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Predicting individual responses to the electroconvulsive therapy with hippocampal subfield volumes in major depression disorder
Electroconvulsive therapy (ECT) is one of the most effective treatments for major depression disorder (MDD). ECT can induce neurogenesis and synaptogenesis in hippocampus, which contains distinct subfields, e.g., the cornu ammonis (CA) subfields, a granule cell layer (GCL), a molecular layer (ML), a...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5882798/ https://www.ncbi.nlm.nih.gov/pubmed/29615675 http://dx.doi.org/10.1038/s41598-018-23685-9 |
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author | Cao, Bo Luo, Qinghua Fu, Yixiao Du, Lian Qiu, Tian Yang, Xiangying Chen, Xiaolu Chen, Qibin Soares, Jair C. Cho, Raymond Y. Zhang, Xiang Yang Qiu, Haitang |
author_facet | Cao, Bo Luo, Qinghua Fu, Yixiao Du, Lian Qiu, Tian Yang, Xiangying Chen, Xiaolu Chen, Qibin Soares, Jair C. Cho, Raymond Y. Zhang, Xiang Yang Qiu, Haitang |
author_sort | Cao, Bo |
collection | PubMed |
description | Electroconvulsive therapy (ECT) is one of the most effective treatments for major depression disorder (MDD). ECT can induce neurogenesis and synaptogenesis in hippocampus, which contains distinct subfields, e.g., the cornu ammonis (CA) subfields, a granule cell layer (GCL), a molecular layer (ML), and the subiculum. It is unclear which subfields are affected by ECT and whether we predict the future treatment response to ECT by using volumetric information of hippocampal subfields at baseline? In this study, 24 patients with severe MDD received the ECT and their structural brain images were acquired with magnetic resonance imaging before and after ECT. A state-of-the-art hippocampal segmentation algorithm from Freesurfer 6.0 was used. We found that ECT induced volume increases in CA subfields, GCL, ML and subiculum. We applied a machine learning algorithm to the hippocampal subfield volumes at baseline and were able to predict the change in depressive symptoms (r = 0.81; within remitters, r = 0.93). Receiver operating characteristic analysis also showed robust prediction of remission with an area under the curve of 0.90. Our findings provide evidence for particular hippocampal subfields having specific roles in the response to ECT. We also provide an analytic approach for generating predictions about clinical outcomes for ECT in MDD. |
format | Online Article Text |
id | pubmed-5882798 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-58827982018-04-09 Predicting individual responses to the electroconvulsive therapy with hippocampal subfield volumes in major depression disorder Cao, Bo Luo, Qinghua Fu, Yixiao Du, Lian Qiu, Tian Yang, Xiangying Chen, Xiaolu Chen, Qibin Soares, Jair C. Cho, Raymond Y. Zhang, Xiang Yang Qiu, Haitang Sci Rep Article Electroconvulsive therapy (ECT) is one of the most effective treatments for major depression disorder (MDD). ECT can induce neurogenesis and synaptogenesis in hippocampus, which contains distinct subfields, e.g., the cornu ammonis (CA) subfields, a granule cell layer (GCL), a molecular layer (ML), and the subiculum. It is unclear which subfields are affected by ECT and whether we predict the future treatment response to ECT by using volumetric information of hippocampal subfields at baseline? In this study, 24 patients with severe MDD received the ECT and their structural brain images were acquired with magnetic resonance imaging before and after ECT. A state-of-the-art hippocampal segmentation algorithm from Freesurfer 6.0 was used. We found that ECT induced volume increases in CA subfields, GCL, ML and subiculum. We applied a machine learning algorithm to the hippocampal subfield volumes at baseline and were able to predict the change in depressive symptoms (r = 0.81; within remitters, r = 0.93). Receiver operating characteristic analysis also showed robust prediction of remission with an area under the curve of 0.90. Our findings provide evidence for particular hippocampal subfields having specific roles in the response to ECT. We also provide an analytic approach for generating predictions about clinical outcomes for ECT in MDD. Nature Publishing Group UK 2018-04-03 /pmc/articles/PMC5882798/ /pubmed/29615675 http://dx.doi.org/10.1038/s41598-018-23685-9 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Cao, Bo Luo, Qinghua Fu, Yixiao Du, Lian Qiu, Tian Yang, Xiangying Chen, Xiaolu Chen, Qibin Soares, Jair C. Cho, Raymond Y. Zhang, Xiang Yang Qiu, Haitang Predicting individual responses to the electroconvulsive therapy with hippocampal subfield volumes in major depression disorder |
title | Predicting individual responses to the electroconvulsive therapy with hippocampal subfield volumes in major depression disorder |
title_full | Predicting individual responses to the electroconvulsive therapy with hippocampal subfield volumes in major depression disorder |
title_fullStr | Predicting individual responses to the electroconvulsive therapy with hippocampal subfield volumes in major depression disorder |
title_full_unstemmed | Predicting individual responses to the electroconvulsive therapy with hippocampal subfield volumes in major depression disorder |
title_short | Predicting individual responses to the electroconvulsive therapy with hippocampal subfield volumes in major depression disorder |
title_sort | predicting individual responses to the electroconvulsive therapy with hippocampal subfield volumes in major depression disorder |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5882798/ https://www.ncbi.nlm.nih.gov/pubmed/29615675 http://dx.doi.org/10.1038/s41598-018-23685-9 |
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