Acupuncture modulates emotional network resting-state functional connectivity in patients with insomnia disorder: a randomized controlled trial and fMRI study

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Abstract

Background

Insomnia disorder (ID) is one of the most common sleep problems, usually accompanied by anxiety and depression symptoms. Functional magnetic resonance imaging (fMRI) study suggests that both poor sleep quality and negative emotion are linked to the dysregulation of brain network related to emotion processing in ID patients. Acupuncture therapy has been proven effective in improving sleep quality and mood of ID patients, but the involved neurobiological mechanism remains unclear. We aimed to investigate the modulation effect of acupuncture on resting-state functional connectivity (rsFC) of the emotional network (EN) in patients experiencing insomnia.

Methods

A total of 30 healthy controls (HCs) and 60 ID patients were enrolled in this study. Sixty ID patients were randomly assigned to real and sham acupuncture groups and attended resting-state fMRI scans before and after 4 weeks of acupuncture treatment. HCs completed an MRI/fMRI scan at baseline. The rsFC values within EN were calculated, and Hamilton Anxiety Scale (HAMA), Hamilton Depression Scale (HAMD), Pittsburgh Sleep Quality Index (PSQI), Hyperarousal Scale (HAS), and actigraphy data were collected for clinical efficacy evaluation.

Results

Resting-state FC analysis showed abnormalities in rsFC centered on the thalamus and dorsolateral prefrontal cortex within EN of ID patients compared to HCs. After real acupuncture treatment, rsFC of the anterior cingulate cortex, hippocampus, and amygdala were increased compared with the sham acupuncture group ( p < 0.05, FDR corrected). In real acupuncture group, the rsFC value was decreased between left amygdala and left thalamus after 4 weeks of treatment compared with baseline. A trend of correlation was found that the increased rsFC value between the right amygdala and left hippocampus was positively correlated with the decreased HAMA scores across all ID patients, and the decreased left amygdala rsFC value with the left thalamus was negatively correlated with the increased sleep efficiency in the real acupuncture group.

Conclusion

Our findings showed that real acupuncture could produce a positive effect on modulating rsFC within network related to emotion processing in ID patients, which may illustrate the central mechanism underlying acupuncture for insomnia in improving sleep quality and emotion regulation.

Trial registration

http://www.chictr.org.cn., ChiCTR1800015282, 20/03/2018.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12906-024-04612-0.

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Most cited references 100

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DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging.

Chao-Gan Yan, Xin-Di Wang, Xi-Nian Zuo … (2016)

Brain imaging efforts are being increasingly devoted to decode the functioning of the human brain. Among neuroimaging techniques, resting-state fMRI (R-fMRI) is currently expanding exponentially. Beyond the general neuroimaging analysis packages (e.g., SPM, AFNI and FSL), REST and DPARSF were developed to meet the increasing need of user-friendly toolboxes for R-fMRI data processing. To address recently identified methodological challenges of R-fMRI, we introduce the newly developed toolbox, DPABI, which was evolved from REST and DPARSF. DPABI incorporates recent research advances on head motion control and measurement standardization, thus allowing users to evaluate results using stringent control strategies. DPABI also emphasizes test-retest reliability and quality control of data processing. Furthermore, DPABI provides a user-friendly pipeline analysis toolkit for rat/monkey R-fMRI data analysis to reflect the rapid advances in animal imaging. In addition, DPABI includes preprocessing modules for task-based fMRI, voxel-based morphometry analysis, statistical analysis and results viewing. DPABI is designed to make data analysis require fewer manual operations, be less time-consuming, have a lower skill requirement, a smaller risk of inadvertent mistakes, and be more comparable across studies. We anticipate this open-source toolbox will assist novices and expert users alike and continue to support advancing R-fMRI methodology and its application to clinical translational studies.

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Network-based statistic: identifying differences in brain networks.

Andrew Zalesky, Alex Fornito, Edward T. Bullmore (2010)

Large-scale functional or structural brain connectivity can be modeled as a network, or graph. This paper presents a statistical approach to identify connections in such a graph that may be associated with a diagnostic status in case-control studies, changing psychological contexts in task-based studies, or correlations with various cognitive and behavioral measures. The new approach, called the network-based statistic (NBS), is a method to control the family-wise error rate (in the weak sense) when mass-univariate testing is performed at every connection comprising the graph. To potentially offer a substantial gain in power, the NBS exploits the extent to which the connections comprising the contrast or effect of interest are interconnected. The NBS is based on the principles underpinning traditional cluster-based thresholding of statistical parametric maps. The purpose of this paper is to: (i) introduce the NBS for the first time; (ii) evaluate its power with the use of receiver operating characteristic (ROC) curves; and, (iii) demonstrate its utility with application to a real case-control study involving a group of people with schizophrenia for which resting-state functional MRI data were acquired. The NBS identified a expansive dysconnected subnetwork in the group with schizophrenia, primarily comprising fronto-temporal and occipito-temporal dysconnections, whereas a mass-univariate analysis controlled with the false discovery rate failed to identify a subnetwork. Copyright © 2010 Elsevier Inc. All rights reserved.

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Functional grouping and cortical-subcortical interactions in emotion: a meta-analysis of neuroimaging studies.

Hedy Kober, Lisa Feldman Barrett, Josh Joseph … (2008)

We performed an updated quantitative meta-analysis of 162 neuroimaging studies of emotion using a novel multi-level kernel-based approach, focusing on locating brain regions consistently activated in emotional tasks and their functional organization into distributed functional groups, independent of semantically defined emotion category labels (e.g., "anger," "fear"). Such brain-based analyses are critical if our ways of labeling emotions are to be evaluated and revised based on consistency with brain data. Consistent activations were limited to specific cortical sub-regions, including multiple functional areas within medial, orbital, and inferior lateral frontal cortices. Consistent with a wealth of animal literature, multiple subcortical activations were identified, including amygdala, ventral striatum, thalamus, hypothalamus, and periaqueductal gray. We used multivariate parcellation and clustering techniques to identify groups of co-activated brain regions across studies. These analyses identified six distributed functional groups, including medial and lateral frontal groups, two posterior cortical groups, and paralimbic and core limbic/brainstem groups. These functional groups provide information on potential organization of brain regions into large-scale networks. Specific follow-up analyses focused on amygdala, periaqueductal gray (PAG), and hypothalamic (Hy) activations, and identified frontal cortical areas co-activated with these core limbic structures. While multiple areas of frontal cortex co-activated with amygdala sub-regions, a specific region of dorsomedial prefrontal cortex (dmPFC, Brodmann's Area 9/32) was the only area co-activated with both PAG and Hy. Subsequent mediation analyses were consistent with a pathway from dmPFC through PAG to Hy. These results suggest that medial frontal areas are more closely associated with core limbic activation than their lateral counterparts, and that dmPFC may play a particularly important role in the cognitive generation of emotional states.

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Author and article information

Contributors

Jing Guo: guojing_2002@163.com

Journal

Journal ID (nlm-ta): BMC Complement Med Ther

Journal ID (iso-abbrev): BMC Complement Med Ther

Title: BMC Complementary Medicine and Therapies

Publisher: BioMed Central (London )

ISSN (Electronic): 2662-7671

Publication date (Electronic): 21 August 2024

Publication date PMC-release: 21 August 2024

Publication date Collection: 2024

Volume: 24

Electronic Location Identifier: 311

Affiliations

[1 ]GRID grid.24696.3f, ISNI 0000 0004 0369 153X, Department of Acupuncture and Moxibustion, Beijing Key Laboratory of Acupuncture Neuromodulation, , Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, ; Beijing, 100010 China

[2 ]School of Traditional Chinese Medicine, Capital Medical University, ( https://ror.org/013xs5b60) Beijing, 100069 China

[3 ]Department of Radiology, Dong Zhimen Hospital Beijing University of Chinese Medicine, ( https://ror.org/05damtm70) Beijing, 100010 China

Article

Publisher ID: 4612

DOI: 10.1186/s12906-024-04612-0

PMC ID: 11340108

PubMed ID: 39169368

SO-VID: 4544fb30-532f-45d7-87cf-a301c1c3e8d3

License:

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, 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 you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. 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-nc-nd/4.0/.

History

Date received : 22 August 2023

Date accepted : 13 August 2024

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;

Award ID: 81774391

Funded by: FundRef http://dx.doi.org/10.13039/501100004826, Natural Science Foundation of Beijing Municipality;

Award ID: 7212170

Custom metadata

Keywords: insomnia disorder,acupuncture,emotional network,resting-state functional connectivity (rsfc),functional magnetic resonance imaging (fmri)

Data availability:

Keywords: insomnia disorder, acupuncture, emotional network, resting-state functional connectivity (rsfc), functional magnetic resonance imaging (fmri)

Acupuncture modulates emotional network resting-state functional connectivity in patients with insomnia disorder: a randomized controlled trial and fMRI study

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Abstract

Background

Methods

Results

Conclusion

Trial registration

Supplementary Information

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Most cited references 100

DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging.

Network-based statistic: identifying differences in brain networks.

Functional grouping and cortical-subcortical interactions in emotion: a meta-analysis of neuroimaging studies.

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