Publication

Moving From Static to Dynamic Models of the Onset of Mental Disorder A Review

Nelson, B., McGorry, P. D., Wichers, M., Wigman, J. T. W. & Hartmann, J. A., May-2017, In : Jama psychiatry. 74, 5, p. 528-534 7 p.

Research output: Contribution to journalArticleAcademicpeer-review

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

Author

Nelson, Barnaby ; McGorry, Patrick D. ; Wichers, Marieke ; Wigman, Johanna T. W. ; Hartmann, Jessica A. / Moving From Static to Dynamic Models of the Onset of Mental Disorder A Review. In: Jama psychiatry. 2017 ; Vol. 74, No. 5. pp. 528-534.

Harvard

Nelson, B, McGorry, PD, Wichers, M, Wigman, JTW & Hartmann, JA 2017, 'Moving From Static to Dynamic Models of the Onset of Mental Disorder A Review', Jama psychiatry, vol. 74, no. 5, pp. 528-534. https://doi.org/10.1001/jamapsychiatry.2017.0001

Standard

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.

In: Jama psychiatry, Vol. 74, No. 5, 05.2017, p. 528-534.

Research output: Contribution to journalArticleAcademicpeer-review

Vancouver

Nelson B, McGorry PD, Wichers M, Wigman JTW, Hartmann JA. Moving From Static to Dynamic Models of the Onset of Mental Disorder A Review. Jama psychiatry. 2017 May;74(5):528-534. https://doi.org/10.1001/jamapsychiatry.2017.0001


BibTeX

@article{f90d9bef40a54e3eac4d99a286c6bd7f,
title = "Moving From Static to Dynamic Models of the Onset of Mental Disorder A Review",
abstract = "IMPORTANCE In recent years, there has been increased focus on subthreshold stages of mental disorders, with attempts to model and predict which individuals will progress to full-threshold disorder. Given this research attention and the clinical significance of the issue, this article analyzes the assumptions of the theoretical models in the field.OBSERVATIONS Psychiatric research into predicting the onset of mental disorder has shown an overreliance on one-off sampling of cross-sectional data (ie, a snapshot of clinical state and other risk markers) and may benefit from taking dynamic changes into account in predictive modeling. 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 the emergence (or decline) of psychopathology, including psychosis prediction, as well as to transdiagnostic emergence of symptoms.CONCLUSIONS AND RELEVANCE Psychiatric research may benefit from approaching psychopathology as a system rather than as a category, identifying dynamics of system change (eg, abrupt vs gradual psychosis onset), and determining the factors to which these systems are most sensitive (eg, interpersonal dynamics and neurochemical change) and the individual variability in system architecture and change. These goals can be advanced by testing hypotheses that emerge from cross-disciplinary models of complex systems. Future studies require repeated longitudinal assessment of relevant variables through either (or a combination of) micro-level (momentary and day-to-day) and macro-level (month and year) assessments. Ecological momentary assessment is a data collection technique appropriate for micro-level assessment. Relevant statistical approaches are joint modeling and time series analysis, includingmetric-based and model-based methods that draw on the mathematical principles of dynamical systems. This next generation of prediction studies may more accurately model the dynamic nature of psychopathology and system change as well as have treatment implications, such as introducing a means of identifying critical periods of risk for mental state deterioration.",
keywords = "CRITICAL SLOWING-DOWN, NETWORK APPROACH, TIPPING POINTS, DSM-V, PSYCHOSIS, RISK, PSYCHIATRY, PSYCHOPATHOLOGY, DEPRESSION, DIAGNOSIS",
author = "Barnaby Nelson and McGorry, {Patrick D.} and Marieke Wichers and Wigman, {Johanna T. W.} and Hartmann, {Jessica A.}",
year = "2017",
month = "5",
doi = "10.1001/jamapsychiatry.2017.0001",
language = "English",
volume = "74",
pages = "528--534",
journal = "Jama psychiatry",
issn = "2168-622X",
publisher = "AMER MEDICAL ASSOC",
number = "5",

}

RIS

TY - JOUR

T1 - Moving From Static to Dynamic Models of the Onset of Mental Disorder A Review

AU - Nelson, Barnaby

AU - McGorry, Patrick D.

AU - Wichers, Marieke

AU - Wigman, Johanna T. W.

AU - Hartmann, Jessica A.

PY - 2017/5

Y1 - 2017/5

N2 - IMPORTANCE In recent years, there has been increased focus on subthreshold stages of mental disorders, with attempts to model and predict which individuals will progress to full-threshold disorder. Given this research attention and the clinical significance of the issue, this article analyzes the assumptions of the theoretical models in the field.OBSERVATIONS Psychiatric research into predicting the onset of mental disorder has shown an overreliance on one-off sampling of cross-sectional data (ie, a snapshot of clinical state and other risk markers) and may benefit from taking dynamic changes into account in predictive modeling. 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 the emergence (or decline) of psychopathology, including psychosis prediction, as well as to transdiagnostic emergence of symptoms.CONCLUSIONS AND RELEVANCE Psychiatric research may benefit from approaching psychopathology as a system rather than as a category, identifying dynamics of system change (eg, abrupt vs gradual psychosis onset), and determining the factors to which these systems are most sensitive (eg, interpersonal dynamics and neurochemical change) and the individual variability in system architecture and change. These goals can be advanced by testing hypotheses that emerge from cross-disciplinary models of complex systems. Future studies require repeated longitudinal assessment of relevant variables through either (or a combination of) micro-level (momentary and day-to-day) and macro-level (month and year) assessments. Ecological momentary assessment is a data collection technique appropriate for micro-level assessment. Relevant statistical approaches are joint modeling and time series analysis, includingmetric-based and model-based methods that draw on the mathematical principles of dynamical systems. This next generation of prediction studies may more accurately model the dynamic nature of psychopathology and system change as well as have treatment implications, such as introducing a means of identifying critical periods of risk for mental state deterioration.

AB - IMPORTANCE In recent years, there has been increased focus on subthreshold stages of mental disorders, with attempts to model and predict which individuals will progress to full-threshold disorder. Given this research attention and the clinical significance of the issue, this article analyzes the assumptions of the theoretical models in the field.OBSERVATIONS Psychiatric research into predicting the onset of mental disorder has shown an overreliance on one-off sampling of cross-sectional data (ie, a snapshot of clinical state and other risk markers) and may benefit from taking dynamic changes into account in predictive modeling. 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 the emergence (or decline) of psychopathology, including psychosis prediction, as well as to transdiagnostic emergence of symptoms.CONCLUSIONS AND RELEVANCE Psychiatric research may benefit from approaching psychopathology as a system rather than as a category, identifying dynamics of system change (eg, abrupt vs gradual psychosis onset), and determining the factors to which these systems are most sensitive (eg, interpersonal dynamics and neurochemical change) and the individual variability in system architecture and change. These goals can be advanced by testing hypotheses that emerge from cross-disciplinary models of complex systems. Future studies require repeated longitudinal assessment of relevant variables through either (or a combination of) micro-level (momentary and day-to-day) and macro-level (month and year) assessments. Ecological momentary assessment is a data collection technique appropriate for micro-level assessment. Relevant statistical approaches are joint modeling and time series analysis, includingmetric-based and model-based methods that draw on the mathematical principles of dynamical systems. This next generation of prediction studies may more accurately model the dynamic nature of psychopathology and system change as well as have treatment implications, such as introducing a means of identifying critical periods of risk for mental state deterioration.

KW - CRITICAL SLOWING-DOWN

KW - NETWORK APPROACH

KW - TIPPING POINTS

KW - DSM-V

KW - PSYCHOSIS

KW - RISK

KW - PSYCHIATRY

KW - PSYCHOPATHOLOGY

KW - DEPRESSION

KW - DIAGNOSIS

U2 - 10.1001/jamapsychiatry.2017.0001

DO - 10.1001/jamapsychiatry.2017.0001

M3 - Article

VL - 74

SP - 528

EP - 534

JO - Jama psychiatry

JF - Jama psychiatry

SN - 2168-622X

IS - 5

ER -

ID: 41472251