Altered Regional Spontaneous Brain Activity and Default Mode Network Functional Connectivity in Herpes Zoster-related Pain Patients with High Anxiety

doi:10.21203/rs.3.rs-3585473/v1

Download PDF

Research Article

Altered Regional Spontaneous Brain Activity and Default Mode Network Functional Connectivity in Herpes Zoster-related Pain Patients with High Anxiety

https://doi.org/10.21203/rs.3.rs-3585473/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Pain and psychological disorders are two common symptom clusters in patients with herpes zoster-related pain (HZ-RP). Numerous neuroimaging studies have revealed abnormal brain structure and function in patients with HZ-RP. However, very few studies have focused on the functional characteristics of the brain in HZ-RP patients with different levels of anxiety. Fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo) and functional connectivity (FC) were compared between HZ-RP patients with high anxiety (HZ-RP with HA n = 25) and HZ-RP patients with low anxiety (HZ-RP with LA, n = 26). Multivariable linear regression models were used to investigate the impact of clinical characteristics on the significant abnormalities, and moderation analysis was used to examine the interaction between pain and anxiety. The two groups showed significant differences in regional spontaneous brain activity in the right Rolandic operculum (RO), left middle temporal gyrus (MTG) and right precentral gyrus (PrCG). The left anterior prefrontal cortex showed significantly decreased FC with the left medial superior frontal gyrus while the left occipital cortex showed increased FC with the right fusiform, right MTG and left precuneus in the HZ-RP with HA group compared with that in the LA group. The degree of pain and anxiety contributed to explaining the variance in the fALFF of the right RO. However, the abnormal ReHo in the right PrCG was solely related to the degree of anxiety. The abnormal brain dysfunctional pattern may be an indicator of underlying pathophysiological mechanisms linking HZ-RP and anxiety. The right PrCG plays a vital role in regulating anxiety in HZ-RP patients.

herpes zoster-related pain (HZ-RP)

anxiety

fractional amplitude of low-frequency fluctuations (fALFF)

regional homogeneity (ReHo)

default mode network (DMN)

functional connectivity (FC)

Herpes zoster (HZ) is a common human skin disease caused by reactivation of latent varicella zoster virus in sensory ganglia (Jeon, 2015). The incidence of HZ-related pain (HZ-RP) is remarkably increasing in middle-aged and elderly people and includes acute zoster pain and postherpetic neuralgia (PHN) (Luo et al., 2021). PHN is a refractory chronic pain syndrome caused by HZ with complex aetiology and pathogenesis that lasts for months to years after resolution of HZ (Liu et al., 2020; Wan and Song, 2021). The incidence of HZ ranges from 3.4%-5.3%, and the incidence of PHN in patients with HZ ranges from 9%-34% (Liu et al., 2020). Patients with HZ-RP may experience various phenotypes of abnormal sensations, such as itching, burning, hyperesthesia, and severe pain, as well as mood disorders, such as depression, anxiety and sleep disorders (Baron et al., 2010; Fashner and Bell, 2011). One prospective study showed that acute HZ disturbs all healthy domains, such as sleep, enjoyment of life and general activities, and PHN most frequently leads to anxiety, depression, poor mood and poor sleep (Drolet et al., 2010). In a previous study, the incidence of anxiety in patients with PHN was 69.0%, higher than that of patients with low back pain and patients with diabetic neuropathic pain (Du et al., 2021). Additionally, an animal experiment on HZ-RP showed that varicella zoster virus-infected rats exhibited anxiety-like behaviour (Hasnie et al., 2007). Conversely, long-term anxiety may further aggravate HZ-RP symptoms. Therefore, it is very important to improve the psychological state to enhance the quality of life of patients with HZ-RP. However, the neurophysiological mechanisms underlying HZ-RP with anxiety are not yet adequately understood. Thus, it is necessary to further explore the underlying brain mechanisms of HZ-RP with anxiety and provide a neurobiological basis for subsequent interventions to improve the quality of life of patients.

Functional magnetic resonance imaging (fMRI) has become the mainstream of neuroimaging in brain diseases and cognitive neuroscience (Logothetis, 2008). In resting-state fMRI signals, an index called the amplitude of low-frequency fluctuation (ALFF) reflects the intensity of regional spontaneous brain activity (Zou et al., 2008). Fractional ALFF (fALFF) is also one of the most common metrics, which improves the sensitivity and specificity of detecting spontaneous brain activity (Li et al., 2021; Zou et al., 2008). Another of the most commonly used indicators, called regional homogeneity (ReHo), represents local consistency in local spontaneous neuronal activity (Cao et al., 2018). Previous studies have shown that HZ-RP patients exhibited significantly abnormal ALFF, fALFF and ReHo intensity in many brain regions, such as the classic pain-related brain regions (such as the frontal lobe, thalamus, insula, and cerebellum), brainstem, limbic lobe, temporal gyrus and cerebellum anterior lobe (Cao et al., 2017a; Cao et al., 2017b; Gu et al., 2019; Huang et al., 2020). Patients with anxiety also exhibited significantly abnormal ALFF, fALFF and ReHo intensity in many brain regions, such as the postcentral gyrus, precentral gyrus, cingulate cortex, frontal gyrus, cerebellar posterior lobe and fusiform gyrus (Cui et al., 2020; Shen et al., 2020a; Yuan et al., 2018a; Yuan et al., 2018b).

Functional connectivity (FC) is an important metric reflecting the coordinated activity between spatially remote neuronal assemblies to allow individuals to perform complex cognitive tasks or perceptual processes in the human brain and has become a vital concept in modern cognitive neuroscience (Fingelkurts et al., 2005). In recent decades, a growing number of researchers have shifted their focus from isolated brain regions to brain networks. The brain network is made up of a large number of interacting components, and the default mode network (DMN) is the key architecture for functional organization in the human brain (Clancy et al., 2022; Mears and Pollard, 2016). The DMN, composed of discrete, bilateral, and symmetrical cortical regions, is more active in the resting state than other networks in the human brain (Raichle, 2015; Xiong et al., 2020). DMN dysfunction is associated with many pathological conditions, such as Alzheimer’s disease, schizophrenia, Parkinson’s disease, depression and anxiety (Broyd et al., 2009; Clancy et al., 2022; Wang et al., 2020). However, it is still unclear whether there are alterations in regional spontaneous brain activity and FC characteristics in HZ-RP patients with different levels of anxiety.

Consequently, the present study aimed to explore the alterations in regional spontaneous brain activity and FC characteristics in HZ-RP patients with different levels of anxiety. The study investigated fALFF, ReHo and FC to identify the difference in regional brain dysfunction and spatial dysfunction among remote brain regions. Additionally, multivariable linear regression models were used to investigate the impact of clinical characteristics on significant abnormalities in brain function, and moderation analysis was used to examine the interaction between pain and anxiety. To the best of our knowledge, this was the first resting-state fMRI study focused on brain functional characteristics to investigate anxiety comorbidity in HZ-RP patients.

Participants

Fifty-one participants with a diagnosis of HZ-RP were recruited from the Pain Department of the Affiliated Hospital of Southwest Medical University. Two pain specialists made the clinical diagnosis of HZ-RP based on the International Association for the Study of Pain criteria.

The recruited participants were selected based on their clinical diagnosis of HZ-RP. The inclusion criteria were as follows: (1) clinically diagnosed with HZ or PHN, (2) a pain score ≥ 5 according to the visual analogue scale (VAS) before the magnetic resonance imaging (MRI) scan, and (3) right-handedness. The exclusion criteria were as follows: (1) history of other acute or chronic pain disorders; (2) failed MRI scans, for reasons such as metallic implants or claustrophobia; (3) maximal head motion or displacement exceeding 2.5 mm, maximal head rotation exceeding 2.5°; and (4) remarkable cerebral infarctions or other abnormalities visible.

The intensity of spontaneous pain was assessed using the VAS, with a range from 0 (no pain) to 10 (highest intolerable pain) (see Supplementary Fig. S1). Additionally, all of the participants completed the short-form McGill pain questionnaire (SF-MPQ) to evaluate the sensory and affective aspects of their pain (see Supplementary table S1). Furthermore, a self-rating anxiety scale (SAS) was used to evaluate the degree of anxiety of the participants with HZ-RP (see Supplementary table S2).

The normality of the SAS score was tested using statistical software (SPSS 17.0; Inc., Chicago, IL, USA), which showed that the data of the current study were normally distributed (p > 0.05). After checking the normality of the distribution of SAS scores, participants were divided into two groups based on the SAS median value. A similar method was used in a previous neuroimaging study (Wang et al., 2021). In the current study, participants with SAS scores equal to or less than 38 were assigned to the HZ-RP with low anxiety (HZ-RP with LA) group; participants with SAS scores greater than 38 were assigned to the HZ-RP with high anxiety (HZ-RP with HA) group.

The Ethics Committee of the Affiliated Hospital of Southwest Medical University approved this study, and relevant written informed consent was obtained from all of the participants in accordance with the Declaration of Helsinki.

Image acquisition

An MRI scan was performed on the Philips 3.0 Tesla MRI scanner by using a standard eight-channel head coil. Three-dimensional, high-resolution T1-weighted images were collected using a fast-field echo pulse sequence. Data acquisition parameters were as follows: repetition time (TR)/echo time (TE) = 8/4 ms, flip angle (FA) = 7°, acquisition matrix = 256 × 256, voxel size = 1 mm × 1 mm × 1 mm, slice thickness = 1.0 mm, and 160 sagittal slices.

The fMRI data were collected using an echo-planar image sequence (EPI). Data acquisition parameters were as follows: TR/TE = 2000/30 ms, matrix = 64 × 64, field of view = 240 × 240 × 152, voxel size = 3.75 × 3.75 × 4 mm, FA = 90°, scan order interleaved, slice thickness = 4.0 mm (no slice gap), and scan time = 546 s. Each brain volume comprised 38 axial slices, and each functional run contained 210 image volumes. For the resting-state scan, participants were asked to keep their eyes closed, keep their mind clear, and not fall asleep.

Preprocessing of resting-state fMRI data

The resting-state fMRI data were analysed using the Data Processing and Analysis of Brain Imaging (DPABI) toolbox (http://rfmri.org/dpabi). The EPI scans were analysed via subsequent steps of procedures: (1) format conversion, converting EPI DICOM images to NIFTI format; (2) removal of the first 10 time points per subject for signal equilibrium and participant adaptation in the scan; (3) slice-timing correction; (4) head-motion correction; (5) realignment and registration of functional images; and (6) spatial normalization to standard Montreal Neurological Institute (MNI) space, and each voxel was resampled to 3 × 3 ×3 mm³. Participants with maximal head motion or displacement > 2.5 mm or head rotation > 2.5° in any direction were excluded from further processing.

For ReHo analysis, the procedures included the following steps: (1) linear detrending; (2) bandpass filtering (0.01–0.1 Hz); (3) regressing out the nuisance signals, which included 24 head motion parameters, white matter signals, and cerebrospinal fluid signals; and (4) the Kendall coefficient concordance (KCC) of the time series was used to generate a standardized ReHo map for each subject by calculating a given voxel with its neighbours (27 voxels) in a voxelwise manner.. Fisher r-to-z transformation was used to improve normality.

For fALFF analysis, the procedures also included the following steps: (1) spatial smoothing was performed with a Gaussian kernel with an FWHM of 4 mm; (2) detrending; (3) regressing out the nuisance signals, which included 24 head motion parameters, white matter signals, and cerebrospinal fluid signals; (4) the fast Fourier transform (FFT) algorithm was used to transform the time courses into the frequency band; and (5) the fALFF values were obtained by calculating the square root of the power spectrum. As previously stated, Fisher r-to-z transformation was also used to improve normality.

FC analysis

Procedures of the FC analysis were performed using DPABI. Preprocessing steps included the following steps: removing the first 10 time points, slice timing, head motion correction, realignment, spatial normalization to MNI space and resampling to 3 mm × 3 mm × 3 mm, nuisance covariate regression (24 head motion parameters, white matter and cerebrospinal fluid signals), and bandpass filtering (0.01–0.1 Hz). Afterwards, the time points that were more than the framewise displacement threshold (0.5 mm) were censored together with the one time point before and two time points after that time point (Power et al., 2013).

The brain regions with significant differences in regional spontaneous brain activity between the two groups were used as “seeds” for the subsequent seed-based FC analysis. Furthermore, 34 regions of interest (ROIs) of the DMN were defined as 6 mm radius circular spheres placed at MNI coordinates based on the Dosenbach 160 atlas (see Supplementary Table S3) (Dosenbach et al., 2010). Correlation functional analyses were performed by calculating the voxel-level temporal correlations between each seed and the rest of the brain. To improve normality, the correlation coefficients of each voxel were converted into z score images using Fisher r-to-z transformation. Finally, a complete brain z score map for each individual was created for subsequent statistical analysis.

Statistical analysis

SPSS was used to analyse the demographic and clinical data. Two-sample t tests were used to compare the ages, SAS scores, illness duration, VAS scores and SF-MPQ scores between the two groups. The χ2 test was used to compare sex data between the two groups. We set the significance threshold to P < 0.05.

The Resting State fMRI Data Analysis Toolkit (REST V1.8, http://rest.restfmri. net) was used to detect the differences in fALFF and ReHo between the two groups with age and sex as covariates. We set the threshold for statistical significance to P < 0.01 at the voxel level and P < 0.05 at the cluster level (AlphaSim corrected). For FC analysis, a voxel-based comparison of z value maps between the two groups was performed with age and sex as covariates. Similarly, we set the threshold for statistical significance to P < 0.01 at the voxel level and P < 0.05 at the cluster level (AlphaSim corrected).

We further used multivariable linear regression models to investigate the impact of independent variables (demographic and clinical data) on the fALFF, ReHo and FC values of clusters showing significant differences between the two groups. The mean fALFF, ReHo and FC values of the clusters were calculated for each subject using REST. Additionally, to examine whether anxiety symptoms may exacerbate the associations between pain and abnormal brain function, we used moderation analysis to examine the interaction between SAS sores and VAS scores on fALFF, ReHo and FC values of clusters showing significant differences.

Demographic and clinical features

A total of 51 participants were selected in this study, including 25 participants in the HZ-RP with HA group (13 males, mean age: 60.12 years) and 26 participants in the HZ-RP with LA group (17 males, mean age: 61.85 years). The demographic and clinical details of all participants are listed in Table 1. The SAS scores, SF-MPQ-Sensory scores and SF-MPQ-Affective scores of the HZ-RP with HA group were significantly higher than those of the HZ-RP with LA group. There were no significant differences in age, sex, skin lesions, illness duration or VAS scores between the two groups.

Group differences in fALFF and ReHo

The results of the two-sample t test regarding fALFF and ReHo are shown in Fig. 1 and Table 2. Compared with the HZ-RP with LA group, participants in the HZ-RP with HA group exhibited higher fALFF values in the right Rolandic operculum (RO) (AlphaSim corrected, cluster-level P < 0.05, voxel-level P <0.01, cluster size >19 voxels). ReHo values were significantly increased in the left middle temporal gyrus (MTG) and decreased in the right precentral gyrus (PrCG) in the HZ-RP with HA group (AlphaSim corrected, cluster-level P < 0.05, voxel-level P <0.01, cluster size >18 voxels).

FC analysis

The seed-based FC analysis based on the above results did not show significant differences between the two groups. Based on the DMN FC analysis, the left anterior prefrontal cortex (aPFC) showed a significantly decreased connectivity pattern with the left medial superior frontal gyrus (mSFG) (AlphaSim corrected, cluster-level P < 0.05, voxel-level P <0.01, cluster size >18 voxels) in the HZ-RP with HA group compared with that in the LA group. In addition, the left occipital cortex showed an increased FC pattern with the right fusiform, right MTG and left precuneus in the HZ-RP with HA group compared with that in the LA group (AlphaSim corrected, cluster-level P < 0.05, voxel-level P <0.01, cluster size >18 voxels). The results of the two-sample t test regarding the DMN are shown in Fig. 2 and Table 3.

Multivariable linear regression analysis and moderation analysis

The results of multivariable linear regression analysis are shown in Table 4 and Table 5. VAS scores and SAS scores contributed to explaining the variance in fALFF of the right RO. SAS scores solely contributed to explaining the variance in ReHo of the right PrCG. However, the remaining multivariate regression models were not significant (P＞0.05). Moderation analysis showed that there was no interaction between VAS scores and SAS scores on clusters that had significant differences (P＞0.05).

This study investigated fALFF and ReHo to explore the differences in resting-state brain activity between the HZ-RP with HA group and the HZ-RP with LA group. The results of the two-sample t test showed significant differences in regional spontaneous brain activity of the right RO, left MTG and right PrCG between the two groups. Seed-based FC analysis based on significant brain regions of spontaneous brain activity did not show significant differences between the two groups. However, FC analyses based on the DMN revealed significantly decreased FC between the left aPFC and left mSFG and increased FC between the left occipital cortex and right fusiform, right MTG and left precuneus in HZ-RP patients with HA compared with the FC of those in the HZ-RP with LA group. Multivariable linear regression analysis showed that the VAS score and SAS score were associated with alterations of fALFF in the right RO, and the SAS score was solely associated with alterations of ReHo in the right PrCG.

The RO, also called the subcentral gyrus or the central/basal operculum, consists of the inferior junction of the precentral and postcentral gyrus (Triarhou, 2021). The RO is involved in emotional processing as well as taste and visceral sensations, and its complex connectivity pattern is thought to play a role in sensory-auditory integration (Mălîia et al., 2018; Triarhou, 2021). Additionally, the RO processes and integrates exteroceptive-interoceptive signals and plays a key role in self-awareness and consciousness (Blefari et al., 2017). A previous study suggested that lesions in the right RO lead to worse psychological conditions, such as high levels of apathy, depression, anxiety and perceived stress (Sutoko et al., 2020). In a ⁹⁹mTc-ECD SPECT/CT brain imaging study, the RO blood flow was altered in the anxiety disorder group compared with that in the healthy control group (Zhuang et al., 2015). Additionally, a study of the PHN showed a significant correlation between degree centrality changes in the RO and changes in pain, emotion, and sleep quality (Fan et al., 2022). In our study, increased fALFF values of the right RO were found in the HZ-RP with HA group compared with those of the HZ-RP with LA group, and the VAS and SAS scores contributed to explaining the variance in the brain region. Therefore, we speculate that alterations in the right RO may indicate an adaptive compensatory mechanism in chronic pain and help explain the regulation of emotion in HZ-RP patients with anxiety.

The MTG is a brain region unique to human beings, and there is no homology to the human MTG (Binney et al., 2012). The MTG plays an important role in language-related tasks and has many other functions, such as observing movement, deductive reasoning, and dynamic facial expressions (Briggs et al., 2021; Xu et al., 2015). Observing and analysing human movements and facial expressions is an important part of everyday interaction. Studies have suggested that patients with anxiety tend to view static faces in a negative or vigilant manner (Qiu et al., 2014; Winton et al., 1995). Relative to the nonpatient population, patients diagnosed with anxiety disorder have changes in the characteristics of the left MTG, such as ADC values, FA values, and FC with the right amygdala (Pannekoek et al., 2013; Qiu et al., 2014). One study showed that in patients with anxiety disorders, damage to the left MTG was proportional to the severity of clinical symptom-related functional impairment (Yun et al., 2017). In addition, the MTG is a pain-related area. Previous studies have identified that HZ-RP patients showed significant functional changes in the MTG (Cao et al., 2018; Huang et al., 2020; Schmaal et al., 2017). Differences in the left MTG were also found between the two groups in the present study. In the multivariable linear regression analysis, SAS scores may help explain the variance in ReHo for the left MTG, although the regression model outcomes were not significant. These results suggested that functional abnormalities in the MTG may be related to the pathophysiological mechanism of HZ-RP with anxiety. However, the results should be interpreted with caution, and further studies are needed to understand the MTG abnormalities more fully in HZ-RP patients with anxiety.

The primary motor area is located in the PrCG, and the PrCG has clear significance in preventing tactile allodynia (Liu et al., 2019; Wei et al., 2022). The PrCG belongs to the "pain matrix", which is closely related to aversive experiences we call "pain" (Garcia-Larrea and Bastuji, 2018; Tang et al., 2021). An increasing body of evidence suggests that the PrCG exhibits brain functional and structural reorganization in many pain-related diseases, such as shoulder pain, neck pain, migraine, or facial pain (Cunningham et al., 2015; Niddam et al., 2019; Wei et al., 2022; Yue and Du, 2020). A systematic review revealed that the PHN was closely associated with structural and functional abnormalities of the PrCG (Tang et al., 2021). Additionally, the PrCG consists of parts of the prefrontal and posterior central cortices (Shen et al., 2020b). Among the emotion-related brain systems, the prefrontal cortex plays a key role in the production and regulation of emotion (Dixon et al., 2017). A previous study showed that patients with anxiety had reduced ALFF and ReHo values in the right PrCG compared to healthy controls (Shen et al., 2020b). In the present study, multivariable linear regression analysis showed that SAS scores solely contributed to explaining the variance in the right PrCG. Overall, functional and structural abnormalities in the PrCG may play a vital role in the pathophysiology of HZ-RP with anxiety.

The DMN is the most active network in the resting state, and its activity is associated with autobiographical memories, self-monitoring, thinking about past events and the future, and emotional regulation (Buckner et al., 2008; Xiong et al., 2020). Previous studies have suggested that major nodes of the DMN, such as the medial prefrontal cortex and posterior cingulate cortex, are altered in patients with anxiety disorder when they are in the resting state (Xiong et al., 2020; Zhao et al., 2007). Abnormalities of the DMN in patients with HZ-RP were also shown in previous studies. One study showed altered precuneus connectivity in both the HZ and PHN, indicating DMN disruption (Hong et al., 2018). One study showed that the intrinsic connectivity of the DMN in patients with PHN was widely disrupted (Jiang et al., 2016). Our study also found altered FC of the DMN in the HZ-RP with HA group compared to that in the LA group. Interestingly, the mSFG, MTG and precuneus all belong to nodes of the DMN (Dosenbach et al., 2010; Uddin et al., 2019). The fusiform cortex is regulated by emotional valence, and the right fusiform cortex is more active than the left during emotional processing (Geday et al., 2003). Increased FC between the DMN and right fusiform may account for the excessive focus on negative feelings or thoughts in HZ-RP patients with anxiety. Hence, we inferred that altered FC of the DMN may be related to the pathophysiological mechanism of HZ-RP with anxiety.

There are several limitations to be considered in the current study. First, the sample size was relatively limited, which may have reduced the generalizability of the findings. Second, the effect of medication on our results cannot be completely ruled out because some patients cannot tolerate severe pain. Unfortunately, the sample size was too small for further subgroup analysis in the current study. Third, as a cross-sectional study, this research still cannot be used to accurately speculate on the cause-and-effect relationship between clinical characteristics and cerebral abnormalities. Fourth, we focused on assessing FC changes of the DMN, and the changes in other networks need to be further evaluated.

In conclusion, the present study demonstrated that abnormalities in regional spontaneous brain activity in the RO, MTG and PrCG and FC characteristics of the DMN were closely associated with the neurobiological mechanisms of HZ-RP with anxiety. Furthermore, the abnormal ReHo in the right PrCG was solely related to SAS scores, suggesting that the right PrCG plays a vital role in regulating anxiety in HZ-RP patients. Therefore, the abnormal brain dysfunctional pattern may be an indicator of underlying pathophysiological mechanisms linking HZ-RP and anxiety. This may provide novel insights that may guide the development of new biomarkers or even the identification of treatment targets of anxiety comorbidity in HZ-RP patients.

aPFC

anterior prefrontal cortex

DMN

default mode network

fALFF

fractional amplitude of low frequency fluctuations

functional connectivity

fMRI

functional magnetic resonance imaging

herpes zoster

HZ-RP

herpes zoster-related pain

HZ-RP with HA

herpes zoster-related pain patients with high anxiety

HZ-RP with LA

herpes zoster-related pain patients with low anxiety

left

MNI

the Montreal Neurological Institute

mSFG

medial superior frontal gyrus

MTG

middle temporal gyrus

PHN

postherpetic neuralgia

PrCG

precentral gyrus

right. ReHo,regional homogeneity

Rolandic operculum

SAS

self-rating anxiety scale

VAS

visual analogue scale.

Acknowledgements We would like to thank the funding support provided by relevant organizations. The authors would also like to thank the relevant researchers in the Department of Radiology and Department of Pain of Southwest Medical University for their support of this research.

Authors’ contributions L.Z., Q.L. and G.C. designed and drafted the manuscript. L. Z., Q.L., S.L., X. L., Q.L., Y.H., C.R. contributed to the acquisition of data and analyzed them. C.R. and G.C. revised the manuscript. All authors read and approved the final manuscript.

Funding This study was supported by the Sichuan Science & Technology Department of China (Grant No. 2019YJ0692), the Sichuan University & Luzhou Collaborative Foundation (Grant No. 2017CDLZ-G27, 2018CDLZ-11), the Project of Cadres Health Protection of Sichuan Province (Grant No. 2019-1502), and the Project of Health Commission of Sichuan Province (Grant No. 20PJ128), the Luzhou Science & Technology Department (Grant No. 2022-SYF-60 ), the Project for Doctors of Affiliated Hospital of Southwest Medical University (Grant No. 2018-17129).

Data availability Data will be made available on request.

Ethics approval and consent to participate The Ethics Committee of the Affiliated Hospital of Southwest Medical University approved this study (KY2020190), and relevant written informed consent was obtained from all of the participants in accordance with the Declaration of Helsinki.

Competing interests The authors declare that they have no competing interests.

Baron, R., Binder, A., Wasner, G. (2010). Neuropathic pain: diagnosis, pathophysiological mechanisms, and treatment. Lancet Neurol,9(8), 807-19.
Binney, R.J., Parker, G.J., Lambon Ralph, M.A. (2012). Convergent connectivity and graded specialization in the rostral human temporal lobe as revealed by diffusion-weighted imaging probabilistic tractography. J Cogn Neurosci,24(10), 1998-2014.
Blefari, M.L., Martuzzi, R., Salomon, R., Bello-Ruiz, J., Herbelin, B., Serino, A., Blanke, O. (2017). Bilateral Rolandic operculum processing underlying heartbeat awareness reflects changes in bodily self-consciousness. Eur J Neurosci,45(10), 1300-1312.
Briggs, R.G., Tanglay, O., Dadario, N.B., Young, I.M., Fonseka, R.D., Hormovas, J., Dhanaraj, V., Lin, Y.H., et al. (2021). The Unique Fiber Anatomy of Middle Temporal Gyrus Default Mode Connectivity. Oper Neurosurg (Hagerstown),21(1), E8-E14.
Broyd, S.J., Demanuele, C., Debener, S., Helps, S.K., James, C.J., Sonuga-Barke, E.J. (2009). Default-mode brain dysfunction in mental disorders: a systematic review. Neurosci Biobehav Rev,33(3), 279-96.
Buckner, R.L., Andrews-Hanna, J.R., Schacter, D.L. (2008). The brain's default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci,1124(1-38.
Cao, S., Li, Y., Deng, W., Qin, B., Zhang, Y., Xie, P., Yuan, J., Yu, B., et al. (2017a). Local Brain Activity Differences Between Herpes Zoster and Postherpetic Neuralgia Patients: A Resting-State Functional MRI Study. Pain Physician,20(5), E687-e699.
Cao, S., Song, G., Zhang, Y., Xie, P., Tu, Y., Li, Y., Yu, T., Yu, B. (2017b). Abnormal Local Brain Activity Beyond the Pain Matrix in Postherpetic Neuralgia Patients: A Resting-State Functional MRI Study. Pain Physician,20(2), E303-e314.
Cao, S., Qin, B., Zhang, Y., Yuan, J., Fu, B., Xie, P., Song, G., Li, Y., et al. (2018). Herpes zoster chronification to postherpetic neuralgia induces brain activity and grey matter volume change. Am J Transl Res,10(1), 184-199.
Clancy, K.J., Andrzejewski, J.A., You, Y., Rosenberg, J.T., Ding, M., Li, W. (2022). Transcranial stimulation of alpha oscillations up-regulates the default mode network. Proc Natl Acad Sci U S A,119(1).
Cui, Q., Sheng, W., Chen, Y., Pang, Y., Lu, F., Tang, Q., Han, S., Shen, Q., et al. (2020). Dynamic changes of amplitude of low-frequency fluctuations in patients with generalized anxiety disorder. Hum Brain Mapp,41(6), 1667-1676.
Cunningham, G., Zanchi, D., Emmert, K., Kopel, R., Van De Ville, D., Ladermann, A., Haller, S., Hoffmeyer, P. (2015). Neural Correlates of Clinical Scores in Patients with Anterior Shoulder Apprehension. Med Sci Sports Exerc,47(12), 2612-20.
Dixon, M.L., Thiruchselvam, R., Todd, R., Christoff, K. (2017). Emotion and the prefrontal cortex: An integrative review. Psychol Bull,143(10), 1033-1081.
Dosenbach, N.U., Nardos, B., Cohen, A.L., Fair, D.A., Power, J.D., Church, J.A., Nelson, S.M., Wig, G.S., et al. (2010). Prediction of individual brain maturity using fMRI. Science,329(5997), 1358-61.
Drolet, M., Brisson, M., Schmader, K.E., Levin, M.J., Johnson, R., Oxman, M.N., Patrick, D., Blanchette, C., et al. (2010). The impact of herpes zoster and postherpetic neuralgia on health-related quality of life: a prospective study. CMAJ,182(16), 1731-6.
Du, J., Sun, G., Ma, H., Xiang, P., Guo, Y., Deng, Y., Li, S., Li, X. (2021). Prevalence and Risk Factors of Anxiety and Depression in Patients with Postherpetic Neuralgia: A Retrospective Study. Dermatology,237(6), 891-895.
Fan, X., Ren, H., Bu, C., Lu, Z., Wei, Y., Xu, F., Fu, L., Ma, L., et al. (2022). Alterations in local activity and functional connectivity in patients with postherpetic neuralgia after short-term spinal cord stimulation. Front Mol Neurosci,15(938280.
Fashner, J., Bell, A.L. (2011). Herpes zoster and postherpetic neuralgia: prevention and management. Am Fam Physician,83(12), 1432-7.
Fingelkurts, A.A., Fingelkurts, A.A., Kahkonen, S. (2005). Functional connectivity in the brain--is it an elusive concept? Neurosci Biobehav Rev,28(8), 827-36.
Garcia-Larrea, L., Bastuji, H. (2018). Pain and consciousness. Prog Neuropsychopharmacol Biol Psychiatry,87(Pt B), 193-199.
Geday, J., Gjedde, A., Boldsen, A.-S., Kupers, R. (2003). Emotional valence modulates activity in the posterior fusiform gyrus and inferior medial prefrontal cortex in social perception. NeuroImage,18(3), 675-684.
Gu, L., Hong, S., Jiang, J., Liu, J., Cao, X., Huang, Q., Zeng, X., Zhou, F., et al. (2019). Bidirectional alterations in ALFF across slow-5 and slow-4 frequencies in the brains of postherpetic neuralgia patients. J Pain Res,12(39-47.
Hasnie, F.S., Breuer, J., Parker, S., Wallace, V., Blackbeard, J., Lever, I., Kinchington, P.R., Dickenson, A.H., et al. (2007). Further characterization of a rat model of varicella zoster virus-associated pain: Relationship between mechanical hypersensitivity and anxiety-related behavior, and the influence of analgesic drugs. Neuroscience,144(4), 1495-508.
Hong, S., Gu, L., Zhou, F., Liu, J., Huang, M., Jiang, J., He, L., Gong, H., et al. (2018). Altered functional connectivity density in patients with herpes zoster and postherpetic neuralgia. J Pain Res,11(881-888.
Huang, J., Li, Y., Xie, H., Yang, S., Jiang, C., Sun, W., Li, D., Liao, Y., et al. (2020). Abnormal Intrinsic Brain Activity and Neuroimaging-Based fMRI Classification in Patients With Herpes Zoster and Postherpetic Neuralgia. Front Neurol,11(532110.
Jeon, Y.H. (2015). Herpes Zoster and Postherpetic Neuralgia: Practical Consideration for Prevention and Treatment. Korean J Pain,28(3), 177-84.
Jiang, J., Gu, L., Bao, D., Hong, S., He, W., Tan, Y., Zeng, X., Gong, H., et al. (2016). Altered homotopic connectivity in postherpetic neuralgia: a resting state fMRI study. J Pain Res,9(877-886.
Li, X., Yu, R., Huang, Q., Chen, X., Ai, M., Zhou, Y., Dai, L., Qin, X., et al. (2021). Alteration of Whole Brain ALFF/fALFF and Degree Centrality in Adolescents With Depression and Suicidal Ideation After Electroconvulsive Therapy: A Resting-State fMRI Study. Front Hum Neurosci,15(762343.
Liu, B., Yang, Y., Zhang, Z., Wang, H., Fan, B., Sima, L. (2020). Clinical Study of Spinal Cord Stimulation and Pulsed Radiofrequency for Management of Herpes Zoster-Related Pain Persisting Beyond Acute Phase in Elderly Patients. Pain Physician,23(3), 263-270.
Liu, J., Gu, L., Huang, Q., Hong, S., Zeng, X., Zhang, D., Zhou, F., Jiang, J. (2019). Altered gray matter volume in patients with herpes zoster and postherpetic neuralgia. J Pain Res,12(605-616.
Logothetis, N.K. (2008). What we can do and what we cannot do with fMRI. Nature,453(7197), 869-78.
Luo, G., Zhang, Z., Zhu, J., Xie, K., Ni, H., Tao, J., Yao, M. (2021). Association between the Risk of Relapse and the Type of Surgical Procedure for Herpes Zoster-related Pain. Pain Physician,24(8), E1227-1236.
Mălîia, M.D., Donos, C., Barborica, A., Popa, I., Ciurea, J., Cinatti, S., Mîndruţă, I. (2018). Functional mapping and effective connectivity of the human operculum. Cortex,109(303-321.
Mears, D., Pollard, H.B. (2016). Network science and the human brain: Using graph theory to understand the brain and one of its hubs, the amygdala, in health and disease. J Neurosci Res,94(6), 590-605.
Niddam, D.M., Lee, S.H., Su, Y.T., Chan, R.C. (2019). Altered cortical morphology in patients with chronic shoulder pain. Neurosci Lett,712(134515.
Pannekoek, J.N., Veer, I.M., van Tol, M.J., van der Werff, S.J., Demenescu, L.R., Aleman, A., Veltman, D.J., Zitman, F.G., et al. (2013). Resting-state functional connectivity abnormalities in limbic and salience networks in social anxiety disorder without comorbidity. Eur Neuropsychopharmacol,23(3), 186-95.
Power, J.D., Barnes, K.A., Snyder, A.Z., Schlaggar, B.L., Petersen, S.E. (2013). Steps toward optimizing motion artifact removal in functional connectivity MRI; a reply to Carp. NeuroImage,76(439-441.
Qiu, C., Zhu, C., Zhang, J., Nie, X., Feng, Y., Meng, Y., Wu, R., Huang, X., et al. (2014). Diffusion tensor imaging studies on chinese patients with social anxiety disorder. Biomed Res Int,2014(860658.
Raichle, M.E. (2015). The brain's default mode network. Annu Rev Neurosci,38(433-47.
Schmaal, L., Hibar, D.P., Sämann, P.G., Hall, G.B., Baune, B.T., Jahanshad, N., Cheung, J.W., van Erp, T.G.M., et al. (2017). Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the ENIGMA Major Depressive Disorder Working Group. Mol Psychiatry,22(6), 900-909.
Shen, Z., Zhu, J., Ren, L., Qian, M., Shao, Y., Yuan, Y., Shen, X. (2020a). Aberrant amplitude low-frequency fluctuation (ALFF) and regional homogeneity (ReHo) in generalized anxiety disorder (GAD) and their roles in predicting treatment remission. Ann Transl Med,8(20), 1319.
Shen, Z., Zhu, J., Ren, L., Qian, M., Shao, Y., Yuan, Y., Shen, X. (2020b). Aberrant amplitude low-frequency fluctuation (ALFF) and regional homogeneity (ReHo) in generalized anxiety disorder (GAD) and their roles in predicting treatment remission. Annals of Translational Medicine,8(20), 1319-1319.
Sutoko, S., Atsumori, H., Obata, A., Funane, T., Kandori, A., Shimonaga, K., Hama, S., Yamawaki, S., et al. (2020). Lesions in the right Rolandic operculum are associated with self-rating affective and apathetic depressive symptoms for post-stroke patients. Scientific Reports,10(1).
Tang, Y., Wang, M., Zheng, T., Xiao, Y., Wang, S., Han, F., Chen, G. (2021). Structural and functional brain abnormalities in postherpetic neuralgia: A systematic review of neuroimaging studies. Brain Res,1752(147219.
Triarhou, L.C. (2021). Cytoarchitectonics of the Rolandic operculum: morphofunctional ponderings. Brain Struct Funct,226(4), 941-950.
Uddin, L.Q., Yeo, B.T.T., Spreng, R.N. (2019). Towards a Universal Taxonomy of Macro-scale Functional Human Brain Networks. Brain Topogr,32(6), 926-942.
Wan, C.F., Song, T. (2021). Efficacy of Pulsed Radiofrequency or Short-Term Spinal Cord Stimulation for Acute/Subacute Zoster-Related Pain: A Randomized, Double-Blinded, Controlled Trial. Pain Physician,24(3), 215-222.
Wang, C., Wang, Y., Lau, W.K.W., Wei, X., Feng, X., Zhang, C., Liu, Y., Huang, R., et al. (2021). Anomalous static and dynamic functional connectivity of amygdala subregions in individuals with high trait anxiety. Depress Anxiety,38(8), 860-873.
Wang, S., Tepfer, L.J., Taren, A.A., Smith, D.V. (2020). Functional parcellation of the default mode network: a large-scale meta-analysis. Sci Rep,10(1), 16096.
Wei, X., Shi, G., Tu, J., Zhou, H., Duan, Y., Lee, C.K., Wang, X., Liu, C. (2022). Structural and Functional Asymmetry in Precentral and Postcentral Gyrus in Patients With Unilateral Chronic Shoulder Pain. Front Neurol,13(792695.
Winton, E.C., Clark, D.M., Edelmann, R.J. (1995). Social anxiety, fear of negative evaluation and the detection of negative emotion in others. Behav Res Ther,33(2), 193-6.
Xiong, H., Guo, R.J., Shi, H.W. (2020). Altered Default Mode Network and Salience Network Functional Connectivity in Patients with Generalized Anxiety Disorders: An ICA-Based Resting-State fMRI Study. Evid Based Complement Alternat Med,2020(4048916.
Xu, J., Wang, J., Fan, L., Li, H., Zhang, W., Hu, Q., Jiang, T. (2015). Tractography-based Parcellation of the Human Middle Temporal Gyrus. Sci Rep,5(18883.
Yuan, C., Zhu, H., Ren, Z., Yuan, M., Gao, M., Zhang, Y., Li, Y., Meng, Y., et al. (2018a). Precuneus-related regional and network functional deficits in social anxiety disorder: A resting-state functional MRI study. Compr Psychiatry,82(22-29.
Yuan, M., Zhu, H., Qiu, C., Meng, Y., Zhang, Y., Ren, Z., Li, Y., Yuan, C., et al. (2018b). Altered regional and integrated resting-state brain activity in general social anxiety disorder patients before and after group cognitive behavior therapy. Psychiatry Res Neuroimaging,272(30-37.
Yue, X., Du, Y. (2020). Altered intrinsic brain activity and regional cerebral blood flow in patients with chronic neck and shoulder pain. Pol J Radiol,85(e155-e162.
Yun, J.Y., Kim, J.C., Ku, J., Shin, J.E., Kim, J.J., Choi, S.H. (2017). The left middle temporal gyrus in the middle of an impaired social-affective communication network in social anxiety disorder. J Affect Disord,214(53-59.
Zhao, X.H., Wang, P.J., Li, C.B., Hu, Z.H., Xi, Q., Wu, W.Y., Tang, X.W. (2007). Altered default mode network activity in patient with anxiety disorders: an fMRI study. Eur J Radiol,63(3), 373-8.
Zhuang, L., Shenxun, S., Xingdang, L., Menglong, Z., Congjin, L., Yuankai, W., Miao, L. (2015). [Evaluation of 99mTc-ECD SPECT/CT brain imaging with scenium analysis in patients with generalized anxiety disorder]. Zhonghua Yi Xue Za Zhi,95(13), 1017-20.
Zou, Q.H., Zhu, C.Z., Yang, Y., Zuo, X.N., Long, X.Y., Cao, Q.J., Wang, Y.F., Zang, Y.F. (2008). An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF. J Neurosci Methods,172(1), 137-41.

Table 1 Demographic and clinical characteristics of the participants.
Clinical information	HZ-RP with HA	HZ-RP with LA	Statistic results (P values)
Clinical information	n=25	n=26	Statistic results (P values)
Age (years)	60.12±9.79	61.85±7.92	t = -0.693 (0.491)
Sex (male: female)	13:12	17:9	χ2 = 0.943 (0.332)
Skin lesions (L: R)	11:14	12:14	χ2 = 0.024 (0.877)
Illness duration (days)	50.72±72.19	104.77±283.37	t= -0.925 (0.360)
VAS score	7.20±1.19	6.79±1.22	t = 1.220 (0.228)
SAS score	45.36±6.19	32.73±3.12	t =9.145 (＜0.001**)
SF-MPQ
SF-MPQ-Sensory	12.84±5.44	8.54±3.43	t =3.393 (＜0.001**)
SF-MPQ-Affective	9.16±5.14	5.08±3.72	t =3.261 (0.002*)
HZ-RP with HA, herpes zoster-related pain with high anxiety; HZ-RP with LA, herpes zoster-related pain with low anxiety; L, left; R, right; SAS, self-rating anxiety scale; SF-MPQ, short-form McGill pain questionnaire; VAS, visual analogue scale; , P value < 0.05; *, P value < 0.001.

Table 2 Regions showing fALFF and ReHo differences between the two groups.
	Brain area	Side	MNI coordinates			Peak intensity	Number of voxels
	Brain area	Side	X	Y	Z	Peak intensity	Number of voxels
fALFF
	RO	R	51	-12	18	4.47	21
ReHo
	MTG	L	-63	-6	-9	4.2806	32
	PrCG	R	24	-27	39	-4.7479	75
fALFF, fractional amplitude of low-frequency fluctuation; ReHo, regional homogeneity; MNI, Montreal Neurological Institute; RO, Rolandic operculum; MTG, middle temporal gyrus; PrCG, precentral gyrus; L, left; R, right.

Table 3 Regions showing FC alterations of the DMN between the two groups.
Seed area	Brain area	Side	MNI coordinates			Peak intensity	Number of voxels
Seed area	Brain area	Side	X	Y	Z	Peak intensity	Number of voxels
aPFC _ L
Cluster 1	mSFG	L	-3	63	9	-4.4318	32
Occipital _ L
Cluster 1	Fusiform	R	27	-81	-12	3.6946	87
Cluster 2	MTG	R	45	-72	9	3.5845	36
Cluster 3	Precuneus	L	-3	-60	36	3.1134	18
FC, functional connectivity; DMN, default mode network; aPFC, anterior prefrontal cortex; mSFG, medial superior frontal gyrus; MTG, middle temporal gyrus; MNI, Montreal Neurological Institute; L, left; R, right.

Table 4 Summary of multivariable linear regression models linking demographic and clinical data with fALFF and ReHo.
	fALFF					ReHo					ReHo
	B	β	95% CI	P value		B	β	95% CI	P value		B	β	95% CI	P value
R RO	R²= 0.283		P=0.035*		L MTG	R² = 0.204		P=0.168		R PrCG	R² = 0.341		P=0.009*
Age	-0.003	-0.037	-0.025 to 0.02	0.804	Age	0.002	0.03	-0.016 to 0.020	0.847	Age	-0.001	-0.031	-0.012 to 0.01	0.825
Sex	-0.192	-0.143	-0.544 to 0.16	0.278	Sex	-0.049	-0.049	-0.331 to 0.232	0.725	Sex	-0.085	-0.127	-0.254 to 0.084	0.315
Illness duration	0	-0.092	-0.001 to 0.001	0.503	Illness duration	0	-0.174	-0.001 to 0	0.232	Illness duration	0	0.204	0 to 0.001	0.125
VAS score	0.187	0.341	0.019 to 0.335	0.03*	VAS score	-0.016	-0.038	-0.151 to 0.119	0.812	VAS score	0.041	0.148	-0.04 to 0.121	0.314
SAS score	0.029	0.353	0.004 to 0.054	0.022*	SAS score	0.026	0.409	0.006 to 0.046	0.012*	SAS score	-0.017	-0.412	-0.029 to -0.005	0.006*
SF-MPQ-Sensory	-0.008	-0.062	-0.057 to 0.04	0.732	SF-MPQ-Sensory	0.005	0.046	-0.034 to 0.044	0.812	SF-MPQ-Sensory	-0.011	-0.164	-0.034 to 0.012	0.351
SF-MPQ-Affective	0.006	0.047	-0.043 to 0.056	0.797	SF-MPQ-Affective	-0.001	-0.012	-0.041 to 0.039	0.95	SF-MPQ-Affective	-0.004	-0.057	-0.028 to 0.02	0.746
*, P value < 0.05; B, nonstandardized coefficient; β, standardized coefficient; CI, confidence interval; RO, Rolandic operculum; MTG, middle temporal gyrus; PrCG, precentral gyrus; L, left; R, right.

Table 5 Summary of multivariable linear regression models linking demographic and clinical data with FC of the DMN.
	FC					FC
	B	β	95% CI	P value		B	β	95% CI	P value
L aPFC - L mSFG	R²= 0.161		P=0.333		L occipital - R fusiform	R² = 0.116		P=0.586
Age	-0.668	-0.277	-1.442 to 0.105	0.089	Age	0.065	0.023	-0.862 to 0.993	0.888
Sex	8.748	0.204	-3.472 to 20.967	0.156	Sex	-5.296	-0.106	-19.947 to 9.354	0.47
Illness duration	0.008	0.083	-0.022 to 0.039	0.576	Illness duration	-0.003	-0.022	-0.039 to 0.034	0.887
VAS score	-2.388	-0.135	-8.226 to 3.450	0.414	VAS score	0.038	0.002	-6.961 to 7.037	0.991
SAS score	-0.644	-0.241	-1.510 to 0.223	0.141	SAS score	-0.611	-0.196	-1.650 to 0.428	0.242
SF-MPQ-Sensory	0.016	0.004	-1.676 to 1.708	0.985	SF-MPQ-Sensory	0.765	0.153	-1.264 to 2.793	0.451
SF-MPQ-Affective	0.695	0.159	-1.033 to 2.424	0.422	SF-MPQ-Affective	-1.387	-0.272	-3.459 to 0.686	0.184
L occipital - R MTG	R² = 0.128		P=0.511		L occipital - L precuneus	R² = 0.221		P=0.125
Age	-0.017	-0.016	-0.365 to 0.332	0.924	Age	-0.01	-0.003	-1.085 to 1.065	0.985
Sex	-1.845	-0.097	-7.356 to 3.665	0.503	Sex	-12.195	-0.197	-29.174 to 4.785	0.155
Illness duration	0.001	0.026	-0.012 to 0.015	0.862	Illness duration	-0.001	-0.009	-0.043 to 0.041	0.952
VAS score	-0.075	-0.01	-2.708 to 2.557	0.954	VAS score	2.265	0.089	-5.847 to 10.377	0.576
SAS score	-0.248	-0.21	-0.639 to 0.143	0.208	SAS score	-0.451	-0.117	-1.655 to 0.753	0.454
SF-MPQ-Sensory	0.346	0.182	-0.417 to 1.109	0.366	SF-MPQ-Sensory	1.809	0.293	-0.542 to 4.159	0.128
SF-MPQ-Affective	-0.578	-0.299	-1.357 to 0.202	0.142	SF-MPQ-Affective	-3.253	-0.517	-5.655 to -0.851	0.009*
*, P value < 0.05; B, nonstandardized coefficient; β, standardized coefficient; CI, confidence interval; FC, functional connectivity; DMN, default mode network; aPFC, anterior prefrontal cortex; mSFG, medial superior frontal gyrus; MTG, middle temporal gyrus; L, left; R, right.

No competing interests reported.

SupplementaryMaterials.docx

Download PDF

Version 1

posted

You are reading this latest preprint version

Altered Regional Spontaneous Brain Activity and Default Mode Network Functional Connectivity in Herpes Zoster-related Pain Patients with High Anxiety

Status:

Version 1

Abstract

Figures

Introduction

Methods

Participants

Image acquisition

Preprocessing of resting-state fMRI data

FC analysis

Statistical analysis

Results

Discussion

Conclusion

Abbreviations

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1