Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Atluri, Sravya, Wong, Willy, Moreno, Sylvain, Blumberger, Daniel M., Daskalakis, Zafiris J., Farzan, Faranak
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2018
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
_version_ 1783368022625353728
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
work_keys_str_mv AT atlurisravya selectivemodulationofbrainnetworkdynamicsbyseizuretherapyintreatmentresistantdepression
AT wongwilly selectivemodulationofbrainnetworkdynamicsbyseizuretherapyintreatmentresistantdepression
AT morenosylvain selectivemodulationofbrainnetworkdynamicsbyseizuretherapyintreatmentresistantdepression
AT blumbergerdanielm selectivemodulationofbrainnetworkdynamicsbyseizuretherapyintreatmentresistantdepression
AT daskalakiszafirisj selectivemodulationofbrainnetworkdynamicsbyseizuretherapyintreatmentresistantdepression
AT farzanfaranak selectivemodulationofbrainnetworkdynamicsbyseizuretherapyintreatmentresistantdepression