Summary
Anxiety-related disorders - such as phobias, PTSD, social anxiety, panic disorder - are highly prevalent and pose a great burden on society. First-line treatment for such disorders is exposure therapy (ET), which entails safely exposing patients to the source of their anxiety. Although current ET protocols are generally effective, many patients do not respond to the therapy or experience residual symptoms and relapses. How to optimize many aspects of ET protocols, such as intensity and timing of the exposures, remains uncertain. Moreover, the current research approach of studying the effect of at most few variables using between-group trials, has produced mixed results, despite large efforts.
Finding optimized ET protocols is a high-dimensional search problem, with complexly interacting variables. To efficiently search this high-dimensional space, this action will develop a new paradigm for improving ET by utilizing modern artificial intelligence (AI) and biosignal analysis methods. In particular, the new bio-adaptive ET paradigm will make use of latest advances in reinforcement learning algorithms and psychophysiological models. Reinforcement learning will allow intelligently optimizing the exposure procedure, by sequentially learning from each trial and each participant, and psychophysiological models will allow to estimate the participant's anxiety level better than through overt behavior or physiological signals alone. Although aimed at anxiety disorders, the proposed bio-adaptive ET paradigm has the potential to serve as a blueprint for optimizing behavioral therapies in general.
This action will allow the fellow to gain valuable knowledge of latest AI techniques, which will put him at the forefront of the emerging discipline of computational psychiatry. Furthermore, the proposed agenda will lay the foundation for innovative translational research that will ultimately benefit patients in the EU and beyond.
Finding optimized ET protocols is a high-dimensional search problem, with complexly interacting variables. To efficiently search this high-dimensional space, this action will develop a new paradigm for improving ET by utilizing modern artificial intelligence (AI) and biosignal analysis methods. In particular, the new bio-adaptive ET paradigm will make use of latest advances in reinforcement learning algorithms and psychophysiological models. Reinforcement learning will allow intelligently optimizing the exposure procedure, by sequentially learning from each trial and each participant, and psychophysiological models will allow to estimate the participant's anxiety level better than through overt behavior or physiological signals alone. Although aimed at anxiety disorders, the proposed bio-adaptive ET paradigm has the potential to serve as a blueprint for optimizing behavioral therapies in general.
This action will allow the fellow to gain valuable knowledge of latest AI techniques, which will put him at the forefront of the emerging discipline of computational psychiatry. Furthermore, the proposed agenda will lay the foundation for innovative translational research that will ultimately benefit patients in the EU and beyond.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101067101 |
| Start date: | 01-09-2023 |
| End date: | 28-02-2026 |
| Total budget - Public funding: | - 229 501,00 Euro |
Cordis data
Original description
Anxiety-related disorders - such as phobias, PTSD, social anxiety, panic disorder - are highly prevalent and pose a great burden on society. First-line treatment for such disorders is exposure therapy (ET), which entails safely exposing patients to the source of their anxiety. Although current ET protocols are generally effective, many patients do not respond to the therapy or experience residual symptoms and relapses. How to optimize many aspects of ET protocols, such as intensity and timing of the exposures, remains uncertain. Moreover, the current research approach of studying the effect of at most few variables using between-group trials, has produced mixed results, despite large efforts.Finding optimized ET protocols is a high-dimensional search problem, with complexly interacting variables. To efficiently search this high-dimensional space, this action will develop a new paradigm for improving ET by utilizing modern artificial intelligence (AI) and biosignal analysis methods. In particular, the new bio-adaptive ET paradigm will make use of latest advances in reinforcement learning algorithms and psychophysiological models. Reinforcement learning will allow intelligently optimizing the exposure procedure, by sequentially learning from each trial and each participant, and psychophysiological models will allow to estimate the participant's anxiety level better than through overt behavior or physiological signals alone. Although aimed at anxiety disorders, the proposed bio-adaptive ET paradigm has the potential to serve as a blueprint for optimizing behavioral therapies in general.
This action will allow the fellow to gain valuable knowledge of latest AI techniques, which will put him at the forefront of the emerging discipline of computational psychiatry. Furthermore, the proposed agenda will lay the foundation for innovative translational research that will ultimately benefit patients in the EU and beyond.
Status
TERMINATEDCall topic
HORIZON-MSCA-2021-PF-01-01Update Date
09-02-2023
Geographical location(s)
