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 dysconnectivity of visual and somatomotor networks yields a simple and robust biomarker for psychosis

Molecular Psychiatry volume 30, pages 1539–1547 (2025)Cite this article

Subjects

Abstract

People with psychosis exhibit thalamo-cortical hyperconnectivity and cortico-cortical hypoconnectivity with sensory networks, however, it remains unclear if this applies to all sensory networks, whether it arises from other illness factors, or whether such differences could form the basis of a viable biomarker. To address the foregoing, we harnessed data from the Human Connectome Early Psychosis Project and computed resting-state functional connectivity (RSFC) matrices for 54 healthy controls and 105 psychosis patients. Primary visual, secondary visual (“visual2”), auditory, and somatomotor networks were defined via a recent brain network partition. RSFC was determined for 718 regions via regularized partial correlation. Psychosis patients—both affective and non-affective—exhibited cortico-cortical hypoconnectivity and thalamo-cortical hyperconnectivity in somatomotor and visual2 networks but not in auditory or primary visual networks. When we averaged and normalized the visual2 and somatomotor network connections, and subtracted the thalamo-cortical and cortico-cortical connectivity values, a robust psychosis biomarker emerged (p = 2e-10, Hedges’ g = 1.05). This “somato-visual” biomarker was present in antipsychotic-naive patients and did not depend on confounds such as psychiatric comorbidities, substance/nicotine use, stress, anxiety, or demographics. It had moderate test-retest reliability (ICC = 0.62) and could be recovered in five-minute scans. The marker could discriminate groups in leave-one-site-out cross-validation (AUC = 0.79) and improve group classification upon being added to a well-known neurocognition task. Finally, it could differentiate later-stage psychosis patients from healthy or ADHD controls in two independent data sets. These results introduce a simple and robust RSFC biomarker that can distinguish psychosis patients from controls by the early illness stages.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

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

Fig. 1: The four sensory networks of the brain network partition.
Fig. 2: Group comparisons in cortico-cortical and thalamo-cortical connectivity for each sensory network.
Fig. 3: Network-wise group differences in connectivity across the four sensory networks using the combined patient sample (threshold FDR q < 0.05).
Fig. 4: Demonstrating robustness of the somato-visual RSFC biomarker.
Fig. 5: ROC curves.

Similar content being viewed by others

Data availability

The HCP data are public (https://nda.nih.gov/edit_collection.html?id=2914). The UCLA data are located on OpenNeuro.org (accession number: ds000030), and so too are the Rutgers patient and control data (ds005073, ds003404, respectively).

Code availability

Preprocessing was done with fmriprep v.21.0.1 (www.fmriprep.org). Code for estimating RSFC and applying the Cole-Anticevic Brain Network Partition are on GitHub (https://github.com/ColeLab/ActflowToolbox/; https://github.com/ColeLab/ColeAnticevicNetPartition). Code for estimating intraclass correlation are on the Matlab Central File Exchange: https://www.mathworks.com/matlabcentral/fileexchange/26885-intraclass-correlation-coefficient-with-confidence-intervals.

References

  1. Abi‐Dargham A, Moeller SJ, Ali F, DeLorenzo C, Domschke K, Horga G, et al. Candidate biomarkers in psychiatric disorders: state of the field. World Psychiatry. 2023;22:236–62.

    PubMed Central  PubMed  Google Scholar 

  2. Damaraju E, Allen EA, Belger A, Ford JM, McEwen S, Mathalon DH, et al. Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia. Neuroimage Clin. 2014;5:298–308.

    CAS  PubMed Central  PubMed  Google Scholar 

  3. Anticevic A, Cole MW, Repovs G, Murray JD, Brumbaugh MS, Winkler AM, et al. Characterizing thalamo-cortical disturbances in schizophrenia and bipolar illness. Cerebral Cortex. 2014;24:3116–30.

    PubMed  Google Scholar 

  4. Cao H, Chén 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 Comms. 2018;9:3836–9.

    Google Scholar 

  5. Ferri J, Ford JM, Roach BJ, Turner JA, Erp TGV, Voyvodic J, et al. Resting-state thalamic dysconnectivity in schizophrenia and relationships with symptoms. Psychol Med. 2018;48:2492–99.

  6. Fu Z, Iraji A, Sui J, Calhoun VD. Whole-brain functional network connectivity abnormalities in affective and non-affective early phase psychosis. Front Neurosci. 2021;15:682110.

    PubMed Central  PubMed  Google Scholar 

  7. Reid AT, Headley DB, Mill RD, Sanchez-Romero R, Uddin LQ, Marinazzo D, et al. Advancing functional connectivity research from association to causation. Nat Neurosci. 2019;22:1751–60.

    CAS  PubMed Central  PubMed  Google Scholar 

  8. Wagenmakers E-J, Sarafoglou A, Aczel B. One statistical analysis must not rule them all. Nature. 2022;605:423–5.

    CAS  PubMed  Google Scholar 

  9. Poldrack RA, Huckins G, Varoquaux G. Establishment of best practices for evidence for prediction. JAMA Psychiatry. 2020;77:534–7.

    PubMed Central  PubMed  Google Scholar 

  10. Kraguljac NV, McDonald WM, Widge AS, Rodriguez CI, Tohen M, Nemeroff CB. Neuroimaging biomarkers in Schizophrenia. Am J Psychiatry. 2021;178:509–21.

    PubMed Central  PubMed  Google Scholar 

  11. Ji JL, Spronk M, Kulkarni K, Repovs G, Anticevic A, Cole MW. Mapping the human brain’s cortical-subcortical functional network organization. Neuroimage. 2019;185:35–57.

    PubMed  Google Scholar 

  12. Noble S, Mejia AF, Zalesky A, Scheinost D. Improving power in functional magnetic resonance imaging by moving beyond cluster-level inference. Proc Natl Acad Sci USA. 2022;119:e2203020119.

    CAS  PubMed Central  PubMed  Google Scholar 

  13. Peterson KL, Sanchez-Romero R, Mill RD, Cole MW Regularized partial correlation provides reliable functional connectivity estimates while correcting for widespread confounding. 2023. 2023. https://doi.org/10.1101/2023.09.16.558065.

  14. Stephan KE, Friston KJ, Frith CD. Dysconnection in Schizophrenia: from abnormal synaptic plasticity to failures of self-monitoring. Schizophr Bull. 2009;35:509–27.

    PubMed Central  PubMed  Google Scholar 

  15. American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 5th ed. 2013.

  16. First MB, Williams J, Karg RS, Spitzer RL. Structured clinical interview for DSM-5--research version (SCID-5 for DSM-5, research version; SCID-5-RV). American Psychiatric Association; 2015.

  17. Kay SR, Fiszbein A, Opler LA. The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophr Bull. 1987;13:261–76.

    CAS  PubMed  Google Scholar 

  18. Wallwork RS, Fortgang R, Hashimoto R, Weinberger DR, Dickinson D. Searching for a consensus five-factor model of the positive and negative syndrome scale for schizophrenia. Schizophr Res. 2012;137:246–50.

    CAS  PubMed Central  PubMed  Google Scholar 

  19. Gardner DM, Murphy AL, O’Donnell H, Centorrino F, Baldessarini RJ. International consensus study of antipsychotic dosing. Am J Psychiatry. 2010;167:686–93.

    PubMed  Google Scholar 

  20. Ciric R, Wolf DH, Power JD, Roalf DR, Baum GL, Ruparel K, et al. Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity. Neuroimage. 2017;154:174–87.

    PubMed  Google Scholar 

  21. Ciric R, Rosen AFG, Erus G, Cieslak M, Adebimpe A, Cook PA, et al. Mitigating head motion artifact in functional connectivity MRI. Nat Protoc. 2018;13:2801–26.

    CAS  PubMed Central  PubMed  Google Scholar 

  22. Muschelli J, Nebel MB, Caffo BS, Barber AD, Pekar JJ, Mostofsky SH. Reduction of motion-related artifacts in resting state fMRI using aCompCor. NeuroImage. 2014;96:22–35.

    PubMed  Google Scholar 

  23. 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 

  24. Siegel JS, Mitra A, Laumann TO, Seitzman BA, Raichle M, Corbetta M, et al. Data quality influences observed links between functional connectivity and behavior. Cereb Cortex. 2017;27:4492–502.

    PubMed  Google Scholar 

  25. Parkes L, Fulcher B, Yücel M, Fornito A. An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI. Neuroimage. 2018;171:415–36.

    PubMed  Google Scholar 

  26. Marek S, Tervo-Clemmens B, Calabro FJ, Montez DF, Kay BP, Hatoum AS, et al. Reproducible brain-wide association studies require thousands of individuals. Nature. 2022;603:654–60.

    CAS  PubMed Central  PubMed  Google Scholar 

  27. Glasser MF, Coalson TS, Robinson EC, Hacker CD, Harwell J, Yacoub E, et al. A multi-modal parcellation of human cerebral cortex. Nature. 2016;536:171–8.

    CAS  PubMed Central  PubMed  Google Scholar 

  28. Schaipp F, Vlasovets O, Müller C. GGLasso - a Python package for general graphical lasso computation. J Open Source Softw. 2021;6:3865.

    Google Scholar 

  29. Hentschke H, Stüttgen MC. Computation of measures of effect size for neuroscience data sets. Eur J Neurosci. 2011;34:1887–94.

    PubMed  Google Scholar 

  30. Benjamini Y, Hochberg J. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Statis Soc Ser B-Methodol. 1995;57:289–300.

    Google Scholar 

  31. Keane BP, Krekelberg B, Mill RD, Silverstein SM, Thompson JL, Serody MR, et al. Dorsal attention network activity during perceptual organization is distinct in schizophrenia and predictive of cognitive disorganization. Eur J Neurosci. 2022;57:458–78.

    Google Scholar 

  32. 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;3:160110–2.

    CAS  PubMed Central  PubMed  Google Scholar 

  33. Hearne LJ, Mill RD, Keane BP, Repovs G, Anticevic A, Cole MW. Activity flow underlying abnormalities in brain activations and cognition in schizophrenia. Sci Adv. 2021;7:eabf2513.

    PubMed Central  PubMed  Google Scholar 

  34. Christodoulou E, Ma J, Collins GS, Steyerberg EW, Verbakel JY, Calster BV. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J Clin Epidemiol. 2019;110:12–22.

    PubMed  Google Scholar 

  35. Schulz M-A, Yeo BTT, Vogelstein JT, Mourao-Miranada J, Kather JN, Kording K, et al. Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets. Nat Comms. 2020;11:4238–15.

    CAS  Google Scholar 

  36. Rubio JM, Lencz T, Cao H, Kraguljac N, Dhamala E, Homan P, et al. Replication of a neuroimaging biomarker for striatal dysfunction in psychosis. Mol Psychiatry. 2024;29:929–38.

  37. Li A, Zalesky A, Yue W, Howes O, Yan H, Liu Y, et al. A neuroimaging biomarker for striatal dysfunction in schizophrenia. Nat Med. 2020;26:558–65.

  38. 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.

    PubMed Central  PubMed  Google Scholar 

  39. Dijk KRAV, Sabuncu MR, Buckner RL. The influence of head motion on intrinsic functional connectivity MRI. NeuroImage. 2012;59:431–8.

    PubMed  Google Scholar 

  40. 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  Google Scholar 

  41. Jong JJA, de, Jansen JFA, Vergoossen LWM, Schram MT, Stehouwer CDA, Wildberger JE, et al. Effect of magnetic resonance image quality on structural and functional brain connectivity: the maastricht study. Brain Sci. 2024;14:62.

    PubMed Central  PubMed  Google Scholar 

  42. Li J, Kong R, Liégeois R, Orban C, Tan Y, Sun N, et al. Global signal regression strengthens association between resting-state functional connectivity and behavior. Neuroimage. 2019;196:126–41.

    PubMed  Google Scholar 

  43. Laumann TO, Snyder AZ, Mitra A, Gordon EM, Gratton C, Adeyemo B, et al. On the stability of BOLD fMRI correlations. Cereb Cortex. 2016;27:4719–32.

    PubMed Central  Google Scholar 

  44. Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. J Heal Soc Behav. 1983;24:385–96.

    CAS  Google Scholar 

  45. Rashid B, Arbabshirani MR, Damaraju E, Cetin MS, Miller R, Pearlson GD, et al. Classification of schizophrenia and bipolar patients using static and dynamic resting-state fMRI brain connectivity. NeuroImage. 2016;134:645–57.

    PubMed  Google Scholar 

  46. Li J, Bzdok D, Chen J, Tam A, Ooi LQR, Holmes AJ, et al. Cross-ethnicity/race generalization failure of behavioral prediction from resting-state functional connectivity. Sci Adv. 2022;8:eabj1812.

    PubMed Central  PubMed  Google Scholar 

  47. Lynch CJ, Power JD, Scult MA, Dubin M, Gunning FM, Liston C. Rapid precision functional mapping of individuals using multi-echo fMRI. Cell Rep. 2020;33:108540.

    CAS  PubMed Central  PubMed  Google Scholar 

  48. Koo TK, Li MY. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med. 2016;15:155–63.

    PubMed Central  PubMed  Google Scholar 

  49. Rodrigue AL, Mastrovito D, Esteban O, Durnez J, Koenis MMG, Janssen R, et al. Searching for imaging biomarkers of psychotic dysconnectivity. Biol Psychiatry Cogn Neurosci Neuroimag. 2021;6:1135–44.

    Google Scholar 

  50. Phillips WA, Silverstein SM. Convergence of biological and psychological perspectives on cognitive coordination in schizophrenia. Behav Brain Sci. 2003;26:65–82.

    PubMed  Google Scholar 

  51. Phillips WA, Clark A, Silverstein SM. On the functions, mechanisms, and malfunctions of intracortical contextual modulation. Neurosci Biobehav Rev. 2015;52:1–20.

    CAS  PubMed  Google Scholar 

  52. Abram SV, Roach BJ, Fryer SL, Calhoun VD, Preda A, Erp TGMV, et al. Validation of ketamine as a pharmacological model of thalamic dysconnectivity across the illness course of schizophrenia. Mol Psychiatry. 2022;27:2448–56.

    CAS  PubMed Central  PubMed  Google Scholar 

  53. Peer M, Prüss H, Ben-Dayan I, Paul F, Arzy S, Finke C. Functional connectivity of large-scale brain networks in patients with anti-NMDA receptor encephalitis: an observational study. Lancet. Psychiatry. 2017;4:768–74.

    PubMed  Google Scholar 

  54. Giraldo-Chica M, Rogers BP, Damon SM, Landman BA, Woodward ND. Prefrontal-thalamic anatomical connectivity and executive cognitive function in schizophrenia. Biol Psychiatry. 2018;83:509–17.

    PubMed  Google Scholar 

  55. Wannan CMJ, Nelson B, Addington J, Allott K, Anticevic A, Arango C, et al. Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ): rationale and study design of the largest global prospective cohort study of clinical high risk for psychosis. Schizophr Bull. 2024;50:496–512.

  56. Zweerings J, Hummel B, Keller M, Zvyagintsev M, Schneider F, Klasen M, et al. Neurofeedback of core language network nodes modulates connectivity with the default-mode network: a double-blind fMRI neurofeedback study on auditory verbal hallucinations. NeuroImage. 2019;189:533–42.

    PubMed  Google Scholar 

  57. Drysdale AT, Grosenick L, Downar J, Dunlop K, Mansouri F, Meng Y, et al. Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat Med. 2017;23:28–38.

    CAS  PubMed  Google Scholar 

  58. Noble S, Scheinost D, Constable RT. A decade of test-retest reliability of functional connectivity: a systematic review and meta-analysis. NeuroImage. 2019;203:116157.

    PubMed  Google Scholar 

  59. Kundu P, Voon V, Balchandani P, Lombardo MV, Poser BA, Bandettini PA. Multi-echo fMRI: a review of applications in fMRI denoising and analysis of BOLD signals. NeuroImage. 2017;154:59–80.

    PubMed  Google Scholar 

  60. Vizioli L, Moeller S, Dowdle L, Akçakaya M, Martino FD, Yacoub E, et al. Lowering the thermal noise barrier in functional brain mapping with magnetic resonance imaging. Nat Commun. 2021;12:5181.

    CAS  PubMed Central  PubMed  Google Scholar 

  61. Riedl V, Bienkowska K, Strobel C, Tahmasian M, Grimmer T, Förster S, et al. Local activity determines functional connectivity in the resting human brain: a simultaneous FDG-PET/fMRI study. J Neurosci. 2014;34:6260–6.

    CAS  PubMed Central  PubMed  Google Scholar 

  62. Diamond A, Silverstein SM, Keane BP. Visual system assessment for predicting a transition to psychosis. Transl Psychiatry. 2022;12:351–9.

    PubMed Central  PubMed  Google Scholar 

  63. Silverstein SM, Keane BP. Perceptual organization impairment in schizophrenia and associated brain mechanisms: review of research from 2005 to 2010. Schizophr Bull. 2011;37:690–9.

    PubMed Central  PubMed  Google Scholar 

  64. Keane BP, Barch DM, Mill RD, Silverstein SM, Krekelberg B, Cole MW. Brain network mechanisms of visual shape completion. Neuroimage. 2021;236:118069.

    PubMed  Google Scholar 

  65. Walther S, Strik W. Motor symptoms and schizophrenia. Neuropsychobiology. 2012;66:77–92.

    PubMed  Google Scholar 

  66. Huang PS-B, Chen C-L, Yeung K-T, Hsu M-Y, Wan S-W, Lou S-Z. Effects of different types of sensory signals on reaching performance in persons with chronic schizophrenia. PLoS ONE. 2020;15:e0234976.

    CAS  PubMed Central  PubMed  Google Scholar 

  67. Clair DS, MacLennan G, Beedie SA, Nouzová E, Lemmon H, Rujescu D, et al. Eye movement patterns can distinguish schizophrenia from the major affective disorders and healthy control subjects. Schizophr Bullet Open. 2022;3:sgac032.

    Google Scholar 

  68. Kim HE, Avraham G, Ivry RB. The psychology of reaching: action selection, movement implementation, and sensorimotor learning. Annu Rev Psychol. 2020;72:1–35.

    Google Scholar 

  69. Coiner B, Pan H, Bennett ML, Bodien YG, Iyer S, O’Neil-Pirozzi TM, et al. Functional neuroanatomy of the human eye movement network: a review and atlas. Brain Struct Funct. 2019;224:2603–17.

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by a K01MH108783 to BPK and a Hendershot pilot grant to BPK through the Psychiatry Department at the University of Rochester. We also thank Kirsten Peterson for assistance in using the Graphical Lasso python code.

Author information

Authors and Affiliations

  1. Departments of Psychiatry and Neuroscience, University of Rochester Medical Center, 430 Elmwood Ave, Rochester, NY, 14642, USA

    Brian P. Keane

  2. Center for Visual Science, University of Rochester, 601 Elmwood Ave, P.O. Box 319, Rochester, NY, 14642, USA

    Brian P. Keane & Yonatan T. Abrham

  3. Department of Brain & Cognitive Science, University of Rochester, 358 Meliora Hall, P.O. Box 270268, Rochester, NY, 14627-0268, USA

    Brian P. Keane, Yonatan T. Abrham & Boyang Hu

  4. Center for Molecular and Behavioral Neuroscience, Rutgers, The State University of New Jersey, 197 University Ave, Newark, NJ, 07102, USA

    Michael W. Cole

  5. Department of Biostatistics, University of Rochester Medical Center, 601 Elmwood Ave, P.O. Box 630, Rochester, NY, USA

    Brent A. Johnson

  6. Department of Psychology, Yale University, 100 College St, New Haven, CT, 06510, USA

    Carrisa V. Cocuzza

Authors
  1. Brian P. Keane
    View author publications

    Search author on:PubMed Google Scholar

  2. Yonatan T. Abrham
    View author publications

    Search author on:PubMed Google Scholar

  3. Michael W. Cole
    View author publications

    Search author on:PubMed Google Scholar

  4. Brent A. Johnson
    View author publications

    Search author on:PubMed Google Scholar

  5. Boyang Hu
    View author publications

    Search author on:PubMed Google Scholar

  6. Carrisa V. Cocuzza
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Conceptualization—BK, MC; Methodology and formal analysis—BK, BJ, YA, CC; Software/Scripting—BK, YA, BH, MC; Data Curation—YA, BK, MC; Writing, original draft—BK, CC; Writing, review and editing: BK, BJ, CC, MC, YA, BH; Visualization—BK, BH, CC; Supervision, funding acquisition, and resources (including prioritized access to the University of Rochester BlueHive cluster)—BK.

Corresponding author

Correspondence to Brian P. Keane.

Ethics declarations

Competing interests

The authors declare no competing interests.

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

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Keane, B.P., Abrham, Y.T., Cole, M.W. et al. Functional dysconnectivity of visual and somatomotor networks yields a simple and robust biomarker for psychosis. Mol Psychiatry 30, 1539–1547 (2025). https://doi.org/10.1038/s41380-024-02767-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41380-024-02767-3

Search

Advanced search

Quick links