Personalized Models of Psychopathology

Wright, Aidan G.C.; Woods, William C.

doi:10.1146/annurev-clinpsy-102419-125032

Cited by 223 publications

(243 citation statements)

References 132 publications

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“…First there is need for better quality between-person (nomothetic) evidence in terms of longitudinal designs and experimental manipulations. Second, there is a long recognised inferential jump that evidence from between-person studies automatically applies to within-person models (see Molenaar, 2004) and within-person or idiographic studies are required (see Wright and Woods, 2020), despite the challenges of defining, operationalising and analysing such models (e.g. Fried, 2020).…”

Section: Caveatsmentioning

confidence: 99%

Towards a model of uncertainty distress in the context of Coronavirus (COVID-19)

Freeston

Tiplady²,

Mawn

et al. 2020

tCBT

149

126

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Abstract The paper forms part of a series of papers outlining the theoretical framework for a new model of uncertainty distress (this paper), treatment implications arising from the model, and empirical tests of the model. We define uncertainty distress as the subjective negative emotions experienced in response to the as yet unknown aspects of a given situation. In the first paper we drawn on a robust body of research on distinct areas including: threat models of anxiety, perceived illness uncertainty and intolerance of uncertainty. We explore how threat and uncertainty are separable in anxiety and how we can understand behaviours in response to uncertainty. Finally, we propose a clinically, theoretically, and empirically informed model for uncertainty distress and outline how this model can be tested. Caveats, clinical applications and practitioner key points are briefly included, although these are more fully outlined in the treatment implications article. While we outline this model in the context of novel coronavirus (Covid-19), the model has broader applications to both mental and physical health care settings.

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Section: Caveatsmentioning

confidence: 99%

Towards a model of uncertainty distress in the context of Coronavirus (COVID-19)

Freeston

Tiplady²,

Mawn

et al. 2020

tCBT

149

126

View full text Add to dashboard Cite

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“…Because of the heterogeneity between patients and the intrinsic non-stationarity of clinical change processes, evidence-based practice guidelines based on group-level comparisons are only of limited use for the care of individual patients [10]. This is why scholars have called for a personalized approach to psychopathology, in which treatment is tailored to the specifics of the individual patient [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Complexity in psychological self-ratings: implications for research and practice

Olthof

Hasselman

Lichtwarck‐Aschoff

2020

BMC Med

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Background Psychopathology research is changing focus from group-based “disease models” to a personalized approach inspired by complex systems theories. This approach, which has already produced novel and valuable insights into the complex nature of psychopathology, often relies on repeated self-ratings of individual patients. So far, it has been unknown whether such self-ratings, the presumed observables of the individual patient as a complex system, actually display complex dynamics. We examine this basic assumption of a complex systems approach to psychopathology by testing repeated self-ratings for three markers of complexity: memory, the presence of (time-varying) short- and long-range temporal correlations; regime shifts, transitions between different dynamic regimes; and sensitive dependence on initial conditions, also known as the “butterfly effect,” the divergence of initially similar trajectories. Methods We analyzed repeated self-ratings (1476 time points) from a single patient for the three markers of complexity using Bartels rank test, (partial) autocorrelation functions, time-varying autoregression, a non-stationarity test, change point analysis, and the Sugihara-May algorithm. Results Self-ratings concerning psychological states (e.g., the item “I feel down”) exhibited all complexity markers: time-varying short- and long-term memory, multiple regime shifts, and sensitive dependence on initial conditions. Unexpectedly, self-ratings concerning physical sensations (e.g., the item “I am hungry”) exhibited less complex dynamics and their behavior was more similar to random variables. Conclusions Psychological self-ratings display complex dynamics. The presence of complexity in repeated self-ratings means that we have to acknowledge that (1) repeated self-ratings yield a complex pattern of data and not a set of (nearly) independent data points, (2) humans are “moving targets” whose self-ratings display non-stationary change processes including regime shifts, and (3) long-term prediction of individual trajectories may be fundamentally impossible. These findings point to a limitation of popular statistical time series models whose assumptions are violated by the presence of these complexity markers. We conclude that a complex systems approach to mental health should appreciate complexity as a fundamental aspect of psychopathology research by adopting the models and methods of complexity science. Promising first steps in this direction, such as research on real-time process monitoring, short-term prediction, and just-in-time interventions, are discussed.

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“…Such an approach could determine whether some people represent a phenomena of interest as a common cause or causal system. This in turn could offer inferences into individualized psychopathological intervention (Wright & Woods, 2020), providing more specific answers to the question about whether it would be more effective for a clinician to treat an underlying disorder or specific symptoms.…”

Section: Discussionmentioning

confidence: 99%

Factor or Network Model? Predictions From Neural Networks

Christensen¹,

Golino²

2020

Preprint

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The nature of associations between variables is important for constructing theory about psychological phenomena. In the last decade, this topic has received renewed interested with the introduction of psychometric network models. In psychology, these models are often contrasted with latent variable (e.g., factor) models. Recent research has shown that the differences between the two tend to be more substantive than statistical (i.e., equivalent statistical representations but differing data generating hypotheses). Still, some researchers have shown that there may exist differences when data are generated from these models (e.g., sparse network models and unidimensional factor models). One recently developed algorithm called the Loadings Comparison Test (LCT) takes advantage of the loading structures of network and factor models to predict whether data were generated from a random, factor, or network model. A significant limitation in the current implementation of the LCT is that it’s based on heuristics that were derived from descriptive statistics of a limited set of data structures. In the present study, we used artificial neural networks to replace these heuristics, and develop a more robust and generalizable algorithm. We performed a simulation study that compared neural networks to the original LCT algorithm as well as logistic regression models that were trained on the same data. We found that the neural networks performed as well as or better than both methods, demonstrating generalizablity across data generating models. We echo the call for more formal theories about the relations between variables and discuss the role of the LCT in this process.

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Personalized Models of Psychopathology

Cited by 223 publications

References 132 publications

Towards a model of uncertainty distress in the context of Coronavirus (COVID-19)

Towards a model of uncertainty distress in the context of Coronavirus (COVID-19)

Complexity in psychological self-ratings: implications for research and practice

Factor or Network Model? Predictions From Neural Networks

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