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Chapter 4 - A Moving Target

How Risk for Mental Disorder Can Be Modelled in Dynamic Rather than Static Terms

from Section 1 - Conceptual and Strategic Issues

Published online by Cambridge University Press:  08 August 2019

Patrick D. McGorry
University of Melbourne
Ian B. Hickie
University of Sydney
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Recently, there has been increased focus on sub-threshold stages of mental disorders, with attempts to model and predict progression to full-threshold disorder. Given this considerable research attention and clinical significance, it is timely to analyse the assumptions of theoretical models in the field. Research into predicting onset of mental disorder has shown an overreliance on one-off sampling of cross-sectional data (i.e., a "snapshot" of clinical state and other risk markers) and may benefit from taking dynamic changes into account. Cross-disciplinary approaches to complex system structures and changes, such as dynamical systems theory, network theory, instability mechanisms, chaos theory and catastrophe theory, offer potent models that can be applied to emergence (or decline) of psychopathology, including psychosis prediction and transdiagnostic symptom emergence. Staging provides a useful framework to research dynamic prediction in psychiatry. Psychiatric research may benefit from approaching psychopathology as a system rather than a category, identifying dynamics of system change (e.g., abrupt/gradual psychosis onset), factors to which these systems are most sensitive (e.g., interpersonal dynamics, neurochemical change), and individual variability in system architecture and change. The next generation of prediction studies may more accurately model the highly dynamic nature of psychopathology and system change, with treatment implications, such as introducing a means of identifying critical risk periods for mental state deterioration.

Clinical Staging in Psychiatry
Making Diagnosis Work for Research and Treatment
, pp. 67 - 80
Publisher: Cambridge University Press
Print publication year: 2019

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Arzouan, Y., Moses, E., Peled, A., & Levit-Binnun, N. (2014). Impaired network stability in schizophrenia revealed by TMS perturbations. Schizophrenia Research, 152(1), 322324.CrossRefGoogle ScholarPubMed
Bak, M., Drukker, M., Hasmi, L., & van Os, J. (2016). An n=1 clinical network analysis of symptoms and treatment in psychosis. PLoS One, 11(9), e0162811.CrossRefGoogle ScholarPubMed
Bedi, G., Carrillo, F., Cecchi, G. A., Slezak, D. F., Sigman, M., Mota, N. B., … Corcoran, C. M. (2015). Automated analysis of free speech predicts psychosis onset in high-risk youths. NPJ Schizophrenia, 1, 15030.CrossRefGoogle ScholarPubMed
Birchwood, M. (1995). Early intervention in psychotic relapse: cognitive approaches to detection and management. Behaviour Change, 12, 29.CrossRefGoogle Scholar
Borsboom, D. (2017). A network theory of mental disorders. World Psychiatry, 16(1), 513.CrossRefGoogle ScholarPubMed
Borsboom, D., & Cramer, A. O. (2013). Network analysis: an integrative approach to the structure of psychopathology. Annual Review of Clinical Psychology, 9, 91121.CrossRefGoogle ScholarPubMed
Bystritsky, A., Nierenberg, A. A., Feusner, J. D., & Rabinovich, M. (2012). Computational non-linear dynamical psychiatry: a new methodological paradigm for diagnosis and course of illness. Journal of Psychiatric Research, 46(4), 428435.CrossRefGoogle ScholarPubMed
Cannon, T. D., Yu, C., Addington, J., Bearden, C. E., Cadenhead, K. S., Cornblatt, B. A., … Kattan, M. W. (2016). An individualized risk calculator for research in prodromal psychosis. American Journal of Psychiatry, 173(10), 980988.CrossRefGoogle ScholarPubMed
Carpenter, S. R., Cole, J. J., Pace, M. L., Batt, R., Brock, W. A., Cline, T., … Weidel, B. (2011). Early warnings of regime shifts: a whole-ecosystem experiment. Science, 332(6033), 10791082.CrossRefGoogle ScholarPubMed
Conrad, K. (1958). Die beginnende Schizophrenie. Versuch einer Gestaltanalyse des Wahns. Stuttgart: Thieme.Google Scholar
Dai, L., Vorselen, D., Korolev, K. S., & Gore, J. (2012). Generic indicators for loss of resilience before a tipping point leading to population collapse. Science, 336(6085), 11751177.CrossRefGoogle ScholarPubMed
Dunlop, P., Clark, C. D., & Hindmarsh, R. C. A. (2008). Bed ribbing instability explanation: testing a numerical model of ribbed moraine formation arising from coupled flow of ice and subglacial sediment. Journal of Geophysical Research: Earth Surface, 113(F3), F03005.CrossRefGoogle Scholar
Early Warning Signals Toolbox. What is a critical transition? Retrieved from
Fowler, A. C. (2010a). The formation of subglacial streams and mega-scale glacial lineations. Proceedings of the Royal Society A: Mathematical Physical and Engineering Sciences, 466(2123), 31813201.CrossRefGoogle Scholar
Fowler, A. C. (2010b). The instability theory of drumlin formation applied to Newtonian viscous ice of finite depth. Proceedings of the Royal Society A: Mathematical Physical and Engineering Sciences, 466(2121), 26732694.CrossRefGoogle Scholar
Fusar-Poli, P., Borgwardt, S., Bechdolf, A., Addington, J., Riecher-Rossler, A., Schultze-Lutter, F., … Yung, A. (2013). The psychosis high-risk state: a comprehensive state-of-the-art review. JAMA Psychiatry, 70(1), 107120.CrossRefGoogle ScholarPubMed
Fusar-Poli, P., & Schultze-Lutter, F. (2016). Predicting the onset of psychosis in patients at clinical high risk: practical guide to probabilistic prognostic reasoning. Evidence Based Mental Health, 19(1), 1015.CrossRefGoogle ScholarPubMed
Gharibzadeh, S., Zendehrouh, S., Vafadoost, M., & Bakouie, F. (2011). Is the functional state of schizophrenic patients located in the vicinity of a bifurcation point? Journal of Neuropsychiatry and Clinical Neurosciences, 23(2), E11.CrossRefGoogle ScholarPubMed
Globus, G. G., & Arpaia, J. P. (1994). Psychiatry and the new dynamics. Biological Psychiatry, 35(5), 352364.CrossRefGoogle ScholarPubMed
Guloksuz, S., Pries, L. K., & van Os, J. (2017). Application of network methods for understanding mental disorders: pitfalls and promise. Psychological Medicine, 47(16), 27432752.CrossRefGoogle ScholarPubMed
Hartmann, J., Nelson, B., Ratheesh, A., Treen, D., & McGorry, P. D. (2019). At-risk studies and clinical antecedents of psychosis, bipolar disorder and depression: a scoping review in the context of clinical staging. Psychological Medicine, 49(2), 177189.CrossRefGoogle ScholarPubMed
Hartmann, J., Nelson, B., Spooner, R., Amminger, G. P., Chanen, A., Davey, C. G., … McGorry, P. D. (in press). Broad clinical high-risk mental state (CHARMS): methodology of a cohort study validating criteria for pluripotent risk. Early Intervention in Psychiatry, in press.
Hickie, I. B., Scott, E. M., Hermens, D. F., Naismith, S. L., Guastella, A. J., Kaur, M., … McGorry, P. D. (2013). Applying clinical staging to young people who present for mental health care. Early Intervention in Psychiatry, 7(1), 3143.CrossRefGoogle ScholarPubMed
Hofmann, S. G., Curtiss, J., & McNally, R. J. (2016). A complex network perspective on clinical science. Perspectives on Psychological Science, 11(5), 597605.CrossRefGoogle ScholarPubMed
Isvoranu, A. M., Borsboom, D., van Os, J., & Guloksuz, S. (2016). A network approach to environmental impact in psychotic disorder: brief theoretical framework. Schizophrenia Bulletin, 42(4), 870873.CrossRefGoogle ScholarPubMed
Isvoranu, A. M., van Borkulo, C. D., Boyette, L. L., Wigman, J. T., Vinkers, C. H., Borsboom, D.; Group Investigators. (2017). A network approach to psychosis: pathways between childhood trauma and psychotic symptoms. Schizophrenia Bulletin, 43(1), 187196.CrossRefGoogle ScholarPubMed
Jaspers, K. (1963). General psychopathology. Trans. Hamilton, J. H. a. M. W.. Chicago, IL: University of Chicago Press.Google Scholar
Koutsouleris, N., Meisenzahl, E. M., Davatzikos, C., Bottlender, R., Frodl, T., Scheuerecker, J., … Gaser, C. (2009). Use of neuroanatomical pattern classification to identify subjects in at-risk mental states of psychosis and predict disease transition. Archives of General Psychiatry, 66(7), 700712.CrossRefGoogle ScholarPubMed
Lenton, T. M. (2011). Early warning of climate tipping points. Nature Climate Change, 1, 201209.CrossRefGoogle Scholar
Levit-Binnun, N., Litvak, V., Pratt, H., Moses, E., Zaroor, M., & Peled, A. (2010). Differences in TMS-evoked responses between schizophrenia patients and healthy controls can be observed without a dedicated EEG system. Clinical Neurophysiology, 121(3), 332339.CrossRefGoogle ScholarPubMed
Loh, M., Rolls, E. T., & Deco, G. (2007). A dynamical systems hypothesis of schizophrenia. PLoS Computational Biology, 3(11), e228.CrossRefGoogle ScholarPubMed
Lysaker, P. H., Dimaggio, G., Buck, K. D., Callaway, S. S., Salvatore, G., Carcione, A., … Stanghellini, G. (2011). Poor insight in schizophrenia: links between different forms of metacognition with awareness of symptoms, treatment need, and consequences of illness. Comprehensive Psychiatry, 52(3), 253260.CrossRefGoogle Scholar
Mandell, A. J., & Selz, K. A. (1992). Dynamical systems in psychiatry: now what? Biological Psychiatry, 32(4), 299301.CrossRefGoogle Scholar
McGorry, P. D. (2007). Issues for DSM-V: clinical staging – a heuristic pathway to valid nosology and safer, more effective treatment in psychiatry. American Journal of Psychiatry, 164(6), 859860.CrossRefGoogle Scholar
McGorry, P. D. (2010). Risk syndromes, clinical staging and DSM V: new diagnostic infrastructure for early intervention in psychiatry. Schizophrenia Research, 120(1–3), 4953.CrossRefGoogle Scholar
McGorry, P. D. (2013). Early clinical phenotypes, clinical staging, and strategic biomarker research: building blocks for personalized psychiatry. Biological Psychiatry, 74(6), 394395.CrossRefGoogle ScholarPubMed
McGorry, P. D., Hickie, I. B., Yung, A. R., Pantelis, C., & Jackson, H. J. (2006). Clinical staging of psychiatric disorders: a heuristic framework for choosing earlier, safer and more effective interventions. Australian and New Zealand Journal of Psychiatry, 40(8), 616622.CrossRefGoogle ScholarPubMed
McGorry, P., & Nelson, B. (2016). Why we need a transdiagnostic staging approach to emerging psychopathology, early diagnosis, and treatment. JAMA Psychiatry, 73(3), 191192.CrossRefGoogle Scholar
McGorry, P., & van Os, J. (2013). Redeeming diagnosis in psychiatry: timing versus specificity. Lancet, 381(9863), 343345.CrossRefGoogle ScholarPubMed
Minkowski, E. (1926). La Notion de Perte de Contact Vital avec la Réalité et ses Applications en Psychopathologie. Paris: Jouve & Cie.Google Scholar
Myin-Germeys, I., Oorschot, M., Collip, D., Lataster, J., Delespaul, P., & van Os, J. (2009). Experience sampling research in psychopathology: opening the black box of daily life. Psychological Medicine, 39(9), 15331547.CrossRefGoogle Scholar
Nelson, B., McGorry, P. D., Wichers, M., Wigman, J. T. W., & Hartmann, J. (2017). Moving from static to dynamic models of the onset of mental disorder. JAMA Psychiatry, 74(5), 528534.CrossRefGoogle ScholarPubMed
Odgers, C. L., Mulvey, E. P., Skeem, J. L., Gardner, W., Lidz, C. W., & Schubert, C. (2009). Capturing the ebb and flow of psychiatric symptoms with dynamical systems models. American Journal of Psychiatry, 166(5), 575582.CrossRefGoogle ScholarPubMed
Olde Rikkert, M. G., Dakos, V., Buchman, T. G., Boer, R., Glass, L., Cramer, A. O., … Scheffer, M. (2016). Slowing down of recovery as generic risk marker for acute severity transitions in chronic diseases. Critical Care Medicine, 44(3), 601606.CrossRefGoogle ScholarPubMed
Parnas, J., & Henriksen, M. G. (2014). Disordered self in the schizophrenia spectrum: a clinical and research perspective. Harvard Review of Psychiatry, 22(5), 251265.CrossRefGoogle ScholarPubMed
Paulus, M. P., & Braff, D. L. (2003). Chaos and schizophrenia: does the method fit the madness? Biological Psychiatry, 53, 311.CrossRefGoogle ScholarPubMed
Poston, T., & Steward, I. (1978). Catastrophe theory and its applications. London: Pitman.Google Scholar
Scheffer, M. (2009). Critical transitions in nature and society. Princeton, NJ: Princeton University Press.Google Scholar
Scheffer, M. (2010). Complex systems: foreseeing tipping points. Nature, 467(7314), 411412.CrossRefGoogle ScholarPubMed
Scheffer, M., Carpenter, S. R., Lenton, T. M., Bascompte, J., Brock, W., Dakos, V., … Vandermeer, J. (2012). Anticipating critical transitions. Science, 338(6105), 344348.CrossRefGoogle ScholarPubMed
Schultze-Lutter, F. (2009). Subjective symptoms of schizophrenia in research and the clinic: the basic symptoms concept. Schizophrenia Bulletin, 35(1), 58.CrossRefGoogle ScholarPubMed
Scott, D. W. (1985). Catastrophe theory applications in clinical psychology: a review. Current Psychological Research and Reviews, 4(1), 6986.CrossRefGoogle Scholar
Strobl, E. V., Eack, S. M., Swaminathan, V., & Visweswaran, S. (2012). Predicting the risk of psychosis onset: advances and prospects. Early Intervention in Psychiatry, 6(4), 368379.CrossRefGoogle ScholarPubMed
Tschacher, W., Scheier, C., & Hashimoto, Y. (1997). Dynamical analysis of schizophrenia courses. Biological Psychiatry, 41(4), 428437.CrossRefGoogle ScholarPubMed
van Borkulo, C., Boschloo, L., Borsboom, D., Penninx, B. W., Waldorp, L. J., & Schoevers, R. A. (2015). Association of symptom network structure with the course of longitudinal depression. JAMA Psychiatry, 72(12), 12191226.CrossRefGoogle Scholar
van de Leemput, I. A., Wichers, M., Cramer, A. O. J., Borsboom, D., Tuerlinckx, F., Kuppens, P., … Scheffer, M. (2014). Critical slowing down as early warning for the onset and termination of depression. Proceedings of the National Academy of Sciences of the United States of America, 111(1), 8792.CrossRefGoogle Scholar
van Os, J. (2013). The dynamics of subthreshold psychopathology: implications for diagnosis and treatment. American Journal of Psychiatry, 170(7), 695698.CrossRefGoogle ScholarPubMed
van Os, J., & Linscott, R. J. (2012). Introduction: the extended psychosis phenotype – relationship with schizophrenia and with ultrahigh risk status for psychosis. Schizophrenia Bulletin, 38(2), 227230.CrossRefGoogle Scholar
Veraart, A. J., Faassen, E. J., Dakos, V., van Nes, E. H., Lurling, M., & Scheffer, M. (2012). Recovery rates reflect distance to a tipping point in a living system. Nature, 481(7381), 357359.CrossRefGoogle Scholar
Vinogradov, S., King, R. J., & Huberman, B. A. (1992). An associationist model of the paranoid process: application of phase transitions in spreading activation networks. Psychiatry, 55(1), 7994.CrossRefGoogle ScholarPubMed
Wichers, M. (2014). The dynamic nature of depression: a new micro-level perspective of mental disorder that meets current challenges. Psychological Medicine, 44(7), 13491360.CrossRefGoogle ScholarPubMed
Wichers, M., Groot, P. C., & Psychosystems, ESM Group, EWS Group. (2016). Critical slowing down as a personalized early warning signal for depression. Psychotherapy and Psychosomatics, 85(2), 114116.CrossRefGoogle Scholar
Wichers, M., Wigman, J. T. W., & Myin-Germeys, I. (2015). Micro-level affect dynamics in psychopathology viewed from complex dynamical system theory. Emotion Review, 7(4), 362367.CrossRefGoogle Scholar
Wigman, J. T., Collip, D., Wichers, M., Delespaul, P., Derom, C., Thiery, E., … van Os, J. (2013a). Altered transfer of momentary mental states (ATOMS) as the basic unit of psychosis liability in interaction with environment and emotions. PLoS One, 8(2), e54653.CrossRefGoogle ScholarPubMed
Wigman, J. T. W., van Os, J., Thiery, E., Derom, C., Collip, D., Jacobs, N., & Wichers, M. (2013b). Psychiatric diagnosis revisited: towards a system of staging and profiling combining nomothetic and idiographic parameters of momentary mental states. PLoS One, 8(3), e59559.CrossRefGoogle ScholarPubMed
Yuen, H. P., & Mackinnon, A. (2016). Performance of joint modelling of time-to-event data with time-dependent predictors: an assessment based on transition to psychosis data. PeerJ, 4, e2582.CrossRefGoogle ScholarPubMed
Yuen, H. P., Mackinnon, A., & Nelson, B. (2018). A new method for analysing transition to psychosis: joint modelling of time‐to‐event outcome with time‐dependent predictors. International Journal of Methods in Psychiatric Research, 27(1), e1588.CrossRefGoogle ScholarPubMed
Yung, A. R., Nelson, B., Thompson, A., & Wood, S. J. (2010). The psychosis threshold in ultra high risk (prodromal) research: is it valid? Schizophrenia Research, 120(1–3), 16.CrossRefGoogle ScholarPubMed

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