Shu Liu1,2, Ang Li2,3, Yong Liu2,4, Hao Yan5,6, Meng Wang2,3, Yuqing Sun2,3, Lingzhong Fan2,4, Ming Song2,7, Kaibin Xu2,3, Jun Chen8, Yunchun Chen9, Huaning Wang9, Hua Guo10, Ping Wan10, Luxian Lv11,12, Yongfeng Yang12,13, Peng Li6,5, Lin Lu6,14, Jun Yan6,14, Huiling Wang15, Hongxing Zhang12,11, Huawang Wu16, Yuping Ning17, Dai Zhang6,14, Tianzi Jiang2,4, Bing Liu2,4. 1. MSc Student, Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences. 2. School of Artificial Intelligence, University of Chinese Academy of Sciences, China. 3. PhD Student, Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences. 4. Professor, Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences. 5. Associate Professor, Peking University Sixth Hospital, Institute of Mental Health. 6. Key Laboratory of Mental Health, Ministry of Health (Peking University), China. 7. Associate Professor, Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences. 8. Associate Professor, Department of Radiology, Renmin Hospital of Wuhan University, China. 9. Associate Professor, Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, China. 10. Professor, Zhumadian Psychiatric Hospital, China. 11. Professor, Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University. 12. Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, China. 13. Attending Doctor, Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University. 14. Professor, Peking University Sixth Hospital, Institute of Mental Health. 15. Professor, Department of Radiology, Renmin Hospital of Wuhan University, China. 16. Attending Doctor, Guangzhou Brain Hospital, The Affiliated Brain Hospital of Guangzhou Medical University, China. 17. Professor, Guangzhou Brain Hospital, The Affiliated Brain Hospital of Guangzhou Medical University, China.
Abstract
BACKGROUND: Schizophrenia is a complex mental disorder with high heritability and polygenic inheritance. Multimodal neuroimaging studies have also indicated that abnormalities of brain structure and function are a plausible neurobiological characterisation of schizophrenia. However, the polygenic effects of schizophrenia on these imaging endophenotypes have not yet been fully elucidated. AIMS: To investigate the effects of polygenic risk for schizophrenia on the brain grey matter volume and functional connectivity, which are disrupted in schizophrenia. METHOD: Genomic and neuroimaging data from a large sample of Han Chinese patients with schizophrenia (N = 509) and healthy controls (N = 502) were included in this study. We examined grey matter volume and functional connectivity via structural and functional magnetic resonance imaging, respectively. Using the data from a recent meta-analysis of a genome-wide association study that comprised a large number of Chinese people, we calculated a polygenic risk score (PGRS) for each participant. RESULTS: The imaging genetic analysis revealed that the individual PGRS showed a significantly negative correlation with the hippocampal grey matter volume and hippocampus-medial prefrontal cortex functional connectivity, both of which were lower in the people with schizophrenia than in the controls. We also found that the observed neuroimaging measures showed weak but similar changes in unaffected first-degree relatives of patients with schizophrenia. CONCLUSIONS: These findings suggested that genetically influenced brain grey matter volume and functional connectivity may provide important clues for understanding the pathological mechanisms of schizophrenia and for the early diagnosis of schizophrenia.
BACKGROUND:Schizophrenia is a complex mental disorder with high heritability and polygenic inheritance. Multimodal neuroimaging studies have also indicated that abnormalities of brain structure and function are a plausible neurobiological characterisation of schizophrenia. However, the polygenic effects of schizophrenia on these imaging endophenotypes have not yet been fully elucidated. AIMS: To investigate the effects of polygenic risk for schizophrenia on the brain grey matter volume and functional connectivity, which are disrupted in schizophrenia. METHOD: Genomic and neuroimaging data from a large sample of Han Chinese patients with schizophrenia (N = 509) and healthy controls (N = 502) were included in this study. We examined grey matter volume and functional connectivity via structural and functional magnetic resonance imaging, respectively. Using the data from a recent meta-analysis of a genome-wide association study that comprised a large number of Chinese people, we calculated a polygenic risk score (PGRS) for each participant. RESULTS: The imaging genetic analysis revealed that the individual PGRS showed a significantly negative correlation with the hippocampal grey matter volume and hippocampus-medial prefrontal cortex functional connectivity, both of which were lower in the people with schizophrenia than in the controls. We also found that the observed neuroimaging measures showed weak but similar changes in unaffected first-degree relatives of patients with schizophrenia. CONCLUSIONS: These findings suggested that genetically influenced brain grey matter volume and functional connectivity may provide important clues for understanding the pathological mechanisms of schizophrenia and for the early diagnosis of schizophrenia.
Authors: Benson S Ku; Katrina Aberizk; Jean Addington; Carrie E Bearden; Kristin S Cadenhead; Tyrone D Cannon; Ricardo E Carrión; Michael T Compton; Barbara A Cornblatt; Benjamin G Druss; Daniel H Mathalon; Diana O Perkins; Ming T Tsuang; Scott W Woods; Elaine F Walker Journal: Schizophr Bull Date: 2022-09-01 Impact factor: 7.348