Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Functional connectome organization predicts conversion to psychosis in clinical high-risk youth from the SHARP program

Molecular Psychiatry volume 25, pages 2431–2440 (2020)Cite this article

Subjects

Abstract

The emergence of prodromal symptoms of schizophrenia and their evolution into overt psychosis may stem from an aberrant functional reorganization of the brain during adolescence. To examine whether abnormalities in connectome organization precede psychosis onset, we performed a functional connectome analysis in a large cohort of medication-naive youth at risk for psychosis from the Shanghai At Risk for Psychosis (SHARP) study. The SHARP program is a longitudinal study of adolescents and young adults at Clinical High Risk (CHR) for psychosis, conducted at the Shanghai Mental Health Center in collaboration with neuroimaging laboratories at Harvard and MIT. Our study involved a total of 251 subjects, including 158 CHRs and 93 age-, sex-, and education-matched healthy controls. During 1-year follow-up, 23 CHRs developed psychosis. CHRs who would go on to develop psychosis were found to show abnormal modular connectome organization at baseline, while CHR non-converters did not. In all CHRs, abnormal modular connectome organization at baseline was associated with a threefold conversion rate. A region-specific analysis showed that brain regions implicated in early-course schizophrenia, including superior temporal gyrus and anterior cingulate cortex, were most abnormal in terms of modular assignment. Our results show that functional changes in brain network organization precede the onset of psychosis and may drive psychosis development in at-risk youth.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Liu CH, Keshavan MS, Tronick E, Seidman LJ. Perinatal risks and childhood premorbid indicators of later psychosis: next steps for early psychosocial interventions. Schizophr Bull. 2015;41:801–16.

    PubMed  PubMed Central  Google Scholar 

  2. Keshavan MS, Delisi LE, Seidman LJ. Early and broadly defined psychosis risk mental states. Schizophr Res. 2011;126:1–10.

    PubMed  Google Scholar 

  3. Fusar-poli P, Bonoldi I, Yung AR, Borgwardt S, Kempton MJ, Valmaggia L, et al. Predicting psychosis: meta-analysis of transition outcomes in individuals at high clinical risk. JAMA Psychiatry. 2012;69:220–9.

    Google Scholar 

  4. Yung AR, McGorry PD. The prodromal phase of first-episode psychosis: past and current conceptualizations. Schizophr Bull. 1996;22:353–70.

    CAS  PubMed  Google Scholar 

  5. Tandon R, Nasrallah HA, Keshavan MS. Schizophrenia, ‘just the facts’ 4. Clinical features and conceptualization. Schizophr Res. 2009;110:1–23.

    PubMed  Google Scholar 

  6. Insel TR. Rethinking schizophrenia. Nature. 2010;468:187–93.

    CAS  PubMed  Google Scholar 

  7. Simon AE, Borgwardt S, Riecher-Rössler A, Velthorst E, de Haan L, Fusar-Poli P. Moving beyond transition outcomes: meta-analysis of remission rates in individuals at high clinical risk for psychosis. Psychiatry Res. 2013;209:266–72.

    PubMed  Google Scholar 

  8. Schlosser DA, Jacobson S, Chen Q, Sugar CA, Niendam TA, Li G, et al. Recovery from an at-risk state: clinical and functional outcomes of putatively prodromal youth who do not develop psychosis. Schizophr Bull. 2012;38:1225–33.

    PubMed  Google Scholar 

  9. Wang C, Lee J, Ho NF, Lim JKW, Poh JS, Rekhi G, et al. Large-scale network topology reveals heterogeneity in individuals with at risk mental state for psychosis: findings from the Longitudinal Youth-at-Risk Study. Cereb Cortex. 2017;1–10. https://doi.org/10.1093/cercor/bhx278

  10. Anticevic A, Haut K, Murray JD, Repovs G, Yang GJ, Diehl C, et al. Association of thalamic dysconnectivity and conversion to psychosis in youth and young adults at elevated clinical risk. JAMA Psychiatry. 2015;72:882–91.

    PubMed  PubMed Central  Google Scholar 

  11. Gu S, Satterthwaite TD, Medaglia JD, Yang M, Gur RE, Gur RC, et al. Emergence of system roles in normative neurodevelopment. Proc Natl Acad Sci USA. 2015;112:13681–6.

    CAS  PubMed  Google Scholar 

  12. Meunier D, Lambiotte R, Bullmore ET. Modular and hierarchically modular organization of brain networks. Front Neurosci. 2010;4:200.

    PubMed  PubMed Central  Google Scholar 

  13. Sporns O, Betzel RF. Modular brain networks. Annu Rev Psychol. 2016;67:613–40.

    PubMed  Google Scholar 

  14. Fair DA, Cohen AL, Power JD, Dosenbach NUF, Church JA, Miezin FM, et al. Functional brain networks develop from a ‘local to distributed’ organization. PLoS Comput Biol. 2009;5:e1000381.

    PubMed  PubMed Central  Google Scholar 

  15. Satterthwaite TD, Wolf DH, Ruparel K, Erus G, Elliott MA, Eickhoff SB, et al. Heterogeneous impact of motion on fundamental patterns of developmental changes in functional connectivity during youth. Neuroimage. 2013;83:45–57.

    PubMed  Google Scholar 

  16. Collin G, Keshavan MS. Connectome development and a novel extension to the neurodevelopmental model of schizophrenia. Dialog Clin Neurosci. 2018;20:101–10.

    Google Scholar 

  17. Sporns O. Contributions and challenges for network models in cognitive neuroscience. Nat Neurosci. 2014;17:652–60.

    CAS  PubMed  Google Scholar 

  18. Zhang T, Li H, Tang Y, Niznikiewicz MA, Shenton ME, Keshavan MS, et al. Validating the predictive accuracy of the NAPLS-2 psychosis risk calculator in a clinical high-risk sample from the SHARP (Shanghai At Risk for Psychosis) Program. Am J Psychiatry. 2018;175:906–8.

    PubMed  Google Scholar 

  19. Zheng L, Wang J, Zhang T, Li H, Li C, Jiang K. The Chinese version of the SIPS/SOPS: a pilot study of reliability and validity. Chin Ment Health J. 2012;26:571–6.

    Google Scholar 

  20. Miller TJ, McGlashan TH, Rosen JL, Cadenhead K, Ventura J, Mcfarlane W, et al. Prodromal assessment with the structured interview for prodromal syndromes and the scale of prodromal symptoms: predictive validity, interrater reliability, and training to reliability. Schizophr Bull. 2003;29:703–15.

    PubMed  Google Scholar 

  21. Wechsler D. WASI manual. San Antonio, TX: Psychological Corporation, Harcourt Brace; 1999.

    Google Scholar 

  22. McGlashan T, Walsh B, Woods S. The psychosis-risk syndrome: handbook for diagnosis and follow-up. New York: Oxford University Press; 2010.

    Google Scholar 

  23. Fornito A, Zalesky A, Breakspear M. Graph analysis of the human connectome: promise, progress, and pitfalls. Neuroimage. 2013;80:426–44.

    PubMed  Google Scholar 

  24. Sohn WS, Yoo K, Lee YB, Seo SW, Na DL, Jeong Y. Influence of ROI selection on resting functional connectivity: an individualized approach for resting fMRI analysis. Front Neurosci. 2015;9:1–10.

    Google Scholar 

  25. Fischl B. FreeSurfer. Neuroimage. 2012;62:774–81.

    PubMed  PubMed Central  Google Scholar 

  26. Whitfield-Gabrieli S, Nieto-Castanon A. Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain. 2012;2:125–41.

    Google Scholar 

  27. Makris N, Meyer JW, Bates JF, Yeterian EH, Kennedy DN, Caviness VS. MRI-based topographic parcellation of human cerebral white matter and nuclei. Neuroimage. 1999;9:18–45.

    CAS  PubMed  Google Scholar 

  28. Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E. Fast unfolding of communities in large networks. J Stat Mech. 2008;P10008. https://doi.org/10.1088/1742-5468/2008/10/P10008

  29. Rubinov M, Sporns O. Weight-conserving characterization of complex functional brain networks. Neuroimage. 2011;56:2068–79.

    PubMed  Google Scholar 

  30. Good BH, de Montjoye YA, Clauset A. Performance of modularity maximization in practical contexts. Phys Rev E. 2010;81:46106.

    Google Scholar 

  31. Doron KW, Bassett DS, Gazzaniga MS. Dynamic network structure of interhemispheric coordination. Proc Natl Acad Sci USA. 2012;109:18661–8.

    CAS  PubMed  Google Scholar 

  32. Traud AL, Kelsic ED, Mucha PJ, Porter MA. Comparing community structure to characteristics in online collegiate social networks. SIAM Rev. 2008;53:526–43.

    Google Scholar 

  33. Bordier C, Nicolini C, Forcellini G, Bifone A. Disrupted modular organization of primary sensory brain areas in schizophrenia. Neuroimage Clin. 2018;18:682–93.

    PubMed  PubMed Central  Google Scholar 

  34. Lerman-Sinkoff DB, Barch DM. Network community structure alterations in adult schizophrenia: identification and localization of alterations. Neuroimage Clin. 2016;10:96–106.

    PubMed  Google Scholar 

  35. Hoffman R, Dobschka S. Cortical pruning and the development of schizophrenia: a computer model. Schizophr Bull. 1989;15:477–90.

    CAS  PubMed  Google Scholar 

  36. Hoffman R, McGlashan T. Parallel distributed processing and the emergence of schizophrenic symptoms. Schizophr Bull. 1993;19:119–40.

    CAS  PubMed  Google Scholar 

  37. Hoffman RE, McGlashan TH. Synaptic elimination, neurodevelopment, and the mechanism of hallucinated ‘voices’ in schizophrenia. Am J Psychiatry. 1997;154:1683–9.

    CAS  PubMed  Google Scholar 

  38. David AS. Dysmodularity: a neurocognitive model for schizophrenia. Schizophr Bull. 1994;20:249–55.

    CAS  PubMed  Google Scholar 

  39. Yu Q, Plis SM, Erhardt EB, Allen EA, Sui J, Kiehl KA, et al. Modular organization of functional network connectivity in healthy controls and patients with schizophrenia during the resting state. Front Syst Neurosci. 2012;5:1–16.

    Google Scholar 

  40. Van den Heuvel MP, Sporns O, Collin G, Scheewe T, Mandl RCW, Cahn W, et al. Abnormal rich club organization and functional brain dynamics in schizophrenia. JAMA Psychiatry. 2013;70:783–92.

    PubMed  Google Scholar 

  41. Schmidt A, Crossley NA, Harrisberger F, Smieskova R, Lenz C, Riecher-Rössler A, et al. Structural network disorganization in subjects at clinical high risk for psychosis. Schizophr Bull. 2017;43:583–91.

    PubMed  Google Scholar 

  42. Alexander-Bloch AF, Gogtay N, Meunier D, Birn R, Clasen L, Lalonde F, et al. Disrupted modularity and local connectivity of brain functional networks in childhood-onset schizophrenia. Front Syst Neurosci. 2010;4:147.

    PubMed  PubMed Central  Google Scholar 

  43. Supekar K, Musen M, Menon V. Development of large-scale functional brain networks in children. PLoS Biol. 2009;7:e1000157.

    PubMed  PubMed Central  Google Scholar 

  44. Casey B, Jones R, Somerville L. Braking and accelerating of the adolescent brain. J Res Adolesc. 2011;21:21–33.

    PubMed  PubMed Central  Google Scholar 

  45. Skudlarski P, Jagannathan K, Anderson K, Stevens MC, Calhoun VD, Skudlarska BA, et al. Brain connectivity is not only lower but different in schizophrenia: a combined anatomical and functional approach. Biol Psychiatry. 2010;68:61–9.

    PubMed  PubMed Central  Google Scholar 

  46. Pettersson-Yeo W, Allen P, Benetti S, McGuire P, Mechelli A. Dysconnectivity in schizophrenia: where are we now? Neurosci Biobehav Rev. 2011;35:1110–24.

    PubMed  Google Scholar 

  47. Zalesky A, Cocchi L, Fornito A, Murray MM, Bullmore E. Connectivity differences in brain networks. Neuroimage. 2012;60:1055–62.

    PubMed  Google Scholar 

  48. Honea R, Sc B, Crow TJ, Ph D, Passingham D, Ph D, et al. Regional deficits in brain volume in schizophrenia: a meta-analysis of voxel-based morphometry studies. Am J Psychiatry. 2005;162:2233–45.

    PubMed  Google Scholar 

  49. Glahn DC, Laird AR, Ellison-Wright I, Thelen SM, Robinson JL, Lancaster JL, et al. Meta-analysis of gray matter anomalies in schizophrenia: application of anatomic likelihood estimation and network analysis. Biol Psychiatry. 2008;64:774–81.

    PubMed  PubMed Central  Google Scholar 

  50. Vita A, De Peri L, Deste G, Sacchetti E. Progressive loss of cortical gray matter in schizophrenia: A meta-analysis and meta-regression of longitudinal MRI studies. Transl Psychiatry. 2012;2:e190–13.

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Shenton ME, Dickey CC, Frumin M, Mccarley RW. A review of MRI findings in schizophrenia. Schizophr Res. 2001;49:1–52.

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Jung WH, Borgwardt S, Fusar-Poli P, Kwon JS. Gray matter volumetric abnormalities associated with the onset of psychosis. Front Psychiatry. 2012;3:1–21.

    Google Scholar 

  53. Tracy DK, Shergill SS. Mechanisms underlying auditory hallucinations—understanding perception without stimulus. Brain Sci. 2013;3:642–69.

    PubMed  PubMed Central  Google Scholar 

  54. Satterthwaite TD, Wolf DH, Loughead J, Ruparel K, Elliott MA, Hakonarson H, et al. Impact of in-scanner head motion on multiple measures of functional connectivity: relevance for studies of neurodevelopment in youth. Neuroimage. 2012;60:623–32.

    PubMed  PubMed Central  Google Scholar 

  55. Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage. 2012;59:2142–54.

    PubMed  Google Scholar 

  56. Behzadi Y, Restom K, Liau J, Liu TT. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. Neuroimage. 2007;37:90–101.

    PubMed  PubMed Central  Google Scholar 

  57. Murphy K, Birn RM, Handwerker DA, Jones TB, Bandettini PA. The impact of global signal regression on resting state correlations: are anti-correlated networks introduced? Neuroimage. 2009;44:893–905.

    PubMed  Google Scholar 

  58. Chai XJ, Castañán AN, Öngür D, Whitfield-Gabrieli S. Anticorrelations in resting state networks without global signal regression. Neuroimage. 2012;59:1420–8.

    PubMed  Google Scholar 

  59. Murphy K, Birn RM, Bandettini PA. Resting-state FMRI confounds and cleanup. Neuroimage. 2013;80:349–59.

    PubMed  PubMed Central  Google Scholar 

  60. Rangaprakash D, Wu GR, Marinazzo D, Hu X, Deshpande G. Hemodynamic response function (HRF) variability confounds resting-state fMRI functional connectivity. Magn Reson Med. 2018;80:1697–713.

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This study was supported by the Ministry of Science and Technology of China (2016 YFC 1306803) and US National Institute of Mental Health (R21 MH 093294, R01 MH 101052, and R01 MH 111448). GC was supported by an EU Marie Curie Global Fellowship (Grant no. 749201); MSK was supported by an NIMH Grant (R01 MH 64023); MES and RWM were supported by a VA Merit Award.

Author contributions

LJS passed away on 7 September 2017 and RWM passed away on 27 May 2017. LJS and RWM were two of the initiators and principal investigators of the Shanghai At Risk for Psychosis (SHARP) study.

Author information

Authors and Affiliations

  1. Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA

    Guusje Collin, Larry J. Seidman, Matcheri S. Keshavan, William S. Stone & Zhenghan Qi

  2. McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

    Guusje Collin, Sheeba Arnold Anteraper & Susan Whitfield-Gabrieli

  3. Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Guusje Collin & Martha E. Shenton

  4. Department of Linguistics and Cognitive Science, University of Delaware, Newark, DE, USA

    Zhenghan Qi

  5. Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Tianhong Zhang, Yingying Tang & Jijun Wang

  6. Department of Psychology, Florida A&M University, Tallahassee, FL, USA

    Huijun Li

  7. Alan and Lorraine Bressler Clinical and Research Program for Autism Spectrum Disorder, Massachusetts General Hospital, Boston, MA, USA

    Sheeba Arnold Anteraper

  8. Department of Psychiatry, VA Boston Healthcare System, Brockton Division, Brockton, MA, USA

    Margaret A. Niznikiewicz & Robert W. McCarley

  9. Research and Development, VA Boston Healthcare System, Brockton Division, Brockton, MA, USA

    Martha E. Shenton

  10. Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Martha E. Shenton

  11. Poitras Center for Affective Disorders, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

    Susan Whitfield-Gabrieli

Authors
  1. Guusje Collin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Larry J. Seidman
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Matcheri S. Keshavan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. William S. Stone
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhenghan Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Tianhong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yingying Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Huijun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Sheeba Arnold Anteraper
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Margaret A. Niznikiewicz
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Robert W. McCarley
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Martha E. Shenton
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Jijun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Susan Whitfield-Gabrieli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Guusje Collin or Jijun Wang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Deceased authors: Larry J. Seidman, Robert W. McCarley

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Collin, G., Seidman, L.J., Keshavan, M.S. et al. Functional connectome organization predicts conversion to psychosis in clinical high-risk youth from the SHARP program. Mol Psychiatry 25, 2431–2440 (2020). https://doi.org/10.1038/s41380-018-0288-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41380-018-0288-x

This article is cited by

Search

Advanced search

Quick links