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:

Dynamic and progressive changes in thalamic functional connectivity over the first five years of psychosis

Molecular Psychiatry volume 27, pages 1177–1183 (2022)Cite this article

Subjects

Abstract

The early stage of psychosis (ESP) is a critical period where effective intervention has the most favorable impact on outcomes. Thalamic connectivity abnormalities have been consistently found in psychosis, and are associated with clinical symptoms and cognitive deficits. However, most studies consider ESP patients as a homogeneous population and fail to take the duration of illness into account. In this study, we aimed to capture the progression of thalamic connectivity changes over the first five years of psychosis. Resting-state functional MRI scans were collected from 156 ESP patients (44 with longitudinal data) and 82 healthy controls (24 with longitudinal data). We first performed a case-control analysis comparing thalamic connectivity with 13 networks in the cortex and cerebellum. Next, we modelled the shape (flat, linear, curvilinear) of thalamic connectivity trajectories by comparing flexible non-linear versus linear models. We then tested the significance of the duration of illness and diagnosis in trajectories that changed over time. Connectivity changed over the ESP period between the thalamus and default mode network (DMN) and fronto-parietal network (FPN) nodes in both the cortex and cerebellum. Three models followed a curvilinear trajectory (early increase followed by a subsequent decrease), while thalamo-cerebellar FPN connectivity followed a linear trajectory of steady reductions over time, indicating different rates of change. Finally, diagnosis significantly predicted thalamic connectivity. Thalamo-cortical and thalamo-cerebellar connectivity change in a dynamic fashion during the ESP period. A better understanding of these changes may provide insights into the compensatory and progressive changes in functional connectivity in the early stages of illness.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

Fig. 1: Study design and analysis pipeline.
Fig. 2: Results summarizing case-control analysis of thalamic connectivity by cortical/cerebellar networks.
Fig. 3: Results summarizing the 4 thalamic connectivity trajectories that changed over the ESP period.
Fig. 4: Results summarizing the effects of diagnosis specificity on thalamic connectivity trajectories that changed over the ESP period.

Similar content being viewed by others

References

  1. Waghorn G, Saha S, Harvey C, Morgan VA, Waterreus A, Bush R, et al. ‘Earning and learning’ in those with psychotic disorders: the second Australian national survey of psychosis. The Aust NZ J Psychiatry. 2012;46:774–85.

    Article  Google Scholar 

  2. Hill SK, Reilly JL, Keefe RS, Gold JM, Bishop JR, Gershon ES, et al. Neuropsychological impairments in schizophrenia and psychotic bipolar disorder: findings from the bipolar and schizophrenia network on intermediate phenotypes (B-SNIP) study. Am J Psychiatry. 2013;170:1275–84.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Chan SY, Brady R, Hwang M, Higgins A, Nielsen K, Ongur D, et al. Heterogeneity of outcomes and network connectivity in early-stage psychosis: a longitudinal study. Schizophr Bull. 2020;47:138–48.

    Article  PubMed Central  Google Scholar 

  4. Stain HJ, Galletly CA, Clark S, Wilson J, Killen EA, Anthes L, et al. Understanding the social costs of psychosis: the experience of adults affected by psychosis identified within the second Australian national survey of psychosis. Aust NZ J psychiatry. 2012;46:879–89.

    Article  Google Scholar 

  5. Brady RO Jr., Tandon N, Masters GA, Margolis A, Cohen BM, Keshavan M, et al. Differential brain network activity across mood states in bipolar disorder. J Affect Disord. 2017;207:367–76.

    Article  PubMed  Google Scholar 

  6. Lewandowski KE, Bouix S, Ongur D, Shenton ME. Neuroprogression across the Early Course of Psychosis. J Psychiatr Brain Sci. 2020;5:e200002.

  7. Rund BR, Melle I, Friis S, Johannessen JO, Larsen TK, Midbøe LJ, et al. The course of neurocognitive functioning in first-episode psychosis and its relation to premorbid adjustment, duration of untreated psychosis, and relapse. Schizophr Res. 2007;91:132–40.

    Article  PubMed  Google Scholar 

  8. Barder HE, Sundet K, Rund BR, Evensen J, Haahr U. Ten Velden Hegelstad W, et al. Ten year neurocognitive trajectories in first-episode psychosis. Front Hum Neurosci. 2013;7:643.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Andreasen NC, Nopoulos P, Magnotta V, Pierson R, Ziebell S, Ho BC. Progressive brain change in schizophrenia: a prospective longitudinal study of first-episode schizophrenia. Biol Psychiatry. 2011;70:672–9.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Woodward ND. The course of neuropsychological impairment and brain structure abnormalities in psychotic disorders. Neurosci Res. 2016;102:39–46.

    Article  PubMed  Google Scholar 

  11. Pantelis C, Yucel M, Wood SJ, Velakoulis D, Sun D, Berger G, et al. Structural brain imaging evidence for multiple pathological processes at different stages of brain development in schizophrenia. Schizophr Bull. 2005;31:672–96.

    Article  PubMed  Google Scholar 

  12. Jindal RD, Keshavan MS. Neurobiology of the early course of schizophrenia. Expert Rev Neurotherapeutics. 2008;8:1093–100.

    Article  Google Scholar 

  13. The Recognition and Management of Early Psychosis: A Preventive Approach. McGorry P, Jackson HJ, editors: Cambridge University Press; 1999.

  14. 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.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Woodward ND, Heckers S. Mapping Thalamocortical Functional Connectivity in Chronic and Early Stages of Psychotic Disorders. Biol Psychiatry. 2016;79:1016–25.

    Article  PubMed  Google Scholar 

  16. Steullet P. Thalamus-related anomalies as candidate mechanism-based biomarkers for psychosis. Schizophr Res. 2020;226:147–57.

    Article  PubMed  Google Scholar 

  17. Chen P, Ye E, Jin X, Zhu Y, Wang L. Association between thalamocortical functional connectivity abnormalities and cognitive deficits in schizophrenia. Sci Rep. 2019;9:2952.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Bernard JA, Orr JM, Mittal VA. Cerebello-thalamo-cortical networks predict positive symptom progression in individuals at ultra-high risk for psychosis. NeuroImage Clin 2017;14:622–8.

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  Google Scholar 

  20. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Woodward ND, Karbasforoushan H, Heckers S. Thalamocortical dysconnectivity in schizophrenia. Am J Psychiatry. 2012;169:1092–9.

    Article  PubMed  Google Scholar 

  22. Hua J, Blair NIS, Paez A, Choe A, Barber AD, Brandt A, et al. Altered functional connectivity between sub-regions in the thalamus and cortex in schizophrenia patients measured by resting state BOLD fMRI at 7 T. Schizophr Res. 2019;206:370–7.

    Article  PubMed  Google Scholar 

  23. Klingner CM, Langbein K, Dietzek M, Smesny S, Witte OW, Sauer H, et al. Thalamocortical connectivity during resting state in schizophrenia. Eur Arch psychiatry Clin Neurosci. 2014;264:111–9.

    Article  PubMed  Google Scholar 

  24. Collin G, Hulshoff Pol HE, Haijma SV, Cahn W, Kahn RS, van den Heuvel MP. Impaired cerebellar functional connectivity in schizophrenia patients and their healthy siblings. Front Psychiatry. 2011;2:73.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Chen YL, Tu PC, Lee YC, Chen YS, Li CT, Su TP. Resting-state fMRI mapping of cerebellar functional dysconnections involving multiple large-scale networks in patients with schizophrenia. Schizophr Res. 2013;149:26–34.

    Article  PubMed  Google Scholar 

  26. Andreasen NC, Paradiso S, O’Leary DS. “Cognitive dysmetria” as an integrative theory of schizophrenia: a dysfunction in cortical-subcortical-cerebellar circuitry? Schizophr Bull. 1998;24:203–18.

    Article  CAS  PubMed  Google Scholar 

  27. Buckner RL, Krienen FM, Castellanos A, Diaz JC, Yeo BT. The organization of the human cerebellum estimated by intrinsic functional connectivity. J Neurophysiol. 2011;106:2322–45.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Yeo BT, Krienen FM, Sepulcre J, Sabuncu MR, Lashkari D, Hollinshead M, et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol 2011;106:1125–65.

    Article  PubMed  Google Scholar 

  29. Heslin M, Lomas B, Lappin JM, Donoghue K, Reininghaus U, Onyejiaka A, et al. Diagnostic change 10 years after a first episode of psychosis. Psychological Med. 2015;45:2757–69.

    Article  CAS  Google Scholar 

  30. Torrent C, Reinares M, Martinez-Arán A, Cabrera B, Amoretti S, Corripio I, et al. Affective versus non-affective first episode psychoses: A longitudinal study. J Affect Disord 2018;238:297–304.

    Article  CAS  PubMed  Google Scholar 

  31. Lewandowski KE, Cohen BM, Ongur D. Evolution of neuropsychological dysfunction during the course of schizophrenia and bipolar disorder. Psychological Med. 2011;41:225–41.

    Article  CAS  Google Scholar 

  32. De Peri L, Crescini A, Deste G, Fusar-Poli P, Sacchetti E, Vita A. Brain structural abnormalities at the onset of schizophrenia and bipolar disorder: a meta-analysis of controlled magnetic resonance imaging studies. Curr Pharm Des. 2012;18:486–94.

    Article  PubMed  Google Scholar 

  33. Ellison-Wright I, Bullmore E. Anatomy of bipolar disorder and schizophrenia: a meta-analysis. Schizophr Res. 2010;117:1–12.

    Article  PubMed  Google Scholar 

  34. Prell T, Gaur N, Steinbach R, Witte OW, Grosskreutz J. Modelling disease course in amyotrophic lateral Sclerosis: pseudo-longitudinal insights from cross-sectional health-related quality of life data. Health Qual Life Outcomes. 2020;18:117.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Albery L, Munafo M. Chapter 2: Research Methods and Measurement. Los Angeles: SAGE; 2008.

  36. Shinn AK, Bolton KW, Karmacharya R, Lewandowski KE, Yuksel C, Baker JT, et al. McLean OnTrack: a transdiagnostic program for early intervention in first-episode psychosis. Early Inter Psychiatry. 2017;11:83–90.

    Article  Google Scholar 

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

    Article  PubMed  Google Scholar 

  38. Wood SN. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J R Stat Soc. 2011;73:3–36.

    Article  Google Scholar 

  39. Forsyth JK, Lewis DA. Mapping the consequences of impaired synaptic plasticity in schizophrenia through development: an integrative model for diverse clinical features. Trends Cogn Sci. 2017;21:760–78.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Palaniyappan L. Inefficient neural system stabilization: a theory of spontaneous resolutions and recurrent relapses in psychosis. J Psychiatry Neurosci. 2019;44:367–83.

    Article  PubMed  Google Scholar 

  41. Crossley NA, Mechelli A, Scott J, Carletti F, Fox PT, McGuire P, et al. The hubs of the human connectome are generally implicated in the anatomy of brain disorders. Brain. 2014;137:2382–95.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Marsman A, van den Heuvel MP, Klomp DW, Kahn RS, Luijten PR, Hulshoff HE, et al. Glutamate in schizophrenia: a focused review and meta-analysis of ¹H-MRS studies. Schizophr Bull. 2013;39:120–9.

    Article  PubMed  Google Scholar 

  43. Hwang K, Bertolero MA, Liu WB, D’Esposito M. The Human Thalamus Is an Integrative Hub for Functional Brain Networks. J Neurosci. 2017;37:5594–607.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Rikhye RV, Gilra A, Halassa MM. Thalamic regulation of switching between cortical representations enables cognitive flexibility. Nat Neurosci. 2018;21:1753–63.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Najdenovska E, Aleman-Gomez Y, Battistella G, Descoteaux M, Hagmann P, Jacquemont S, et al. In-vivo probabilistic atlas of human thalamic nuclei based on diffusion- weighted magnetic resonance imaging. Sci Data. 2018;5:180270.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Cho KI, Shenton ME, Kubicki M, Jung WH, Lee TY, Yun JY, et al. Altered thalamo-cortical white matter connectivity: probabilistic tractography study in clinical-high risk for psychosis and first-episode psychosis. Schizophr Bull. 2016;42:723–31.

    Article  PubMed  Google Scholar 

  47. Tu PC, Lee YC, Chen YS, Li CT, Su TP. Schizophrenia and the brain’s control network: aberrant within- and between-network connectivity of the frontoparietal network in schizophrenia. Schizophr Res. 2013;147:339–47.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Institute of Health/National Institute of Mental Health (R01MH116170 (ROB), R01MH117012 (KEL), P50MH115846 (DO), R01MH109687 (MHH)). This work was conducted with statistical support from Professor Garrett Fitzmaurice, Harvard Catalyst | The Harvard Clinical and Translational Science Center (National Center for Advancing Translational Sciences, National Institutes of Health Award UL 1TR002541) and financial contributions from Harvard University and its affiliated academic healthcare centers. The content is solely the responsibility of the authors and does not necessarily represent the official views of Harvard Catalyst, Harvard University and its affiliated academic healthcare centers, or the National Institutes of Health.

Author information

Authors and Affiliations

  1. Schizophrenia and Bipolar Disorder Research Program, McLean Hospital, Belmont, MA, USA

    Shi Yu Chan, Roscoe O. Brady, Kathryn E. Lewandowski, Amy Higgins, Dost Öngür & Mei-Hua Hall

  2. Psychosis Neurobiology Laboratory, McLean Hospital, Belmont, MA, USA

    Shi Yu Chan, Amy Higgins, Dost Öngür & Mei-Hua Hall

  3. Department of Psychiatry, Harvard Medical School, Boston, MA, USA

    Shi Yu Chan, Roscoe O. Brady, Kathryn E. Lewandowski, Dost Öngür & Mei-Hua Hall

  4. Department of Psychiatry, Beth Israel Deaconess Medical Center and Massachusetts Mental Health Center, Boston, MA, USA

    Roscoe O. Brady

Authors
  1. Shi Yu Chan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Roscoe O. Brady
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kathryn E. Lewandowski
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Amy Higgins
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dost Öngür
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mei-Hua Hall
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

SYC contributed to the study design, processing and analysis of the data, and preparation of the manuscript. AH contributed to data collection and processing. ROB, KEL, DO and MHH contributed to the study design, interpretation of data and preparation of the paper. All authors gave final approval of the version to be published.

Corresponding author

Correspondence to Shi Yu Chan.

Ethics declarations

Competing interests

Dr. Lewandowski has received research funding from diaMentis. All other authors report no financial relationships with commercial 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

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chan, S.Y., Brady, R.O., Lewandowski, K.E. et al. Dynamic and progressive changes in thalamic functional connectivity over the first five years of psychosis. Mol Psychiatry 27, 1177–1183 (2022). https://doi.org/10.1038/s41380-021-01319-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41380-021-01319-3

This article is cited by

Search

Advanced search

Quick links