Mathematical Characterization of Changes in Fear During Exposure Therapy

During exposure therapy, patients report increases in fear that generally decrease within and across exposure sessions. Our main aim was to characterize these changes in fear ratings mathematically; a secondary aim was to test whether the resulting model would help to predict treatment outcome. We a...

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Published inBiological psychiatry : cognitive neuroscience and neuroimaging Vol. 6; no. 11; pp. 1090 - 1099
Main Authors Portêlo, Ana, Shiban, Youssef, Maia, Tiago V.
Format Journal Article
LanguageEnglish
Published United States Elsevier Inc 01.11.2021
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ISSN2451-9022
2451-9030
2451-9030
DOI10.1016/j.bpsc.2021.01.005

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Abstract During exposure therapy, patients report increases in fear that generally decrease within and across exposure sessions. Our main aim was to characterize these changes in fear ratings mathematically; a secondary aim was to test whether the resulting model would help to predict treatment outcome. We applied tools of computational psychiatry to a previously published dataset in which 30 women with spider phobia were randomly assigned to virtual-reality exposures in a single context or in multiple contexts (n = 15 each). Patients provided fear ratings every minute during exposures. We characterized fear decrease within exposures and return of fear between exposures using a set of mathematical models; we selected the best model using Bayesian techniques. In the multiple-contexts group, we tested the predictions of the best model in a separate, test exposure, and we investigated the ability of model parameters to predict treatment outcome. The best model characterized fear decrease within exposures in both groups as an exponential decay with constant decay rate across exposures. The best model for each group had only two parameters but captured with remarkable accuracy the patterns of fear change, both at the group level and for individual subjects. The best model also made remarkably accurate predictions for the test exposure. One of the model’s parameters helped predict treatment outcome. Individual patterns of fear change during exposure therapy can be characterized mathematically. This mathematical characterization helps predict treatment outcome.
AbstractList During exposure therapy, patients report increases in fear that generally decrease within and across exposure sessions. Our main aim was to characterize these changes in fear ratings mathematically; a secondary aim was to test whether the resulting model would help to predict treatment outcome.BACKGROUNDDuring exposure therapy, patients report increases in fear that generally decrease within and across exposure sessions. Our main aim was to characterize these changes in fear ratings mathematically; a secondary aim was to test whether the resulting model would help to predict treatment outcome.We applied tools of computational psychiatry to a previously published dataset in which 30 women with spider phobia were randomly assigned to virtual-reality exposures in a single context or in multiple contexts (n = 15 each). Patients provided fear ratings every minute during exposures. We characterized fear decrease within exposures and return of fear between exposures using a set of mathematical models; we selected the best model using Bayesian techniques. In the multiple-contexts group, we tested the predictions of the best model in a separate, test exposure, and we investigated the ability of model parameters to predict treatment outcome.METHODSWe applied tools of computational psychiatry to a previously published dataset in which 30 women with spider phobia were randomly assigned to virtual-reality exposures in a single context or in multiple contexts (n = 15 each). Patients provided fear ratings every minute during exposures. We characterized fear decrease within exposures and return of fear between exposures using a set of mathematical models; we selected the best model using Bayesian techniques. In the multiple-contexts group, we tested the predictions of the best model in a separate, test exposure, and we investigated the ability of model parameters to predict treatment outcome.The best model characterized fear decrease within exposures in both groups as an exponential decay with constant decay rate across exposures. The best model for each group had only two parameters but captured with remarkable accuracy the patterns of fear change, both at the group level and for individual subjects. The best model also made remarkably accurate predictions for the test exposure. One of the model's parameters helped predict treatment outcome.RESULTSThe best model characterized fear decrease within exposures in both groups as an exponential decay with constant decay rate across exposures. The best model for each group had only two parameters but captured with remarkable accuracy the patterns of fear change, both at the group level and for individual subjects. The best model also made remarkably accurate predictions for the test exposure. One of the model's parameters helped predict treatment outcome.Individual patterns of fear change during exposure therapy can be characterized mathematically. This mathematical characterization helps predict treatment outcome.CONCLUSIONSIndividual patterns of fear change during exposure therapy can be characterized mathematically. This mathematical characterization helps predict treatment outcome.
During exposure therapy, patients report increases in fear that generally decrease within and across exposure sessions. Our main aim was to characterize these changes in fear ratings mathematically; a secondary aim was to test whether the resulting model would help to predict treatment outcome. We applied tools of computational psychiatry to a previously published dataset in which 30 women with spider phobia were randomly assigned to virtual-reality exposures in a single context or in multiple contexts (n = 15 each). Patients provided fear ratings every minute during exposures. We characterized fear decrease within exposures and return of fear between exposures using a set of mathematical models; we selected the best model using Bayesian techniques. In the multiple-contexts group, we tested the predictions of the best model in a separate, test exposure, and we investigated the ability of model parameters to predict treatment outcome. The best model characterized fear decrease within exposures in both groups as an exponential decay with constant decay rate across exposures. The best model for each group had only two parameters but captured with remarkable accuracy the patterns of fear change, both at the group level and for individual subjects. The best model also made remarkably accurate predictions for the test exposure. One of the model's parameters helped predict treatment outcome. Individual patterns of fear change during exposure therapy can be characterized mathematically. This mathematical characterization helps predict treatment outcome.
Author Portêlo, Ana
Maia, Tiago V.
Shiban, Youssef
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Issue 11
Keywords Fear
Exponentially decaying fear
Phobia
Computational psychiatry
Mathematical model
Exposure therapy
Language English
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Snippet During exposure therapy, patients report increases in fear that generally decrease within and across exposure sessions. Our main aim was to characterize these...
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SubjectTerms Computational psychiatry
Exponentially decaying fear
Exposure therapy
Fear
Mathematical model
Phobia
Title Mathematical Characterization of Changes in Fear During Exposure Therapy
URI https://www.clinicalkey.com/#!/content/1-s2.0-S2451902221000240
https://dx.doi.org/10.1016/j.bpsc.2021.01.005
https://www.ncbi.nlm.nih.gov/pubmed/33508496
https://www.proquest.com/docview/2483813227
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