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Morphometric dis-similarity between cortical and subcortical areas underlies cognitive function and psychiatric symptomatology: a preadolescence study from ABCD

Molecular Psychiatry volume 28, pages 1146–1158 (2023)Cite this article

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Abstract

Preadolescence is a critical period characterized by dramatic morphological changes and accelerated cortico-subcortical development. Moreover, the coordinated development of cortical and subcortical regions underlies the emerging cognitive functions during this period. Deviations in this maturational coordination may underlie various psychiatric disorders that begin during preadolescence, but to date these deviations remain largely uncharted. We constructed a comprehensive whole-brain morphometric similarity network (MSN) from 17 neuroimaging modalities in a large preadolescence sample (N = 8908) from Adolescent Brain Cognitive Development (ABCD) study and investigated its association with 10 cognitive subscales and 27 psychiatric subscales or diagnoses. Based on the MSNs, each brain was clustered into five modules with distinct cytoarchitecture and evolutionary relevance. While morphometric correlation was positive within modules, it was negative between modules, especially between isocortical and paralimbic/subcortical modules; this developmental dissimilarity was genetically linked to synapse and neurogenesis. The cortico-subcortical dissimilarity becomes more pronounced longitudinally in healthy children, reflecting developmental differentiation of segregated cytoarchitectonic areas. Higher cortico-subcortical dissimilarity (between the isocortical and paralimbic/subcortical modules) were related to better cognitive performance. In comparison, children with poor modular differentiation between cortex and subcortex displayed higher burden of externalizing and internalizing symptoms. These results highlighted cortical-subcortical morphometric dissimilarity as a dynamic maturational marker of cognitive and psychiatric status during the preadolescent stage and provided insights into brain development.

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Fig. 1: Overall flowchart of MSN construction and analysis.
Fig. 2: Modular structure of morphometric similarity network and its consistency with cytoarchitecture and functional network.
Fig. 3: Morphometric similarity network (MSN) and morphometric co-developmental network (MCDN).
Fig. 4: Correlation between baseline module-level MSN and cognition (NIH toolbox) and psychiatric phenotypes (CBCL scores and KSADS diagnosis).
Fig. 5: Principal Component Analysis (PCA) results of all t-value maps obtained from the association analyses between MSN and cognitive/psychiatric phenotypes.
Fig. 6: Enrichment results of genes associated with morphometric similarity.

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Data availability

GWAS summary statistics of MSN could be downloaded on (https://drive.google.com/drive/folders/1cOfYZe60PAI2JjlEhKL3N-qcJCGtmzUv?usp=sharing).

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Acknowledgements

JZ was supported by Science and Technology Innovation 2030 - Brain Science and Brain-Inspired Intelligence Project (Grant No. 2021ZD0200204), Shanghai Municipal Science and Technology Major Project (No.2018SHZDZX01) and ZJLab, NSFC 61973086, and Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China. KZ was supported by National Natural Science Foundation 62276099, and Shanghai Pujiang Program. LP acknowledges research support from the Tanna Schulich Chair of Neuroscience and Mental Health (Schulich School of Medicine, Western University: 2019 – 2022); Monique H. Bourgeois Chair in Developmental Disorders and Graham Boeckh Foundation (Douglas Research Centre, McGill University) and a salary award from the Fonds de recherche du Quebec-Sante ́ (FRQS). JF was supported by the 111 Project (No. B18015), the key project of Shanghai Science and Technology (No. 16JC1420402), National Key R&D Program of China (No. 2018YFC1312900), National Natural Science Foundation of China (NSFC 91630314). Xiang-Zhen Kong is supported by the National Natural Science Foundation of China (32171031), the Fundamental Research Funds for the Central Universities (2021XZZX006), and Information Technology Center of Zhejiang University.

Author information

Author notes

  1. These authors contributed equally: Xinran Wu, Lena Palaniyappan, Gechang Yu, Kai Zhang.

Authors and Affiliations

  1. Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China

    Xinran Wu, Jianfeng Feng, Trevor W. Robbins & Jie Zhang

  2. Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China

    Xinran Wu, Jianfeng Feng & Jie Zhang

  3. Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Quebec, QC, Canada

    Lena Palaniyappan

  4. Robarts Research Institute, University of Western Ontario, London, ON, Canada

    Lena Palaniyappan

  5. Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, 999077, Hong Kong SAR, China

    Gechang Yu

  6. School of Computer Science and Technology, East China Normal University, 200062, Shanghai, China

    Kai Zhang

  7. Department of Child and Adolescent Psychiatry and Behavioral Science, Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Jakob Seidlitz

  8. Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA

    Jakob Seidlitz

  9. Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA

    Zhaowen Liu

  10. Department of Psychology and Behavioral Sciences, Zhejiang University, Zhejiang, China

    Xiangzhen Kong

  11. The Centre for Population Neuroscience and Stratified Medicine (PONS), ISTBI, Fudan University, Shanghai, China

    Gunter Schumann

  12. PONS Centre and SGDP Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK

    Gunter Schumann

  13. PONS Centre, Charite Mental Health, Dept. of Psychiatry and Psychotherapie, CCM, Charite Universitaetsmedizin Berlin, Berlin, Germany

    Gunter Schumann

  14. Shanghai Center for Mathematical Sciences, Shanghai, 200433, China

    Jianfeng Feng

  15. Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, 200433, China

    Jianfeng Feng

  16. Department of Psychiatry, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK

    Barbara J. Sahakian

  17. Cambridge shire and Peterborough NHS Trust, Elizabeth House, Fulbourn Hospital, Cambridge, UK

    Trevor W. Robbins

  18. Department of Psychiatry and Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK

    Edward Bullmore

  19. Department of Psychology and Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK

    Edward Bullmore

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  1. Xinran Wu
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  2. Lena Palaniyappan
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  3. Gechang Yu
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  4. Kai Zhang
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  5. Jakob Seidlitz
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  6. Zhaowen Liu
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  7. Xiangzhen Kong
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  8. Gunter Schumann
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  9. Jianfeng Feng
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  11. Trevor W. Robbins
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  12. Edward Bullmore
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  13. Jie Zhang
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Contributions

XW and JZ contributed to the conception of the study; XW, GY and KZ performed the data analysis; XW, JZ and LP wrote the manuscript; LP, KZ, ZL helped perform the analysis with constructive discussions; LP, KZ, JS, ZL, GS, JF, BS, TR, EB helped with the revision of the article.

Corresponding author

Correspondence to Jie Zhang.

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Wu, X., Palaniyappan, L., Yu, G. et al. Morphometric dis-similarity between cortical and subcortical areas underlies cognitive function and psychiatric symptomatology: a preadolescence study from ABCD. Mol Psychiatry 28, 1146–1158 (2023). https://doi.org/10.1038/s41380-022-01896-x

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