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Suppressed activity of the rostral anterior cingulate cortex as a biomarker for depression remission
BACKGROUND: Suppression of the rostral anterior cingulate cortex (rACC) has shown promise as a prognostic biomarker for depression. We aimed to use machine learning to characterise its ability to predict depression remission. METHODS: Data were obtained from 81 15- to 25-year-olds with a major depre...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cambridge University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10123826/ https://www.ncbi.nlm.nih.gov/pubmed/36762975 http://dx.doi.org/10.1017/S0033291721004323 |
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author | Davey, Christopher G. Cearns, Micah Jamieson, Alec Harrison, Ben J. |
author_facet | Davey, Christopher G. Cearns, Micah Jamieson, Alec Harrison, Ben J. |
author_sort | Davey, Christopher G. |
collection | PubMed |
description | BACKGROUND: Suppression of the rostral anterior cingulate cortex (rACC) has shown promise as a prognostic biomarker for depression. We aimed to use machine learning to characterise its ability to predict depression remission. METHODS: Data were obtained from 81 15- to 25-year-olds with a major depressive disorder who had participated in the YoDA-C trial, in which they had been randomised to receive cognitive behavioural therapy plus either fluoxetine or placebo. Prior to commencing treatment patients performed a functional magnetic resonance imaging (fMRI) task to assess rACC suppression. Support vector machines were trained on the fMRI data using nested cross-validation, and were similarly trained on clinical data. We further tested our fMRI model on data from the YoDA-A trial, in which participants had completed the same fMRI paradigm. RESULTS: Thirty-six of 81 (44%) participants in the YoDA-C trial achieved remission. Our fMRI model was able to predict remission status (AUC = 0.777 [95% confidence interval (CI) 0.638–0.916], balanced accuracy = 67%, negative predictive value = 74%, p < 0.0001). Clinical models failed to predict remission status at better than chance levels. Testing the model on the alternative YoDA-A dataset confirmed its ability to predict remission (AUC = 0.776, balanced accuracy = 64%, negative predictive value = 70%, p < 0.0001). CONCLUSIONS: We confirm that rACC activity acts as a prognostic biomarker for depression. The machine learning model can identify patients who are likely to have difficult-to-treat depression, which might direct the earlier provision of enhanced support and more intensive therapies. |
format | Online Article Text |
id | pubmed-10123826 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cambridge University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-101238262023-04-25 Suppressed activity of the rostral anterior cingulate cortex as a biomarker for depression remission Davey, Christopher G. Cearns, Micah Jamieson, Alec Harrison, Ben J. Psychol Med Original Article BACKGROUND: Suppression of the rostral anterior cingulate cortex (rACC) has shown promise as a prognostic biomarker for depression. We aimed to use machine learning to characterise its ability to predict depression remission. METHODS: Data were obtained from 81 15- to 25-year-olds with a major depressive disorder who had participated in the YoDA-C trial, in which they had been randomised to receive cognitive behavioural therapy plus either fluoxetine or placebo. Prior to commencing treatment patients performed a functional magnetic resonance imaging (fMRI) task to assess rACC suppression. Support vector machines were trained on the fMRI data using nested cross-validation, and were similarly trained on clinical data. We further tested our fMRI model on data from the YoDA-A trial, in which participants had completed the same fMRI paradigm. RESULTS: Thirty-six of 81 (44%) participants in the YoDA-C trial achieved remission. Our fMRI model was able to predict remission status (AUC = 0.777 [95% confidence interval (CI) 0.638–0.916], balanced accuracy = 67%, negative predictive value = 74%, p < 0.0001). Clinical models failed to predict remission status at better than chance levels. Testing the model on the alternative YoDA-A dataset confirmed its ability to predict remission (AUC = 0.776, balanced accuracy = 64%, negative predictive value = 70%, p < 0.0001). CONCLUSIONS: We confirm that rACC activity acts as a prognostic biomarker for depression. The machine learning model can identify patients who are likely to have difficult-to-treat depression, which might direct the earlier provision of enhanced support and more intensive therapies. Cambridge University Press 2023-04 2021-12-09 /pmc/articles/PMC10123826/ /pubmed/36762975 http://dx.doi.org/10.1017/S0033291721004323 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited. |
spellingShingle | Original Article Davey, Christopher G. Cearns, Micah Jamieson, Alec Harrison, Ben J. Suppressed activity of the rostral anterior cingulate cortex as a biomarker for depression remission |
title | Suppressed activity of the rostral anterior cingulate cortex as a biomarker for depression remission |
title_full | Suppressed activity of the rostral anterior cingulate cortex as a biomarker for depression remission |
title_fullStr | Suppressed activity of the rostral anterior cingulate cortex as a biomarker for depression remission |
title_full_unstemmed | Suppressed activity of the rostral anterior cingulate cortex as a biomarker for depression remission |
title_short | Suppressed activity of the rostral anterior cingulate cortex as a biomarker for depression remission |
title_sort | suppressed activity of the rostral anterior cingulate cortex as a biomarker for depression remission |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10123826/ https://www.ncbi.nlm.nih.gov/pubmed/36762975 http://dx.doi.org/10.1017/S0033291721004323 |
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