Identifying Transdiagnostic Mechanisms in Mental Health Using Computational Factor Modeling

Wise, Toby; Robinson, Oliver J.; Gillan, Claire

doi:10.1016/j.biopsych.2022.09.034

Cited by 28 publications

(29 citation statements)

References 151 publications

(161 reference statements)

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“…There is a growing interest in these confidence abnormalities in psychiatry, with studies implicating alterations of metacognition in depression, obsessive-compulsive disorder, and psychosis (5). While case-control studies have mainly found patterns of reduced confidence across several disorders, newer methods that can separate transdiagnostic dimensions of mental health using large online samples have revealed specific and bi-directional effects of confidence (6). Using these methods, studies have shown that the transdiagnostic dimension 'anxious-depression' is linked to under-confidence in one's own performance, while a separate dimension 'compulsivity and intrusive thought' is related to elevated confidence (7)(8)(9)(10)(11).…”

Section: Introductionmentioning

confidence: 99%

An observational treatment study of metacognition in anxious-depression

Fox¹,

Lee²,

Hanlon³

et al. 2023

Preprint

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Objective: Prior studies have found metacognitive impairments are linked to a transdiagnostic dimension of anxious-depression, manifesting as reduced confidence in performance (‘metacognitive bias’). However, previous work has been cross-sectional and so it is unclear if under-confidence is a trait-like marker of anxious-depression vulnerability, or if it resolves when anxious-depression improves.Methods: Data were collected as part of the ‘Precision in Psychiatry’ study, a large-scale transdiagnostic, four-week observational study of individuals initiating internet-based cognitive behavioural therapy (iCBT) or antidepressant medication. Self-reported clinical questionnaires and perceptual task performance were gathered to assess anxious-depression and metacognitive bias at baseline and four-week follow-up. Primary analyses were conducted for individuals who received iCBT (n=649), with comparisons between smaller samples that received antidepressant medication (n=88) and a control group receiving no intervention (n=82).Results: Prior to receiving treatment, anxious-depression severity was associated with under-confidence in performance in the iCBT arm, replicating previous work. From baseline to follow-up, levels of anxious-depression were significantly reduced, and this was accompanied by a significant increase in metacognitive confidence (B=0.17, SE=0.02, p<0.001). These changes were correlated (r(647)=-0.12, p=0.002); those with the greatest reductions in anxious-depression levels had the largest increase in confidence. In the antidepressant arm, anxious-depression reduced (B=-0.61, SE=0.09, p<0.001) and confidence increased (B=0.31, SE=0.08, p<0.001). Among controls, confidence remained stable from baseline to follow-up (B=0.11, SE=0.07, p=0.103). Conclusions: Metacognitive biases in anxious-depression are state-dependent; when symptoms improve with treatment, so does confidence in performance. Our results suggest this is not specific to the type of intervention.

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Section: Introductionmentioning

confidence: 99%

An observational treatment study of metacognition in anxious-depression

Fox¹,

Lee²,

Hanlon³

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…Further, whilst we included tests to check whether participants were wearing headphones for this online study, we could not be certain that the sounds were not played over speakers and/or played at a sufficiently high volume to be aversive. Finally, it may simply be that the relationships between psychiatric symptoms and behaviour are small due to the inherent variability of individuals, the lack of precision of self-reported symptoms or the unreliability of mental health diagnoses (Freedman et al, 2013;Wise et al, 2022). Despite these limitations, an advantage of using web-based auditory stimuli is their efficiency in scaling with sample size, as only a web-browser is needed to complete the task.…”

Section: Discussionmentioning

confidence: 99%

Approach-avoidance reinforcement learning as a translational and computational model of anxiety-related avoidance

Yamamori

Robinson

Roiser

2023

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Although avoidance is a prevalent feature of anxiety-related psychopathology, differences in existing measures of avoidance between humans and non-human animals impede progress in its theoretical understanding and treatment. To address this, we developed a novel translational measure of anxiety-related avoidance in the form of an approach-avoidance reinforcement learning task, by adapting a paradigm from the non-human animal literature to study the same cognitive processes in human participants. We used computational modelling to probe the putative cognitive mechanisms underlying approach-avoidance behaviour in this task and investigated how they relate to subjective task-induced anxiety. In a large online study, participants (n = 372) who experienced greater task-induced anxiety avoided choices associated with punishment, even when this resulted in lower overall reward. Computational modelling revealed that this effect was explained by greater individual sensitivities to punishment relative to rewards. We replicated these findings in an independent sample (n = 627) and we also found fair-to-excellent reliability of measures of task performance in a sub-sample retested one week later (n = 57). Our findings demonstrate the potential of approach-avoidance reinforcement learning tasks as translational and computational models of anxiety-related avoidance. Future studies should assess the predictive validity of this approach in clinical samples and experimental manipulations of anxiety.

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“…A considerable amount of research using temporal reproduction tasks conflates these processes, thereby hindering progress in understanding their neurophysiological and cognitive correlates. This work thus may provide an analytic approach that could be harnessed in computational factor modelling, whereby factor analytic and computational modelling approaches are integrated in attempts to characterise symptom heterogeneity in clinical and non-clinical samples (Wise et al, 2023). In particular, we expect that this method could inform future research on distortions in time perception in response to pharmacological agents (e.g., (Sadibolova et al, 2023;Wittmann et al, 2007;Yanakieva et al, 2019) and in psychiatric and neurological disorders (Bschor et al, 2004;DiMarco et al, 2023;Thoenes & Oberfeld, 2017).…”

Section: Discussionmentioning

confidence: 99%

Uncovering the latent structure of human time perception

Sadibolova¹,

Widmer²,

Fletcher³

et al. 2023

Preprint

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One of the ongoing controversies in interval timing concerns whether human time perception is subserved by multiple distinct mechanisms. The crux of this debate concerns whether timing over subsecond and suprasecond intervals can be attributed to a single semi-uniform mechanism or requires distinct or interacting mechanisms. Whereas past studies provide some valuable insights, our study overcomes previous limitations by adopting multiple convergent statistical approaches in a design with strong statistical power. We conducted two large online experiments in which participants reproduced temporal intervals ranging from 400 to 2400ms (Experiment 1; N=302) and 1000 to 2000ms (Experiment 2; N=302). We contrasted the application of exploratory factor analysis and structural equation modelling (ESEM; bifactor analysis) to distinguish different latent structures underlying duration reproduction patterns and compared model results against the results from subject-level changepoint analyses. In both experiments, the bifactor analysis model was the most parsimonious and best-fitting. This analysis yielded evidence for a two-factor model comprising a general timing factor spanning the full interval range and a second factor that quadratically scaled relative to middle intervals, which may reflect a Bayesian mechanism subserving the central tendency bias (reproduced intervals regressed to the mean of stimulus interval range). We observed a low proportion of changepoints in median reproduced intervals with the changepoint detection predicted by factor scores. Our findings contribute to the ongoing debate concerning the mechanisms in millisecond-to-second interval range and highlight the role of central tendency effects in investigating the changepoint and therefore the nature of continuity between timing systems.

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Identifying Transdiagnostic Mechanisms in Mental Health Using Computational Factor Modeling

Cited by 28 publications

References 151 publications

An observational treatment study of metacognition in anxious-depression

An observational treatment study of metacognition in anxious-depression

Approach-avoidance reinforcement learning as a translational and computational model of anxiety-related avoidance

Uncovering the latent structure of human time perception

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