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Yu H, Ni P, Tian Y, Zhao L, Li M, Li X, Wei W, Wei J, Wang Q, Guo W, Deng W, Ma X, Coid J, Li T. Association of elevated levels of peripheral complement components with cortical thinning and impaired logical memory in drug-naïve patients with first-episode schizophrenia. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2023; 9:79. [PMID: 37935744 PMCID: PMC10630449 DOI: 10.1038/s41537-023-00409-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 10/25/2023] [Indexed: 11/09/2023]
Abstract
Schizophrenia has been linked to polymorphism in genes encoding components of the complement system, and hyperactive complement activity has been linked to immune dysfunction in schizophrenia patients. Whether and how specific complement components influence brain structure and cognition in the disease is unclear. Here we compared 52 drug-naïve patients with first-episode schizophrenia and 52 healthy controls in terms of levels of peripheral complement factors, cortical thickness (CT), logical memory and psychotic symptoms. We also explored the relationship between complement factors with CT, cognition and psychotic symptoms. Patients showed significantly higher levels of C1q, C4, factor B, factor H, and properdin in plasma. Among patients, higher levels of C3 in plasma were associated with worse memory recall, while higher levels of C4, factor B and factor H were associated with thinner sensory cortex. These findings link dysregulation of specific complement components to abnormal brain structure and cognition in schizophrenia.
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Affiliation(s)
- Hua Yu
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, 310058, China
| | - Peiyan Ni
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, 310058, China
| | - Yang Tian
- Psychiatric Laboratory and Mental Health Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Liansheng Zhao
- Psychiatric Laboratory and Mental Health Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Mingli Li
- Psychiatric Laboratory and Mental Health Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Xiaojing Li
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, 310058, China
| | - Wei Wei
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, 310058, China
| | - Jinxue Wei
- Psychiatric Laboratory and Mental Health Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Qiang Wang
- Psychiatric Laboratory and Mental Health Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Wanjun Guo
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, 310058, China
| | - Wei Deng
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, 310058, China
| | - Xiaohong Ma
- Psychiatric Laboratory and Mental Health Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Jeremy Coid
- Psychiatric Laboratory and Mental Health Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Tao Li
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China.
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, 311121, China.
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, 310058, China.
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Esmaeilzadeh Kiabani N, Kazemi R, Hadipour AL, Khomami S, Kalloch B, Hlawitschka M. Targeting the insula with transcranial direct current stimulation; A simulation study. Psychiatry Res Neuroimaging 2023; 335:111718. [PMID: 37738706 DOI: 10.1016/j.pscychresns.2023.111718] [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: 02/10/2023] [Revised: 08/26/2023] [Accepted: 09/11/2023] [Indexed: 09/24/2023]
Abstract
Insula is considered an important region of the brain in the generation and maintenance of a wide range of psychiatric symptoms, possibly due to being key in fundamental functions such as interoception and cognition in general. Investigating the possibility of targeting this area using non-invasive brain stimulation techniques can open new possibilities to probe the normal and abnormal functioning of the brain and potentially new treatment protocols to alleviate symptoms of different psychiatric disorders. In the current study, COMETS2, a MATLAB based toolbox was used to simulate the magnitude of the current density and electric field in the brain caused by different transcranial direct current stimulation (tDCS) protocols to find an optimum montage to target the insula and its 6 subregions for three different current intensities, namely 2, 3, and 4 mA. Frontal and occipital regions were found to be optimal candidate regions.. The results of the current study showed that it is viable to reach the insula and its individual subregions using tDCS.
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Affiliation(s)
| | - Reza Kazemi
- Faculty of Entrepreneurship, University of Tehran, Tehran, Iran.
| | - Abed L Hadipour
- Department of Cognitive Sciences, University of Messina, Messina, Italy
| | - Sanaz Khomami
- Department of Psychology, South Tehran Branch, Islamic Azad University, Tehran, Iran
| | - Benjamin Kalloch
- Max Planck Institute for Human Cognitive and Brain Sciences, Instiute of Biomedical Engineering and Informatics, Germany & Technische Universität Ilmenau, Ilmenau, Leipzig, Germany
| | - Mario Hlawitschka
- Faculty of Computer Science and Media, Leipzig University of Applied Science, Leipzig, Germany
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Mao Q, Lin X, Yin Q, Liu P, Zhang Y, Qu S, Xu J, Cheng W, Luo X, Kang L, Taximaimaiti R, Zheng C, Zhang H, Wang X, Ren H, Cao Y, Lin J, Luo X. A significant, functional and replicable risk KTN1 variant block for schizophrenia. Sci Rep 2023; 13:3890. [PMID: 36890161 PMCID: PMC9995530 DOI: 10.1038/s41598-023-27448-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 01/02/2023] [Indexed: 03/10/2023] Open
Abstract
Cortical and subcortical structural alteration has been extensively reported in schizophrenia, including the unusual expansion of gray matter volumes (GMVs) of basal ganglia (BG), especially putamen. Previous genome-wide association studies pinpointed kinectin 1 gene (KTN1) as the most significant gene regulating the GMV of putamen. In this study, the role of KTN1 variants in risk and pathogenesis of schizophrenia was explored. A dense set of SNPs (n = 849) covering entire KTN1 was analyzed in three independent European- or African-American samples (n = 6704) and one mixed European and Asian Psychiatric Genomics Consortium sample (n = 56,418 cases vs. 78,818 controls), to identify replicable SNP-schizophrenia associations. The regulatory effects of schizophrenia-associated variants on the KTN1 mRNA expression in 16 cortical or subcortical regions in two European cohorts (n = 138 and 210, respectively), the total intracranial volume (ICV) in 46 European cohorts (n = 18,713), the GMVs of seven subcortical structures in 50 European cohorts (n = 38,258), and the surface areas (SA) and thickness (TH) of whole cortex and 34 cortical regions in 50 European cohorts (n = 33,992) and eight non-European cohorts (n = 2944) were carefully explored. We found that across entire KTN1, only 26 SNPs within the same block (r2 > 0.85) were associated with schizophrenia across ≥ 2 independent samples (7.5 × 10-5 ≤ p ≤ 0.048). The schizophrenia-risk alleles, which increased significantly risk for schizophrenia in Europeans (q < 0.05), were all minor alleles (f < 0.5), consistently increased (1) the KTN1 mRNA expression in 12 brain regions significantly (5.9 × 10-12 ≤ p ≤ 0.050; q < 0.05), (2) the ICV significantly (6.1 × 10-4 ≤ p ≤ 0.008; q < 0.05), (3) the SA of whole (9.6 × 10-3 ≤ p ≤ 0.047) and two regional cortices potentially (2.5 × 10-3 ≤ p ≤ 0.042; q > 0.05), and (4) the TH of eight regional cortices potentially (0.006 ≤ p ≤ 0.050; q > 0.05), and consistently decreased (1) the BG GMVs significantly (1.8 × 10-19 ≤ p ≤ 0.050; q < 0.05), especially putamen GMV (1.8 × 10-19 ≤ p ≤ 1.0 × 10-4; q < 0.05, (2) the SA of four regional cortices potentially (0.010 ≤ p ≤ 0.048), and (3) the TH of four regional cortices potentially (0.015 ≤ p ≤ 0.049) in Europeans. We concluded that we identified a significant, functional, and robust risk variant block covering entire KTN1 that might play a critical role in the risk and pathogenesis of schizophrenia.
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Affiliation(s)
- Qiao Mao
- Department of Psychosomatic Medicine, People's Hospital of Deyang City, Deyang, 618000, Sichuan, China
| | - Xiandong Lin
- Laboratory of Radiation Oncology and Radiobiology, Fujian Provincial Cancer Hospital, the Teaching Hospital of Fujian Medical University, Fuzhou, 350014, Fujian, China
| | - Qin Yin
- Department of Respiratory and Critical Care Medicine, Hubei Provincial Hospital of Integrated Chinese and Western Medicine, Wuhan, 430000, Hubei, China
| | - Ping Liu
- Department of Psychosomatic Medicine, People's Hospital of Deyang City, Deyang, 618000, Sichuan, China
| | - Yong Zhang
- Tianjin Mental Health Center, Tianjin, 300222, China
| | - Shihao Qu
- Zhuhai Center for Maternal and Child Health Care, Zhuhai, Guangdong, 519001, China
| | - Jianying Xu
- Zhuhai Center for Maternal and Child Health Care, Zhuhai, Guangdong, 519001, China
| | - Wenhong Cheng
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Xinqun Luo
- Department of Neurosurgery, The First Hospital, Fujian Medical University, Fuzhou, 350004, Fujian, China
| | - Longli Kang
- Key Laboratory for Molecular Genetic Mechanisms and Intervention Research On High Altitude Diseases of Tibet Autonomous Region, Xizang Minzu University School of Medicine, Xiangyang, 712082, Shaanxi, China
| | - Reyisha Taximaimaiti
- Department of Neurology, Shanghai Tongren Hospital, Shanghai Jiao Tong University, Shanghai, 200080, China
| | - Chengchou Zheng
- Minqing Psychiatric Hospital, Minqing, 350800, Fujian, China
| | - Huihao Zhang
- The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350001, China
| | - Xiaoping Wang
- Department of Neurology, The 1st People's Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 201620, USA
| | - Honggang Ren
- Department of Internal Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuping Cao
- Department of Psychiatry, Second Xiangya Hospital, Central South University, China National Clinical Research Center On Mental Disorders, China National Technology Institute On Mental Disorders, Changsha, 410011, Hunan, China.
| | - Jie Lin
- Fujian Center for Disease Control and Prevention, Fuzhou, 350012, Fujian, China.
- Fujian Institute of Preventive Medicine, Fuzhou, 350012, Fujian, China.
| | - Xingguang Luo
- Beijing Huilongguan Hospital, Peking University Huilongguan School of Clinical Medicine, Beijing, 100096, China.
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Walker DL, Palermo R, Callis Z, Gignac GE. The association between intelligence and face processing abilities: A conceptual and meta-analytic review. INTELLIGENCE 2023. [DOI: 10.1016/j.intell.2022.101718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Zhang M, Yang F, Fan H, Fan F, Wang Z, Xiang H, Huang W, Tan Y, Tan S, Hong LE. Increased connectivity of insula sub-regions correlates with emotional dysregulation in patients with first-episode schizophrenia. Psychiatry Res Neuroimaging 2022; 326:111535. [PMID: 36084435 DOI: 10.1016/j.pscychresns.2022.111535] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 08/05/2022] [Accepted: 08/24/2022] [Indexed: 11/21/2022]
Abstract
Dysfunctional insula is crucial in the development of social cognition deficits, especially emotional dysregulation in patients with schizophrenia. However, function networks of insula sub-regions in schizophrenia are rarely investigated. In this study, functional connectivity between insula sub-regions and whole-brain voxels and its relationship with social cognition ability were investigated in patients with first-episode schizophrenia (FES). This study included 47 patients with FES and 47 healthy controls (HCs). Resting-state functional connectivity (rsFC) was assessed using a seed-based approach, and social cognition was measured by the "managing emotions" branch of the Mayer-Salovey-Caruso Emotional Intelligence Test. Differences in rsFC of insula sub-regions between the two groups were examined. Patients with FES showed increased rsFC between the left anterior insula (AI) and the right inferior frontal gyrus or the right anterior middle cingulate cortex (aMCC) and between the right middle insula and the right aMCC. Moreover, the increased AI-aMCC connectivity correlated negatively with the "managing emotion" scores in patients. This study highlights the altered functional connectivity of insula sub-regions and its correlation with emotional dysregulation in patients with FES. Our findings provide some insights into underlying neuropathological mechanisms associated with emotional regulation deficiency in patients with schizophrenia.
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Affiliation(s)
- Meng Zhang
- Beijing HuiLongGuan Hospital, Peking University HuiLongGuan Clinical Medical School, Beijing 100096, China
| | - Fude Yang
- Beijing HuiLongGuan Hospital, Peking University HuiLongGuan Clinical Medical School, Beijing 100096, China.
| | - Hongzhen Fan
- Beijing HuiLongGuan Hospital, Peking University HuiLongGuan Clinical Medical School, Beijing 100096, China
| | - Fengmei Fan
- Beijing HuiLongGuan Hospital, Peking University HuiLongGuan Clinical Medical School, Beijing 100096, China
| | - Zhiren Wang
- Beijing HuiLongGuan Hospital, Peking University HuiLongGuan Clinical Medical School, Beijing 100096, China
| | - Hong Xiang
- Chongqing University Three Gorges Hospital, Chongqing 404000, China
| | - Wenqian Huang
- Beijing HuiLongGuan Hospital, Peking University HuiLongGuan Clinical Medical School, Beijing 100096, China
| | - Yunlong Tan
- Beijing HuiLongGuan Hospital, Peking University HuiLongGuan Clinical Medical School, Beijing 100096, China
| | - Shuping Tan
- Beijing HuiLongGuan Hospital, Peking University HuiLongGuan Clinical Medical School, Beijing 100096, China.
| | - L Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD 21288, United States of America
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Oomen PP, Gangadin SS, Begemann MJH, Visser E, Mandl RCW, Sommer IEC. The neurobiological characterization of distinct cognitive subtypes in early-phase schizophrenia-spectrum disorders. Schizophr Res 2022; 241:228-237. [PMID: 35176721 DOI: 10.1016/j.schres.2022.02.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 01/28/2022] [Accepted: 02/04/2022] [Indexed: 10/19/2022]
Abstract
INTRODUCTION Cognitive deficits are present in some, but not all patients with schizophrenia-spectrum disorders (SSD). We and others have demonstrated three cognitive clusters: cognitively intact patients, patients with deficits in a few domains and those with global cognitive deficits. This study aimed to identify cognitive subtypes of early-phase SSD with matched controls as a reference group, and evaluated cognitive subgroups regarding clinical and brain volumetric measures. METHODS Eighty-six early-phase SSD patients were included. Hierarchical cluster analysis was conducted using global performance on the Brief Assessment of Cognition in Schizophrenia (BACS). Cognitive subgroups were subsequently related to clinical and brain volumetric measures (cortical, subcortical and cortical thickness) using ANCOVA. RESULTS Three distinct cognitive clusters emerged: relative to controls we found one cluster of patients with preserved cognition (n = 25), one moderately impaired cluster (n = 38) and one severely impaired cluster (n = 23). Cognitive subgroups were characterized by differences in volume of the left postcentral gyrus, left middle caudal frontal gyrus and left insula, while differences in cortical thickness were predominantly found in fronto-parietal regions. No differences were demonstrated in subcortical brain volume. DISCUSSION Current results replicate the existence of three distinct cognitive subgroups including one relatively large group with preserved cognitive function. Cognitive subgroups were characterized by differences in cortical regional brain volume and cortical thickness, suggesting associations with cortical, but not subcortical development and cognitive functioning such as attention, executive functions and speed of processing.
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Affiliation(s)
- P P Oomen
- Department of Biomedical Sciences of Cells & Systems, Section Cognitive Neurosciences, University Medical Centre Groningen, University of Groningen, Groningen, the Netherlands.
| | - S S Gangadin
- Department of Biomedical Sciences of Cells & Systems, Section Cognitive Neurosciences, University Medical Centre Groningen, University of Groningen, Groningen, the Netherlands
| | - M J H Begemann
- Department of Biomedical Sciences of Cells & Systems, Section Cognitive Neurosciences, University Medical Centre Groningen, University of Groningen, Groningen, the Netherlands
| | - E Visser
- Department of Psychiatry, University Medical Center, Utrecht Brain Center, Utrecht University, Utrecht, the Netherlands
| | - R C W Mandl
- Department of Psychiatry, University Medical Center, Utrecht Brain Center, Utrecht University, Utrecht, the Netherlands
| | - I E C Sommer
- Department of Biomedical Sciences of Cells & Systems, Section Cognitive Neurosciences, University Medical Centre Groningen, University of Groningen, Groningen, the Netherlands
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Karantonis JA, Carruthers SP, Rossell SL, Pantelis C, Hughes M, Wannan C, Cropley V, Van Rheenen TE. A Systematic Review of Cognition-Brain Morphology Relationships on the Schizophrenia-Bipolar Disorder Spectrum. Schizophr Bull 2021; 47:1557-1600. [PMID: 34097043 PMCID: PMC8530395 DOI: 10.1093/schbul/sbab054] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
The nature of the relationship between cognition and brain morphology in schizophrenia-spectrum disorders (SSD) and bipolar disorder (BD) is uncertain. This review aimed to address this, by providing a comprehensive systematic investigation of links between several cognitive domains and brain volume, cortical thickness, and cortical surface area in SSD and BD patients across early and established illness stages. An initial search of PubMed and Scopus databases resulted in 1486 articles, of which 124 met inclusion criteria and were reviewed in detail. The majority of studies focused on SSD, while those of BD were scarce. Replicated evidence for specific regions associated with indices of cognition was minimal, however for several cognitive domains, the frontal and temporal regions were broadly implicated across both recent-onset and established SSD, and to a lesser extent BD. Collectively, the findings of this review emphasize the significance of both frontal and temporal regions for some domains of cognition in SSD, while highlighting the need for future BD-related studies on this topic.
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Affiliation(s)
- James A Karantonis
- Centre for Mental Health, Faculty of Health, Arts and Design, School of Health Sciences, Swinburne University, Melbourne, Australia
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Melbourne, Australia
| | - Sean P Carruthers
- Centre for Mental Health, Faculty of Health, Arts and Design, School of Health Sciences, Swinburne University, Melbourne, Australia
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Melbourne, Australia
| | - Susan L Rossell
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Melbourne, Australia
- St Vincent’s Mental Health, St Vincent’s Hospital, Melbourne, Australia
| | - Christos Pantelis
- Centre for Mental Health, Faculty of Health, Arts and Design, School of Health Sciences, Swinburne University, Melbourne, Australia
- Florey Institute of Neuroscience and Mental Health, Parkville, Australia
- Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, Australia
| | - Matthew Hughes
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Melbourne, Australia
| | - Cassandra Wannan
- Centre for Mental Health, Faculty of Health, Arts and Design, School of Health Sciences, Swinburne University, Melbourne, Australia
| | - Vanessa Cropley
- Centre for Mental Health, Faculty of Health, Arts and Design, School of Health Sciences, Swinburne University, Melbourne, Australia
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Melbourne, Australia
| | - Tamsyn E Van Rheenen
- Centre for Mental Health, Faculty of Health, Arts and Design, School of Health Sciences, Swinburne University, Melbourne, Australia
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Melbourne, Australia
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Kujala T, Sihvonen AJ, Thiede A, Palo-Oja P, Virtala P, Numminen J, Laasonen M. Voxel and surface based whole brain analysis shows reading skill associated grey matter abnormalities in dyslexia. Sci Rep 2021; 11:10862. [PMID: 34035329 PMCID: PMC8149879 DOI: 10.1038/s41598-021-89317-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 04/01/2021] [Indexed: 01/18/2023] Open
Abstract
Developmental dyslexia (DD) is the most prevalent neurodevelopmental disorder with a substantial negative influence on the individual's academic achievement and career. Research on its neuroanatomical origins has continued for half a century, yielding, however, inconsistent results, lowered total brain volume being the most consistent finding. We set out to evaluate the grey matter (GM) volume and cortical abnormalities in adult dyslexic individuals, employing a combination of whole-brain voxel- and surface-based morphometry following current recommendations on analysis approaches, coupled with rigorous neuropsychological testing. Whilst controlling for age, sex, total intracranial volume, and performance IQ, we found both decreased GM volume and cortical thickness in the left insula in participants with DD. Moreover, they had decreased GM volume in left superior temporal gyrus, putamen, globus pallidus, and parahippocampal gyrus. Higher GM volumes and cortical thickness in these areas correlated with better reading and phonological skills, deficits of which are pivotal to DD. Crucially, total brain volume did not influence our results, since it did not differ between the groups. Our findings demonstrating abnormalities in brain areas in individuals with DD, which previously were associated with phonological processing, are compatible with the leading hypotheses on the neurocognitive origins of DD.
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Affiliation(s)
- Teija Kujala
- Cognitive Brain Research Unit, Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Haartmaninkatu 3 B, P.O. Box 21, 00014, Helsinki, Finland.
| | - Aleksi J Sihvonen
- Cognitive Brain Research Unit, Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Haartmaninkatu 3 B, P.O. Box 21, 00014, Helsinki, Finland.,Department of Neurosciences, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Anja Thiede
- Cognitive Brain Research Unit, Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Haartmaninkatu 3 B, P.O. Box 21, 00014, Helsinki, Finland
| | - Peter Palo-Oja
- Cognitive Brain Research Unit, Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Haartmaninkatu 3 B, P.O. Box 21, 00014, Helsinki, Finland
| | - Paula Virtala
- Cognitive Brain Research Unit, Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Haartmaninkatu 3 B, P.O. Box 21, 00014, Helsinki, Finland
| | - Jussi Numminen
- Department of Radiology, Töölö Hospital, Helsinki University Central Hospital, Helsinki, Finland
| | - Marja Laasonen
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland.,Department of Phoniatrics, Helsinki University Hospital, Helsinki, Finland.,School of Humanities, Philosophical Faculty, University of Eastern Finland, Joensuu, Finland
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Zhao B, Shan Y, Yang Y, Yu Z, Li T, Wang X, Luo T, Zhu Z, Sullivan P, Zhao H, Li Y, Zhu H. Transcriptome-wide association analysis of brain structures yields insights into pleiotropy with complex neuropsychiatric traits. Nat Commun 2021; 12:2878. [PMID: 34001886 PMCID: PMC8128893 DOI: 10.1038/s41467-021-23130-y] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Accepted: 04/16/2021] [Indexed: 02/03/2023] Open
Abstract
Structural variations of the human brain are heritable and highly polygenic traits, with hundreds of associated genes identified in recent genome-wide association studies (GWAS). Transcriptome-wide association studies (TWAS) can both prioritize these GWAS findings and also identify additional gene-trait associations. Here we perform cross-tissue TWAS analysis of 211 structural neuroimaging and discover 278 associated genes exceeding Bonferroni significance threshold of 1.04 × 10-8. The TWAS-significant genes for brain structures have been linked to a wide range of complex traits in different domains. Through TWAS gene-based polygenic risk scores (PRS) prediction, we find that TWAS PRS gains substantial power in association analysis compared to conventional variant-based GWAS PRS, and up to 6.97% of phenotypic variance (p-value = 7.56 × 10-31) can be explained in independent testing data sets. In conclusion, our study illustrates that TWAS can be a powerful supplement to traditional GWAS in imaging genetics studies for gene discovery-validation, genetic co-architecture analysis, and polygenic risk prediction.
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Affiliation(s)
- Bingxin Zhao
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yue Shan
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yue Yang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Zhaolong Yu
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | - Tengfei Li
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Xifeng Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Tianyou Luo
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ziliang Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Patrick Sullivan
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Hongyu Zhao
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
- Department of Biostatistics, Yale University, New Haven, CT, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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10
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Is there a “g-neuron”? Establishing a systematic link between general intelligence (g) and the von Economo neuron. INTELLIGENCE 2021. [DOI: 10.1016/j.intell.2021.101540] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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11
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Wei W, Zhang Y, Li Y, Meng Y, Li M, Wang Q, Deng W, Ma X, Palaniyappan L, Zhang N, Li T. Depth-dependent abnormal cortical myelination in first-episode treatment-naïve schizophrenia. Hum Brain Mapp 2020; 41:2782-2793. [PMID: 32239735 PMCID: PMC7294057 DOI: 10.1002/hbm.24977] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Revised: 02/14/2020] [Accepted: 02/25/2020] [Indexed: 02/05/2023] Open
Abstract
Myelination is key to effective message passing in the central nervous system and is likely linked to the pathogenesis of schizophrenia (SZ). Emerging evidence indicates that a large portion of intracortical myelin insulates inhibitory interneurons that are highly relevant to pathogenesis of schizophrenia. Here for the first time, we characterized intracortical myelination across the entire cortical surface in first‐episode treatment‐naïve patients with schizophrenia (FES) using T1w/T2w ratio of structural MRI, FES patients exhibited significantly higher myelin content in the left inferior parietal lobe, supramarginal gyrus, and superior temporal gyrus in the superficial layer, as well as left IPL in the middle layer, but significantly lower myelin content in the left middle insula and posterior cingulate gyrus. Years of education, a proxy for onset of functional decline, significantly altered the relationship between abnormal parietal and posterior cingulate myelination and clinical symptoms, indicating that the pathoplastic role of myelination hinges on the age of onset of functional decline. In addition, higher myelination generally related to better cognitive function in younger subjects but worse cognitive function in older subjects. We conclude that FES is characterized by increased myelination of the superficial layers of the parietal–temporal association cortex, but reduced myelination of the cingulo‐insular midcortical layer cortex. Intracortical myelin content affects both cognitive functioning and symptom burden in FES, with the effect conditional upon age and timing of onset of functional decline. These results suggest myelination might be a critical biological target for procognitive interventions in SZ.
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Affiliation(s)
- Wei Wei
- Mental Health Center and Psychiatric Laboratory, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Yamin Zhang
- Mental Health Center and Psychiatric Laboratory, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Yinfei Li
- Mental Health Center and Psychiatric Laboratory, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Yajin Meng
- Mental Health Center and Psychiatric Laboratory, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Mingli Li
- Mental Health Center and Psychiatric Laboratory, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Qiang Wang
- Mental Health Center and Psychiatric Laboratory, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Wei Deng
- Mental Health Center and Psychiatric Laboratory, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Xiaohong Ma
- Mental Health Center and Psychiatric Laboratory, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Lena Palaniyappan
- Robarts Research Institute & Department of Psychiatry, University of Western Ontario, London, Ontario, Canada.,Lawson Health Research Institute, London, Ontario, Canada
| | - Nanyin Zhang
- Department of Biomedical Engineering, The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania, USA
| | - Tao Li
- Mental Health Center and Psychiatric Laboratory, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China
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12
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Zhao B, Luo T, Li T, Li Y, Zhang J, Shan Y, Wang X, Yang L, Zhou F, Zhu Z, Zhu H. Genome-wide association analysis of 19,629 individuals identifies variants influencing regional brain volumes and refines their genetic co-architecture with cognitive and mental health traits. Nat Genet 2019; 51:1637-1644. [PMID: 31676860 PMCID: PMC6858580 DOI: 10.1038/s41588-019-0516-6] [Citation(s) in RCA: 139] [Impact Index Per Article: 27.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2019] [Accepted: 09/23/2019] [Indexed: 12/19/2022]
Abstract
Volumetric variations of the human brain are heritable and are associated with many brain-related complex traits. Here we performed genome-wide association studies (GWAS) of 101 brain volumetric phenotypes using the UK Biobank sample including 19,629 participants. GWAS identified 365 independent genetic variants exceeding a significance threshold of 4.9 × 10-10, adjusted for testing multiple phenotypes. A gene-based association study found 157 associated genes (124 new), and functional gene mapping analysis linked 146 additional genes. Many of the discovered genetic variants and genes have previously been implicated in cognitive and mental health traits. Through genome-wide polygenic-risk-score prediction, more than 6% of the phenotypic variance (P = 3.13 × 10-24) in four other independent studies could be explained by the UK Biobank GWAS results. In conclusion, our study identifies many new genetic associations at the variant, locus and gene levels and advances our understanding of the pleiotropy and genetic co-architecture between brain volumes and other traits.
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Affiliation(s)
- Bingxin Zhao
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Tianyou Luo
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Tengfei Li
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jingwen Zhang
- Department of Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Yue Shan
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Xifeng Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Liuqing Yang
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Fan Zhou
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ziliang Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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13
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Song L, Meng J, Liu Q, Huo T, Zhu X, Li Y, Ren Z, Wang X, Qiu J. Polygenic Score of Subjective Well-Being Is Associated with the Brain Morphology in Superior Temporal Gyrus and Insula. Neuroscience 2019; 414:210-218. [PMID: 31173807 DOI: 10.1016/j.neuroscience.2019.05.055] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 05/07/2019] [Accepted: 05/27/2019] [Indexed: 01/15/2023]
Abstract
Subjective well-being (SWB) is closely related to our physical and mental health. Existing studies show that neural or genetic basis underpins individual difference in SWB. Moreover, researchers have found high enrichment of SWB-related mutations in the central nervous system, but the relationship between the genetic architecture of SWB and brain morphology has not been explored. Considering the polygenic nature of SWB, in this study, we aim to establish a measure of additive genetic effect on SWB and explore its relationship to the brain anatomical structure. Based on the results of genome-wide association study (GWAS) on SWB, the polygenic scores (PGSs) of SWB at eight different thresholds were calculated in a large Chinese sample (N = 585). Then, we analyzed the associations between the PGSs of SWB and cortical thickness (CT) or gray matter volume (GMV) measured from 3.0-T structural imaging data. In whole-brain analyses, we found that a higher PGS was significantly associated with increased CT in the right superior temporal gyrus (STG) and GMV in the right insula, both of which are involved in social cognition and emotional processing. More importantly, these findings were repeatable at some different thresholds. The results may suggest that the brain morphology of right STG and insula is partly regulated by SWB-related genetic factors.
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Affiliation(s)
- Li Song
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China; School of Psychology, Southwest University (SWU), Chongqing 400715, China
| | - Jie Meng
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China; School of Psychology, Southwest University (SWU), Chongqing 400715, China
| | - Qiang Liu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China; School of Psychology, Southwest University (SWU), Chongqing 400715, China
| | - Tengbin Huo
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China; School of Psychology, Southwest University (SWU), Chongqing 400715, China
| | - Xingxing Zhu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China; School of Psychology, Southwest University (SWU), Chongqing 400715, China
| | - Yiman Li
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China; School of Psychology, Southwest University (SWU), Chongqing 400715, China
| | - Zhiting Ren
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China; School of Psychology, Southwest University (SWU), Chongqing 400715, China
| | - Xiao Wang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China; School of Psychology, Southwest University (SWU), Chongqing 400715, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China; School of Psychology, Southwest University (SWU), Chongqing 400715, China; Southwest University Branch, Collaborative Innovation Center of Assessment toward Basic Education Quality, Beijing Normal University, Beijing 100875, China.
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