-frequency fluctuation (ALFF/fALFF), and regional homogeneity (ReHo). Afterward, we conducted a coordinate-based meta-analysis by using the activation likelihood estimation algorithm. RESULTS Twenty-five structural MRI studies and thirty-two functional MRI studies were included in the meta-analyses. Our analysis revealed the presence of structural alterations in patients with PNS in some brain regions including the bilateral insula, medial frontal gyrus, anterior cingulate gyrus, left amygdala, superior temporal gyrus, inferior frontal gyrus, cingulate gyrus and middle temporal gyrus, as well as functional differences in some brain regions including the bilateral precuneus, thalamus, left lentiform nucleus, posterior cingulate gyrus, medial frontal gyrus, and superior frontal gyrus. CONCLUSION Our study suggests that structural brain abnormalities are consistently located in the prefrontal, temporal, limbic and subcortical regions, and functional alterations are concentrated in the thalamo-cortical circuits and the default mode network (DMN). This study provides new insights for targeted treatment and intervention to delay further progression of negative symptoms. SYSTEMATIC REVIEW REGISTRATION [https://www.crd.york.ac.uk/prospero/], identifier [CRD42022338669].
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Affiliation(s)
- Tingting Zhu
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Zixu Wang
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Chao Zhou
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xinyu Fang
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Chengbing Huang
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Department of Psychiatry, The Third People's Hospital of Huai'an, Huaian, China
| | - Chunming Xie
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine Southeast University, Nanjing, China
| | - Honglin Ge
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Zheng Yan
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xiangrong Zhang
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,The Affiliated Xuzhou Oriental Hospital of Xuzhou Medical University, Xuzhou, China
| | - Jiu Chen
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
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Zhao G, Lau WKW, Wang C, Yan H, Zhang C, Lin K, Qiu S, Huang R, Zhang R. A Comparative Multimodal Meta-analysis of Anisotropy and Volume Abnormalities in White Matter in People Suffering From Bipolar Disorder or Schizophrenia. Schizophr Bull 2021; 48:69-79. [PMID: 34374427 PMCID: PMC8781378 DOI: 10.1093/schbul/sbab093] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Schizophrenia (SZ) and bipolar disorder (BD) share some similarities in terms of genetic-risk genes and abnormalities of gray-matter structure in the brain, but white matter (WM) abnormalities have not been studied in depth. We undertook a comparative multimodal meta-analysis to identify common and disorder-specific abnormalities in WM structure between SZ and BD. Anisotropic effect size-signed differential mapping software was used to conduct a comparative meta-analysis of 68 diffusion tensor imaging (DTI) and 34 voxel-based morphometry (VBM) studies comparing fractional anisotropy (FA) and white matter volume (WMV), respectively, between patients with SZ (DTI: N = 1543; VBM: N = 1068) and BD (DTI: N = 983; VBM: N = 518) and healthy controls (HCs). The bilateral corpus callosum (extending to the anterior and superior corona radiata) showed shared decreased WMV and FA in SZ and BD. Compared with BD patients, SZ patients showed remarkable disorder-specific WM abnormalities: decreased FA and increased WMV in the left cingulum, and increased FA plus decreased WMV in the right anterior limb of the internal capsule. SZ patients showed more extensive alterations in WM than BD cases, which may be the pathophysiological basis for the clinical continuity of both disorders. The disorder-specific regions in the left cingulum and right anterior limb of the internal capsule provided novel insights into both disorders. Our study adds value to further understanding of the pathophysiology, classification, and differential diagnosis of SZ and BD.
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Affiliation(s)
- Guorui Zhao
- Laboratory of Cognitive Control and Brain Healthy, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Way K W Lau
- Department of Special Education and Counselling, The Education University of Hong Kong, Hong Kong, China
| | - Chanyu Wang
- Laboratory of Cognitive Control and Brain Healthy, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Haifeng Yan
- Laboratory of Cognitive Control and Brain Healthy, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Chichen Zhang
- School of Management, Southern Medical University, Guangzhou, China
| | - Kangguang Lin
- Department of Affective Disorders, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Shijun Qiu
- Department of Radiology, The First Affiliated Hospital of Guangzhou Chinese traditional Medical University, Guangzhou, China
| | - Ruiwang Huang
- School of Psychology, South China Normal University, Guangzhou, China
| | - Ruibin Zhang
- Laboratory of Cognitive Control and Brain Healthy, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China,Department of Psychiatry, Zhujiang Hospital, Southern Medical University, Guangzhou, China,To whom correspondence should be addressed; Laboratory of Cognitive Control and Brain Healthy, Department of Psychology, School of Public Health, Southern Medical University, tel/fax:020-62789234, e-mail:
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