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Uncovering the genetic profiles underlying the intrinsic organization of the human cerebellum

Molecular Psychiatry volume 27, pages 2619–2634 (2022)Cite this article

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Abstract

The functional diversity of the human cerebellum is largely believed to be derived more from its extensive connections rather than being limited to its mostly invariant architecture. However, whether and how the determination of cerebellar connections in its intrinsic organization interact with microscale gene expression is still unknown. Here we decode the genetic profiles of the cerebellar functional organization by investigating the genetic substrates simultaneously linking cerebellar functional heterogeneity and its drivers, i.e., the connections. We not only identified 443 network-specific genes but also discovered that their co-expression pattern correlated strongly with intra-cerebellar functional connectivity (FC). Ninety of these genes were also linked to the FC of cortico-cerebellar cognitive-limbic networks. To further discover the biological functions of these genes, we performed a “virtual gene knock-out” by observing the change in the coupling between gene co-expression and FC and divided the genes into two subsets, i.e., a positive gene contribution indicator (GCI+) involved in cerebellar neurodevelopment and a negative gene set (GCI−) related to neurotransmission. A more interesting finding is that GCI− is significantly linked with the cerebellar connectivity-behavior association and many recognized brain diseases that are closely linked with the cerebellar functional abnormalities. Our results could collectively help to rethink the genetic substrates underlying the cerebellar functional organization and offer possible micro-macro interacted mechanistic interpretations of the cerebellum-involved high order functions and dysfunctions in neuropsychiatric disorders.

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Fig. 1: Analysis pipeline.
Fig. 2: Network-specific gene co-expression correlates with functional connectivity (FC) within the cerebellum.
Fig. 3: Cortical genetic and functional correlation of cerebellar limbic and control networks seeds.
Fig. 4: The Behavior-FC-Gene mapping analysis.
Fig. 5: The gene ontology (GO) and disease enrichment analysis for GCI+ and GCI−.
Fig. 6: Integrative temporal specificity analysis within the human cerebellum for GCI+ and GCI−.

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Data availability

R 3.6.1 and custom scripts were used to perform statistical analysis. All R packages were mentioned explicitly in the text where the package was used. The code is freely available at https://github.com/FANLabCASIA/CerebellarGeneFCCorrelation. The ToppGene (https://toppgene.cchmc.org) and CSEA tool (http://genetics.wustl.edu/jdlab/csea-tool-2/) which used to do the functional annotation of genes were all freely accessible. All data needed to evaluate the conclusions in this study are present in the article and the Supplementary Materials.

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Acknowledgements

This work was partially supported by the Science and Technology Innovation 2030—Brain Science and Brain-Inspired Intelligence Project (Grant No. 2021ZD0200203), the Natural Science Foundation of China (Grant Nos. 82072099 and 91432302), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB32030214), the Youth Innovation Promotion Association, the Beijing Advanced Discipline Fund and the Major Scientific Project of Zhejiang Lab (Grant No. 2021ND0PI01). Data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen, and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. The authors appreciate the English language and editing assistance of Rhoda E. and Edmund F. Perozzi, PhDs.

Author information

Author notes

  1. These authors contributed equally: Yaping Wang, Lin Chai.

Authors and Affiliations

  1. Sino-Danish Center, University of Chinese Academy of Sciences, 100190, Beijing, China

    Yaping Wang, Chaohong Gao, Jens Randel Nyengaard, Kristoffer Hougaard Madsen, Tianzi Jiang & Lingzhong Fan

  2. University of Chinese Academy of Sciences, 100190, Beijing, China

    Yaping Wang, Lin Chai, Congying Chu, Deying Li, Chaohong Gao, Xia Wu, Zhengyi Yang, Tianzi Jiang & Lingzhong Fan

  3. Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China

    Yaping Wang, Lin Chai, Congying Chu, Deying Li, Chaohong Gao, Xia Wu, Zhengyi Yang, Tianzi Jiang & Lingzhong Fan

  4. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China

    Yaping Wang, Lin Chai, Congying Chu, Deying Li, Chaohong Gao, Xia Wu, Zhengyi Yang, Tianzi Jiang & Lingzhong Fan

  5. Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, 311100, China

    Yu Zhang

  6. School of Computer Science and Technology, Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, 300350, China

    Junhai Xu

  7. Core Centre for Molecular Morphology, Section for Stereology and Microscopy, Department of Clinical Medicine, Aarhus University, 8000, Aarhus, Denmark

    Jens Randel Nyengaard

  8. Department of Pathology, Aarhus University Hospital, 8200, Aarhus, Denmark

    Jens Randel Nyengaard

  9. Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425, Jülich, Germany

    Simon B. Eickhoff

  10. Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany

    Simon B. Eickhoff

  11. State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, 100875, Beijing, China

    Bing Liu

  12. Department of Informatics and Mathematical Modelling, Technical University of Denmark, 2800, Kongens Lyngby, Denmark

    Kristoffer Hougaard Madsen

  13. Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital-Amager and Hvidovre, 2650, Hvidovre, Denmark

    Kristoffer Hougaard Madsen

  14. CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China

    Tianzi Jiang & Lingzhong Fan

Authors
  1. Yaping Wang
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  2. Lin Chai
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  3. Congying Chu
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  4. Deying Li
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  5. Chaohong Gao
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  6. Xia Wu
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  7. Zhengyi Yang
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  8. Yu Zhang
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  9. Junhai Xu
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  10. Jens Randel Nyengaard
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  11. Simon B. Eickhoff
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  12. Bing Liu
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  13. Kristoffer Hougaard Madsen
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  14. Tianzi Jiang
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  15. Lingzhong Fan
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Contributions

YW and LF designed the research; YW and LC performed the experiments; YW, LF, CC, TJ, BL, and ZY contributed new analytic tools; YW, LC, DL, XW, and CG analyzed data; YW and LF wrote the paper; and LF, CC, KHM, JRN, YZ, ZY, JX, SBE, BL, and TJ contributed analytic expertise, theoretical guidance, paper revisions, and informed interpretation of the results; and LF and CC supervised the project.

Corresponding authors

Correspondence to Congying Chu or Lingzhong Fan.

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The authors declare no competing interests.

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Wang, Y., Chai, L., Chu, C. et al. Uncovering the genetic profiles underlying the intrinsic organization of the human cerebellum. Mol Psychiatry 27, 2619–2634 (2022). https://doi.org/10.1038/s41380-022-01489-8

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  • DOI: https://doi.org/10.1038/s41380-022-01489-8

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