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Using fMRI and machine learning to predict symptom improvement following cognitive behavioural therapy for psychosis
Cognitive behavioural therapy for psychosis (CBTp) involves helping patients to understand and reframe threatening appraisals of their psychotic experiences to reduce distress and increase functioning. Whilst CBTp is effective for many, it is not effective for all patients and the factors predicting...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6197386/ https://www.ncbi.nlm.nih.gov/pubmed/30343250 http://dx.doi.org/10.1016/j.nicl.2018.10.011 |
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author | Tolmeijer, Eva Kumari, Veena Peters, Emmanuelle Williams, Steven C.R. Mason, Liam |
author_facet | Tolmeijer, Eva Kumari, Veena Peters, Emmanuelle Williams, Steven C.R. Mason, Liam |
author_sort | Tolmeijer, Eva |
collection | PubMed |
description | Cognitive behavioural therapy for psychosis (CBTp) involves helping patients to understand and reframe threatening appraisals of their psychotic experiences to reduce distress and increase functioning. Whilst CBTp is effective for many, it is not effective for all patients and the factors predicting a good outcome remain poorly understood. Machine learning is a powerful approach that allows new predictors to be identified in a data-driven way, which can inform understanding of the mechanisms underlying therapeutic interventions, and ultimately make predictions about symptom improvement at the individual patient level. Thirty-eight patients with a diagnosis of schizophrenia completed a social affect task during functional MRI. Multivariate pattern analysis assessed whether treatment response in those receiving CBTp (n = 22) could be predicted by pre-therapy neural responses to facial affect that was either threat-related (ambiguous ‘neutral’ faces perceived as threatening in psychosis, in addition to angry and fearful faces) or prosocial (happy faces). The models predicted improvement in psychotic (r = 0.63, p = 0.003) and affective (r = 0.31, p = 0.05) symptoms following CBTp, but not in the treatment-as-usual group (n = 16). Psychotic symptom improvement was predicted by neural responses to threat-related affect across sensorimotor and frontal-limbic regions, whereas affective symptom improvement was predicted by neural responses to fearful faces only as well as prosocial affect across sensorimotor and frontal regions. These findings suggest that CBTp most likely improves psychotic and affective symptoms in those endorsing more threatening appraisals and mood-congruent processing biases, respectively, which are explored and reframed as part of the therapy. This study improves our understanding of the neurobiology of treatment response and provides a foundation that will hopefully lead to greater precision and tailoring of the interventions offered to patients. |
format | Online Article Text |
id | pubmed-6197386 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-61973862018-10-24 Using fMRI and machine learning to predict symptom improvement following cognitive behavioural therapy for psychosis Tolmeijer, Eva Kumari, Veena Peters, Emmanuelle Williams, Steven C.R. Mason, Liam Neuroimage Clin Regular Article Cognitive behavioural therapy for psychosis (CBTp) involves helping patients to understand and reframe threatening appraisals of their psychotic experiences to reduce distress and increase functioning. Whilst CBTp is effective for many, it is not effective for all patients and the factors predicting a good outcome remain poorly understood. Machine learning is a powerful approach that allows new predictors to be identified in a data-driven way, which can inform understanding of the mechanisms underlying therapeutic interventions, and ultimately make predictions about symptom improvement at the individual patient level. Thirty-eight patients with a diagnosis of schizophrenia completed a social affect task during functional MRI. Multivariate pattern analysis assessed whether treatment response in those receiving CBTp (n = 22) could be predicted by pre-therapy neural responses to facial affect that was either threat-related (ambiguous ‘neutral’ faces perceived as threatening in psychosis, in addition to angry and fearful faces) or prosocial (happy faces). The models predicted improvement in psychotic (r = 0.63, p = 0.003) and affective (r = 0.31, p = 0.05) symptoms following CBTp, but not in the treatment-as-usual group (n = 16). Psychotic symptom improvement was predicted by neural responses to threat-related affect across sensorimotor and frontal-limbic regions, whereas affective symptom improvement was predicted by neural responses to fearful faces only as well as prosocial affect across sensorimotor and frontal regions. These findings suggest that CBTp most likely improves psychotic and affective symptoms in those endorsing more threatening appraisals and mood-congruent processing biases, respectively, which are explored and reframed as part of the therapy. This study improves our understanding of the neurobiology of treatment response and provides a foundation that will hopefully lead to greater precision and tailoring of the interventions offered to patients. Elsevier 2018-10-10 /pmc/articles/PMC6197386/ /pubmed/30343250 http://dx.doi.org/10.1016/j.nicl.2018.10.011 Text en © 2018 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Regular Article Tolmeijer, Eva Kumari, Veena Peters, Emmanuelle Williams, Steven C.R. Mason, Liam Using fMRI and machine learning to predict symptom improvement following cognitive behavioural therapy for psychosis |
title | Using fMRI and machine learning to predict symptom improvement following cognitive behavioural therapy for psychosis |
title_full | Using fMRI and machine learning to predict symptom improvement following cognitive behavioural therapy for psychosis |
title_fullStr | Using fMRI and machine learning to predict symptom improvement following cognitive behavioural therapy for psychosis |
title_full_unstemmed | Using fMRI and machine learning to predict symptom improvement following cognitive behavioural therapy for psychosis |
title_short | Using fMRI and machine learning to predict symptom improvement following cognitive behavioural therapy for psychosis |
title_sort | using fmri and machine learning to predict symptom improvement following cognitive behavioural therapy for psychosis |
topic | Regular Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6197386/ https://www.ncbi.nlm.nih.gov/pubmed/30343250 http://dx.doi.org/10.1016/j.nicl.2018.10.011 |
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