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Poortman SR, Barendse ME, Setiaman N, van den Heuvel MP, de Lange SC, Hillegers MH, van Haren NE. Age Trajectories of the Structural Connectome in Child and Adolescent Offspring of Individuals With Bipolar Disorder or Schizophrenia. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2024; 4:100336. [PMID: 39040431 PMCID: PMC11260845 DOI: 10.1016/j.bpsgos.2024.100336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 04/08/2024] [Accepted: 05/09/2024] [Indexed: 07/24/2024] Open
Abstract
Background Offspring of parents with severe mental illness (e.g., bipolar disorder or schizophrenia) are at elevated risk of developing psychiatric illness owing to both genetic predisposition and increased burden of environmental stress. Emerging evidence indicates a disruption of brain network connectivity in young offspring of patients with bipolar disorder and schizophrenia, but the age trajectories of these brain networks in this high-familial-risk population remain to be elucidated. Methods A total of 271 T1-weighted and diffusion-weighted scans were obtained from 174 offspring of at least 1 parent diagnosed with bipolar disorder (n = 74) or schizophrenia (n = 51) and offspring of parents without severe mental illness (n = 49). The age range was 8 to 23 years; 97 offspring underwent 2 scans. Anatomical brain networks were reconstructed into structural connectivity matrices. Network analysis was performed to investigate anatomical brain connectivity. Results Offspring of parents with schizophrenia had differential trajectories of connectivity strength and clustering compared with offspring of parents with bipolar disorder and parents without severe mental illness, of global efficiency compared with offspring of parents without severe mental illness, and of local connectivity compared with offspring of parents with bipolar disorder. Conclusions The findings of this study suggest that familial high risk of schizophrenia is related to deviations in age trajectories of global structural connectome properties and local connectivity strength.
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Affiliation(s)
- Simon R. Poortman
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center, Sophia Children’s Hospital, Rotterdam, the Netherlands
| | - Marjolein E.A. Barendse
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center, Sophia Children’s Hospital, Rotterdam, the Netherlands
| | - Nikita Setiaman
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center, Sophia Children’s Hospital, Rotterdam, the Netherlands
- Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht, the Netherlands
| | - Martijn P. van den Heuvel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Department of Child Psychiatry, Amsterdam University Medical Center, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Siemon C. de Lange
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Department of Sleep and Cognition, Netherlands Institute for Neuroscience, an institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, the Netherlands
| | - Manon H.J. Hillegers
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center, Sophia Children’s Hospital, Rotterdam, the Netherlands
- Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht, the Netherlands
| | - Neeltje E.M. van Haren
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center, Sophia Children’s Hospital, Rotterdam, the Netherlands
- Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht, the Netherlands
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Li D, Mao M, Zhang X, Hou D, Zhang S, Hao J, Cui X, Niu Y, Xiang J, Wang B. Gender effects on the controllability of hemispheric white matter networks. Cereb Cortex 2023; 33:1643-1658. [PMID: 35483707 DOI: 10.1093/cercor/bhac162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 04/03/2022] [Accepted: 04/04/2022] [Indexed: 11/13/2022] Open
Abstract
Male and female adults exhibited significant group differences in brain white matter (WM) asymmetry and WM network controllability. However, gender differences in controllability of hemispheric WM networks between males and females remain to be determined. Based on 1 principal atlas and 1 replication atlas, this work characterized the average controllability (AC) and modal controllability (MC) of hemispheric WM network based on 1 principal dataset and 2 replication datasets. All results showed that males had higher AC of left hemispheric networks than females. And significant hemispheric asymmetry was revealed in regional AC and MC. Furthermore, significant gender differences in the AC asymmetry were mainly found in regions lie in the frontoparietal network, and the MC asymmetry was found in regions involving auditory and emotion process. Finally, we found significant associations between regional controllability and cognitive features. Taken together, this work could provide a novel perspective for understanding gender differences in hemispheric WM asymmetry and cognitive function between males and females.
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Affiliation(s)
- Dandan Li
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Min Mao
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Xi Zhang
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Dianni Hou
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Shanshan Zhang
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Jiangping Hao
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Xiaohong Cui
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Yan Niu
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Jie Xiang
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Bin Wang
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
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