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.

A transdiagnostic network analysis of motivation and pleasure, expressivity and social functioning

Abstract

Negative symptoms, comprising the motivation and pleasure (MAP) factor and the expressivity (EXP) factor, are key determinants of social functioning in schizophrenia (SCZ). Although negative symptoms are also found in major depressive disorder (MDD) and bipolar disorder (BD), it remains unclear whether the two factors would have different impacts on social functioning from a transdiagnostic perspective. Here we adopt network analysis to examine the inter-relationship pattern between the MAP and EXP factors, social functioning and other clinical characteristics in 192 patients with SCZ, 67 patients with BD and 92 patients with MDD. The results show that the MAP factor is the central node in the transdiagnostic network at symptom-domain and symptom-item levels. The MAP factor is closely connected to social functioning and makes a greater contribution to the variance explained for social functioning. These findings support that the MAP factor is the core symptom in determining social functioning across different psychiatric disorders.

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

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

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

Fig. 1: Transdiagnostic flow network and centrality estimates of the transdiagnostic symptom-domain network.
Fig. 2: Transdiagnostic network and centrality estimates of the transdiagnostic symptom-item network.
Fig. 3: Regularized partial correlation ‘symptom-domain’ network of the three clinical groups.

Data availability

The minimum anonymous data that support the findings of this study are available upon reasonable request from R.C.K.C. or Z.-H.Y. The participants did not consent to the sharing of the raw data to the public.

Code availability

R scripts are provided in Supplementary Information.

References

  1. Kirkpatrick, B., Fenton, W. S., Carpenter, W. T. Jr & Marder, S. R. The NIMH-MATRICS consensus statement on negative symptoms. Schizophr. Bull. 32, 214–219 (2006).

    Article  PubMed Central  PubMed  Google Scholar 

  2. Strauss, G. P. & Cohen, A. S. A transdiagnostic review of negative symptom phenomenology and etiology. Schizophr. Bull. 43, 712–719 (2017).

    Article  PubMed Central  PubMed  Google Scholar 

  3. Guessoum, S. B., Le Strat, Y., Dubertret, C. & Mallet, J. A transnosographic approach of negative symptoms pathophysiology in schizophrenia and depressive disorders. Prog. Neuropsychopharmacol. Biol. Psychiatry 99, 109862 (2020).

    Article  PubMed  Google Scholar 

  4. Mucci, A. et al. Factors associated with real-life functioning in persons with schizophrenia in a 4-year follow-up study of the Italian network for research on psychoses. JAMA Psychiat. 78, 550–559 (2021).

    Article  Google Scholar 

  5. Ventura, J., Hellemann, G. S., Thames, A. D., Koellner, V. & Nuechterlein, K. H. Symptoms as mediators of the relationship between neurocognition and functional outcome in schizophrenia: a meta-analysis. Schizophr. Res. 113, 189–199 (2009).

    Article  PubMed Central  PubMed  Google Scholar 

  6. Insel, T. et al. Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. Am. J. Psychiatry 167, 748–751 (2010).

    Article  PubMed  Google Scholar 

  7. Mansell, W., Harvey, A., Watkins, E. & Shafran, R. Conceptual foundations of the transdiagnostic approach to CBT. J. Cogn. Psychother. 23, 6–19 (2009).

    Article  Google Scholar 

  8. Mahmood, Z., Burton, C. Z., Vella, L. & Twamley, E. W. Neuropsychological predictors of performance-based measures of functional capacity and social skills in individuals with severe mental illness. J. Psychiatr. Res. 102, 201–206 (2018).

    Article  PubMed Central  PubMed  Google Scholar 

  9. Kring, A. M., Gur, R. E., Blanchard, J. J., Horan, W. P. & Reise, S. P. The Clinical Assessment Interview for Negative Symptoms (CAINS): final development and validation. Am. J. Psychiatry 170, 165–172 (2013).

    Article  PubMed Central  PubMed  Google Scholar 

  10. Xie, D. J. et al. Cross cultural validation and extension of the Clinical Assessment Interview for Negative Symptoms (CAINS) in the Chinese context: evidence from a spectrum perspective. Schizophr. Bull. 44, S547–S555 (2018).

    Article  PubMed Central  PubMed  Google Scholar 

  11. Pratt, D. N. et al. Reliability and replicability of implicit and explicit reinforcement learning paradigms in people with psychotic disorders. Schizophr. Bull. 47, 731–739 (2021).

    Article  PubMed  Google Scholar 

  12. Richter, J., Hölz, L., Hesse, K., Wildgruber, D. & Klingberg, S. Measurement of negative and depressive symptoms: discriminatory relevance of affect and expression. Eur. Psychiatry 55, 23–28 (2019).

    Article  PubMed  Google Scholar 

  13. Peralta, V., Gil-Berrozpe, G. J., Librero, J., Sánchez-Torres, A. & Cuesta, M. J. The symptom and domain structure of psychotic disorders: a network analysis approach. Schizophr. Bull. Open 1, sgaa008 (2020).

    Article  Google Scholar 

  14. Galderisi, S., Mucci, A., Buchanan, R. W. & Arango, C. Negative symptoms of schizophrenia: new developments and unanswered research questions. Lancet Psychiatry 5, 664–677 (2018).

    Article  PubMed  Google Scholar 

  15. Hu, H. X. et al. The important role of motivation and pleasure deficits on social functioning in patients with schizophrenia: a network analysis. Schizophr. Bull. 48, 860–870 (2022).

    Article  PubMed Central  PubMed  Google Scholar 

  16. Quek, Y. F., Yang, Z., Dauwels, J. & Lee, J. The impact of negative symptoms and neurocognition on functioning in MDD and schizophrenia. Front. Psychiatry 12, 648108 (2021).

    Article  PubMed Central  PubMed  Google Scholar 

  17. Borsboom, D. A network theory of mental disorders. World Psychiatry 16, 5–13 (2017).

    Article  PubMed Central  PubMed  Google Scholar 

  18. McNally, R. J. Can network analysis transform psychopathology? Behav. Res. Ther. 86, 95–104 (2016).

    Article  PubMed  Google Scholar 

  19. Guloksuz, S., Pries, L. K. & van Os, J. Application of network methods for understanding mental disorders: pitfalls and promise. Psychol. Med. 47, 2743–2752 (2017).

    Article  PubMed  Google Scholar 

  20. Strauss, G. P. et al. Network analysis indicates that avolition is the most central domain for the successful treatment of negative symptoms: evidence from the roluperidone randomized clinical trial. Schizophr. Bull. 46, 964–970 (2020).

    Article  PubMed Central  PubMed  Google Scholar 

  21. Epskamp, S., Borsboom, D. & Fried, E. I. Estimating psychological networks and their accuracy: a tutorial paper. Behav. Res. Methods 50, 195–212 (2018).

    Article  PubMed  Google Scholar 

  22. Stein, F. et al. Factor analyses of multidimensional symptoms in a large group of patients with major depressive disorder, bipolar disorder, schizoaffective disorder and schizophrenia. Schizophr. Res. 218, 38–47 (2020).

    Article  PubMed  Google Scholar 

  23. Krynicki, C. R., Upthegrove, R., Deakin, J. & Barnes, T. The relationship between negative symptoms and depression in schizophrenia: a systematic review. Acta Psychiatr. Scand. 137, 380–390 (2018).

    Article  PubMed  Google Scholar 

  24. Cohen, A. S. et al. Using machine learning of computerized vocal expression to measure blunted vocal affect and alogia. npj Schizophr. 6, 26 (2020).

    Article  PubMed Central  PubMed  Google Scholar 

  25. Whitton, A. E., Treadway, M. T. & Pizzagalli, D. A. Reward processing dysfunction in major depression, bipolar disorder and schizophrenia. Curr. Opin. Psychiatry 28, 7–12 (2015).

    Article  PubMed Central  PubMed  Google Scholar 

  26. Zou, Y. M. et al. Effort-cost computation in a transdiagnostic psychiatric sample: differences among patients with schizophrenia, bipolar disorder and major depressive disorder. Psych. J. 9, 210–222 (2020).

    Article  PubMed  Google Scholar 

  27. Wang, Y. Y. et al. Shared and distinct reward neural mechanisms among patients with schizophrenia, major depressive disorder and bipolar disorder: an effort-based functional imaging study. Eur. Arch. Psychiatry Clin. Neurosci. 272, 859–871 (2022).

    Article  PubMed  Google Scholar 

  28. Pelizza, L. et al. Disorganization in first episode schizophrenia: treatment response and psychopathological findings from the 2-year follow-up of the ‘Parma Early Psychosis’ program. J. Psychiatr. Res. 141, 293–300 (2021).

    Article  PubMed  Google Scholar 

  29. Bringmann, L. F. et al. What do centrality measures measure in psychological networks? J. Abnorm. Psychol. 128, 892–903 (2019).

    Article  PubMed  Google Scholar 

  30. Galderisi, S. et al. Interplay among psychopathologic variables, personal resources, context-related factors and real-life functioning in individuals with schizophrenia: a network analysis. JAMA Psychiatry 75, 396–404 (2018).

    Article  PubMed Central  PubMed  Google Scholar 

  31. Diagnostic and Statistical Manual of Mental Disorders 4th edn (American Psychiatric Association, 1994).

  32. Shen, Y. & Wang, C. A Handbook of Epidemiological Investigation of Mental Illness (Renming Health Press, 1985).

  33. Wu, W. Y. in Handbook of Rating Scales in Psychiatry (ed. Zhang, M. Y.) 163–166 (Hunan Science and Technology Press, 1998).

  34. Kay, S. R., Fiszbein, A. & Opler, L. A. The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophr. Bull. 13, 261–276 (1987).

    Article  PubMed  Google Scholar 

  35. Wallwork, R. S., Fortgang, R., Hashimoto, R., Weinberger, D. R. & Dickinson, D. Searching for a consensus five-factor model of the Positive and Negative Syndrome Scale for schizophrenia. Schizophr. Res. 137, 246–250 (2012).

    Article  PubMed Central  PubMed  Google Scholar 

  36. Si, T. M. et al. The reliability, validity of PANSS and its implication. Chin. Ment. Health J. 18, 45–47 (2004).

    Google Scholar 

  37. Shafer, A. & Dazzi, F. Meta-analysis of the Positive And Negative Syndrome Scale (PANSS) factor structure. J. Psychiatr. Res. 115, 113–120 (2019).

    Article  PubMed  Google Scholar 

  38. IBM SPSS Statistics for Macintosh, Version 22.0 (IBM Corporation, 2013).

  39. R Core Team R: A Language and Environment for Statistical Computing, Version 4.0.0 (R Foundation for Statistical Computing, 2016); http://www.R-project.org/

  40. Constantin, M. A. & Cramer, A. O. J. Sample Size Recommendations for Estimating Cross-Sectional Network Models (Tilburg Univ., 2020).

  41. Epskamp, S. qgraph: Graph Plotting Methods, Psychometric Data Visualization and Graphical Model Estimation (R Foundation for Statistical Computing, 2023); https://cran.r-project.org/package=qgraph

  42. Liu, H., Lafferty, J. & Wasserman, L. The nonparanormal: semiparametric estimation of high dimensional undirected graphs. J. Mach. Learn. Res. 10, 2295–2328 (2009).

    Google Scholar 

  43. Friedman, J. H., Hastie, T. & Tibshirani, R. Sparse inverse covariance estimation with the graphical LASSO. Biostatistics 9, 432–441 (2008).

    Article  PubMed  Google Scholar 

  44. Chen, J. & Chen, Z. Extended Bayesian information criteria for model selection with large model spaces. Biometrika 95, 759–771 (2008).

    Article  Google Scholar 

  45. Foygel, R. & Drton, M. Extended Bayesian information criteria for Gaussian graphical models. In Proc. Advances in Neural Information Processing Systems 23 (eds Lafferty, J. D. et al.) 604–612 (Curran Associates, 2010).

  46. Fruchterman, T. M. J. & Reingold, E. M. Graph drawing by force-directed placement. Softw. Pract. Exp. 21, 1129–1164 (1991).

    Article  Google Scholar 

  47. Reingold, E. & Tilford, J. Tidier drawing of trees. IEEE Trans. Software Eng. 7, 223–228 (1981).

    Article  Google Scholar 

  48. Borsboom, D. & Cramer, A. O. J. Network analysis: an integrative approach to the structure of psychopathology. Annu. Rev. Clin. Psychol. 9, 91–121 (2013).

    Article  PubMed  Google Scholar 

  49. Robinaugh, D. J., Millner, A. J. & McNally, R. J. Identifying highly influential nodes in the complicated grief network. J. Abnorm. Psychol. 125, 747–757 (2016).

    Article  PubMed Central  PubMed  Google Scholar 

  50. Van Borkulo, C. D., Epskamp, S., Jones, P., Haslbeck, J. & Millner, A. Network Comparison Test: Statistical Comparison of Two Networks Based on Three Invariance Measures (R Foundation for Statistical Computing, 2019); https://cran.r-project.org/package=NetworkComparisonTest

  51. Lindeman, R. H., Merenda, P. F. & Gold, R. Z. Introduction to Bivariate and Multivariate Analysis (Scott, Foresman & Co, 1980).

  52. Grömping, U. Relative importance for linear regression in R: the package relaimpo. J. Stat. Softw. 17, 1–27 (2007).

    Google Scholar 

Download references

Acknowledgements

L.H., Z.-H.Y. and R.C.K.C. were supported by the Jiangsu Provincial Key Research and Development Program (BE2020661). R.C.K.C. was also supported by the CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, the Scientific Foundation of the Institute of Psychology, the Chinese Academy of Sciences (E2CX3415CX), the Science Foundation of Shanghai Mental Health Centre (SMHCRSD01) and the Philip K. H. Wong Foundation. The funding agents had no role in study design, in the collection, analysis and interpretation of the data, in the writing of the manuscript, and in the decision to submit the paper for publication.

Author information

Authors and Affiliations

Authors

Contributions

H.-X.H. contributed to study design, data analysis and interpretation, and drafting and revision of the paper. C.L. and J.-B.Z. contributed to data collection and the interpretation of findings. L.-L.W., M.-Y.C. and S.-B.L. contributed to data interpretation and drafting of the paper. Q.-Y.L. contributed to the supervision of data collection and interpretation of the findings. S.S.Y.L. and L.H. contributed to data interpretation and paper revision. Z.-H.Y. contributed to study conceptualization, the supervision of data collection and interpretation and paper revision. R.C.K.C. contributed to study conceptualization, study design, data interpretation and paper revision.

Corresponding authors

Correspondence to Zheng-hui Yi or Raymond C. K. Chan.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Mental Health thanks Chi C. Chan, David E. Gard and William Horan for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Methods, Results, Tables 1–6, Figs. 1–6 and References.

Reporting Summary

Supplementary Code 1

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

Hu, Hx., Liu, C., Zhang, Jb. et al. A transdiagnostic network analysis of motivation and pleasure, expressivity and social functioning. Nat. Mental Health 1, 586–595 (2023). https://doi.org/10.1038/s44220-023-00102-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s44220-023-00102-3

Search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research