Cargando…

Functional network connectivity patterns predicting the efficacy of repetitive transcranial magnetic stimulation in the spectrum of Alzheimer’s disease

BACKGROUND: Neuro-navigated repetitive transcranial magnetic stimulation (rTMS) is potentially effective in enhancing cognitive performance in the spectrum of Alzheimer’s disease (AD). We explored the effect of rTMS-induced network reorganization and its predictive value for individual treatment res...

Descripción completa

Detalles Bibliográficos
Autores principales: Chen, Haifeng, Li, Mengyun, Qin, Zhiming, Yang, Zhiyuan, Lv, Tingyu, Yao, Weina, Hu, Zheqi, Qin, Ruomeng, Zhao, Hui, Bai, Feng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Vienna 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10593644/
https://www.ncbi.nlm.nih.gov/pubmed/37872457
http://dx.doi.org/10.1186/s41747-023-00376-3
_version_ 1785124483986620416
author Chen, Haifeng
Li, Mengyun
Qin, Zhiming
Yang, Zhiyuan
Lv, Tingyu
Yao, Weina
Hu, Zheqi
Qin, Ruomeng
Zhao, Hui
Bai, Feng
author_facet Chen, Haifeng
Li, Mengyun
Qin, Zhiming
Yang, Zhiyuan
Lv, Tingyu
Yao, Weina
Hu, Zheqi
Qin, Ruomeng
Zhao, Hui
Bai, Feng
author_sort Chen, Haifeng
collection PubMed
description BACKGROUND: Neuro-navigated repetitive transcranial magnetic stimulation (rTMS) is potentially effective in enhancing cognitive performance in the spectrum of Alzheimer’s disease (AD). We explored the effect of rTMS-induced network reorganization and its predictive value for individual treatment response. METHODS: Sixty-two amnestic mild cognitive impairment (aMCI) and AD patients were recruited. These subjects were assigned to multimodal magnetic resonance imaging scanning before and after a 4-week stimulation. Then, we investigated the neural mechanism underlying rTMS treatment based on static functional network connectivity (sFNC) and dynamic functional network connectivity (dFNC) analyses. Finally, the support vector regression was used to predict the individual rTMS treatment response through these functional features at baseline. RESULTS: We found that rTMS at the left angular gyrus significantly induced cognitive improvement in multiple cognitive domains. Participants after rTMS treatment exhibited significantly the increased sFNC between the right frontoparietal network (rFPN) and left frontoparietal network (lFPN) and decreased sFNC between posterior visual network and medial visual network. We revealed remarkable dFNC characteristics of brain connectivity, which was increased mainly in higher-order cognitive networks and decreased in primary networks or between primary networks and higher-order cognitive networks. dFNC characteristics in state 1 and state 4 could further predict individual higher memory improvement after rTMS treatment (state 1, R = 0.58; state 4, R = 0.54). CONCLUSION: Our findings highlight that neuro-navigated rTMS could suppress primary network connections to compensate for higher-order cognitive networks. Crucially, dynamic regulation of brain networks at baseline may serve as an individualized predictor of rTMS treatment response. RELEVANCE STATEMENT: Dynamic reorganization of brain networks could predict the efficacy of repetitive transcranial magnetic stimulation in the spectrum of Alzheimer’s disease. KEY POINTS: • rTMS at the left angular gyrus could induce cognitive improvement. • rTMS could suppress primary network connections to compensate for higher-order networks. • Dynamic reorganization of brain networks could predict individual treatment response to rTMS. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s41747-023-00376-3.
format Online
Article
Text
id pubmed-10593644
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Springer Vienna
record_format MEDLINE/PubMed
spelling pubmed-105936442023-10-25 Functional network connectivity patterns predicting the efficacy of repetitive transcranial magnetic stimulation in the spectrum of Alzheimer’s disease Chen, Haifeng Li, Mengyun Qin, Zhiming Yang, Zhiyuan Lv, Tingyu Yao, Weina Hu, Zheqi Qin, Ruomeng Zhao, Hui Bai, Feng Eur Radiol Exp Original Article BACKGROUND: Neuro-navigated repetitive transcranial magnetic stimulation (rTMS) is potentially effective in enhancing cognitive performance in the spectrum of Alzheimer’s disease (AD). We explored the effect of rTMS-induced network reorganization and its predictive value for individual treatment response. METHODS: Sixty-two amnestic mild cognitive impairment (aMCI) and AD patients were recruited. These subjects were assigned to multimodal magnetic resonance imaging scanning before and after a 4-week stimulation. Then, we investigated the neural mechanism underlying rTMS treatment based on static functional network connectivity (sFNC) and dynamic functional network connectivity (dFNC) analyses. Finally, the support vector regression was used to predict the individual rTMS treatment response through these functional features at baseline. RESULTS: We found that rTMS at the left angular gyrus significantly induced cognitive improvement in multiple cognitive domains. Participants after rTMS treatment exhibited significantly the increased sFNC between the right frontoparietal network (rFPN) and left frontoparietal network (lFPN) and decreased sFNC between posterior visual network and medial visual network. We revealed remarkable dFNC characteristics of brain connectivity, which was increased mainly in higher-order cognitive networks and decreased in primary networks or between primary networks and higher-order cognitive networks. dFNC characteristics in state 1 and state 4 could further predict individual higher memory improvement after rTMS treatment (state 1, R = 0.58; state 4, R = 0.54). CONCLUSION: Our findings highlight that neuro-navigated rTMS could suppress primary network connections to compensate for higher-order cognitive networks. Crucially, dynamic regulation of brain networks at baseline may serve as an individualized predictor of rTMS treatment response. RELEVANCE STATEMENT: Dynamic reorganization of brain networks could predict the efficacy of repetitive transcranial magnetic stimulation in the spectrum of Alzheimer’s disease. KEY POINTS: • rTMS at the left angular gyrus could induce cognitive improvement. • rTMS could suppress primary network connections to compensate for higher-order networks. • Dynamic reorganization of brain networks could predict individual treatment response to rTMS. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s41747-023-00376-3. Springer Vienna 2023-10-24 /pmc/articles/PMC10593644/ /pubmed/37872457 http://dx.doi.org/10.1186/s41747-023-00376-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Article
Chen, Haifeng
Li, Mengyun
Qin, Zhiming
Yang, Zhiyuan
Lv, Tingyu
Yao, Weina
Hu, Zheqi
Qin, Ruomeng
Zhao, Hui
Bai, Feng
Functional network connectivity patterns predicting the efficacy of repetitive transcranial magnetic stimulation in the spectrum of Alzheimer’s disease
title Functional network connectivity patterns predicting the efficacy of repetitive transcranial magnetic stimulation in the spectrum of Alzheimer’s disease
title_full Functional network connectivity patterns predicting the efficacy of repetitive transcranial magnetic stimulation in the spectrum of Alzheimer’s disease
title_fullStr Functional network connectivity patterns predicting the efficacy of repetitive transcranial magnetic stimulation in the spectrum of Alzheimer’s disease
title_full_unstemmed Functional network connectivity patterns predicting the efficacy of repetitive transcranial magnetic stimulation in the spectrum of Alzheimer’s disease
title_short Functional network connectivity patterns predicting the efficacy of repetitive transcranial magnetic stimulation in the spectrum of Alzheimer’s disease
title_sort functional network connectivity patterns predicting the efficacy of repetitive transcranial magnetic stimulation in the spectrum of alzheimer’s disease
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10593644/
https://www.ncbi.nlm.nih.gov/pubmed/37872457
http://dx.doi.org/10.1186/s41747-023-00376-3
work_keys_str_mv AT chenhaifeng functionalnetworkconnectivitypatternspredictingtheefficacyofrepetitivetranscranialmagneticstimulationinthespectrumofalzheimersdisease
AT limengyun functionalnetworkconnectivitypatternspredictingtheefficacyofrepetitivetranscranialmagneticstimulationinthespectrumofalzheimersdisease
AT qinzhiming functionalnetworkconnectivitypatternspredictingtheefficacyofrepetitivetranscranialmagneticstimulationinthespectrumofalzheimersdisease
AT yangzhiyuan functionalnetworkconnectivitypatternspredictingtheefficacyofrepetitivetranscranialmagneticstimulationinthespectrumofalzheimersdisease
AT lvtingyu functionalnetworkconnectivitypatternspredictingtheefficacyofrepetitivetranscranialmagneticstimulationinthespectrumofalzheimersdisease
AT yaoweina functionalnetworkconnectivitypatternspredictingtheefficacyofrepetitivetranscranialmagneticstimulationinthespectrumofalzheimersdisease
AT huzheqi functionalnetworkconnectivitypatternspredictingtheefficacyofrepetitivetranscranialmagneticstimulationinthespectrumofalzheimersdisease
AT qinruomeng functionalnetworkconnectivitypatternspredictingtheefficacyofrepetitivetranscranialmagneticstimulationinthespectrumofalzheimersdisease
AT zhaohui functionalnetworkconnectivitypatternspredictingtheefficacyofrepetitivetranscranialmagneticstimulationinthespectrumofalzheimersdisease
AT baifeng functionalnetworkconnectivitypatternspredictingtheefficacyofrepetitivetranscranialmagneticstimulationinthespectrumofalzheimersdisease