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Neuroimaging and multiomics reveal cross-scale circuit abnormalities in schizophrenia

Nature Mental Health volume 1, pages 633–654 (2023)Cite this article

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Abstract

Schizophrenia (SCZ) is a highly heterogeneous disorder with diverse clinical manifestations and macro- and microscale biological variations, usually observed at dissociable levels. Here we propose a cross-scale, circuit-based framework to connect heterogeneous clinical symptoms, large-scale brain circuit dysfunctions, and genetic, molecular and cellular abnormalities in SCZ. Using connectomic and predictive models on three independent neuroimaging datasets (n = 1,199, including patients with SCZ and healthy controls), we first identified two macroscale dysconnectivity dimensions for corticocortical and corticostriatal circuits, each associated with specific clinical symptoms. We then associated macroscale dysconnectivity with disrupted cellular circuits using extended imaging transcriptomic and genetic analyses on multiomics data. Our findings suggest a two-dimensional cross-scale heterogeneity model of SCZ, which reveals how distinct genetic disruptions affect specific cellular-level deficits, resulting in system-level brain circuit dysconnectivity responsible for the heterogeneous symptoms in SCZ. These findings significantly improve our understanding of cross-scale heterogeneity in SCZ, advancing its pathophysiology and treatment development.

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Fig. 1: Highly reproducible corticocortical and corticostriatal dysconnectivity patterns in SCZ.
Fig. 2: Corticocortical and corticostriatal dysconnectivity associated with clinical symptoms.
Fig. 3: Cell enrichment of dysconnectivity transcriptional correlates.
Fig. 4: Population-based and person-specific genomic analyses reveal the cell types implicated in the genetic risk of SCZ.
Fig. 5: Cell-type-specific PRS linked to dysconnectivity.
Fig. 6: A hypothetical model linking the macroscale dysconnectivity to microscale cellular circuits and clinical symptoms in SCZ.

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Data availability

The COBRE dataset can be accessed through the COINS data exchange portal112 (https://coins.trendscenter.org/). The UKB data can be requested through a standard protocol (https://www.ukbiobank.ac.uk/register-apply/). The Brainnetome atlas can be downloaded at https://atlas.brainnetome.org/download.html. The Neurosynth database can be downloaded at https://github.com/neurosynth/neurosynth-data. The AHBA is freely available at https://human.brain-map.org/. The Lake68 single-cell data are publicly available at the National Center for Biotechnology Information under the SuperSeries accession code GSE97942. The ABA69 single-cell data are publicly available at https://portal.brain-map.org/atlases-and-data/rnaseq. The SCZ GWAS78 data are publicly available at https://pgc.unc.edu/for-researchers/download-results/. All study data supporting the findings are provided within the paper and in its Supplementary Information. All raw data from the SCZ-I and SCZ-II datasets will be made available upon reasonable request to the corresponding authors.

Code availability

The preprocessing software for resting-state fMRI data is freely available (BRANT114 v3.35, http://brant.brainnetome.org/en/latest/). The SurfStat toolbox for surface-wide statistical comparisons is freely available at https://www.math.mcgill.ca/keith/surfstat/. Corticocortical connectivity (first functional gradient) was calculated based on open-source codes at https://github.com/NeuroanatomyAndConnectivity/gradient_analysis. The toolbox for performing spatial permutation test (spin test) is freely available at https://github.com/spin-test/spin-test. Functional decodings were performed based on the Neurosynth package openly available at https://github.com/neurosynth/neurosynth. The pyGAM package for performing symptom prediction analysis is openly available at https://github.com/dswah/pyGAM. The abagen toolbox for AHBA data processing is freely available at https://github.com/rmarkello/abagen. The pipeline for single-cell data processing is consistent with that performed by ref. 71, and the codes are openly available at https://github.com/kevmanderson/2020_PNAS_Depression. FGSEA was performed based on the fgsea package, which is openly available at https://github.com/ctlab/fgsea. The MAGMA (v 1.08)76 software and reference data are publicly available at https://ctg.cncr.nl/software/magma. The PLINK (v1.07)117 software is freely available at https://www.cog-genomics.org/plink/. GO enrichment analysis was performed using the Metascape137 platform (https://metascape.org/gp/index.html#/main/step1). The LDSC package is available at https://github.com/bulik/ldsc. All custom codes used in the analysis are publicly available at https://github.com/BingLiu-Lab/scz_cross-scale_abnormalities.

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Acknowledgements

This work was supported by the Startup Funds of Beijing Normal University (to B.L.), the National Key Basic Research and Development Program (973) (grant 2011CB707800 to T.J.), the National Key Research and Development Plan (grant 2016YFC0904300 to B.L.), the Natural Science Foundation of China (grant 81771451 to B.L.; grant 82171543 to A.L.), the Science and Technology Innovation 2030—Brain Science and Brain-Inspired Intelligence Project of China (grant 2021ZD0200200 to T.J.).

Author information

Authors and Affiliations

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

    Meng Wang, Xiaohan Tian, Yuqing Sun, Jing Lou & Bing Liu

  2. Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China

    Hao Yan, Weihua Yue, Peng Li, Jun Yan, Lin Lu & Dai Zhang

  3. NHC Key Laboratory of Mental Health (Peking University) and National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China

    Hao Yan, Weihua Yue, Peng Li, Jun Yan, Lin Lu & Dai Zhang

  4. School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China

    Yong Liu

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

    Lingzhong Fan, Ke Hu, Yuxin Zhao, Ming Song & Tianzi Jiang

  6. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China

    Lingzhong Fan, Ke Hu, Yuxin Zhao, Ming Song & Tianzi Jiang

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

    Lingzhong Fan & Tianzi Jiang

  8. Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China

    Jun Chen

  9. Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, Xi’an, China

    Yunchun Chen, Huaning Wang & Wenming Liu

  10. Zhumadian Psychiatric Hospital, Zhumadian, China

    Zhigang Li & Hua Guo

  11. Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China

    Yongfeng Yang, Luxian Lv & Hongxing Zhang

  12. Henan Key Lab of Biological Psychiatry of Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China

    Yongfeng Yang, Luxian Lv & Hongxing Zhang

  13. Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China

    Huiling Wang

  14. Department of Psychology, Xinxiang Medical University, Xinxiang, China

    Hongxing Zhang

  15. The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China

    Huawang Wu & Yuping Ning

  16. Center for Life Sciences/ PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China

    Dai Zhang

  17. State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China

    Ang Li

  18. Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou, China

    Tianzi Jiang

  19. Innovation Academy for Artificial Intelligence, Chinese Academy of Sciences, Beijing, China

    Tianzi Jiang

  20. IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China

    Bing Liu

  21. Chinese Institute for Brain Research, Beijing, China

    Bing Liu

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  1. Meng Wang
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Contributions

B.L. and T.J. led the project. B.L. and M.W. were responsible for the study concept and the design of the study. H.Y. and W.Y. provided crucial advice for the study. A.L. made substantial contributions to the paper and provided critical comments. M.W. and B.L. analyzed the data, created the figures and wrote the paper. Y.L., L.F., K.H., Y.S., Y.Z., J.L., X.T. and M.S. participated in discussions of the results and the paper. P.L., J.C., Y.C., Huaning Wang, W.L., Z.L., Y.Y., H.G., L. Lv, L. Lu, J.Y., Huiling Wang, H.Z., H. Wu, Y.N. and D.Z. contributed to the data acquisition.

Corresponding authors

Correspondence to Ang Li, Tianzi Jiang or Bing Liu.

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Nature Mental Health thanks Marta Bosia, Katharina Schmack and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Supra-threshold permutation test based on cluster size (10,000 iterations, cluster defining threshold (cdt)=0.001).

Surface-based corticocortical and corticostriatal connectivity comparisons (based on a two-sample two-sided t-test) between patients with SCZ and NC, with multiple comparisons corrected using permutation-based cluster thresholding instead of random field theory (see Fig. 2a,c).

Extended Data Fig. 2 Nested 10-fold cross-validation process.

The procedure was applied for model training and selection in the SCZ-I dataset.

Extended Data Fig. 3 Comparisons of functional connectivity between high and low PANSS dimension subgroups.

a. Based on the negative dimension of the PANSS 5-factor model, the top and bottom 30% of SCZ patients (n = 513) from the combined SCZ-I and SCZ-II datasets were selected, forming two subgroups: N-high and N-low. The two subgroups were further screened using a two-sample t-test (two-sided) to ensure no differences in the other four PANSS dimensions. b. Corticocortical connectivity was compared between the N-high and N-low subgroups using the methodology in Fig. 1, resulting in a differential map, termed N-map. The N-map demonstrated a significant positive spatial correlation with the dysconnectivity t-map from the SCZ and NC group comparison (see Fig. 1). Mean connectivity scores from the ten dysconnectivity clusters (Fig. 2a) were significantly higher in the N-high subgroup. c. Corticostriatal connectivity was compared between the N-high and N-low subgroups. The corticostriatal N-map and dysconnectivity t-map displayed a significant positive spatial correlation. The absolute connectivity values from eight clusters (Fig. 2c) significantly decreased in the N-high subgroup. d-f. A similar analysis, like panels a-c, compared functional connectivity between high and low positive subgroups. The corticocortical P-map and dysconnectivity t-map showed no correlation, with no significant differences in connectivity strength (absolute value) of the ten clusters between P-high and P-low subgroups (e). However, a significant positive correlation was found between the corticostriatal P-map and dysconnectivity t-map, while the eight clusters’ connectivity strength significantly decreased in the P-high subgroup (f). g-i. Like panels a-c and d-f, functional connectivity differences were examined between the high and low cognitive subgroups. Significant differences were found in corticocortical connectivity for C-high and C-low subgroups, while corticostriatal connectivity showed no significant differences. Spatial correlations between maps were quantified using two-sided Pearson’s r. The significance (Pspin) was determined using the spin-based permutation test. Connectivity strength differences were measured using the two-sample t-test (two-sided). The error bars represent mean values ± 95% confidence interval. The box plot presents minimum, 25th percentile, median, 75th percentile, and maximum values (excluding outliers) for the combined high and low groups.

Extended Data Fig. 4 Functional decodings of corticocortical and corticostriatal dysconnectivity.

The spatial correlations (two-sided Pearson’s r) between t-maps and 24 predefined topic maps (each is composed of related terms) from the Neurosynth database were calculated. The significance (Pperm) was estimated using permutation tests. The warm or cool color indicates a positive or negative correlation, respectively. Only significant correlations (Pperm<0.05) are shown with colors, and darker colors indicate a higher significance. The face/affective processing map (the label is marked in bold and surrounded by a red box) is significantly, positively, and consistently correlated with corticocortical dysconnectivity t-maps across three datasets (a). There were three cognitive maps (pain, cued attention, and inhibition error) that were significantly, negatively, and consistently correlated with corticostriatal dysconnectivity t-maps across three datasets (b).

Extended Data Fig. 5 Cell enrichment of individual dysconnectivity.

a, c, e, g, The line graphs illustrate individual-level enrichment scores (NESs) of Lake and ABA cells for corticocortical and corticostriatal dysconnectivity. The participants were derived from the SCZ-I, SCZ-II, and COBRE datasets (total SCZ/NC=585/614). The error bars represent mean values ± 95% confidence interval. b, d, f, h, The heatmaps show pairwise comparisons of individual NES scores within the SCZ group (n = 585), using a two-sample t-test (two-sided). Blank squares indicate no significant differences between cells.

Extended Data Fig. 6 Cell enrichment comparisons of dysconnectivity transcriptional correlates between SCZ and MDD.

a, b, Cortical renderings show corticocortical (a) and corticostriatal (b) connectivity differences (t-maps) between patients with MDD and NC in the MDD-XX dataset (MDD, n = 75; NC, n = 74). The MDD dysconnectivity t-maps were obtained by applying the same analytical method as in Fig. 1. The warm or cool color indicates connectivity is increased or decreased in MDD. c. Cell enrichment of MDD dysconnectivity transcriptional correlates was conducted as with SCZ in Fig. 3. MDD corticocortical dysconnectivity transcriptional correlated genes were most significantly positively enriched in In6 and negatively enriched in Ast. The positively and negatively enriched cells with the most significance for MDD corticostriatal dysconnectivity were Ex3 and Ast, respectively. The solid circles with warm or cool colors indicate positive or negative enrichment, determined by NES. Darker colors mean greater NES. The radius is quantified by -log10(adjusted P value) (two-sided, FDR correction). Larger circles mean more significance of enrichment. NS, non-significant. NES, normalized enrichment score.

Extended Data Fig. 7 Gene Ontology enrichment of dysconnectivity transcriptional correlates.

Metascape enrichment networks show inter-cluster and intra-cluster similarities of enriched biological process terms. Each term is characterized by a circle node, where its size represents the significance level of enrichment, and its color denotes cluster identity (nodes with the same color belong to the same cluster). For each kind of dysconnectivity, a sorted gene list (in descending order) was obtained based on averaged spatial correlations between all AHBA genes and dysconnectivity t-maps. The gene list was then split into deciles. Enrichment analyses were separately conducted for the top and bottom gene deciles.

Extended Data Fig. 8 Cell enrichment of SCZ GWAS based on LDSC method.

Vertical bar plots show Lake (a) and ABA (b) cells enrichment of polygenic risk in SCZ from GWAS data. Cell-specific genes were defined using Lake data from the dorsal frontal cortex (DFC) and visual cortex (VIS), and ABA data from the middle temporal gyrus (MTG). The dashed line indicates unadjusted P < 0.05.

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Wang, M., Yan, H., Tian, X. et al. Neuroimaging and multiomics reveal cross-scale circuit abnormalities in schizophrenia. Nat. Mental Health 1, 633–654 (2023). https://doi.org/10.1038/s44220-023-00110-3

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