University of Groningen
Moving From Static to Dynamic Models of the Onset of Mental Disorder A Review
Nelson, Barnaby; McGorry, Patrick D.; Wichers, Marieke; Wigman, Johanna T. W.; Hartmann,
Jessica A.
Published in:
Jama psychiatry
DOI:
10.1001/jamapsychiatry.2017.0001
IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from
it. Please check the document version below.
Document Version
Final author's version (accepted by publisher, after peer review)
Publication date:
2017
Link to publication in University of Groningen/UMCG research database
Citation for published version (APA):
Nelson, B., McGorry, P. D., Wichers, M., Wigman, J. T. W., & Hartmann, J. A. (2017). Moving From Static
to Dynamic Models of the Onset of Mental Disorder A Review.
Jama psychiatry
,
74
(5), 528-534.
https://doi.org/10.1001/jamapsychiatry.2017.0001
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Moving from static to dynamic models of the onset of mental disorder 6
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Nelson, Barnaby PhD
1, 2*
, McGorry, Patrick D. PhD
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, Wichers, Marieke PhD
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, Wigman, 9
Johanna T.W. PhD
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, Hartmann, Jessica A. PhD
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Orygen, The National Centre of Excellence in Youth Mental Health, The University of Melbourne 14
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Centre for Youth Mental Health, The University of Melbourne 15
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University of Groningen, University Medical Center Groningen (UMCG), Dept. of Psychiatry, 16
Interdisciplinary Center for Emotion regulation and Psychopathology (ICPE), Groningen, the 17
Netherlands 18
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Corresponding author. E-mail: Barnaby.nelson@orygen.org.au; +61 3 9342 2800 19
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Abstract 24
Importance: In recent years there has been increased focus on sub-threshold stages of mental 25
disorders, with attempts to model and predict which individuals will progress to full-threshold 26
disorder. Given this considerable research attention and clinical significance of the issue it is timely 27
to analyse the assumptions of the theoretical models in the field. 28
Observations: Psychiatric research into predicting onset of mental disorder has shown an 29
overreliance on one-off sampling of cross-sectional data (i.e., a "snapshot" of clinical state and 30
other risk markers) and may benefit from taking dynamic changes into account in predictive 31
modeling. Cross-disciplinary approaches to complex system structures and changes, such as 32
dynamical systems theory, network theory, instability mechanisms, chaos theory and catastrophe 33
theory, offer potent models that can be applied to emergence (or decline) of psychopathology, 34
including psychosis prediction but also to transdiagnostic emergence of symptoms. 35
Conclusions and Relevance: Psychiatric research may benefit from approaching psychopathology 36
as a system rather than as a category, identifying dynamics of system change (e.g., abrupt versus 37
gradual psychosis onset), identifying the factors to which these systems are most sensitive (e.g., 38
interpersonal dynamics, neurochemical change), and individual variability in system architecture 39
and change. These goals can be advanced by testing hypotheses that emerge from cross-disciplinary 40
models of complex systems. Future studies require repeat longitudinal assessment of relevant 41
variables through either, or a combination of, micro- (momentary, day-to-day) and macro- (months, 42
years) level assessments. Ecological momentary assessment is a data collection technique 43
appropriate for micro-level assessment. Relevant statistical approaches include joint modelling and 44
time series analysis, including metric- and model-based methods that draw on the mathematical 45
principles of dynamic systems. This next generation of prediction studies may more accurately 46
model the highly dynamic nature of psychopathology and system change, as well as have treatment 47
3
implications, such as introducing a means of identifying critical periods of risk for mental state 48
deterioration. 49
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In recent years there has been increased focus on sub-threshold stages of mental disorders, with 57
attempts to predict which individuals will progress to full-threshold (i.e., DSM or ICD diagnosable) 58
disorder
1,2
. A prototype for this line of research has been prediction of onset of psychotic disorder 59
in high risk cohorts defined through a combination of risk factors
3
. The standard research approach 60
consists of assessing a range of variables (clinical, neurocognitive, neurobiological, etc.) at clinical 61
service entry and investigating whether these variables predict the emergence of more severe 62
psychopathology (i.e., onset of psychotic disorder) over time. In the case of psychosis prediction 63
research this point of disorder onset has traditionally been defined as “transition” to first episode 64
psychosis
4
. The assumption here is that a single baseline assessment of clinical variables (e.g., 65
intensity of paranoid ideation or frequency of perceptual disturbances) may index level of risk for 66
emergence of diagnosable mental disorder (schizophrenia, major depression, etc.) over time
5
. In 67
other words, the approach assumes that a one-off sampling of cross-sectional data (i.e., a “snapshot” 68
of clinical state and other risk markers) can reliably predict future emergence of a particular mental 69
disorder or progression to more advanced stages of disorder
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. 70
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However, there is increasing recognition of psychopathology as being highly dynamic and 72
changeable in nature
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. Symptoms can vary substantially over time both on a “macro” (months, 73
years) level and a “micro” (momentary, day-to-day) level and also defy diagnostic boundaries, 74
changing from one clinical picture to another, particularly in the early phases of disorder
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. In 75
addition, these patterns of symptom development can differ substantially between individuals, 76
adding to the heterogeneous nature of emerging psychopathology. These characteristics of 77
psychopathology suggest that the ‘static’ model of prediction described above (i.e., predictions 78
based on single baseline assessments) may not be fit for purpose. This is also reflected in the 79
modest accuracy and replicability of static prediction models in the psychosis prediction field
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. 80
Rather, theoretical models and associated analytic techniques built on the dynamic nature of 81