Unravelling Repetitive Negative Thinking With Reinforcement Learning DOI Open Access

Rachel L Bedder,

Peter Hitchcock, Paul B. Sharp

et al.

Published: Sept. 2, 2024

Recent advances in the computational dynamics of planning and state inference from interdisciplinary field reinforcement learning offer rich opportunities for insights into repetitive negative thinking (RNT), specifically rumination worry. In this perspective, we apply key principles meta-reasoning to provide a normative foundation clinical phenomena associated with RNT, including excessive focus on potential events, impact overly abstract thinking, perpetuation RNT over time. We explore how these factors may contribute clinically relevant behavioral outcomes such as avoidance. propose two algorithmic accounts RNT: worry-as-planning rumination-as-inference, where agents learn through mentally simulating states actions. Furthermore, discuss algorithms can be viewed cognitive actions subject selection, learning, reinforcement. This integration opens avenues innovative approaches understanding intervening maladaptive thought patterns, ultimately advancing treatment RNT-related conditions.

Language: Английский

Precision Psychiatry for Obsessive-Compulsive Disorder: Clinical Applications of Deep Learning Architectures DOI Open Access
Brian A. Zaboski, Lora Bednarek

Journal of Clinical Medicine, Journal Year: 2025, Volume and Issue: 14(7), P. 2442 - 2442

Published: April 3, 2025

Obsessive-compulsive disorder (OCD) is a complex psychiatric condition characterized by significant heterogeneity in symptomatology and treatment response. Advances neuroimaging, EEG, other multimodal datasets have created opportunities to identify biomarkers predict outcomes, yet traditional statistical methods often fall short analyzing such high-dimensional data. Deep learning (DL) offers powerful tools for addressing these challenges leveraging architectures capable of classification, prediction, data generation. This brief review provides an overview five key DL architectures-feedforward neural networks, convolutional recurrent generative adversarial transformers-and their applications OCD research clinical practice. We highlight how models been used the predictors response, diagnose classify OCD, advance precision psychiatry. conclude discussing implementation DL, summarizing its advances promises underscoring field.

Language: Английский

Citations

0
Using computational models of learning to advance cognitive behavioral therapy DOI Creative Commons
Isabel M. Berwian,

Peter Hitchock,

Sashank Pisupati

et al.

Communications Psychology, Journal Year: 2025, Volume and Issue: 3(1)

Published: April 27, 2025

Abstract Many psychotherapy interventions have a large evidence base and can help substantial number of people with symptoms mental health conditions. However, we still little understanding why treatments work. Early advances in psychotherapy, such as the development exposure therapy, built on theoretical experimental from Pavlovian instrumental conditioning. More generally, all achieves change through learning. The past 25 years seen developments computational models learning, increased precision focus multiple learning mechanisms their interaction. Now might be good time to formalize improve our psychotherapy. To advance research bring together new joint field theory-driven first review literature cognitive behavioral therapy (exposure restructuring) introduce reinforcement representation We then suggest mapping these algorithms processes presumably underlying effects restructuring. Finally, outline how lens inform intervention research.

Language: Английский

Citations

0
Distinct cognitive and functional connectivity features from healthy cohorts inform clinical obsessive-compulsive disorder DOI Creative Commons
Luke J. Hearne, B.T. Thomas Yeo, Lachlan Webb

et al.

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 3, 2024

Improving diagnostic accuracy of obsessive-compulsive disorder (OCD) using models brain imaging data is a key goal the field, but this objective challenging due to limited size and phenotypic depth clinical datasets. Leveraging diversity in large non-clinical datasets such as UK Biobank (UKBB), offers potential solution problem. Nevertheless, it remains unclear whether classification trained on populations will generalise individuals with OCD. This question also relevant for conceptualisation OCD; specifically, symptomology OCD exists continuum from normal pathological. Here, we examined recently published "meta-matching" model functional connectivity five normative (N=45,507) predict cognitive, health demographic variables. Specifically, tested could classify status three independent (N=345). We found that identify out-of-sample individuals. Notably, most predictive features mapped onto known cortico-striatal abnormalities correlated genetic expression maps previously implicated disorder. Further, meta-matching relied upon estimates cognitive functions, flexibility inhibition, successfully These findings suggest variability behavioural can discriminate status. results support dimensional transdiagnostic basis OCD, implications research approaches treatment targets.

Language: Английский

Citations

1
The neuroscience of mental illness: Building toward the future DOI
Joshua A. Gordon, Kafui Dzirasa, Frederike H. Petzschner

et al.

Cell, Journal Year: 2024, Volume and Issue: 187(21), P. 5858 - 5870

Published: Oct. 1, 2024

Language: Английский

Citations

1
Unravelling Repetitive Negative Thinking With Reinforcement Learning DOI Open Access

Rachel L Bedder,

Peter Hitchcock, Paul B. Sharp

et al.

Published: Sept. 2, 2024

Recent advances in the computational dynamics of planning and state inference from interdisciplinary field reinforcement learning offer rich opportunities for insights into repetitive negative thinking (RNT), specifically rumination worry. In this perspective, we apply key principles meta-reasoning to provide a normative foundation clinical phenomena associated with RNT, including excessive focus on potential events, impact overly abstract thinking, perpetuation RNT over time. We explore how these factors may contribute clinically relevant behavioral outcomes such as avoidance. propose two algorithmic accounts RNT: worry-as-planning rumination-as-inference, where agents learn through mentally simulating states actions. Furthermore, discuss algorithms can be viewed cognitive actions subject selection, learning, reinforcement. This integration opens avenues innovative approaches understanding intervening maladaptive thought patterns, ultimately advancing treatment RNT-related conditions.

Language: Английский

Citations

0