Skip to main content
Download PDF

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

BMC Complementary Medicine and Therapies volume 24, Article number: 311 (2024) Cite this article

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.

Peer Review reports

Introduction

As the most frequently encountered sleep affliction, insomnia disorder (ID) is characterized by difficulties in initiating sleep or waking up during the night or earlier in the morning than one would like [1]. Approximately 22.1% of the general population suffer from chronic insomnia [2], and most of them experience persistent negative moods such as anxiety and depression [3]. The coexistence of insomnia and negative emotions will prolong the course of disease and reduce the quality of life in patients [4].

It is believed that insomnia and negative emotions have partially similar pathophysiological pathways [5]. Hyperarousal is widely accepted as a hypothesis to explain the pathogenesis of insomnia, meaning a state of excessive arousal of cognition, physiology, and cerebral cortex across the sleep-wake cycle [6]. Excessive cortical arousal can lead to emotional rumination and anxiety [7, 8]. The constant worry about the inability to sleep causes mental hyperarousal, leading to the vicious circle of insomnia and anxiety [5, 9]. Additionally, functional magnetic resonance imaging (fMRI) studies have shown that insomnia, anxiety, and depression are all linked to dysregulation of emotion-related regions, further demonstrating a close relationship between insomnia and negative emotion [10, 11].

It has been proven that emotional management could be helpful in improving sleep quality [12]. Although cognitive behavioral therapy (CBT) and pharmacotherapy are standard therapies for treating ID, the high cost and inconsistent efficacy of CBT and the dependence on medications hinder the effectiveness of treating ID [13, 14]. Acupuncture, as a complementary therapy, has been proven effective in the therapy of insomnia and some psychological disorders [15, 16]. Acupuncture is valuable for improving sleep quality [17], decreasing hyperarousal [18], relieving anxiety [19] and depression [20] in patients with ID. Acupuncture has the advantages of being safe, affordable, and effective [21]. However, the neurological mechanism underlying the effect of acupuncture remains unclear.

FMRI studies have shown abnormal neural activity of brain regions underlying emotional reception and expression in ID patients. In general, these emotional related areas are usually concentrated in the amygdala, anterior cingulate cortex (ACC), thalamus, hippocampus and prefrontal cortex (PFC) [22, 23], which are collectively considered as the emotional network (EN) [23, 24]. Dysregulation of the EN has been demonstrated to be associated with the occurrence of insomnia [25, 26]. Klumpp et al. found that worse sleep quality corresponded with increased amygdala-ACC functional connectivity [27]. Kim et al. discovered increased FC between the thalamus and cortical areas in the frontal, parietal, and temporal lobes in ID patients, and the FC values were negatively correlated with sleep efficiency [17]. Furthermore, Zhu et al. and Sanford et al. proved that the FC value between ACC and insula was closely related to the severity of anxiety and the FC of PFC was involved in sleep and cognition regulation [28, 29].

Recently, acupuncture has been shown to have a positive effect on regulating regional functional activity in hippocampus [30], amygdala, ACC [31], PFC [32, 33] and thalamus [34] in ID patients. Therefore, we speculate that modulation of functional activity in the emotion-related areas might be the potential mechanism of acupuncture’s effect in relieving negative mood and insomnia. However, there is a lack of research on the modulation of EN in insomnia patients by acupuncture.

Resting-state FC analysis provides temporal correlation between discrete or continuous timeseries of brain regions [35]. Investigation of brain rsFC would be helpful for a comprehensive understanding of the synchronization among different brain regions [36], which could reveal changes in brain networks in psychiatric mental illness and insomnia [37, 38]. In this study, we aimed to investigate the changes of rsFC in the EN regions in patients with insomnia modulated by acupuncture.

Materials and methods

This trial was performed at the Beijing Hospital of Traditional Chinese Medicine, Capital Medical University (Beijing TCM Hospital). It was approved by the ethics committee of the hospital and registered on http://www.chictr.org.cn (Chinese Clinical Trials Register, ChiCTR1800015282, 20/03/2018). The protocol of this study has been published previously [39].

Participants

According to the Diagnostic and Statistical Manual of Mental Disorders (5th edition) (DSM-5), ID patients were recruited from outpatient acupuncture clinics of Beijing TCM Hospital from September 2019 to September 2021. All patients with ID were required to sign an informed consent. Insomnia was diagnosed based on the DSM-5 by Jing Guo, a specialist working at the neurology outpatient service of the Beijing TCM Hospital. In addition, we also recruited 30 age-, education-, and sex-matched healthy participants from the local community to serve as the control group through advertising posters.

The inclusion criteria of ID patients were as follows: (1) age 18–60 years; (2) Pittsburgh Sleep Quality Index (PSQI) score > 8 [40]; (3) Hamilton Depression Scale (HAMD) score < 7 [41]; (4) Hamilton Anxiety Scale (HAMA) score < 14 [42]; (5) Hyperarousal Scale (HAS) score > 32 [43]; (6) informed consent; (7) patients not taking anti-anxiety, anti-depressant, or sleep medication in the past month; (8) right-handedness. ID patients were excluded from the study if any of the following criteria were met: (1) depression, severe anxiety, schizophrenia, or other serious mental illnesses; (2) severe heart, brain, kidney, or liver disease; (3) failure to cooperate with the examination and treatment; (4) apnea syndrome; pregnant and lactating women; (5) claustrophobia or other contraindications to MRI examination; 7) and a definite lesion on MRI or grossly asymmetrical anatomy of the head. Healthy controls (HCs) were included in the study according to the following criteria: (i) they had good sleep habits and PSQI score < 3; (ii) HAMA score < 7; (iii) Hyperarousal Scale (HAS) score < 32; (iv) no serious heart, lung, kidney, or neuropsychological disease; (v) no structural abnormalities on conventional brain magnetic resonance imaging (MRI); (vi) females were not pregnant or nursing; and (vii) right-handedness.

Experimental design

The study is a randomized, single-blind, controlled clinical trial. ID patients were included and randomly assigned to the real acupuncture group and the sham acupuncture group in a 1:1 ratio, using opaque, sequentially numbered sealed envelopes. All ID patients were blinded to their group allocation. Due to the feature of acupuncture, the acupuncturist was not blinded to patients’ allocation; however, ID patients and acupuncturist are prohibited from discussing group allocation. To comply with the single-blind principle, assessors and statisticians involved in data collection and analysis were blinded to the assignments.

Acupuncture treatment

In the trial, all acupuncture procedures were performed by a licensed acupuncturist with 20 years of experience. All ID patients in the real and sham acupuncture groups received 12 sessions of acupuncture treatment in 4 weeks with different acupoint prescriptions and manipulation, with 3 sessions per week.

ID patients in real acupuncture group received manual acupuncture in Baihui (DU20), Shenting (DU24), Sishencong (EX-HN1), bilateral Shenmen (HT7), bilateral Benshen (GB13), bilateral Neiguan (PC6), and bilateral Sanyinjiao (SP6) acupoints after skin disinfection using 75% alcohol. The positioning standard refers to the National Standard for Acupuncture and Moxibustion Meridian Point Positioning promulgated by the National Standard GB12346-90 of China. Details of more information on acupuncture (location of real and sham acupoints, depth of insertion) are shown in Figs. 1 and 2. The needles were twisted (bi-directionally, 90°–180°), lifted, and thrust (3–5 mm) by an acupuncturist to induce the Deqi sensation (distension, heaviness, and numbness sensation; the key to effect of acupuncture). ID patients were given 30 min of treatment time per session. Disposable sterile needles (0.25 × 25 mm, Huatuo Medical Instrument Co., ltd, Suzhou, China) were used in this study.

ID patients in sham acupuncture group received acupuncture in bilateral Binao (LI14), Shousanli (LI10), Fengshi (GB31), Futu (ST32), and Liangqiu (ST34). These acupoints had no therapeutic effect on insomnia according to literature search and previous research [44, 45]. The needles were inserted 1–2 mm vertically to penetrate superficial skin at these non-effective acupoints to avoid the sensation of Deqi. The sham control method is commonly used in randomized controlled trials of the efficacy of acupuncture [46]. ID patients were also given 30 min of treatment time per session. The frequency of treatments and needles was the same as that in the real acupuncture group.

Fig. 1
figure 1

Acupoints location of real acupuncture group

Full size image
Fig. 2
figure 2

Acupoints location of sham acupuncture group

Full size image

If a subject suffers a serious adverse injury from needling during the experiment (e.g., severe local and systemic infection, significant vascular injury, puncture of organs and nerves), adverse events should be recorded in detail. All ID patients were given practical sleep guidance during the treatment. If necessary, ID patients were allowed to take zopiclone, and the dosage and times of administration should be recorded in detail.

Sample size estimate

In our previous pilot study [47], the PSQI score significantly decreased by 4.43 ± 3.60 in the acupuncture group and by 1.30 ± 2.58 in the control group. Using a t-test of two independent samples with uneven variance in PASS for calculation, the withdrawal rate is 20% to ensure that the results are statistically different (α = 0.05, 1-β = 0.9); therefore, each group enrolled 30 subjects.

Clinical assessment and data analysis

Sleep quality evaluation: PSQI

The primary outcome measure was the PSQI scale. The PSQI scale was developed by Dr. Buysse in 1989; it is the most widely used indicator for assessing sleep quality in insomniacs, including sleep duration, efficiency, depth, and abnormal sleep sensations [40]. The higher PSQI score, the poorer sleep quality. Reference of PSQI scores: 0 ~ 5 Sleep quality is very good; 6 ~ 10 Sleep quality is okay; 11 ~ 15 Average sleep quality; 16 ~ 21 Sleep quality is very poor. It was collected at baseline and after 4 weeks acupuncture treatment.

Emotion evaluation: HAMD and HAMA

The emotion evaluation outcomes were measured by HAMD and HAMA scales. The 17-items HAMD is a widely used rating scale to assess the severity of depression [41]. Reference of HAMD scores: 0 ~ 6 none or may have mild depressive symptoms but do not reach a diagnosis of depression disease [48]; 7 ~ 17 possible diagnosis of depressive disorder; 18 ~ 24 diagnosed depression; 25–52 major depression.

The 14-items HAMA was developed by Hamilton in 1959; it is one of the more widely used physician-rated scales to assess the severity of anxiety [42]. Each item has five possible answers with scores from 0 to 4. Reference of HAMA scores: 0 ~ 6 without anxiety symptoms; 7 ~ 14 may have anxiety; 15 ~ 21 definitely anxious; 22 ~ 29 obvious anxiety; 29 ~ 54 serious anxiety. HAMA and HAMD scales were collected at baseline and after 4 weeks acupuncture treatment.

HAS

The self-reported HAS is used to assess cognitive and emotional hyperarousal in response to stimuli during wakefulness [43]. It could measure symptoms of cortical hyperexcitability in ID participants [49]. The HAS consists of 26 items; each item is scored on a scale of 0–3, with higher scores indicating higher levels of cortical arousal. The total score above 32 indicates a state of hyperarousal [43]. HAS scale were collected at baseline and after 4 weeks acupuncture treatment.

Actigraphy

All of the ID patients were requested to wear an actigraphy unit (MTI Health Services Company, Pensacola, FL, USA) on the left wrist to detect sleep at night. Actigraphy, a wearable sleep device for sleep quality monitoring, is an objective indicator that reflects sleep time and quality in ID patients [50]. Actigraphy data were collected at night for one week before the first acupuncture intervention and for one week after the last acupuncture intervention. The total sleep time (TST), wake after sleep onset (WASO), and sleep efficiency (SE) were recorded to evaluate the objective sleep status of ID patients.

Clinical data analysis

Statistical evaluation of the clinical data was performed using SPSS software V21.0 (IBM SPSS Statistics, IBM Corp, Somers, NY). T-test was used for continuous measurement data conforming to normal distribution, and the Wilcoxon rank sum test was used for data not conforming to normal distribution. We set statistical significance at p < 0.05, with two-tailed analysis. The continuous variables that conform to normal distribution are presented as mean ± standard deviation (SD), those that are not conform to normal distribution are presented as median and interquartile range. One-way analysis of variance (ANOVA) was used for age between HCs and the real and sham acupuncture groups. For the sex and education level, we used the chi-square test to compare the dichotomous data. All ID patients were required to complete a questionnaire about their demographic characteristics and clinical questionnaires before treatment and after 4-weeks acupuncture treatment. Clinical data statistics followed the intention-to-treat (ITT) principle [51], and the abscission of clinical data was replaced by the group mean or median.

fMRI image acquisition

All MRI/fMRI scans were conducted on a Siemens 3.0 Tesla MRI scanner (Siemen Magnetom Verio, Erlangen, Germany) with a 24-channel phased-array head coil at Dong Zhimen Hospital Beijing University of Chinese Medicine. All participants underwent whole-brain conventional structural imaging and resting-state fMRI scans. They were required to stay awake and relaxed, remain motionless, and keep their eyes open during the scan. All MRI/fMRI scans were conducted between 9:00 am and 11:30 am. High-resolution brain T1-weighted brain anatomical images were collected using a volumetric three-dimensional magnetization prepared by a rapid-acquisition gradient-echo (MPRAGE) sequence with the following parameters: repetition time (TR) = 2000 ms, echo time (TE) = 3.51 ms, flip angle (FA) = 7°, slice number = 188, slice thickness/gap = 1.0/0 mm, voxel size = 1.0 mm × 1.0 mm × 1.0 mm, matrix = 256 × 256, and field of view (FOV) = 256 mm × 256 mm. The rs-fMRI images were obtained using an echo-planar imaging (EPI) sequence, and the parameters are as follow: TR = 2000 ms, TE = 30 ms, FA = 90°, slice number = 32, slice thickness/gap = 3.5/0.6 mm, voxel size = 3.5 mm × 3.5 mm × 3.5 mm, FOV = 224 mm × 224 mm, matrix size = 64 × 64, and phase-encoding direction: Anterior > > Posterior. In total, 240 volumes were acquired, and the EPI sequence scan time was 8 min. Each ID patient attended two MRI/fMRI scan visits before and after 4 weeks of acupuncture treatment. HCs completed an MRI/fMRI scan at baseline.

Image pre-processing and analysis

Image pre-processing

The fMRI data pre-processing and analysis were performed with DPABI V5.1_201201 (5.1, advanced edition, https://www.rfmri: dpabi) [52] within MATLAB_R2015b (Mathworks, Inc., Natick, MA). First, we converted the raw data from EPI DICOM format to NIFTI format before preprocessing. Then, the data were preprocessed as follows: (1) the first 10 time points were removed; (2) slice timing was performed to correct for timing differences between slices; (3) realignment to reduce motion-related variance, in which participants were excluded based on the criteria of translational movement > 3.0 mm and > 3.0° rotation [53]; (4) segmentation to split the image into gray matter, white matter and cerebrospinal fluid (CSF); (5) detrending and nuisance covariates regression procedures were preformed to remove linear trends and nuisance signals from the white matter, CSF, global signal time series; (6) the generated images were normalized to the standard template in the Montreal Neurological Institute (MNI) space; (7) low-frequency filtering (0.01–0.08 Hz) was performed to eliminate high frequency noises from the physiology; (8) to increase the signal-to-noise ratio of the image, spatial smoothing was analyzed using a [6] fullwidth at half maximum Gaussian kernel.

Resting-state FC analysis within emotional network

Resting-state FC analysis shows synchronization and cooperation of relevant brain regions [54]. Seed-based FC analysis was used for calculating FC value between specific region pairs within networks [55, 56]. The thalamus, ACC, DLPFC, hippocampus, and amygdala are mainly responsible for emotional management [22, 23]; thus, we predefined these brain regions as regions of interest (ROIs), a total of 10 ROIs in bilateral. The masks of 10 ROIs were extracted from the automated anatomical labeling (AAL) atlas using WFU_PickAtlas Toolbox V3.0.5 (https://www.nitrc.org/projects/wfu_pickatlas/). An ROI was considered a node of EN [57]. The representative BOLD time series of each ROI was calculated by averaging the regressed time series of all the voxels in the ROI mask. The edges of emotional network were determined by calculating the pairwise Pearson’s correlations for all ROI pairs. In this way, a 10 × 10 correlation matrix of each participant was obtained. To follow normal distribution, the correlation coefficients were Z-standardized by Fisher’s R-to-Z transformation.

Two sample t-test was used to compare the rsFC value between groups (HCs vs. ID patients at baseline, the real vs. the sham acupuncture group after 4 weeks of treatment), and paired t-test was used to compare within groups (pre- and post- treatment) using Graph Theoretical Network Analysis (GRETNA; http://www.nitrc.org/projects/gretna/), with head motion parameters, sex, age, education, and duration of insomnia as covariates. Network-based statistics (NBS) method was used to identify functional connectivity of brain network differences between real and sham groups, and the number of permutation tests was 1000 [58, 59]. The results were corrected with the False Discovery Rate (FDR, cluster-level p < 0.05).

Correlational analysis

Pearson’s correlation analysis [60] was performed using SPSS V21.0 to investigate the relationship between rsFC changes and clinical data changes (including PSQI, HAMA, HAMD, HAS, TST, WASO, and SE). The Shapiro-Wilks test was used to check the normality of the data. We extracted the average z-score values of rsFC that had a significant difference between groups and within groups. Next, the relationship between Δ rsFC (post - pre) and Δ clinical outcomes (post - pre) was examined. Bonferroni corrections were used for multiple comparisons. Both uncorrected p value and corrected p value were reported. Hierarchical multiple regression analysis was applied to test the correlation between the mean z-score of changed rsFC and altered clinical data, using age, sex, education, and duration of insomnia as covariates. The significance level was set at p < 0.05.

Results

Participants and baseline demographics

A total of 30 HCs and 60 ID patients were recruited into the study and the 60 ID patients were randomly assigned to the real and sham acupuncture group in a 1:1 ratio. Ten ID patients were withdrawn, including four patients in real acupuncture group due to COVID-19 (n = 3) and time restriction (n = 1), six patients in sham acupuncture group due to COVID-19 (n = 4) and unsatisfactory effect (n = 2). Finally, fifty ID patients (26 in the real acupuncture group and 24 in the sham acupuncture group) completed the study. The procedure for the study is illustrated in Fig. 3. No patient reported the administration of hypnotic drug medications.

Fig. 3
figure 3

Procedure for the study

Full size image

There was no significant difference in demographics between the HCs, the real acupuncture group, and the sham acupuncture group (p > 0.05) (Table 1). There was no significant difference in HAMA, HAMD, PSQI, HAS scores, TST, WASO, and SE between the real and sham acupuncture groups in the baseline period (p > 0.05), suggesting that the baseline conditions of the real and sham acupuncture groups were comparable (Table 2). Statistical significance was set at p < 0.05.

Table 1 Demographics of participants
Full size table
Table 2 Clinical data results in the intention-to-treat population
Full size table

clinical results

After 4 weeks of treatment, the HAMA, HAMD, PSQI, and HAS scores decreased significantly in the real acupuncture group compared with the sham acupuncture group (p < 0.05). Actigraphy results showed that the real acupuncture group had longer TST and higher SE compared with the sham acupuncture group (p < 0.05); however, there was no statistically significant difference between the two groups in reducing the time of nighttime awakenings (p > 0.05) (Table 2). Neither the real nor the sham acupuncture group experienced serious adverse effects during acupuncture treatment.

Resting-state FC results

Baseline comparison between HCs and ID patients of rsFC within EN

In the baseline period, compared with HCs, ID patients showed increased rsFC values in right ACC-left DLPFC (t = 2.045, p = 0.044), right thalamus-left amygdala (t = 2.907, p = 0.005), left thalamus-left amygdala (t = 3.170, p = 0.002), left hippocampus-right thalamus (t = 2.111, p = 0.038), left hippocampus-left thalamus (t = 2.439, p = 0.017), and left DLPFC-right thalamus (t = 2.247, p = 0.027). Results are shown in Table 3; Fig. 4. Statistical significance was set at cluster-level p < 0.05 (FDR corrected).

Table 3 Baseline comparison between HCs and ID patients of rsFC within EN
Full size table
Fig. 4
figure 4

Increased rsFC within the EN in ID patients compared with HCs in the baseline period. Five colors represent five brain regions (bilateral). Red lines indicate enhanced rsFC. The thickness of the line was positively correlated with the degree of increase. ACC, anterior cingulate cortex; DLPFC, dorsolateral prefrontal cortex; Amyg, amygdala; Thal, thalamus; Hipp, hippocampus; L, left; R, right

Full size image

Inter-group comparison of rsFC within EN

A total of fifty ID patients’ fMRI images were acquired. Compared with sham acupuncture group, real acupuncture group showed increase rsFC values in right ACC-left ACC (t = 2.881, p = 0.006), left hippocampus-right ACC (t = 2.066, p = 0.044), right amygdala-left ACC (t = 2.276, p = 0.027), left hippocampus-left ACC (t = 2.779, p = 0.008), left amygdala-right amygdala (t = 2.400, p = 0.020), and left hippocampus-right amygdala (t = 2.552, p = 0.014) after 4-weeks acupuncture treatment. Results are shown in Table 4; Fig. 5. Statistical significance was set at cluster-level p < 0.05 (FDR corrected).

Table 4 Inter-group comparison of rsFC within EN regions
Full size table
Fig. 5
figure 5

Increased rsFC within the EN in real acupuncture group compared with sham acupuncture group after 4 weeks of acupuncture treatment. Five colors represent five brain regions (bilateral). Red lines indicate enhanced rsFC. The thickness of the line was positively correlated with the degree of increase. ACC, anterior cingulate cortex; DLPFC, dorsolateral prefrontal cortex; Amyg, amygdala; Thal, thalamus; Hipp, hippocampus; L, left; R, right

Full size image

Intra-group comparison of rsFC within EN

Paired t-test was used to compare the rsFC of EN within groups before and after treatment. In real acupuncture group, the rsFC decreased in left amygdala-left thalamus after 4-weeks acupuncture treatment compared with baseline period (t = -2.719, p = 0.009). Shown in Table 5; Fig. 6. However, there was no significant difference between before and after treatment in the sham acupuncture group (Fig. 7).

Table 5 Intra-group comparison of rsFC within EN (Real acupuncture group)
Full size table
Fig. 6
figure 6

Decreased rsFC within the EN after 4 weeks of treatment compared with baseline in the real acupuncture group. Five colors represent five brain regions (bilateral). Blue lines indicate decreased rsFC. The thickness of the line was positively correlated with the degree of decline. ACC, anterior cingulate cortex; DLPFC, dorsolateral prefrontal cortex; Amyg, amygdala; Thal, thalamus; Hipp, hippocampus; L, left; R, right

Full size image
Fig. 7
figure 7

There is no significant difference between before and after treatment in the sham acupuncture group. Five colors represent five brain regions (bilateral). ACC, anterior cingulate cortex; DLPFC, dorsolateral prefrontal cortex; Amyg, amygdala; Thal, thalamus; Hipp, hippocampus; L, left; R, right

Full size image

Correlation analysis results

We conducted a Pearson’s correlation analysis between clinical outcome changes (post-treatment minus pre-treatment) and Fisher z-scores of rsFC changes (post-treatment minus pre-treatment) across all ID patients and within-group, with age, sex, education, and duration of insomnia as covariates. Correlation analysis showed that the increased rsFC between the right amygdala and left hippocampus was positively correlated with the decreased HAMA scores (r = 0.309, puncorrected = 0.035, pBonferroni−corrected = 0.245) across all ID patients (Fig. 8). The decreased left amygdala rsFC with the left thalamus was negatively correlated with sleep efficiency (r = -0.514, puncorrected = 0.012, pBonferroni−corrected = 0.084) in real acupuncture group (Fig. 9). These two correlation results did not survive after Bonferroni correction, but showed a significant trend. No correlation was found between other significant Δ rsFC (post - pre) and Δ clinical outcomes (post - pre) (all puncorrected > 0.05).

Fig. 8
figure 8

The positive correlation tendence between rsFC value change in right amygdala with left hippocampus and HAMA change across all ID patients. Five colors represent five brain regions (bilateral). HAMA, Hamilton Anxiety Scale; ACC, anterior cingulate cortex; DLPFC, dorsolateral prefrontal cortex; Amyg, amygdala; Thal, thalamus; Hipp, hippocampus; L, left; R, right

Full size image
Fig. 9
figure 9

The negative correlation tendence between rsFC value change in left amygdala with left thalamus and SE change in real acupuncture group. Five colors represent five brain regions (bilateral). SE, sleep efficiency; ACC, anterior cingulate cortex; DLPFC, dorsolateral prefrontal cortex; Amyg, amygdala; Thal, thalamus; Hipp, hippocampus; L, left; R, right

Full size image

Discussion

We investigated the rsFC changes within EN of insomnia patients evoked by real and sham acupuncture. In previous fMRI studies, the method of seed-based FC analysis had been proved reliable for analyzing the functional changes within networks [55, 56]. Ten brain regions (ROIs) within the emotional network were selected to calculate the rsFC values between each ROIs [55]. In this study, we observed abnormalities in rsFC centered on the thalamus and DLPFC within EN of ID patients compared to HCs. Moreover, compared to the sham acupuncture group, the real acupuncture group significantly increased rsFC of the ACC, hippocampus, and amygdala after 4-weeks treatment. Intra-group comparisons revealed that in the real acupuncture group, the rsFC between the left amygdala and left thalamus decreased after treatment compared with baseline. Although no statistically significant correlation was found after multiple comparisons, a trend of correlation was shown 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 sleep efficiency in real acupuncture group. These findings suggest that real acupuncture shows a positive effect on reducing anxiety and improving sleep efficiency by modulating the connectivity within the emotional related area.

The hippocampus, as one of the principal hubs of the limbic system and EN [61], is important for emotion regulation [62]. Studies have shown that hippocampal volume reduction is associated with difficulty maintaining sleep, hyperarousal, and cognitive dysfunction [63, 64]. Moreover, changes in neurotransmitters such as 5-HT and nitric oxide in the hippocampus [65] interfere with normal sleep-wake patterns and result in insomnia. ACC is another key brain region of the limbic system and EN [61]. Dysregulation of the dorsal ACC is associated with depression and obsessive-compulsive disorder [66]. Plante et al. found abnormal functional activity of the ACC in patients with major depression and ID [67]. A study showed that the functional connection between the hippocampus and ACC is associated with recollect episodic memory [68]. The circuit of ACC to the hippocampus controls the expression of contextual fear generalization, while manipulating the circuit may prevent anxiety and fear [69]. In addition, Feng et al. found that pharmacological and physical interventions can increase rsFC of hippocampus-ACC, which is negatively associated with PSQI score [70]. Moreover, some studies found that acupuncture could increase the FC strength of the ACC in patients with depression [71] and the FC of the hippocampus in older adults with cognitive decline [72]. In our trial, we found real acupuncture could increase rsFC of the bilateral ACC-left hippocampus compared with sham acupuncture. Thus, increasing the activity of hippocampus and its FC with ACC may have a positive effect on emotion regulation [73]. We suggest that the enhanced rsFC between ACC and hippocampus may be an intrinsic mechanism for decreased negative emotion in ID patients.

The amygdala plays a critical role in the formation and storage of memory associated with mood disorders, fear, and pain [74]. Gong et al. reported that amygdala is engaged in controlling emotion and has strong bidirectional interaction with the prefrontal cortex and ACC [75]. Therefore, abnormal amygdala activity has been involved in the neuropathology of major depression and anxiety disorder [76, 77], as well as insomnia [78]. In previous studies, the amygdala showed abnormal rsFC with the hippocampus [79, 80] in both insomnia and depression patients. Li et al. confirmed that neurofeedback training could alter amygdala activity, which reshaped the abnormal FC caused by insomnia and improved the sleep of ID patients [30]. Furthermore, a study found that acupuncture significantly modulated FC between the amygdala and hippocampus which was related to decreased anxiety and depression [81]. Wang et al. found that acupuncture treatment significantly increased rsFC between the amygdala, ACC, and parahippocampus, and the rsFC strength between the amygdala and ACC was positively associated with the depression level [31]. In this study, we also found after real acupuncture treatment, rsFC between bilateral amygdala, right amygdala rsFC with left ACC and left hippocampus were increased. In addition, there was a positive correlation trend between the increased rsFC of the right amygdala and left hippocampus and decreased scores of HAMA. Thus, we speculate that acupuncture may decrease anxiety mood by modulating the connectivity between amygdala and hippocampus.

The thalamus has a crucial role in the integration of sensory, emotional, and cognitive information [82, 83]. Recent studies suggested that the negative mood of insomnia patients was associated with dysregulation of thalamus [84, 85]. Kim et al. indicated that increased thalamocortical activity was related to hypersensitive sensory during sleep time [86]. Kong et al. found that acupuncture altered the rsFC between the thalamus and DMN regions, which may be the mechanism by which acupuncture regulates sensory and emotional changes caused by chronic pain [87]. Zhao et al. indicated that transcutaneous auricular vagus nerve stimulation modulates thalamus-related FC in ID patients, suggesting a potential target for insomnia treatment [88]. In this study, we found increased rsFC in the thalamus-amygdala and thalamus-hippocampus in ID patients compared with HCs, which may be an intrinsic mechanism for the abnormal emotional sensitivity of ID patients. Further, our results revealed that real acupuncture could decrease the rsFC between the left thalamus and left amygdala, and the rsFC showed a negative trend with sleep efficiency. The ACC, amygdala, and thalamus all belong to the autonomic nuclei of the brain and are affected by inputs from the internal and external environments [89]. Acupuncture as an effective stimulus could modulate the autonomic nervous system and further regulate the ACC, amygdala, and thalamus to relieve insomnia and negative moods. Therefore, this may be the neuro-mechanism of acupuncture in improving sleep in ID patients.

DLPFC is a major prefrontal region involved in high-level cognitive and complex emotion processing [90]. Many studies have found that increased DLPFC activity was associated with adverse emotions [91] and excessive self-attention [92]. Consistent with previous studies, our results showed increased rsFC in the DLPFC-ACC and DLPFC-thalamus in ID patients. However, no differences in rsFC of DLPFC were found in inter-group and intra-group comparisons after acupuncture treatment. We speculate that the DLPFC contains a large number of voxels and has different intracerebral regional divisions [93]. Further analysis could be conducted from more subdivided regions.

Acupuncture has been proven to be effective in decreasing hyperarousal level and regulating emotions of ID sufferers [94]. Meanwhile, it could modulate heart rate variability (HRV), indicating that acupuncture can help restore the balance between the sympathetic and parasympathetic nervous system [95]. It has been shown that regions associated with autonomic regulation are mainly ACC, prefrontal cortex, and amygdala [96], which are also responsible for emotional management. The mechanism of modulation of the emotional network by acupuncture may be related to the regulation of sympathetic and parasympathetic nerves balance and the further alteration of FC in EN. Future research should combine HRV and fMRI to explore the central mechanism of acupuncture in regulating sleep and emotions of insomnia sufferers.

There are several limitations to our study. First, the intervention in the sham acupuncture group inevitably has some nonspecific physiological effects. As a type of physical placebo, sham acupuncture necessarily involves touching, which may cause other types of nonspecific physiological effects to occur in addition to the placebo response [97]. Studies have shown that the amygdala, ACC, and PFC are also associated with the processing of painful stimulus signals [98, 99]. A perfect comparison of real acupuncture should be developed in the future, such as designing a sham acupuncture with blunt tip needle so it will not penetrate skin. Second, no sleep poly-conductivity test was carried out in this study, so there was a lack of more accurate judgment on the quality, quantity, and structure of sleep. Polysomnography (PSG) provides a quantitative assessment of nocturnal sleep and is an important tool in the diagnosis of ID [100, 101]. Hence, PSG could be combined with fMRI technology to explore the central nervous mechanisms of acupuncture for insomnia in future studies. Third, there is no statistically significant correlation found after Bonferroni correction; we speculate that it may be related to the sample size limitations. Fourth, the blinding of participants was not assessed in this study. Thus, the blinding questionnaire should be collected in future acupuncture studies. Fifth, due to the specificity of fMRI studies, there are no cut-offs of normality in relation to fMRI results that can be used as comparative parameters. Thus, we compared the results with the literature previously to elucidate the relationship between acupuncture and rsFC changes within EN. Future studies should further explore the more definitive and consistent neuroimaging mechanisms of acupuncture for ID. Sixth, because insomnia is also closely related to the default mode network, salience network [78], and cognitive control network [102], the synergy between brain networks can be analyzed from the perspective of large-scale brain networks in the future. Last, subgroup analyses of insomnia were not performed. Future research should expand the sample size and classify ID into different subtypes, such as difficulty falling asleep and early-awakened insomnia, to explore the neuroimaging differences between different insomnia subtypes.

Conclusion

Our results demonstrated that there is a general increase of rsFC within the EN in ID patients, as well as that real acupuncture could modulate the rsFC of the ACC, hippocampus, and amygdala. Moreover, increased rsFC value in the right amygdala-left hippocampus and decreased rsFC value in the left amygdala-left thalamus were correlated with reduced anxiety and improved sleep efficiency. The findings support the hypothesis that acupuncture may improve sleep and emotion by modulating functional activity of emotion-related regions in ID patients. The results highlight the potential mechanism in acupuncture treatment of insomnia.

Availability of data and materials

The raw data supporting the conclusions of this article can be obtained on reasonable request from the corresponding author. Requests to access the datasets should be directed to JG, guojing_2002@163.com.

Abbreviations

ACC:

Anterior cingulate cortex

CSF:

Cerebrospinal fluid

DLPFC:

Dorsolateral prefrontal cortex

EN:

Emotional network

FA:

Flip angle

FC:

Functional connectivity

FDR:

False discovery rate

FOV:

Field of view

HAMA:

Hamilton Anxiety Scale

HAMD:

Hamilton Depression Scale

HAS:

Hyperarousal Scale

ID:

Insomnia disorder

ITT:

Intention-to-treat

MNI:

Montreal Neurological Institute

PFC:

Prefrontal cortex

PSQI:

Pittsburgh Sleep Quality Index

ROI:

Regions of interest

SE:

Sleep efficiency

SN:

Salience network

TST:

Total sleep time

WASO:

Wake after sleep onset

References

  1. Perlis ML, Posner D, Riemann D, et al. Insomnia Lancet. 2022;400(10357):1047–60. https://doi.org/10.1016/S0140-6736(22)00879-0.

    Article  PubMed  Google Scholar 

  2. Dopheide JA. Insomnia overview: epidemiology, pathophysiology, diagnosis and monitoring, and nonpharmacologic therapy. Am J Manag Care. 2020;26(4 Suppl):S76–84. https://doi.org/10.37765/ajmc.2020.42769.

    Article  PubMed  Google Scholar 

  3. Nguyen VV, Zainal NH, Newman MG. Why sleep is key: poor sleep quality is a mechanism for the bidirectional relationship between major depressive disorder and generalized anxiety disorder across 18 years. J Anxiety Disord. 2022;90:102601. https://doi.org/10.1016/j.janxdis.2022.102601.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Vanek J, Prasko J, Genzor S, et al. Insomnia and emotion regulation. Neuroendocrinol Lett. 2020;1(5):255–69.

    Google Scholar 

  5. Lancel M, Boersma GJ, Kamphuis J. Insomnia disorder and its reciprocal relation with psychopathology. Curr Opin Psychol. 2021;41:34–9. https://doi.org/10.1016/j.copsyc.2021.02.001.

    Article  PubMed  Google Scholar 

  6. Dressle RJ, Riemann D. Hyperarousal in Insomnia disorder: current evidence and potential mechanisms. J Sleep Res. 2023;32(6):e13928. https://doi.org/10.1111/jsr.13928.

    Article  PubMed  Google Scholar 

  7. Li C, Xia L, Ma J, et al. Dynamic functional abnormalities in generalized anxiety disorders and their increased network segregation of a hyperarousal brain state modulated by insomnia. J Affect Disord. 2019;246:338–45. https://doi.org/10.1016/j.jad.2018.12.079.

    Article  PubMed  Google Scholar 

  8. Makovac E, Meeten F, Watson DR, et al. Alterations in amygdala-prefrontal functional connectivity account for excessive worry and autonomic dysregulation in generalized anxiety disorder. Biol Psychiatry. 2016;80(10):786–95. https://doi.org/10.1016/j.biopsych.2015.10.013.

    Article  PubMed  Google Scholar 

  9. Kalmbach DA, Cuamatzi-Castelan AS, Tonnu CV, et al. Hyperarousal and sleep reactivity in insomnia: current insights. Nat Sci Sleep. 2018;10:193. https://doi.org/10.2147/NSS.S138823.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Van Someren EJW. Brain mechanisms of insomnia: new perspectives on causes and consequences. Physiol Rev. 2021;101(3):995–1046. https://doi.org/10.1152/physrev.00046.2019.

    Article  CAS  PubMed  Google Scholar 

  11. Kirwan M, Pickett SM, Jarrett NL. Emotion regulation as a moderator between anxiety symptoms and insomnia symptom severity. Psychiatry Res. 2017;254:40–7. https://doi.org/10.1016/j.psychres.2017.04.028.

    Article  PubMed  Google Scholar 

  12. Lee S, Oh JW, Park KM, et al. Digital cognitive behavioral therapy for insomnia on depression and anxiety: a systematic review and meta-analysis. Npj Digit Med. 2023;6(1):1–11. https://doi.org/10.1038/s41746-023-00800-3.

    Article  Google Scholar 

  13. Do D. Trends in the use of medications with insomnia side effects and the implications for insomnia among US adults. J Sleep Res. 2020;29(4):e13075. https://doi.org/10.1111/jsr.13075.

    Article  PubMed  Google Scholar 

  14. Buntrock C, Lehr D, Smit F, et al. Guided internet-based cognitive behavioral therapy for insomnia: health-economic evaluation from the societal and public health care perspective alongside a randomized controlled trial. J Med Internet Res. 2021;23(5):e25609. https://doi.org/10.2196/25609.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Liu C, Xi H, Wu W, et al. Placebo effect of acupuncture on insomnia: a systematic review and meta-analysis. Ann Palliat Med. 2020;9(1):19–29. https://doi.org/10.21037/apm.2019.11.15.

    Article  PubMed  Google Scholar 

  16. Dong B, Chen Z, Yin X, et al. The efficacy of acupuncture for treating depression-related insomnia compared with a control group: a systematic review and meta-analysis. Biomed Res Int. 2017;2017:9614810. https://doi.org/10.1155/2017/9614810.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Kim SA, Lee SH, Kim JH, et al. Efficacy of acupuncture for insomnia: a systematic review and meta-analysis. Am J Chin Med. 2021;49(05):1135–50. https://doi.org/10.1142/S0192415X21500543.

    Article  CAS  PubMed  Google Scholar 

  18. Ke R, Sn G, Jj M, et al. Hyperarousal and Insomnia in survivors of cancer. Int J Behav Med. 2021;28(6):683–91. https://doi.org/10.1007/s12529-021-09962-4.

    Article  Google Scholar 

  19. Yin X, Li W, Wu H, et al. Efficacy of electroacupuncture on treating depression-related insomnia: a randomized controlled trial. Nat Sci Sleep. 2020;12:497–508. https://doi.org/10.2147/NSS.S253320.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Zhang L, Tang Y, Hui R, et al. The effects of active acupuncture and placebo acupuncture on insomnia patients: a randomized controlled trial. Psychol Health Med. 2020;25(10):1201–15. https://doi.org/10.1080/13548506.2020.1738015.

    Article  PubMed  Google Scholar 

  21. Zhao FY, Spencer SJ, Kennedy GA, et al. Acupuncture for primary insomnia: effectiveness, safety, mechanisms and recommendations for clinical practice. Sleep Med Rev. 2024;74:101892. https://doi.org/10.1016/j.smrv.2023.101892.

    Article  PubMed  Google Scholar 

  22. Kober H, Barrett LF, Joseph J, et al. Functional grouping and cortical-subcortical interactions in emotion: a meta-analysis of neuroimaging studies. NeuroImage. 2008;42(2):998–1031. https://doi.org/10.1016/j.neuroimage.2008.03.059.

    Article  PubMed  Google Scholar 

  23. Zhang Y, Padmanabhan A, Gross JJ, et al. Development of human emotion circuits investigated using a big-data analytic approach: stability, reliability, and robustness. J Neurosci. 2019;39(36):7155–72. https://doi.org/10.1523/JNEUROSCI.0220-19.2019.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Ma Y. Neuropsychological mechanism underlying antidepressant effect: a systematic meta-analysis. Mol Psychiatry. 2015;20(3):311–9. https://doi.org/10.1038/mp.2014.24.

    Article  CAS  PubMed  Google Scholar 

  25. Wang T, Yan J, Li S, et al. Increased insular connectivity with emotional regions in primary insomnia patients: a resting-state fMRI study. Eur Radiol. 2017;27(9):3703–9. https://doi.org/10.1007/s00330-016-4680-0.

    Article  PubMed  Google Scholar 

  26. Qin H, Zhao W, Yu J, et al. Altered resting-state brain networks in insomnia: functional connectivities within and between networks. Adv Psychol Sci. 2019;27(02):289–300. https://doi.org/10.3724/SP.J.1042.2019.00289.

    Article  Google Scholar 

  27. Klumpp H, Hosseini B, Phan KL. Self-reported sleep quality modulates amygdala resting-state functional connectivity in anxiety and depression. Front Psychiatry. 2018;9:220. https://doi.org/10.3389/fpsyt.2018.00220.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Zhu Y, Zhao X, Yin H, et al. Functional connectivity density abnormalities and anxiety in primary insomnia patients. Brain Imaging Behav. 2021;15(1):114–21. https://doi.org/10.1007/s11682-019-00238-w.

    Article  PubMed  Google Scholar 

  29. Ji B, Dai M, Guo Z, et al. Functional connectivity density in the sensorimotor area is associated with sleep latency in patients with primary insomnia. Neuropsychiatr Dis Treat. 2022;18:1–10. https://doi.org/10.2147/NDT.S338489.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Zang S, Chen Y, Chen H, et al. Effects of acupuncture on the brain in primary insomnia: a coordinate-based meta-analysis of fMRI studies. Front Neurol. 2023;14:1180393. https://doi.org/10.3389/fneur.2023.1180393.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Wang X, Wang Z, Liu J, et al. Repeated acupuncture treatments modulate amygdala resting state functional connectivity of depressive patients. Neuroimage Clin. 2016;12:746–52. https://doi.org/10.1016/j.nicl.2016.07.011.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Wang YK, Li T, Ha LJ, et al. Effectiveness and cerebral responses of multi-points acupuncture for primary insomnia: a preliminary randomized clinical trial and fMRI study. BMC Complement Med Ther. 2020;20(1):254. https://doi.org/10.1186/s12906-020-02969-6.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Zhao Ti, Xu H, Zhao T, et al. Effect of Jiannao Anshen acupuncture on insomnia: a functional magnetic resonance imaging study. Chin Acupunct Moxibustion. 2021;41(7):767–73.

    Google Scholar 

  34. Gao L, Zhang M, Gong H, et al. Differential activation patterns of fMRI in sleep-deprived brain: restoring effects of acupuncture. Evid Based Complement Alternat Med. 2014;2014:465760. https://doi.org/10.1155/2014/465760.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Daniel J, Lurie D, Kessler DS, Bassett, et al. Questions and controversies in the study of time-varying functional connectivity in resting fMRI. Netw Neurosci. 2020;4(1):30–69. https://doi.org/10.1162/netn_a_00116.

    Article  Google Scholar 

  36. Wei L, Jing B, Li H. Bootstrapping promotes the RSFC-behavior associations: an application of individual cognitive traits prediction. Hum Brain Mapp. 2020;41(9):2302–16. https://doi.org/10.1002/hbm.24947.

    Article  PubMed  PubMed Central  Google Scholar 

  37. He JK, Jia BH, Wang Y, et al. Transcutaneous auricular vagus nerve stimulation modulates the prefrontal cortex in chronic insomnia patients: fMRI study in the first session. Front Neurol. 2022;13:827749. https://doi.org/10.3389/fneur.2022.827749.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Fu X, Li H, Yan M, et al. Shared and distinct fractional amplitude of low-frequency fluctuation patterns in major depressive disorders with and without gastrointestinal symptoms. Front Psychiatry. 2021;12:744898. https://doi.org/10.3389/fpsyt.2021.744898.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Jiang T, Zhang Q, Yuan F, et al. Efficacy of acupuncture and its influence on the emotional network in adult insomnia patients: protocol for a randomized controlled clinical trial. Trials. 2022;23(1):11. https://doi.org/10.1186/s13063-021-05913-2.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Seow LSE, Abdin E, Chang S, et al. Identifying the best sleep measure to screen clinical insomnia in a psychiatric population. Sleep Med. 2018;41:86–93. https://doi.org/10.1016/j.sleep.2017.09.015.

    Article  PubMed  Google Scholar 

  41. Lin CH, Park C, McIntyre RS. Early improvement in HAMD-17 and HAMD-7 scores predict response and remission in depressed patients treated with fluoxetine or electroconvulsive therapy. J Affect Disord. 2019;253:154–61. https://doi.org/10.1016/j.jad.2019.04.082.

    Article  CAS  PubMed  Google Scholar 

  42. Zimmerman M, Martin J, Clark H, et al. Measuring anxiety in depressed patients: a comparison of the Hamilton anxiety rating scale and the DSM-5 anxious distress specifier interview. J Psychiatr Res. 2017;93:59–63. https://doi.org/10.1016/j.jpsychires.2017.05.014.

    Article  PubMed  Google Scholar 

  43. Ayabe N, Nakajima S, Okajima I, et al. Development and validation of the Japanese version of the Hyperarousal Scale. BMC Psychiatry. 2022;22(1):617. https://doi.org/10.1186/s12888-022-04243-0.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Zhang X, Tang CZ, Luo XZ, et al. A meta-analysis of effectiveness of acupoint catgut embedding therapy for insomnia. Zhen Ci Yan Jiu. 2018;43(9):591–6. https://doi.org/10.13702/j.1000-0607.170221.

    Article  PubMed  Google Scholar 

  45. Yeung WF, Chung KF, Poon MMK, et al. Prescription of Chinese herbal medicine and selection of acupoints in pattern-based traditional Chinese medicine treatment for insomnia: a systematic review. Evidence-Based Complement Altern Med. 2012;2012:e902578. https://doi.org/10.1155/2012/902578.

    Article  Google Scholar 

  46. Zhao L, Chen J, Li Y, et al. The long-term effect of acupuncture for migraine prophylaxis: a randomized clinical trial. JAMA Intern Med. 2017;177(4):508–15. https://doi.org/10.1001/jamainternmed.2016.9378.

    Article  PubMed  Google Scholar 

  47. Li HQ, Zou YH, Guo J, et al. 30 cases of primary insomnia treated by Zhou Shi Tiaoshen acupuncture therapy. Global Traditional Chin Med. 2017;10:882–5.

    Google Scholar 

  48. Alves LP, de Fleck C, de Boni MP. The major depressive disorder hierarchy: rasch analysis of 6 items of the Hamilton depression scale covering the continuum of depressive syndrome. PLoS ONE. 2017;12(1):e0170000. https://doi.org/10.1371/journal.pone.0170000.

    Article  CAS  Google Scholar 

  49. Zhao W, Van Someren EJW, Xu Z, et al. Identifying the insomnia-related psychological issues associated with hyperarousal: a network perspective. Int J Psychophysiol. 2024;195:112276. https://doi.org/10.1016/j.ijpsycho.2023.112276.

    Article  PubMed  Google Scholar 

  50. Ray MA, Youngstedt SD, Zhang H, et al. Examination of wrist and hip actigraphy using a novel sleep estimation procedure. Sleep Sci. 2014;7(2):74–81. https://doi.org/10.1016/j.slsci.2014.09.007.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Abraha I, Cozzolino F, Orso M, et al. A systematic review found that deviations from intention-to-treat are common in randomized trials and systematic reviews. J Clin Epidemiol. 2017;84:37–46. https://doi.org/10.1016/j.jclinepi.2016.11.012.

    Article  PubMed  Google Scholar 

  52. Yan CG, Wang XD, Zuo XN, et al. DPABI: data processing & analysis for (resting-state) brain imaging. Neuroinformatics. 2016;14(3):339–51. https://doi.org/10.1007/s12021-016-9299-4.

    Article  PubMed  Google Scholar 

  53. Patriat R, Reynolds RC, Birn RM. An improved model of motion-related signal changes in fMRI. NeuroImage. 2017;144(Pt A):74–82. https://doi.org/10.1016/j.neuroimage.2016.08.051.

    Article  PubMed  Google Scholar 

  54. Smitha K, Akhil Raja K, Arun K, et al. Resting state fMRI: a review on methods in resting state connectivity analysis and resting state networks. Neuroradiol J. 2017;30(4):305–17. https://doi.org/10.1177/1971400917697342.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Fan F, Tan S, Huang J, et al. Functional disconnection between subsystems of the default mode network in schizophrenia. Psychol Med. 2022;52(12):2270–80. https://doi.org/10.1017/S003329172000416X.

    Article  PubMed  Google Scholar 

  56. Ji JL, Spronk M, Kulkarni K, et al. Mapping the human brain’s cortical-subcortical functional network organization. NeuroImage. 2019;185:35–57. https://doi.org/10.1016/j.neuroimage.2018.10.006.

    Article  PubMed  Google Scholar 

  57. Tomasi D, Volkow ND. Mapping small-world properties through development in the human brain: disruption in schizophrenia. PLoS ONE. 2014;9(4):e96176. https://doi.org/10.1371/journal.pone.0096176.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Zalesky A, Fornito A, Bullmore ET. Network-based statistic: identifying differences in brain networks. NeuroImage. 2010;53(4):1197–207. https://doi.org/10.1016/j.neuroimage.2010.06.041.

    Article  PubMed  Google Scholar 

  59. Kaushal M, Oni-Orisan A, Chen G, et al. Evaluation of whole-brain resting-state functional connectivity in spinal cord injury: a large-scale network analysis using network-based statistic. J Neurotrauma. 2017;34(6):1278–82. https://doi.org/10.1089/neu.2016.4649.

    Article  PubMed  Google Scholar 

  60. Ahmadi H, Fatemizadeh E, Motie-Nasrabadi A. A comparative study of correlation methods in functional connectivity analysis using fMRI data of Alzheimer’s patients. J Biomed Phys Eng. 2023;13(2):125–34. https://doi.org/10.31661/jbpe.v0i0.2007-1134.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Rolls ET. The cingulate cortex and limbic systems for emotion, action, and memory. Brain Struct Funct. 2019;224(9):3001–18. https://doi.org/10.1007/s00429-019-01945-2.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Zhu Y, Gao H, Tong L, et al. Emotion regulation of hippocampus using real-time fMRI neurofeedback in healthy human. Front Hum Neurosci. 2019;13:242. https://doi.org/10.3389/fnhum.2019.00242.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Noh HJ, Joo EY, Kim ST, et al. The relationship between hippocampal volume and cognition in patients with chronic primary insomnia. J Clin Neurol. 2012;8(2):130–8. https://doi.org/10.3988/jcn.2012.8.2.130.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Winkelman JW, Benson KL, Buxton OM, et al. Lack of hippocampal volume differences in primary insomnia and good sleeper controls: an MRI volumetric study at 3 Tesla. Sleep Med. 2010;11(6):576–82. https://doi.org/10.1016/j.sleep.2010.03.009.

    Article  PubMed  Google Scholar 

  65. Wang Y, Wang M, Dai Y, et al. PtNPs/short MWCNT-PEDOT: PSS-modified microelectrode array to detect neuronal firing patterns in the dorsal raphe nucleus and hippocampus of insomnia rats. Micromachines (Basel). 2022;13(3):488. https://doi.org/10.3390/mi13030488.

    Article  PubMed  Google Scholar 

  66. McGovern RA, Sheth SA. Role of the dorsal anterior cingulate cortex in obsessive-compulsive disorder: converging evidence from cognitive neuroscience and psychiatric neurosurgery. J Neurosurg. 2017;126(1):132–47. https://doi.org/10.3171/2016.1.JNS15601.

    Article  PubMed  Google Scholar 

  67. Plante DT, Jensen JE, Schoerning L, et al. Reduced γ-aminobutyric acid in occipital and anterior cingulate cortices in primary insomnia: a link to major depressive disorder? Neuropsychopharmacology. 2012;37(6):1548–57. https://doi.org/10.1038/npp.2012.4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Jin T, Chen R, Shao M, et al. Dorsal hippocampus- and ACC-projecting medial septum neurons differentially contribute to the recollection of episodic-like memory. FASEB J. 2020;34(9):11741–53. https://doi.org/10.1096/fj.202000398R.

    Article  CAS  PubMed  Google Scholar 

  69. Bian XL, Qin C, Cai CY, et al. Anterior cingulate cortex to ventral hippocampus circuit mediates contextual fear generalization. J Neurosci. 2019;39(29):5728–39. https://doi.org/10.1523/JNEUROSCI.2739-18.2019.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Feng F, Yu S, Wang Z, et al. Non-pharmacological and pharmacological interventions relieve insomnia symptoms by modulating a shared network: a controlled longitudinal study. Neuroimage Clin. 2019;22:101745. https://doi.org/10.1016/j.nicl.2019.101745.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Zhang J, Wu X, Nie D, et al. Magnetic resonance imaging studies on acupuncture therapy in depression: a systematic review. Front Psychiatry. 2021;12:670739. https://doi.org/10.3389/fpsyt.2021.670739.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Wang X, Zhou H, Yan CQ, et al. Cognitive and hippocampal changes in older adults with subjective cognitive decline after acupuncture intervention. Am J Geriatr Psychiatry. 2024;32(8):1014–27. https://doi.org/10.1016/j.jagp.2024.03.001.

    Article  PubMed  Google Scholar 

  73. Tsujimoto M, Saito T, Matsuzaki Y, et al. Common and distinct neural bases of multiple positive emotion regulation strategies: a functional magnetic resonance imaging study. NeuroImage. 2022;257:119334. https://doi.org/10.1016/j.neuroimage.2022.119334.

    Article  PubMed  Google Scholar 

  74. Šimić G, Tkalčić M, Vukić V, et al. Understanding emotions: origins and roles of the amygdala. Biomolecules. 2021;11(6):823. https://doi.org/10.3390/biom11060823.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Gong L, Liao T, Liu D, et al. Amygdala changes in chronic insomnia and their association with sleep and anxiety symptoms: insight from shape analysis. Neural Plast. 2019;2019:8549237. https://doi.org/10.1155/2019/8549237.

    Article  PubMed  PubMed Central  Google Scholar 

  76. He C, Gong L, Yin Y, et al. Amygdala connectivity mediates the association between anxiety and depression in patients with major depressive disorder. Brain Imaging Behav. 2019;13(4):1146–59. https://doi.org/10.1007/s11682-018-9923-z.

    Article  PubMed  Google Scholar 

  77. Sun T, Song Z, Tian Y, et al. Basolateral amygdala input to the medial prefrontal cortex controls obsessive-compulsive disorder-like checking behavior. Proc Natl Acad Sci USA. 2019;116(9):3799–804. https://doi.org/10.1073/pnas.1814292116.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Schiel JE, Holub F, Petri R, et al. Affect and arousal in insomnia: through a lens of neuroimaging studies. Curr Psychiatry Rep. 2020;22(9):44. https://doi.org/10.1007/s11920-020-01173-0.

    Article  PubMed  PubMed Central  Google Scholar 

  79. Yang L, Yu S, Zhang L, et al. Gender differences in hippocampal/parahippocampal functional connectivity network in patients diagnosed with chronic insomnia disorder. Nat Sci Sleep. 2022;14:1175–86. https://doi.org/10.2147/NSS.S355922.

    Article  PubMed  PubMed Central  Google Scholar 

  80. Zhu X, Suarez-Jimenez B, Lazarov A, et al. Exposure-based therapy changes amygdala and hippocampus resting-state functional connectivity in patients with posttraumatic stress disorder. Depress Anxiety. 2018;35(10):974–84. https://doi.org/10.1002/da.22816.

    Article  PubMed  PubMed Central  Google Scholar 

  81. Wang Z, Wang X, Liu J, et al. Acupuncture treatment modulates the corticostriatal reward circuitry in major depressive disorder. J Psychiatr Res. 2017;84:18–26. https://doi.org/10.1016/j.jpsychires.2016.09.014.

    Article  PubMed  Google Scholar 

  82. Jin Y, Yang H, Zhang F, et al. The medial Tthalamus plays an important role in the cognitive and emotional modulation of orofacial oain: a functional magnetic resonance imaging-based study. Front Neurol. 2021;11:589125. https://doi.org/10.3389/fneur.2020.589125.

    Article  PubMed  PubMed Central  Google Scholar 

  83. Parnaudeau S, Bolkan SS, Kellendonk C. The mediodorsal thalamus: an essential partner of the prefrontal cortex for cognition. Biol Psychiatry. 2018;83(8):648–56. https://doi.org/10.1016/j.biopsych.2017.11.008.

    Article  PubMed  Google Scholar 

  84. Motomura Y, Katsunuma R, Ayabe N, et al. Decreased activity in the reward network of chronic insomnia patients. Sci Rep. 2021;11(1):3600. https://doi.org/10.1038/s41598-020-79989-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Li M, Wang R, Zhao M, et al. Abnormalities of thalamus volume and resting state functional connectivity in primary insomnia patients. Brain Imaging Behav. 2019;13(5):1193–201. https://doi.org/10.1007/s11682-018-9932-y.

    Article  PubMed  Google Scholar 

  86. Kim N, Won E, Cho SE, et al. Thalamocortical functional connectivity in patients with insomnia using resting-state fMRI. J Psychiatry Neurosci. 2021;46(6):E639–46. https://doi.org/10.1503/jpn.210066.

    Article  PubMed  PubMed Central  Google Scholar 

  87. Kong Q, Sacca V, Walker K, et al. Thalamocortical mechanisms underlying real and imagined acupuncture. Biomedicines. 2023;11(7):1830. https://doi.org/10.3390/biomedicines11071830.

    Article  PubMed  PubMed Central  Google Scholar 

  88. Zhao B, Bi Y, Chen Y, Zhang J, Zhang S, Zhang D, Rong P. Altered functional connectivity of the thalamus in patients with insomnia disorder after transcutaneous auricular vagus nerve stimulation therapy. Front Neurol. 2023;14:1164869. https://doi.org/10.3389/fneur.2023.1164869.

    Article  PubMed  PubMed Central  Google Scholar 

  89. Li YW, Li W, Wang ST, et al. The autonomic nervous system: a potential link to the efficacy of acupuncture. Front Neurosci. 2022;16:1038945. https://doi.org/10.3389/fnins.2022.1038945.

    Article  PubMed  PubMed Central  Google Scholar 

  90. Morawetz C, Bode S, Baudewig J, et al. Changes in effective connectivity between dorsal and ventral prefrontal regions moderate emotion regulation. Cereb Cortex. 2016;26(5):1923–37. https://doi.org/10.1093/cercor/bhv005.

    Article  PubMed  Google Scholar 

  91. Cheng S, Qiu X, Mo L, et al. Asynchronous involvement of VLPFC and DLPFC during negative emotion processing: an online transcranial magnetic stimulation study. Neuroscience. 2024;S0306–4522(24):00251–3. https://doi.org/10.1016/j.neuroscience.2024.05.041.

    Article  CAS  Google Scholar 

  92. Martínez-Pérez V, Campoy G, Palmero LB, et al. Examining the dorsolateral and ventromedial prefrontal cortex involvement in the self-attention network: a randomized, sham-controlled, parallel group, double-blind, and multichannel HD-tDCS study. Front Neurosci. 2020;14:683. https://doi.org/10.3389/fnins.2020.00683.

    Article  PubMed  PubMed Central  Google Scholar 

  93. Jung J, Lambon Ralph MA, Jackson RL. Subregions of DLPFC display graded yet distinct structural and functional connectivity. J Neurosci. 2022;42(15):3241–52. https://doi.org/10.1523/JNEUROSCI.1216-21.2022.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Guo J, Tang C, Wang L. Effect of Tiaoshen acupuncture therapy on sleep quality and hyperarousal state in primary insomnia. J Clin Acupunct Moxibustion. 2017;10(7):882–5.

    Google Scholar 

  95. Hamvas S, Hegyi P, Kiss S, et al. Acupuncture increases parasympathetic tone, modulating HRV - systematic review and meta-analysis. Complement Ther Med. 2023;72:102905. https://doi.org/10.1016/j.ctim.2022.102905.

    Article  PubMed  Google Scholar 

  96. Wei L, Chen H, Wu GR. Structural covariance of the prefrontal-amygdala pathways associated with heart rate variability. Front Hum Neurosci. 2018;12:2. https://doi.org/10.3389/fnhum.2018.00002.

    Article  PubMed  PubMed Central  Google Scholar 

  97. Zeng D, Yan X, Deng H, et al. Placebo response varies between different types of sham acupuncture: a randomized double-blind trial in neck pain patients. Eur J Pain. 2022;26(5):1006–20. https://doi.org/10.1002/ejp.1924.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Fallon N, Roberts C, Stancak A. Shared and distinct functional networks for empathy and pain processing: a systematic review and meta-analysis of fMRI studies. Soc Cognit Affect Neurosci. 2020;15(7):709–23. https://doi.org/10.1093/scan/nsaa090.

    Article  Google Scholar 

  99. Ong WY, Stohler CS, Herr DR. Role of the prefrontal cortex in pain processing. Mol Neurobiol. 2019;56(2):1137–66. https://doi.org/10.1007/s12035-018-1130-9.

    Article  CAS  PubMed  Google Scholar 

  100. Andrillon T, Solelhac G, Bouchequet P, et al. Revisiting the value of polysomnographic data in insomnia: more than meets the eye. Sleep Med. 2020;66:184–200. https://doi.org/10.1016/j.sleep.2019.12.002.

    Article  PubMed  Google Scholar 

  101. Kao CH, D’Rozario AL, Lovato N et al. Insomnia subtypes characterised by objective sleep duration and NREM spectral power and the effect of acute sleep restriction: an exploratory analysis. Sci Rep. 2021;11(1):24331. Published 2021 Dec 21. https://doi.org/10.1038/s41598-021-03564-6

  102. Chen Z, Jiang T, Yin X, Li B, Tan Z, Guo J. The increased functional connectivity between the locus coeruleus and supramarginal gyrus in insomnia disorder with acupuncture modulation. Front Neurosci. 2023;17:1131916. https://doi.org/10.3389/fnins.2023.1131916. Published 2023 Apr 20.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We would like to thank the study participants for volunteering for this study. We gratefully acknowledge the contributions of personnel who implemented acquisition of the data.

Funding

The National Natural Science Foundation of China (81774391), Beijing Natural Science Foundation (7212170) and the Beijing Key Laboratory of Acupuncture Neuromodulation, Beijing, China (BZ0437) funded this study.

Author information

Authors and Affiliations

  1. Department of Acupuncture and Moxibustion, Beijing Key Laboratory of Acupuncture Neuromodulation, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, 100010, China

    Tong-Fei Jiang, Zhao-Yi Chen, Xue-Jiao Yin, Gui-Ling Wang, Bin Li & Jing Guo

  2. School of Traditional Chinese Medicine, Capital Medical University, Beijing, 100069, China

    Jiao Liu

  3. Department of Radiology, Dong Zhimen Hospital Beijing University of Chinese Medicine, Beijing, 100010, China

    Zhong-Jian Tan

Authors
  1. Tong-Fei Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhao-Yi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiao Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xue-Jiao Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhong-Jian Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Gui-Ling Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Bin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jing Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Jing Guo: conception and study design. Xue-Jiao Yin: data collection or acquisition. Zhao-Yi Chen: statistical analysis. Jiao Liu and Zhong-Jian Tan: fMRI data reviewing and analysis. Bin Li: review and editing, methodology. Gui-Ling Wang: clinical intervention design. Tong-Fei Jiang. and Jing Guo: drafting the manuscript work. Jing Guo: revising the manuscript critically for important intellectual content. All authors contributed to the article and approved the submitted version.All authors reviewed the manuscript.

Corresponding author

Correspondence to Jing Guo.

Ethics declarations

Ethics approval and consent to participate

This trial was performed at the Beijing Hospital of Traditional Chinese Medicine, Capital Medical University (Beijing TCM Hospital). It was approved by the ethics committee of the hospital and registered on http://www.chictr.org.cn (Chinese Clinical Trials Register, ChiCTR1800015282). Informed consent was obtained from all subjects in this study. A copy of the informed consent is available for review by the Editor-in-Chief of this journal.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

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/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jiang, TF., Chen, ZY., Liu, J. et al. Acupuncture modulates emotional network resting-state functional connectivity in patients with insomnia disorder: a randomized controlled trial and fMRI study. BMC Complement Med Ther 24, 311 (2024). https://doi.org/10.1186/s12906-024-04612-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12906-024-04612-0

Keywords

BMC Complementary Medicine and Therapies

ISSN: 2662-7671

Contact us