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Selective modulation of brain network dynamics by seizure therapy in treatment-resistant depression
BACKGROUND: Electroconvulsive therapy (ECT) is highly effective for treatment-resistant depression, yet its mechanism of action is still unclear. Understanding the mechanism of action of ECT can advance the optimization of magnetic seizure therapy (MST) towards higher efficacy and less cognitive imp...
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/PMC6214861/ https://www.ncbi.nlm.nih.gov/pubmed/30388600 http://dx.doi.org/10.1016/j.nicl.2018.10.015 |
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author | Atluri, Sravya Wong, Willy Moreno, Sylvain Blumberger, Daniel M. Daskalakis, Zafiris J. Farzan, Faranak |
author_facet | Atluri, Sravya Wong, Willy Moreno, Sylvain Blumberger, Daniel M. Daskalakis, Zafiris J. Farzan, Faranak |
author_sort | Atluri, Sravya |
collection | PubMed |
description | BACKGROUND: Electroconvulsive therapy (ECT) is highly effective for treatment-resistant depression, yet its mechanism of action is still unclear. Understanding the mechanism of action of ECT can advance the optimization of magnetic seizure therapy (MST) towards higher efficacy and less cognitive impairment. Given the neuroimaging evidence for disrupted resting-state network dynamics in depression, we investigated whether seizure therapy (ECT and MST) selectively modifies brain network dynamics for therapeutic efficacy. METHODS: EEG microstate analysis was used to evaluate resting-state network dynamics in patients at baseline and following seizure therapy, and in healthy controls. Microstate analysis defined four classes of brain states (labelled A, B, C, D). Source localization identified the brain regions associated with these states. RESULTS: An increase in duration and decrease in frequency of microstates was specific to responders of seizure therapy. Significant changes in the dynamics of States A, C and D were observed and predicted seizure therapy outcome (specifically ECT). Relative change in the duration of States C and D was shown to be a strong predictor of ECT response. Source localization partly associated C and D to the salience and frontoparietal networks, argued to be impaired in depression. An increase in duration and decrease in frequency of microstates was also observed following MST, however it was not specific to responders. CONCLUSION: This study presents the first evidence for the modulation of global brain network dynamics by seizure therapy. Successful seizure therapy was shown to selectively modulate network dynamics for therapeutic efficacy. |
format | Online Article Text |
id | pubmed-6214861 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-62148612018-11-07 Selective modulation of brain network dynamics by seizure therapy in treatment-resistant depression Atluri, Sravya Wong, Willy Moreno, Sylvain Blumberger, Daniel M. Daskalakis, Zafiris J. Farzan, Faranak Neuroimage Clin Regular Article BACKGROUND: Electroconvulsive therapy (ECT) is highly effective for treatment-resistant depression, yet its mechanism of action is still unclear. Understanding the mechanism of action of ECT can advance the optimization of magnetic seizure therapy (MST) towards higher efficacy and less cognitive impairment. Given the neuroimaging evidence for disrupted resting-state network dynamics in depression, we investigated whether seizure therapy (ECT and MST) selectively modifies brain network dynamics for therapeutic efficacy. METHODS: EEG microstate analysis was used to evaluate resting-state network dynamics in patients at baseline and following seizure therapy, and in healthy controls. Microstate analysis defined four classes of brain states (labelled A, B, C, D). Source localization identified the brain regions associated with these states. RESULTS: An increase in duration and decrease in frequency of microstates was specific to responders of seizure therapy. Significant changes in the dynamics of States A, C and D were observed and predicted seizure therapy outcome (specifically ECT). Relative change in the duration of States C and D was shown to be a strong predictor of ECT response. Source localization partly associated C and D to the salience and frontoparietal networks, argued to be impaired in depression. An increase in duration and decrease in frequency of microstates was also observed following MST, however it was not specific to responders. CONCLUSION: This study presents the first evidence for the modulation of global brain network dynamics by seizure therapy. Successful seizure therapy was shown to selectively modulate network dynamics for therapeutic efficacy. Elsevier 2018-10-17 /pmc/articles/PMC6214861/ /pubmed/30388600 http://dx.doi.org/10.1016/j.nicl.2018.10.015 Text en © 2018 Published by Elsevier Inc. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Regular Article Atluri, Sravya Wong, Willy Moreno, Sylvain Blumberger, Daniel M. Daskalakis, Zafiris J. Farzan, Faranak Selective modulation of brain network dynamics by seizure therapy in treatment-resistant depression |
title | Selective modulation of brain network dynamics by seizure therapy in treatment-resistant depression |
title_full | Selective modulation of brain network dynamics by seizure therapy in treatment-resistant depression |
title_fullStr | Selective modulation of brain network dynamics by seizure therapy in treatment-resistant depression |
title_full_unstemmed | Selective modulation of brain network dynamics by seizure therapy in treatment-resistant depression |
title_short | Selective modulation of brain network dynamics by seizure therapy in treatment-resistant depression |
title_sort | selective modulation of brain network dynamics by seizure therapy in treatment-resistant depression |
topic | Regular Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6214861/ https://www.ncbi.nlm.nih.gov/pubmed/30388600 http://dx.doi.org/10.1016/j.nicl.2018.10.015 |
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