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Cao P, Li Y, Dong Y, Tang Y, Xu G, Si Q, Chen C, Yao Y, Li R, Sui Y. Different structural connectivity patterns in the subregions of the thalamus, hippocampus, and cingulate cortex between schizophrenia and psychotic bipolar disorder. J Affect Disord 2024; 363:269-281. [PMID: 39053628 DOI: 10.1016/j.jad.2024.07.077] [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: 03/14/2024] [Revised: 06/25/2024] [Accepted: 07/14/2024] [Indexed: 07/27/2024]
Abstract
OBJECTIVE Schizophrenia (SCZ) and psychotic bipolar disorder (PBD) are two major psychotic disorders with similar symptoms and tight associations on the psychopathological level, posing a clinical challenge for their differentiation. This study aimed to investigate and compare the structural connectivity patterns of the limbic system between SCZ and PBD, and to identify specific regional disruptions associated with psychiatric symptoms. METHODS Using sMRI data from 146 SCZ, 160 PBD, and 145 healthy control (HC) participants, we employed a data-driven approach to segment the hippocampus, thalamus, hypothalamus, amygdala, and cingulate cortex into subregions. We then investigated the structural connectivity patterns between these subregions at the global and nodal levels. Additionally, we assessed psychotic symptoms by utilizing the subscales of the Brief Psychiatric Rating Scale (BPRS) to examine correlations between symptom severity and network metrics between groups. RESULTS Patients with SCZ and PBD had decreased global efficiency (Eglob) (SCZ: adjusted P<0.001; PBD: adjusted P = 0.003), local efficiency (Eloc) (SCZ and PBD: adjusted P<0.001), and clustering coefficient (Cp) (SCZ and PBD: adjusted P<0.001), and increased path length (Lp) (SCZ: adjusted P<0.001; PBD: adjusted P = 0.004) compared to HC. Patients with SCZ showed more pronounced decreases in Eglob (adjusted P<0.001), Eloc (adjusted P<0.001), and Cp (adjusted P = 0.029), and increased Lp (adjusted P = 0.024) compared to patients with PBD. The most notable structural disruptions were observed in the hippocampus and thalamus, which correlated with different psychotic symptoms, respectively. CONCLUSION This study provides evidence of distinct structural connectivity disruptions in the limbic system of patients with SCZ and PBD. These findings might contribute to our understanding of the neuropathological basis for distinguishing SCZ and PBD.
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Affiliation(s)
- Peiyu Cao
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing 210000, Jiangsu, China
| | - Yuting Li
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing 210000, Jiangsu, China; Huzhou Third People's Hospital, Huzhou 313000, Zhejiang, China
| | - Yingbo Dong
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing 210000, Jiangsu, China
| | - Yilin Tang
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing 210000, Jiangsu, China
| | - Guoxin Xu
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing 210000, Jiangsu, China
| | - Qi Si
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing 210000, Jiangsu, China; Huai'an No. 3 People's Hospital, Huai'an 223001, Jiangsu, China
| | - Congxin Chen
- Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing 210000, Jiangsu, China
| | - Ye Yao
- Nanjing Medical University, Nanjing 210000, Jiangsu, China
| | - Runda Li
- Vanderbilt University, Nashville 37240, TN, USA
| | - Yuxiu Sui
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing 210000, Jiangsu, China.
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2
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Jameei H, Rakesh D, Zalesky A, Cairns MJ, Reay WR, Wray NR, Di Biase MA. Linking Polygenic Risk of Schizophrenia to Variation in Magnetic Resonance Imaging Brain Measures: A Comprehensive Systematic Review. Schizophr Bull 2024; 50:32-46. [PMID: 37354489 PMCID: PMC10754175 DOI: 10.1093/schbul/sbad087] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/26/2023]
Abstract
BACKGROUND AND HYPOTHESIS Schizophrenia is highly heritable, with a polygenic effect of many genes conferring risk. Evidence on whether cumulative risk also predicts alterations in brain morphology and function is inconsistent. This systematic review examined evidence for schizophrenia polygenic risk score (sczPRS) associations with commonly used magnetic resonance imaging (MRI) measures. We expected consistent evidence to emerge for significant sczPRS associations with variation in structure and function, specifically in frontal, temporal, and insula cortices that are commonly implicated in schizophrenia pathophysiology. STUDY DESIGN In accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we searched MEDLINE, Embase, and PsycINFO for peer-reviewed studies published between January 2013 and March 2022. Studies were screened against predetermined criteria and National Institutes of Health (NIH) quality assessment tools. STUDY RESULTS In total, 57 studies of T1-weighted structural, diffusion, and functional MRI were included (age range = 9-80 years, Nrange = 64-76 644). We observed moderate, albeit preliminary, evidence for higher sczPRS predicting global reductions in cortical thickness and widespread variation in functional connectivity, and to a lesser extent, region-specific reductions in frontal and temporal volume and thickness. Conversely, sczPRS does not predict whole-brain surface area or gray/white matter volume. Limited evidence emerged for sczPRS associations with diffusion tensor measures of white matter microstructure in a large community sample and smaller cohorts of children and young adults. These findings were broadly consistent across community and clinical populations. CONCLUSIONS Our review supports the hypothesis that schizophrenia is a disorder of disrupted within and between-region brain connectivity, and points to specific whole-brain and regional MRI metrics that may provide useful intermediate phenotypes.
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Affiliation(s)
- Hadis Jameei
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
| | - Divyangana Rakesh
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
- Faculty of Engineering and Information Technology, The University of Melbourne, Parkville, VIC, Australia
| | - Murray J Cairns
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Newcastle, NSW, Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - William R Reay
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Newcastle, NSW, Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Maria A Di Biase
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
- Department of Anatomy and Physiology, School of Biomedical Sciences, The University of Melbourne, VIC, Australia
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
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3
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Bosma MJ, Cox SR, Ziermans T, Buchanan CR, Shen X, Tucker-Drob EM, Adams MJ, Whalley HC, Lawrie SM. White matter, cognition and psychotic-like experiences in UK Biobank. Psychol Med 2023; 53:2370-2379. [PMID: 37310314 PMCID: PMC10123836 DOI: 10.1017/s0033291721004244] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 09/09/2021] [Accepted: 09/29/2021] [Indexed: 11/05/2022]
Abstract
BACKGROUND Psychotic-like experiences (PLEs) are risk factors for the development of psychiatric conditions like schizophrenia, particularly if associated with distress. As PLEs have been related to alterations in both white matter and cognition, we investigated whether cognition (g-factor and processing speed) mediates the relationship between white matter and PLEs. METHODS We investigated two independent samples (6170 and 19 891) from the UK Biobank, through path analysis. For both samples, measures of whole-brain fractional anisotropy (gFA) and mean diffusivity (gMD), as indications of white matter microstructure, were derived from probabilistic tractography. For the smaller sample, variables whole-brain white matter network efficiency and microstructure were also derived from structural connectome data. RESULTS The mediation of cognition on the relationships between white matter properties and PLEs was non-significant. However, lower gFA was associated with having PLEs in combination with distress in the full available sample (standardized β = -0.053, p = 0.011). Additionally, lower gFA/higher gMD was associated with lower g-factor (standardized β = 0.049, p < 0.001; standardized β = -0.027, p = 0.003), and partially mediated by processing speed with a proportion mediated of 7% (p = < 0.001) for gFA and 11% (p < 0.001) for gMD. CONCLUSIONS We show that lower global white matter microstructure is associated with having PLEs in combination with distress, which suggests a direction of future research that could help clarify how and why individuals progress from subclinical to clinical psychotic symptoms. Furthermore, we replicated that processing speed mediates the relationship between white matter microstructure and g-factor.
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Affiliation(s)
- M. J. Bosma
- Department of Psychology, University of Amsterdam, Amsterdam, Netherlands
| | - S. R. Cox
- School of Philosophy, Psychology and Language Sciences, University of Edinburgh, Edinburgh, UK
| | - T. Ziermans
- Department of Psychology, University of Amsterdam, Amsterdam, Netherlands
| | - C. R. Buchanan
- School of Philosophy, Psychology and Language Sciences, University of Edinburgh, Edinburgh, UK
| | - X. Shen
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, Scotland, UK
| | - E. M. Tucker-Drob
- Department of Psychology, University of Texas at Austin, Austin, USA
| | - M. J. Adams
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, Scotland, UK
| | - H. C. Whalley
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, Scotland, UK
| | - S. M. Lawrie
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, Scotland, UK
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4
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Zorlu N, Bayrakçı A, Karakılıç M, Zalesky A, Seguin C, Tian Y, Gülyüksel F, Yalınçetin B, Oral E, Gelal F, Bora E. Abnormal Structural Network Communication Reflects Cognitive Deficits in Schizophrenia. Brain Topogr 2023; 36:294-304. [PMID: 36971857 DOI: 10.1007/s10548-023-00954-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 03/04/2023] [Indexed: 03/28/2023]
Abstract
Schizophrenia has long been thought to be a disconnection syndrome and several previous studies have reported widespread abnormalities in white matter tracts in individuals with schizophrenia. Furthermore, reductions in structural connectivity may also impair communication between anatomically unconnected pairs of brain regions, potentially impacting global signal traffic in the brain. Therefore, we used different communication models to examine direct and indirect structural connections (polysynaptic) communication in large-scale brain networks in schizophrenia. Diffusion-weighted magnetic resonance imaging scans were acquired from 62 patients diagnosed with schizophrenia and 35 controls. In this study, we used five network communication models including, shortest paths, navigation, diffusion, search information and communicability to examine polysynaptic communication in large-scale brain networks in schizophrenia. We showed less efficient communication between spatially widespread brain regions particulary encompassing cortico-subcortical basal ganglia network in schizophrenia group relative to controls. Then, we also examined whether reduced communication efficiency was related to clinical symptoms in schizophrenia group. Among different measures of communication efficiency, only navigation efficiency was associated with global cognitive impairment across multiple cognitive domains including verbal learning, processing speed, executive functions and working memory, in individuals with schizophrenia. We did not find any association between communication efficiency measures and positive or negative symptoms within the schizophrenia group. Our findings are important for improving our mechanistic understanding of neurobiological process underlying cognitive symptoms in schizophrenia.
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Gao Z, Xiao Y, Zhu F, Tao B, Yu W, Lui S. The whole-brain connectome landscape in patients with schizophrenia: a systematic review and meta-analysis of graph theoretical characteristics. Neurosci Biobehav Rev 2023; 148:105144. [PMID: 36990373 DOI: 10.1016/j.neubiorev.2023.105144] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 03/14/2023] [Accepted: 03/24/2023] [Indexed: 03/29/2023]
Abstract
The alterations of connectome in schizophrenia have been reported, but the results remain inconsistent. We conducted a systematic review and random-effects meta-analysis on structural or functional connectome MRI studies comparing global graph theoretical characteristics between schizophrenia and healthy controls. Meta-regression and subgroup analyses were performed to examine confounding effects. Based on the included 48 studies, Structural connectome in schizophrenia showed a significant decrease in segregation (lower clustering coefficient and local efficiency, Hedge's g= -0.352 and -0.864, respectively) and integration (higher characteristic path length and lower global efficiency, Hedge's g= 0.532 and -0.577 respectively). The functional connectome showed no difference between groups except γ. Moderator analysis indicated that clinical and methodological factors exerted a potential effect on the graph theoretical characteristics. Our analysis revealed a weaker small-worldization trend in structural connectome of schizophrenia. For the relatively unchanged functional connectome, more homogenous and high-quality studies are warranted to elucidate whether the change was blurred by heterogeneity or the presentation of pathophysiological reconfiguration.
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6
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Yue W, Huang H, Duan J. Potential diagnostic biomarkers for schizophrenia. MEDICAL REVIEW (BERLIN, GERMANY) 2022; 2:385-416. [PMID: 37724326 PMCID: PMC10388817 DOI: 10.1515/mr-2022-0009] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 06/20/2022] [Indexed: 09/20/2023]
Abstract
Schizophrenia (SCH) is a complex and severe mental disorder with high prevalence, disability, mortality and carries a heavy disease burden, the lifetime prevalence of SCH is around 0.7%-1.0%, which has a profound impact on the individual and society. In the clinical practice of SCH, key problems such as subjective diagnosis, experiential treatment, and poor overall prognosis are still challenging. In recent years, some exciting discoveries have been made in the research on objective biomarkers of SCH, mainly focusing on genetic susceptibility genes, metabolic indicators, immune indices, brain imaging, electrophysiological characteristics. This review aims to summarize the biomarkers that may be used for the prediction and diagnosis of SCH.
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Affiliation(s)
- Weihua Yue
- Institute of Mental Health, Peking University Sixth Hospital, Beijing, China
- National Clinical Research Center for Mental Disorders & NHC Key Laboratory of Mental Health (Peking University) and Chinese Academy of Medical Sciences Research Unit, Beijing, China
- PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
| | - Hailiang Huang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Jubao Duan
- Center for Psychiatric Genetics, NorthShore University Health System, Evanston, IL, USA
- Department of Psychiatry and Behavioral Neurosciences, University of Chicago, Chicago, IL, USA
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7
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Bayrakçı A, Zorlu N, Karakılıç M, Gülyüksel F, Yalınçetin B, Oral E, Gelal F, Bora E. Negative symptoms are associated with modularity and thalamic connectivity in schizophrenia. Eur Arch Psychiatry Clin Neurosci 2022; 273:565-574. [PMID: 35661912 DOI: 10.1007/s00406-022-01433-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 05/15/2022] [Indexed: 11/30/2022]
Abstract
Negative symptoms, including avolition, anhedonia, asociality, blunted affect and alogia are associated with poor long-term outcome and functioning. However, treatment options for negative symptoms are limited and neurobiological mechanisms underlying negative symptoms in schizophrenia are still poorly understood. Diffusion-weighted magnetic resonance imaging scans were acquired from 64 patients diagnosed with schizophrenia and 35 controls. Global and regional network properties and rich club organization were investigated using graph analytical methods. We found that the schizophrenia group had higher modularity, clustering coefficient and characteristic path length, and lower rich connections compared to controls, suggesting highly connected nodes within modules but less integrated with nodes in other modules in schizophrenia. We also found a lower nodal degree in the left thalamus and left putamen in schizophrenia relative to the control group. Importantly, higher modularity was associated with greater negative symptoms but not with cognitive deficits in patients diagnosed with schizophrenia suggesting an alteration in modularity might be specific to overall negative symptoms. The nodal degree of the left thalamus was associated with both negative and cognitive symptoms. Our findings are important for improving our understanding of abnormal white-matter network topology underlying negative symptoms in schizophrenia.
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Affiliation(s)
- Adem Bayrakçı
- Department of Psychiatry, Katip Celebi University, Ataturk Education and Research Hospital, Izmir, Turkey
| | - Nabi Zorlu
- Department of Psychiatry, Katip Celebi University, Ataturk Education and Research Hospital, Izmir, Turkey.
| | - Merve Karakılıç
- Department of Psychiatry, Katip Celebi University, Ataturk Education and Research Hospital, Izmir, Turkey
| | - Funda Gülyüksel
- Department of Psychiatry, Katip Celebi University, Ataturk Education and Research Hospital, Izmir, Turkey
| | - Berna Yalınçetin
- Department of Neurosciences, Institute of Health Sciences, Dokuz Eylul University, Izmir, Turkey
| | - Elif Oral
- Department of Psychiatry, Katip Celebi University, Ataturk Education and Research Hospital, Izmir, Turkey
| | - Fazıl Gelal
- Department of Radiodiagnostics, Katip Celebi University, Ataturk Education and Research Hospital, Izmir, Turkey
| | - Emre Bora
- Department of Neurosciences, Institute of Health Sciences, Dokuz Eylul University, Izmir, Turkey.,Faculty of Medicine, Department of Psychiatry, Dokuz Eylul University, Izmir, Turkey.,Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne, Melbourne, Australia
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8
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Kang J, Jiao Z, Qin Y, Wang Y, Wang J, Jin L, Feng J, Wang F, Tang Y, Gong X. Associations between polygenic risk scores and amplitude of low-frequency fluctuation of inferior frontal gyrus in schizophrenia. J Psychiatr Res 2022; 147:4-12. [PMID: 34999338 DOI: 10.1016/j.jpsychires.2021.12.043] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 12/14/2021] [Accepted: 12/20/2021] [Indexed: 10/19/2022]
Abstract
Schizophrenia (SCZ) is a serious and complex mental disorder with high heritability. Polygenic risk score (PRS) is a useful tool calculating the accumulating effects of multiple common genetic variants of schizophrenia. The amplitude of low-frequency fluctuation (ALFF) is an efficient index to reflect spontaneous, intrinsic neuronal activity. Aberrant ALFF of brain regions were reported in schizophrenia frequently, but the relationship between PRS and ALFF has not been studied. In the present study, we compared PRS and ALFF in 101 schizophrenia patients and 106 age-matched healthy controls to test their associations with schizophrenia. Then, the correlation of PRS with ALFF was measured to reveal the effect of polygenic risk on brain activity in schizophrenia. We found that schizophrenia patients showed significant differences in PRS and ALFF compared with controls. Twenty-six brain regions showed significant difference of ALFF between schizophrenia cases and controls, of which left inferior frontal gyrus, triangular part (IFGtriang.L) showed increased activity in schizophrenia. PRS-SCZ was positively correlated with ALFF in IFGtriang.L in 57 non-chronic patients. Genes involved in synaptic organization and transmission, especially in glutamatergic synapse, were highly enriched in PRS-SCZ genes, suggesting the dysfunction of synapses in schizophrenia. These results help to understand the molecular mechanism underlying schizophrenia and related brain dysfunction.
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Affiliation(s)
- Jujiao Kang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Shanghai Center for Mathematical Science, Fudan University, Shanghai, China
| | - Zeyu Jiao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Shanghai Center for Mathematical Science, Fudan University, Shanghai, China
| | - Yue Qin
- School of Life Sciences, Fudan University, Shanghai, China
| | - Yi Wang
- School of Life Sciences, Fudan University, Shanghai, China
| | - Jiucun Wang
- School of Life Sciences, Fudan University, Shanghai, China; Human Phoneme Institute, Fudan University, Shanghai, China
| | - Li Jin
- School of Life Sciences, Fudan University, Shanghai, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Shanghai Center for Mathematical Science, Fudan University, Shanghai, China; Department of Computer Science, University of Warwick, Coventry, CV4 7AL, UK
| | - Fei Wang
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Yanqing Tang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, China.
| | - Xiaohong Gong
- School of Life Sciences, Fudan University, Shanghai, China.
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9
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Zhou HY, Shi LJ, Shen YM, Fang YM, He YQ, Li HB, Luo XR, Cheung EFC, Chan RCK. Altered topographical organization of grey matter structural network in early-onset schizophrenia. Psychiatry Res Neuroimaging 2021; 316:111344. [PMID: 34358964 DOI: 10.1016/j.pscychresns.2021.111344] [Citation(s) in RCA: 6] [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: 12/20/2019] [Revised: 05/18/2021] [Accepted: 06/10/2021] [Indexed: 01/21/2023]
Abstract
Schizophrenia is characterized by both disrupted neurodevelopmental processes and abnormal brain connectivity. However, few studies have examined the atypical features of brain network topography associated with schizophrenia during childhood and adolescence. We used graph theory to compare the grey matter structural networks of individuals (aged 10-15 years) with early-onset schizophrenia (EOS) (n = 25) and a typically-developing (TD) comparison group (n = 31). Compared with the TD group, EOS patients showed significantly increased clustering and local efficiency across a range of network densities (0.3 - 0.4). The network of EOS patients also had more modules (6 modules in EOS vs. 3 modules in controls), indicating a more segregated network at the cost of functional integration. Although our results were preliminary and failed to survive corrections for multiple comparisons, EOS patients might be characterized by altered nodal centrality in several higher-order associative regions including the prefrontal cortex, the hippocampus and the cerebellum. The EOS structural network also lacked the typical left-hemispheric-dominant hub distribution compared with the TD group. These findings suggest that brain structural network was not only globally but also regionally altered in EOS patients.
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Affiliation(s)
- Han-Yu Zhou
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Li-Juan Shi
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; School of Education, Hunan University of Science and Technology, Xiangtan, Hunan, China
| | - Yan-Mei Shen
- Mental Health Institute, the Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yu-Min Fang
- Mental Health Institute, the Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yu-Qiong He
- Mental Health Institute, the Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Hua-Bing Li
- Medical Imaging Department, the Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Xue-Rong Luo
- Mental Health Institute, the Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Eric F C Cheung
- Castle Peak Hospital, Hong Kong Special Administrative Region, China
| | - Raymond C K Chan
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.
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10
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Prajapati R, Emerson IA. Construction and analysis of brain networks from different neuroimaging techniques. Int J Neurosci 2020; 132:745-766. [DOI: 10.1080/00207454.2020.1837802] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Affiliation(s)
- Rutvi Prajapati
- Bioinformatics Programming Laboratory, Department of Biotechnology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Isaac Arnold Emerson
- Bioinformatics Programming Laboratory, Department of Biotechnology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India
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11
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Mallet J, Le Strat Y, Dubertret C, Gorwood P. Polygenic Risk Scores Shed Light on the Relationship between Schizophrenia and Cognitive Functioning: Review and Meta-Analysis. J Clin Med 2020; 9:E341. [PMID: 31991840 PMCID: PMC7074036 DOI: 10.3390/jcm9020341] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2019] [Revised: 01/14/2020] [Accepted: 01/23/2020] [Indexed: 12/26/2022] Open
Abstract
Schizophrenia is a multifactorial disease associated with widespread cognitive impairment. Although cognitive deficits are one of the factors most strongly associated with functional impairment in schizophrenia (SZ), current treatment strategies hardly tackle these impairments. To develop more efficient treatment strategies in patients, a better understanding of their pathogenesis is needed. Recent progress in genetics, driven by large genome-wide association studies (GWAS) and the use of polygenic risk scores (PRS), has provided new insights about the genetic architecture of complex human traits, including cognition and SZ. Here, we review the recent findings examining the genetic links between SZ and cognitive functions in population-based samples as well as in participants with SZ. The performed meta-analysis showed a negative correlation between the polygenetic risk score of schizophrenia and global cognition (p < 0.001) when the samples rely on general and healthy participants, while no significant correlation was detected when the three studies devoted to schizophrenia patients were meta-analysed (p > 0.05). Our review and meta-analysis therefore argues against universal pleiotropy for schizophrenia alleles and cognition, since cognition in SZ patients would be underpinned by the same genetic factors than in the general population, and substantially independent of common variant liability to the disorder.
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Affiliation(s)
- Jasmina Mallet
- APHP; Department of Psychiatry, Universitary Hospital Louis Mourier, 92700 Colombes, France; (Y.L.S.); (C.D.)
- Université de Paris, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, F-75014 Paris, France
| | - Yann Le Strat
- APHP; Department of Psychiatry, Universitary Hospital Louis Mourier, 92700 Colombes, France; (Y.L.S.); (C.D.)
- Université de Paris, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, F-75014 Paris, France
| | - Caroline Dubertret
- APHP; Department of Psychiatry, Universitary Hospital Louis Mourier, 92700 Colombes, France; (Y.L.S.); (C.D.)
- Université de Paris, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, F-75014 Paris, France
| | - Philip Gorwood
- Université de Paris, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, F-75014 Paris, France
- GHU Paris Psychiatrie et Neurosciences, Hôpital Sainte Anne, F-75014 Paris, France
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12
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Toulopoulou T, Zhang X, Cherny S, Dickinson D, Berman KF, Straub RE, Sham P, Weinberger DR. Polygenic risk score increases schizophrenia liability through cognition-relevant pathways. Brain 2019; 142:471-485. [PMID: 30535067 DOI: 10.1093/brain/awy279] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Accepted: 09/19/2018] [Indexed: 02/02/2023] Open
Abstract
Cognitive deficit is thought to represent, at least in part, genetic mechanisms of risk for schizophrenia, with recent evidence from statistical modelling of twin data suggesting direct causality from the former to the latter. However, earlier evidence was based on inferences from twin not molecular genetic data and it is unclear how much genetic influence 'passes through' cognition on the way to diagnosis. Thus, we included direct measurements of genetic risk (e.g. schizophrenia polygenic risk scores) in causation models to assess the extent to which cognitive deficit mediates some of the effect of polygenic risk scores on the disorder. Causal models of family data tested relationships among key variables and allowed parsing of genetic variance components. Polygenic risk scores were calculated from summary statistics from the current largest genome-wide association study of schizophrenia and were represented as a latent trait. Cognition was also modelled as a latent trait. Participants were 1313 members of 1078 families: 416 patients with schizophrenia, 290 unaffected siblings, and 607 controls. Modelling supported earlier findings that cognitive deficit has a putatively causal role in schizophrenia. In total, polygenic risk score explained 8.07% [confidence interval (CI) 5.45-10.74%] of schizophrenia risk in our sample. Of this, more than a third (2.71%, CI 2.41-3.85%) of the polygenic risk score influence was mediated through cognition paths, exceeding the direct influence of polygenic risk score on schizophrenia risk (1.43%, CI 0.46-3.08%). The remainder of the polygenic risk score influence (3.93%, CI 2.37-4.48%) reflected reciprocal causation between schizophrenia liability and cognition (e.g. mutual influences in a cyclical manner). Analysis of genetic variance components of schizophrenia liability indicated that 26.87% (CI 21.45-32.57%) was associated with cognition-related pathways not captured by polygenic risk score. The remaining variance in schizophrenia was through pathways other than cognition-related and polygenic risk score. Although our results are based on inference through statistical modelling and do not provide an absolute proof of causality, we find that cognition pathways mediate a significant part of the influence of cumulative genetic risk on schizophrenia. We estimate from our model that 33.51% (CI 27.34-43.82%) of overall genetic risk is mediated through influences on cognition, but this requires further studies and analyses as the genetics of schizophrenia becomes better characterized.
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Affiliation(s)
- Timothea Toulopoulou
- Department of Psychology, Bilkent University, Bilkent, Ankara, Turkey.,The State Key Laboratory of Brain and Cognitive Sciences, the University of Hong Kong, Hong Kong SAR, China.,Department of Psychology, the University of Hong Kong, Hong Kong SAR, China.,Department of Basic and Clinical Neuroscience, Institute of Psychiatry Psychology and Neuroscience at King's College London, London, UK
| | - Xiaowei Zhang
- Department of Psychiatry, The University of Hong Kong, Hong Kong SAR, China
| | - Stacey Cherny
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry Psychology and Neuroscience at King's College London, London, UK.,Department of Psychiatry, The University of Hong Kong, Hong Kong SAR, China
| | - Dwight Dickinson
- Clinical and Translational Neuroscience Branch, National Institute of Mental Health, USA
| | - Karen F Berman
- Clinical and Translational Neuroscience Branch, National Institute of Mental Health, USA
| | - Richard E Straub
- Lieber Institute for Brain Development, Johns Hopkins University, USA
| | - Pak Sham
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry Psychology and Neuroscience at King's College London, London, UK.,Department of Psychiatry, The University of Hong Kong, Hong Kong SAR, China
| | - Daniel R Weinberger
- Lieber Institute for Brain Development, Johns Hopkins University, USA.,Departments of Psychiatry, Neurology, Neuroscience, The McKusick Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Johns Hopkins University, USA
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13
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Vasistha NA, Johnstone M, Barton SK, Mayerl SE, Thangaraj Selvaraj B, Thomson PA, Dando O, Grünewald E, Alloza C, Bastin ME, Livesey MR, Economides K, Magnani D, Makedonopolou P, Burr K, Story DJ, Blackwood DHR, Wyllie DJA, McIntosh AM, Millar JK, ffrench-Constant C, Hardingham GE, Lawrie SM, Chandran S. Familial t(1;11) translocation is associated with disruption of white matter structural integrity and oligodendrocyte-myelin dysfunction. Mol Psychiatry 2019; 24:1641-1654. [PMID: 31481758 PMCID: PMC6814440 DOI: 10.1038/s41380-019-0505-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Revised: 05/31/2019] [Accepted: 06/26/2019] [Indexed: 12/12/2022]
Abstract
Although the underlying neurobiology of major mental illness (MMI) remains unknown, emerging evidence implicates a role for oligodendrocyte-myelin abnormalities. Here, we took advantage of a large family carrying a balanced t(1;11) translocation, which substantially increases risk of MMI, to undertake both diffusion tensor imaging and cellular studies to evaluate the consequences of the t(1;11) translocation on white matter structural integrity and oligodendrocyte-myelin biology. This translocation disrupts among others the DISC1 gene which plays a crucial role in brain development. We show that translocation-carrying patients display significant disruption of white matter integrity compared with familial controls. At a cellular level, we observe dysregulation of key pathways controlling oligodendrocyte development and morphogenesis in induced pluripotent stem cell (iPSC) derived case oligodendrocytes. This is associated with reduced proliferation and a stunted morphology in vitro. Further, myelin internodes in a humanized mouse model that recapitulates the human translocation as well as after transplantation of t(1;11) oligodendrocyte progenitors were significantly reduced when compared with controls. Thus we provide evidence that the t(1;11) translocation has biological effects at both the systems and cellular level that together suggest oligodendrocyte-myelin dysfunction.
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Affiliation(s)
- Navneet A. Vasistha
- 0000 0004 1936 7988grid.4305.2Centre for Clinical Brain Sciences, The University of Edinburgh, Chancellor’s Building, 49 Little France Crescent, Edinburgh, EH16 4SB UK ,0000 0004 1936 7988grid.4305.2MRC Centre for Regenerative Medicine, The University of Edinburgh, 5 Little France Drive, Edinburgh, EH16 4UU UK ,0000 0004 4905 7710grid.475408.aCentre for Brain Development and Repair, Institute for Stem Cell Biology and Regenerative Medicine, GKVK - Post, Bellary Road, Bangalore, 560065 India ,0000 0001 0674 042Xgrid.5254.6Present Address: Biotech Research and Innovation Centre, Ole Maaløes Vej 5, Copenhagen, N 2200 Denmark
| | - Mandy Johnstone
- 0000 0004 1936 7988grid.4305.2Centre for Clinical Brain Sciences, The University of Edinburgh, Chancellor’s Building, 49 Little France Crescent, Edinburgh, EH16 4SB UK ,0000 0004 1936 7988grid.4305.2Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, EH10 5HF UK
| | - Samantha K. Barton
- 0000 0004 1936 7988grid.4305.2Centre for Clinical Brain Sciences, The University of Edinburgh, Chancellor’s Building, 49 Little France Crescent, Edinburgh, EH16 4SB UK ,0000 0004 1936 7988grid.4305.2MRC Centre for Regenerative Medicine, The University of Edinburgh, 5 Little France Drive, Edinburgh, EH16 4UU UK ,0000 0004 1936 7988grid.4305.2UK Dementia Research Institute at Edinburgh, The University of Edinburgh, Chancellor’s Building, 49 Little France Crescent, Edinburgh, EH16 4SB UK
| | - Steffen E. Mayerl
- 0000 0004 1936 7988grid.4305.2MRC Centre for Regenerative Medicine, The University of Edinburgh, 5 Little France Drive, Edinburgh, EH16 4UU UK
| | - Bhuvaneish Thangaraj Selvaraj
- 0000 0004 1936 7988grid.4305.2Centre for Clinical Brain Sciences, The University of Edinburgh, Chancellor’s Building, 49 Little France Crescent, Edinburgh, EH16 4SB UK ,0000 0004 1936 7988grid.4305.2MRC Centre for Regenerative Medicine, The University of Edinburgh, 5 Little France Drive, Edinburgh, EH16 4UU UK ,0000 0004 1936 7988grid.4305.2UK Dementia Research Institute at Edinburgh, The University of Edinburgh, Chancellor’s Building, 49 Little France Crescent, Edinburgh, EH16 4SB UK
| | - Pippa A. Thomson
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, The University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh, EH4 2XU UK
| | - Owen Dando
- 0000 0004 1936 7988grid.4305.2UK Dementia Research Institute at Edinburgh, The University of Edinburgh, Chancellor’s Building, 49 Little France Crescent, Edinburgh, EH16 4SB UK ,0000 0004 1936 7988grid.4305.2Centre for Discovery Brain Sciences, The University of Edinburgh, Hugh Robson Building, 15 George Square, Edinburgh, EH8 9XD UK
| | - Ellen Grünewald
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, The University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh, EH4 2XU UK
| | - Clara Alloza
- 0000 0004 1936 7988grid.4305.2Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, EH10 5HF UK
| | - Mark E. Bastin
- 0000 0004 1936 7988grid.4305.2Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, EH10 5HF UK
| | - Matthew R. Livesey
- 0000 0004 1936 7988grid.4305.2Centre for Discovery Brain Sciences, The University of Edinburgh, Hugh Robson Building, 15 George Square, Edinburgh, EH8 9XD UK
| | | | - Dario Magnani
- 0000 0004 1936 7988grid.4305.2Centre for Clinical Brain Sciences, The University of Edinburgh, Chancellor’s Building, 49 Little France Crescent, Edinburgh, EH16 4SB UK ,0000 0004 1936 7988grid.4305.2MRC Centre for Regenerative Medicine, The University of Edinburgh, 5 Little France Drive, Edinburgh, EH16 4UU UK ,0000 0004 1936 7988grid.4305.2UK Dementia Research Institute at Edinburgh, The University of Edinburgh, Chancellor’s Building, 49 Little France Crescent, Edinburgh, EH16 4SB UK
| | - Paraskevi Makedonopolou
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, The University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh, EH4 2XU UK
| | - Karen Burr
- 0000 0004 1936 7988grid.4305.2Centre for Clinical Brain Sciences, The University of Edinburgh, Chancellor’s Building, 49 Little France Crescent, Edinburgh, EH16 4SB UK ,0000 0004 1936 7988grid.4305.2MRC Centre for Regenerative Medicine, The University of Edinburgh, 5 Little France Drive, Edinburgh, EH16 4UU UK ,0000 0004 1936 7988grid.4305.2UK Dementia Research Institute at Edinburgh, The University of Edinburgh, Chancellor’s Building, 49 Little France Crescent, Edinburgh, EH16 4SB UK
| | - David J. Story
- 0000 0004 1936 7988grid.4305.2Centre for Clinical Brain Sciences, The University of Edinburgh, Chancellor’s Building, 49 Little France Crescent, Edinburgh, EH16 4SB UK ,0000 0004 1936 7988grid.4305.2MRC Centre for Regenerative Medicine, The University of Edinburgh, 5 Little France Drive, Edinburgh, EH16 4UU UK ,0000 0004 1936 7988grid.4305.2UK Dementia Research Institute at Edinburgh, The University of Edinburgh, Chancellor’s Building, 49 Little France Crescent, Edinburgh, EH16 4SB UK
| | - Douglas H. R. Blackwood
- 0000 0004 1936 7988grid.4305.2Centre for Clinical Brain Sciences, The University of Edinburgh, Chancellor’s Building, 49 Little France Crescent, Edinburgh, EH16 4SB UK ,0000 0004 1936 7988grid.4305.2Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, EH10 5HF UK
| | - David J. A. Wyllie
- 0000 0004 4905 7710grid.475408.aCentre for Brain Development and Repair, Institute for Stem Cell Biology and Regenerative Medicine, GKVK - Post, Bellary Road, Bangalore, 560065 India ,0000 0004 1936 7988grid.4305.2Centre for Discovery Brain Sciences, The University of Edinburgh, Hugh Robson Building, 15 George Square, Edinburgh, EH8 9XD UK
| | - Andrew M. McIntosh
- 0000 0004 1936 7988grid.4305.2Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, EH10 5HF UK
| | - J. Kirsty Millar
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, The University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh, EH4 2XU UK
| | - Charles ffrench-Constant
- 0000 0004 1936 7988grid.4305.2MRC Centre for Regenerative Medicine, The University of Edinburgh, 5 Little France Drive, Edinburgh, EH16 4UU UK
| | - Giles E. Hardingham
- 0000 0004 1936 7988grid.4305.2UK Dementia Research Institute at Edinburgh, The University of Edinburgh, Chancellor’s Building, 49 Little France Crescent, Edinburgh, EH16 4SB UK ,0000 0004 1936 7988grid.4305.2Centre for Discovery Brain Sciences, The University of Edinburgh, Hugh Robson Building, 15 George Square, Edinburgh, EH8 9XD UK
| | - Stephen M. Lawrie
- 0000 0004 1936 7988grid.4305.2Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, EH10 5HF UK
| | - Siddharthan Chandran
- Centre for Clinical Brain Sciences, The University of Edinburgh, Chancellor's Building, 49 Little France Crescent, Edinburgh, EH16 4SB, UK. .,MRC Centre for Regenerative Medicine, The University of Edinburgh, 5 Little France Drive, Edinburgh, EH16 4UU, UK. .,Centre for Brain Development and Repair, Institute for Stem Cell Biology and Regenerative Medicine, GKVK - Post, Bellary Road, Bangalore, 560065, India. .,UK Dementia Research Institute at Edinburgh, The University of Edinburgh, Chancellor's Building, 49 Little France Crescent, Edinburgh, EH16 4SB, UK.
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14
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Zhu X, Xu X, Xu C, Zhang J, Zhang X, Ma L, Liu J, Wang K. The interactive effects of stress and coping style on cognitive function in patients with schizophrenia. Neuropsychiatr Dis Treat 2019; 15:523-530. [PMID: 30863074 PMCID: PMC6391124 DOI: 10.2147/ndt.s181471] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
PURPOSE Previous studies have not determined the interactive effects of stress and coping style on cognitive function in patients with schizophrenia, and the current studies have been restricted to the relationship between stress and stress response, which may be associated with cognitive impairment in individuals with schizophrenia. The present research was aimed to determine whether stress is related to cognitive function in patients with schizophrenia. In addition, this research further investigates the moderating effects of coping style on the relationship between stress and cognitive function in patients with schizophrenia on the basis of stress and coping theory. PATIENTS AND METHODS Our sample consisted of 274 patients with a confirmed diagnosis of schizophrenia, and all of them completed the Simple Cope Style Questionnaire, Social Readjustment Rating Scale, and cognitive function assessment. A multivariate regression analysis was performed to investigate the possible correlations between cognitive function and stress, and the moderating effects of coping style on the relationship between stress and cognitive function were tested using the PROCESS macro for SPSS. RESULTS Stress was negatively correlated with working memory. Negative coping but not positive coping moderated the relationship between stress and working memory in patients with schizophrenia, and the Johnson-Neyman technique showed that the moderating effect was significant only above this cutoff (38.32% of all negative coping scores). This means that when exposed to similar stress, patients adopting high negative coping had worse working memory than those who did not. CONCLUSION These findings suggested that the assessment of stress and coping style may help estimate working memory impairment risk in patients with schizophrenia, and reducing negative coping may be a crucial intervention target to prevent further impairment of working memory in patients with schizophrenia suffering from great stress.
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Affiliation(s)
- Xiaodan Zhu
- Division of Nursing Fundamentals, School of Nursing, Shandong University, Jinan, Shandong, China,
| | - Xuebing Xu
- Inpatient Department, Ningxia Mental Health Center, Ningxia Ning-An Hospital, Yinchuan, Ningxia, China
| | - Chao Xu
- Inpatient Department, Ningxia Mental Health Center, Ningxia Ning-An Hospital, Yinchuan, Ningxia, China
| | - Jingyi Zhang
- Inpatient Department, Ningxia Mental Health Center, Ningxia Ning-An Hospital, Yinchuan, Ningxia, China
| | - Xiaofeng Zhang
- Inpatient Department, Ningxia Mental Health Center, Ningxia Ning-An Hospital, Yinchuan, Ningxia, China
| | - Li Ma
- Inpatient Department, Ningxia Mental Health Center, Ningxia Ning-An Hospital, Yinchuan, Ningxia, China
| | - Juan Liu
- Division of Humanities, School of Nursing, Ningxia Medical University, Yinchuan, Ningxia, China,
| | - Kefang Wang
- Division of Nursing Fundamentals, School of Nursing, Shandong University, Jinan, Shandong, China,
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15
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Palmos AB, Breen G, Goodwin L, Frissa S, Hatch SL, Hotopf M, Thuret S, Lewis CM, Powell TR. Genetic Risk for Psychiatric Disorders and Telomere Length. Front Genet 2018; 9:468. [PMID: 30459805 PMCID: PMC6232668 DOI: 10.3389/fgene.2018.00468] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Accepted: 09/24/2018] [Indexed: 01/07/2023] Open
Abstract
Background: Previous studies have revealed associations between psychiatric disorder diagnosis and shorter telomere length. Here, we attempt to discern whether genetic risk for psychiatric disorders, or use of pharmacological treatments (i.e., antidepressants), predict shorter telomere length and risk for aging-related disease in a United Kingdom population sample. Methods: DNA samples from blood were available from 351 participants who were recruited as part of the South East London Community Health (SELCoH) Study, and for which whole-genome genotype data was available. Leukocyte telomere length was characterized using quantitative polymerase chain reactions. Individualized polygenic risk scores for major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SCZ) were calculated using Psychiatric Genomics Consortium summary statistics. We subsequently performed linear models, to discern the impact polygenic risk for psychiatric disorders (an etiological risk factor) and antidepressant use (common pharmacological treatment) have on telomere length, whilst accounting for other lifestyle/health factors (e.g., BMI, smoking). Results: There were no significant associations between polygenic risk for any of the psychiatric disorders tested and telomere length (p > 0.05). Antidepressant use was significantly associated with shorter telomere length and this was independent from a depression diagnosis or current depression severity (p ≤ 0.01). Antidepressant use was also associated with a significantly higher risk of aging-related disease, which was independent from depression diagnosis (p ≤ 0.05). Conclusion: Genetic risk for psychiatric disorders is not associated with shorter telomere length. Further studies are now needed to prospectively characterize if antidepressant use increases risk for aging-related disease and telomere shortening, or whether people who age faster and have aging-related diseases are just more likely to be prescribed antidepressants.
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Affiliation(s)
- Alish B Palmos
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Gerome Breen
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,National Institute for Health Research Biomedical Research Centre for Mental Health, Institute of Psychiatry, Psychology and Neuroscience, Maudsley Hospital, King's College London, London, United Kingdom
| | - Laura Goodwin
- Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.,Department of Psychological Sciences, University of Liverpool, Liverpool, United Kingdom
| | - Souci Frissa
- Health Service & Population Research Department, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Stephani L Hatch
- Health Service & Population Research Department, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Matthew Hotopf
- National Institute for Health Research Biomedical Research Centre for Mental Health, Institute of Psychiatry, Psychology and Neuroscience, Maudsley Hospital, King's College London, London, United Kingdom.,Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.,South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Sandrine Thuret
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Cathryn M Lewis
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,National Institute for Health Research Biomedical Research Centre for Mental Health, Institute of Psychiatry, Psychology and Neuroscience, Maudsley Hospital, King's College London, London, United Kingdom
| | - Timothy R Powell
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
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16
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Alloza C, Cox SR, Blesa Cábez M, Redmond P, Whalley HC, Ritchie SJ, Muñoz Maniega S, Valdés Hernández MDC, Tucker-Drob EM, Lawrie SM, Wardlaw JM, Deary IJ, Bastin ME. Polygenic risk score for schizophrenia and structural brain connectivity in older age: A longitudinal connectome and tractography study. Neuroimage 2018; 183:884-896. [PMID: 30179718 PMCID: PMC6215331 DOI: 10.1016/j.neuroimage.2018.08.075] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2018] [Revised: 08/28/2018] [Accepted: 08/31/2018] [Indexed: 12/14/2022] Open
Abstract
Higher polygenic risk score for schizophrenia (szPGRS) has been associated with lower cognitive function and might be a predictor of decline in brain structure in apparently healthy populations. Age-related declines in structural brain connectivity-measured using white matter diffusion MRI -are evident from cross-sectional data. Yet, it remains unclear how graph theoretical metrics of the structural connectome change over time, and whether szPGRS is associated with differences in ageing-related changes in human brain connectivity. Here, we studied a large, relatively healthy, same-year-of-birth, older age cohort over a period of 3 years (age ∼ 73 years, N = 731; age ∼76 years, N = 488). From their brain scans we derived tract-averaged fractional anisotropy (FA) and mean diffusivity (MD), and network topology properties. We investigated the cross-sectional and longitudinal associations between these structural brain variables and szPGRS. Higher szPGRS showed significant associations with longitudinal increases in MD in the splenium (β = 0.132, pFDR = 0.040), arcuate (β = 0.291, pFDR = 0.040), anterior thalamic radiations (β = 0.215, pFDR = 0.040) and cingulum (β = 0.165, pFDR = 0.040). Significant declines over time were observed in graph theory metrics for FA-weighted networks, such as mean edge weight (β = -0.039, pFDR = 0.048) and strength (β = -0.027, pFDR = 0.048). No significant associations were found between szPGRS and graph theory metrics. These results are consistent with the hypothesis that szPGRS confers risk for ageing-related degradation of some aspects of structural connectivity.
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Affiliation(s)
- C Alloza
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK.
| | - S R Cox
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; Department of Psychology, University of Edinburgh, Edinburgh, UK; Scottish Imaging Network: A Platform for Scientific Excellence (SINAPSE) Collaboration, University of Edinburgh, Edinburgh, UK
| | - M Blesa Cábez
- MRC Centre for Reproductive Health, University of Edinburgh, UK
| | - P Redmond
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - H C Whalley
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - S J Ritchie
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - S Muñoz Maniega
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; Scottish Imaging Network: A Platform for Scientific Excellence (SINAPSE) Collaboration, University of Edinburgh, Edinburgh, UK; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - M Del C Valdés Hernández
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; Scottish Imaging Network: A Platform for Scientific Excellence (SINAPSE) Collaboration, University of Edinburgh, Edinburgh, UK; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - E M Tucker-Drob
- Department of Psychology, University of Texas, Austin, TX, USA
| | - S M Lawrie
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - J M Wardlaw
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; Scottish Imaging Network: A Platform for Scientific Excellence (SINAPSE) Collaboration, University of Edinburgh, Edinburgh, UK; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - I J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - M E Bastin
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; Scottish Imaging Network: A Platform for Scientific Excellence (SINAPSE) Collaboration, University of Edinburgh, Edinburgh, UK; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
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17
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Abstract
Network theory provides an intuitively appealing framework for studying relationships among interconnected brain mechanisms and their relevance to behaviour. As the space of its applications grows, so does the diversity of meanings of the term network model. This diversity can cause confusion, complicate efforts to assess model validity and efficacy, and hamper interdisciplinary collaboration. In this Review, we examine the field of network neuroscience, focusing on organizing principles that can help overcome these challenges. First, we describe the fundamental goals in constructing network models. Second, we review the most common forms of network models, which can be described parsimoniously along the following three primary dimensions: from data representations to first-principles theory; from biophysical realism to functional phenomenology; and from elementary descriptions to coarse-grained approximations. Third, we draw on biology, philosophy and other disciplines to establish validation principles for these models. We close with a discussion of opportunities to bridge model types and point to exciting frontiers for future pursuits.
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Affiliation(s)
- Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA.
| | - Perry Zurn
- Department of Philosophy, American University, Washington, DC, USA
| | - Joshua I Gold
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA
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18
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Sandini C, Zöller D, Scariati E, Padula MC, Schneider M, Schaer M, Van De Ville D, Eliez S. Development of Structural Covariance From Childhood to Adolescence: A Longitudinal Study in 22q11.2DS. Front Neurosci 2018; 12:327. [PMID: 29867336 PMCID: PMC5968113 DOI: 10.3389/fnins.2018.00327] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Accepted: 04/26/2018] [Indexed: 12/18/2022] Open
Abstract
Background: Schizophrenia is currently considered a neurodevelopmental disorder of connectivity. Still few studies have investigated how brain networks develop in children and adolescents who are at risk for developing psychosis. 22q11.2 Deletion Syndrome (22q11DS) offers a unique opportunity to investigate the pathogenesis of schizophrenia from a neurodevelopmental perspective. Structural covariance (SC) is a powerful approach to explore morphometric relations between brain regions that can furthermore detect biomarkers of psychosis, both in 22q11DS and in the general population. Methods: Here we implement a state-of-the-art sliding-window approach to characterize maturation of SC network architecture in a large longitudinal cohort of patients with 22q11DS (110 with 221 visits) and healthy controls (117 with 211 visits). We furthermore propose a new clustering-based approach to group regions according to trajectories of structural connectivity maturation. We correlate measures of SC with development of working memory, a core executive function that is highly affected in both idiopathic psychosis and 22q11DS. Finally, in 22q11DS we explore correlations between SC dysconnectivity and severity of internalizing psychopathology. Results: In HCs network architecture underwent a quadratic developmental trajectory maturing up to mid-adolescence. Late-childhood maturation was particularly evident for fronto-parietal cortices, while Default-Mode-Network-related regions showed a more protracted linear development. Working memory performance was positively correlated with network segregation and fronto-parietal connectivity. In 22q11DS, we demonstrate aberrant maturation of SC with disturbed architecture selectively emerging during adolescence and correlating more severe internalizing psychopathology. Patients also presented a lack of typical network development during late-childhood, that was particularly prominent for frontal connectivity. Conclusions: Our results suggest that SC maturation may underlie critical cognitive development occurring during late-childhood in healthy controls. Aberrant trajectories of SC maturation may reflect core developmental features of 22q11DS, including disturbed cognitive maturation during childhood and predisposition to internalizing psychopathology and psychosis during adolescence.
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Affiliation(s)
- Corrado Sandini
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland
| | - Daniela Zöller
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland.,Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Elisa Scariati
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland
| | - Maria C Padula
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland
| | - Maude Schneider
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland.,Department of Neuroscience, Center for Contextual Psychiatry, Research Group Psychiatry, KU Leuven, Leuven, Belgium
| | - Marie Schaer
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland
| | - Dimitri Van De Ville
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.,Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Stephan Eliez
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland.,Department of Genetic Medicine and Development, University of Geneva School of Medicine, Geneva, Switzerland
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Bogdan R, Baranger DAA, Agrawal A. Polygenic Risk Scores in Clinical Psychology: Bridging Genomic Risk to Individual Differences. Annu Rev Clin Psychol 2018; 14:119-157. [PMID: 29579395 PMCID: PMC7772939 DOI: 10.1146/annurev-clinpsy-050817-084847] [Citation(s) in RCA: 90] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Genomewide association studies (GWASs) across psychiatric phenotypes have shown that common genetic variants generally confer risk with small effect sizes (odds ratio < 1.1) that additively contribute to polygenic risk. Summary statistics derived from large discovery GWASs can be used to generate polygenic risk scores (PRS) in independent, target data sets to examine correlates of polygenic disorder liability (e.g., does genetic liability to schizophrenia predict cognition?). The intuitive appeal and generalizability of PRS have led to their widespread use and new insights into mechanisms of polygenic liability. However, when currently applied across traits they account for small amounts of variance (<3%), are relatively uninformative for clinical treatment, and, in isolation, provide no insight into molecular mechanisms. Larger GWASs are needed to increase the precision of PRS, and novel approaches integrating various data sources (e.g., multitrait analysis of GWASs) may improve the utility of current PRS.
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Affiliation(s)
- Ryan Bogdan
- BRAINLab, Department of Psychological and Brain Sciences, and Division of Biology and Biomedical Sciences, Washington University in St. Louis, St. Louis, Missouri 63110, USA;
| | - David A A Baranger
- BRAINLab, Department of Psychological and Brain Sciences, and Division of Biology and Biomedical Sciences, Washington University in St. Louis, St. Louis, Missouri 63110, USA;
| | - Arpana Agrawal
- Department of Psychiatry, Washington University in St. Louis School of Medicine, St. Louis, Missouri 63110, USA
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