Abstract
Schizophrenia is a highly heritable mental disorder characterized by functional dysconnectivity across the brain. However, the relationships between polygenic risk factors and connectome-wide neural mechanisms are unclear. Here, combining genetic and multiparadigm fMRI data of 623 healthy Caucasian adults drawn from the Human Connectome Project, we found that higher schizophrenia polygenic risk scores were significantly correlated with lower functional connectivity in a large-scale brain network primarily encompassing the visual system, default-mode system, and frontoparietal system. Such correlation was robustly observed across multiple fMRI paradigms, suggesting a brain-state-independent neural phenotype underlying individual genetic liability to schizophrenia. Moreover, using an independent clinical dataset acquired from the Consortium for Neuropsychiatric Phenomics, we further demonstrated that the connectivity of the identified network was reduced in patients with schizophrenia and significantly correlated with general cognitive ability. These findings provide the first evidence for connectome-wide associations of schizophrenia polygenic risk at the systems level and suggest that disrupted integration of sensori–cognitive information may be a hallmark of genetic effects on the brain that contributes to the pathogenesis of schizophrenia.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 12 print issues and online access
$259.00 per year
only $21.58 per issue
Buy this article
- Purchase on SpringerLink
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout



Similar content being viewed by others
Code availability
All toolboxes used in this study are freely available. PLINK 1.9 is available at https://www.cog-genomics.org/plink2, PRSice-2 is available at https://choishingwan.github.io/PRSice/, and the NBS toolbox is available at https://www.nitrc.org/projects/nbs/.
References
Esslinger C, Walter H, Kirsch P, Erk S, Schnell K, Arnold C, et al. Neural mechanisms of a genome-wide supported psychosis variant. Science. 2009;324:605.
Bigos KL, Mattay VS, Callicott JH, Straub RE, Vakkalanka R, Kolachana B, et al. Genetic variation in CACNA1C affects brain circuitries related to mental illness. Arch Gen Psychiatry. 2010;67:939–45.
Cannon TD, Thompson PM, van Erp TGM, Toga AW, Poutanen VP, Huttunen M, et al. Cortex mapping reveals regionally specific patterns of genetic and disease-specific gray-matter deficits in twins discordant for schizophrenia. Proc Natl Acad Sci USA. 2002;99:3228–33.
Cao H, Bertolino A, Walter H, Schneider M, Schafer A, Taurisano P, et al. Altered functional subnetwork during emotional face processing: a potential intermediate phenotype for schizophrenia. JAMA Psychiatry. 2016;73:598–605.
International Schizophrenia C, Purcell SM, Wray NR, Stone JL, Visscher PM, O’Donovan MC, et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature. 2009;460:748–52.
Wray NR, Lee SH, Mehta D, Vinkhuyzen AAE, Dudbridge F, Middeldorp CM. Research review: polygenic methods and their application to psychiatric traits. J Child Psychol Psychiatry. 2014;55:1068–87.
Kauppi K, Westlye LT, Tesli M, Bettella F, Brandt CL, Mattingsdal M, et al. Polygenic risk for schizophrenia associated with working memory-related prefrontal brain activation in patients with schizophrenia and healthy controls. Schizophr Bull. 2015;41:736–43.
Miller JA, Scult MA, Conley ED, Chen Q, Weinberger DR, Hariri AR. Effects of schizophrenia polygenic risk scores on brain activity and performance during working memory subprocesses in healthy young adults. Schizophr Bull. 2018;44:844–53.
Walton E, Geisler D, Lee PH, Hass J, Turner JA, Liu J, et al. Prefrontal inefficiency is associated with polygenic risk for schizophrenia. Schizophr Bull. 2014;40:1263–71.
Chen Q, Ursini G, Romer AL, Knodt AR, Mezeivtch K, Xiao E, et al. Schizophrenia polygenic risk score predicts mnemonic hippocampal activity. Brain. 2018;141:1218–28.
Lancaster TM, Linden DE, Tansey KE. Polygenic risk of psychosis and ventral striatal activation during reward processing in healthy adolescentspg. JAMA Psychiatry. 2016;73:852–61.
Fornito A, Zalesky A, Pantelis C, Bullmore ET. Schizophrenia, neuroimaging and connectomics. Neuroimage. 2012;62:2296–314.
Dezhina Z, Ranlund S, Kyriakopoulos M, Williams SCR, Dima D. A systematic review of associations between functional MRI activity and polygenic risk for schizophrenia and bipolar disorder. Brain Imaging Behav. 2019;13:862–77.
Van Essen DC, Smith SM, Barch DM, Behrens TE, Yacoub E, Ugurbil K, et al. The WU-Minn human connectome project: an overview. Neuroimage. 2013;80:62–79.
Cao H, Chen OY, Chung Y, Forsyth JK, McEwen SC, Gee DG, et al. Cerebello-thalamo-cortical hyperconnectivity as a state-independent functional neural signature for psychosis prediction and characterization. Nat Commun. 2018;9:3836.
Cao H, Ingvar M, Hultman CM, Cannon T. Evidence for cerebello-thalamo-cortical hyperconnectivity as a heritable trait for schizophrenia. Transl Psychiatry. 2019;9:192.
Poldrack RA, Congdon E, Triplett W, Gorgolewski KJ, Karlsgodt KH, Mumford JA, et al. A phenome-wide examination of neural and cognitive function. Sci Data. 2016;3:160110.
Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015;4:7.
Euesden J, Lewis CM, O’Reilly PF. PRSice: polygenic risk score software. Bioinformatics. 2015;31:1466–8.
Schizophrenia Working Group of the Psychiatric Genomics C. Biological insights from 108 schizophrenia-associated genetic loci. Nature. 2014;511:421–7.
Power JD, Cohen AL, Nelson SM, Wig GS, Barnes KA, Church JA, et al. Functional network organization of the human brain. Neuron. 2011;72:665–78.
Cao H, McEwen SC, Forsyth JK, Gee DG, Bearden CE, Addington J, et al. Toward leveraging human connectomic data in large consortia: generalizability of fMRI-based brain graphs across sites, sessions, and paradigms. Cereb Cortex. 2019;29:1263–79.
Zalesky A, Fornito A, Bullmore ET. Network-based statistic: identifying differences in brain networks. Neuroimage. 2010;53:1197–207.
Cao H, Harneit A, Walter H, Erk S, Braun U, Moessnang C, et al. The 5-HTTLPR polymorphism affects network-based functional connectivity in the visual-limbic system in healthy adults. Neuropsychopharmacology. 2018;43:406–14.
Andreasen NC. Methods for assessing positive and negative symptoms. Schizophrenia: positive and negative symptoms and syndromes. Karger: Basel, Switzerland, 1990, 73–88.
Mendrek A, Mancini-Marie A. Sex/gender differences in the brain and cognition in schizophrenia. Neurosci Biobehav Rev. 2016;67:57–78.
Torniainen M, Suvisaari J, Partonen T, Castaneda AE, Kuha A, Perala J, et al. Sex differences in cognition among persons with schizophrenia and healthy first-degree relatives. Psychiatry Res. 2011;188:7–12.
Cao H, Dixson L, Meyer-Lindenberg A, Tost H. Functional connectivity measures as schizophrenia intermediate phenotypes: advances, limitations, and future directions. Curr Opin Neurobiol. 2016;36:7–14.
Fornito A, Bullmore ET. Connectomic intermediate phenotypes for psychiatric disorders. Front Psychiatry. 2012;3:32.
Rasetti R, Sambataro F, Chen Q, Callicott JH, Mattay VS, Weinberger DR. Altered cortical network dynamics: a potential intermediate phenotype for schizophrenia and association with ZNF804A. Arch Gen Psychiatry. 2011;68:1207–17.
Fornito A, Harrison BJ, Goodby E, Dean A, Ooi C, Nathan PJ, et al. Functional dysconnectivity of corticostriatal circuitry as a risk phenotype for psychosis. JAMA Psychiatry. 2013;70:1143–51.
Rasetti R, Mattay VS, White MG, Sambataro F, Podell JE, Zoltick B, et al. Altered hippocampal-parahippocampal function during stimulus encoding: a potential indicator of genetic liability for schizophrenia. JAMA Psychiatry. 2014;71:236–47.
Liu S, Li A, Liu Y, Yan H, Wang M, Sun Y, et al. Polygenic effects of schizophrenia on hippocampal grey matter volume and hippocampus-medial prefrontal cortex functional connectivity. Br J Psychiatry. 2019: 1–8. https://doi.org/10.1192/bjp.2019.127 [Epub ahead of print].
Wang T, Zhang X, Li A, Zhu M, Liu S, Qin W, et al. Polygenic risk for five psychiatric disorders and cross-disorder and disorder-specific neural connectivity in two independent populations. Neuroimage Clin. 2017;14:441–9.
Lieslehto J, Kiviniemi VJ, Nordstrom T, Barnett JH, Murray GK, Jones PB, et al. Polygenic risk score for schizophrenia and face-processing network in young adulthood. Schizophr Bull. 2019;45:835–45.
Zmigrod L, Garrison JR, Carr J, Simons JS. The neural mechanisms of hallucinations: a quantitative meta-analysis of neuroimaging studies. Neurosci Biobehav Rev. 2016;69:113–23.
Silbersweig DA, Stern E, Frith C, Cahill C, Holmes A, Grootoonk S, et al. A functional neuroanatomy of hallucinations in schizophrenia. Nature. 1995;378:176–9.
Baker JT, Holmes AJ, Masters GA, Yeo BT, Krienen F, Buckner RL, et al. Disruption of cortical association networks in schizophrenia and psychotic bipolar disorder. JAMA Psychiatry. 2014;71:109–18.
Baker JT, Dillon DG, Patrick LM, Roffman JL, Brady RO Jr., Pizzagalli DA, et al. Functional connectomics of affective and psychotic pathology. Proc Natl Acad Sci USA. 2019;116:9050–9.
Whitfield-Gabrieli S, Ford JM. Default mode network activity and connectivity in psychopathology. Annu Rev Clin Psychol. 2012;8:49–76.
Orliac F, Naveau M, Joliot M, Delcroix N, Razafimandimby A, Brazo P, et al. Links among resting-state default-mode network, salience network, and symptomatology in schizophrenia. Schizophr Res. 2013;148:74–80.
Kahn RS, Keefe RS. Schizophrenia is a cognitive illness: time for a change in focus. JAMA Psychiatry. 2013;70:1107–12.
Elvevag B, Goldberg TE. Cognitive impairment in schizophrenia is the core of the disorder. Crit Rev Neurobiol. 2000;14:1–21.
Seidman LJ, Shapiro DI, Stone WS, Woodberry KA, Ronzio A, Cornblatt BA, et al. Association of neurocognition with transition to psychosis: baseline functioning in the second phase of the North American Prodrome Longitudinal Study. JAMA Psychiatry. 2016;73:1239–48.
Toulopoulou T, Picchioni M, Rijsdijk F, Hua-Hall M, Ettinger U, Sham P, et al. Substantial genetic overlap between neurocognition and schizophrenia—genetic modeling in twin samples. Arch Gen Psychiatry. 2007;64:1348–55.
Lencz T, Knowles E, Davies G, Guha S, Liewald DC, Starr JM, et al. Molecular genetic evidence for overlap between general cognitive ability and risk for schizophrenia: a report from the Cognitive Genomics consorTium (COGENT). Mol Psychiatry. 2014;19:168–74.
Hubbard L, Tansey KE, Rai D, Jones P, Ripke S, Chambert KD, et al. Evidence of common genetic overlap between schizophrenia and cognition. Schizophr Bull. 2016;42:832–42.
Halari R, Kumari V, Mehrotra R, Wheeler M, Hines M, Sharma T. The relationship of sex hormones and cortisol with cognitive functioning in Schizophrenia. J Psychopharmacol. 2004;18:366–74.
Hilker R, Helenius D, Fagerlund B, Skytthe A, Christensen K, Werge TM, et al. Heritability of schizophrenia and schizophrenia spectrum based on the Nationwide Danish Twin Register. Biol Psychiatry. 2018;83:492–8.
Lee SH, Ripke S, Neale BM, Faraone SV, Purcell SM, Perlis RH, et al. Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nature Genetics. 2013;45:984–94.
Acknowledgements
This work was supported by the Brain and Behavior Research Foundation NARSAD Young Investigator Grant (No. 27068) to HC, by National Institute of Health (NIH) grants U01 MH081902 to TDC, and by gifts from the Staglin Music Festival for Mental Health and International Mental Health Research Organization to TDC. Authors would like to thank Kevin Anderson (Yale Psychology) for suggestions on genetic data analysis.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
TDC has served as a consultant for Boehringer-Ingelheim Pharmaceuticals and Lundbeck A/S. The other authors report no conflicts of interest.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Rights and permissions
About this article
Cite this article
Cao, H., Zhou, H. & Cannon, T.D. Functional connectome-wide associations of schizophrenia polygenic risk. Mol Psychiatry 26, 2553–2561 (2021). https://doi.org/10.1038/s41380-020-0699-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41380-020-0699-3
This article is cited by
-
Attention-mediated genetic influences on psychotic symptomatology in adolescence
Nature Mental Health (2024)
-
Genetic overlap between multivariate measures of human functional brain connectivity and psychiatric disorders
Nature Mental Health (2024)
-
Complex activity and short-term plasticity of human cerebral organoids reciprocally connected with axons
Nature Communications (2024)
-
Polygenic effects on brain functional endophenotype for deficit and non-deficit schizophrenia
Schizophrenia (2024)
-
Associated transcriptional, brain and clinical variations in schizophrenia
Nature Mental Health (2024)