Default mode network-basal ganglia network connectivity predicts the transition to postherpetic neuralgia

doi:10.21203/rs.3.rs-4842138/v1

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Research Article

Default mode network-basal ganglia network connectivity predicts the transition to postherpetic neuralgia

https://doi.org/10.21203/rs.3.rs-4842138/v1

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Background

Neuroimaging study has revealed aberrant network functional connectivities in postherpetic neuralgia (PHN) patients. However, there is a lack of knowledge regarding the relationship between brain network connectivity in acute period and disease prognosis. The purpose was to detect a characteristic network connectivity in the process of herpes zoster (HZ) pain chronification and identify whether the abnormal network connectivity in acute period can predict outcome of HZ patient.

Methods

In this cross-sectional study, 31 PHN patients, 33 recuperation from herpes zoster (RHZ) patients, and 28 acute herpes zoster (AHZ) patients were recruited and underwent resting-state functional magnetic resonance imaging (fMRI). We investigated the differences in four resting-state network (RSN) connectivities among the aboved three groups. Receiver operating characteristic curve (ROC) analysis was performed to identify whether the abnormal network connectivity in acute period can predict the outcome of HZ patient.

Results

Firstly, we found within-basal ganglia network (BGN) and default mode network (DMN)-BGN connectivity differences, with PHN patients showing increased DMN-BGN connectivity compared with AHZ and RHZ patients, and RHZ patients showing increased within-BGN connectivity compared with AHZ and PHN patients. Moreover, DMN-BGN connectivity was associated with the ID pain score in AHZ patients. Finally, the DMN-BGN connectivity of AHZ patients can predict the outcome of HZ patients with sensitivity and specificity of 77.8% and 63.2%, respectively.

Conclusions

Our results provide evidence that DMN-BGN connectivity in acute period confers risk for the development of chronic pain and can act as a neuroimaging biomarker to predict the outcome of HZ patients.

postherpetic neuralgia

acute herpes zoster

fMRI

network functional connectivity

predict outcome

Herpes zoster (HZ) is a disease of acute skin inflammation which present as shingles and severe pain caused by peripheral nerve injury. Postherpetic neuralgia (PHN) is the most intractable complication of Herpes zoster (HZ) (Johnson and Rice, 2014). Epidemiological results indicate that 12.5% of HZ patients aged ≥ 50 years develop PHN at 3 months after the rash and that the proportion affected increases markedly with an advancing age(Forbes et al., 2016). These PHN patients experience persistent pain, emotional and cognitive distress for months or even years, thereby dramatically degrading quality of life for a large sector of society(Johnson et al., 2010; Weaver, 2007). However, it remains unclear why some patients with similarly painful shingles episodes recover while others remain affected by persistent pain. It is worth exploring whether there are risk factors for pain chronification beyond, for example, age and the severity of the acute pain and rash at the initial zoster presentation(Forbes et al., 2016). Previous extensive human and animal results showed that chronic pain was associated with peripheral and central nervous system reorganization, which encompassed a long list of neuronal and glial changes associated with pain persistence(Baron et al., 2010). However, due to the lack of an entirely adequate animal model to mimic the PHN disease course, our understanding of the central mechanism and the risk of HZ chronification based on animal experiments is hampered. Thus, a clinical neuroimaging approach may greatly help to reveal the brain changes during the disease course of HZ.

Neuroimaging studies revealed aberrant brain structure and function in regions that are emotion-/reward-linked (such as amygdala and striatum) in PHN patients, in addition to sensory-discriminative regions, whether compared with healthy subjects or acute HZ patients(Cao et al., 2017; Geha et al., 2007; Geha et al., 2008; Gu et al., 2019; Hong et al., 2018; Zhang et al., 2014; Zhang et al., 2016). Another longitudinal study showed that PHN brains exhibit abnormal regional homogeneity (ReHo), fractional aptitude of low-frequency fluctuation (fALFF), and voxel-based morphometry (VBM) values in the pain matrix (the frontal lobe, thalamus, limbic lobe and cerebellum) as well as the occipital lobe and temporal lobe compared with HZ brains(Cao et al., 2018). These suggested that brain functional and structural changes may be correlated with HZ-PHN chronification.

However, chronic pain, as one of higher-grade brain functions, rely on the collaboration of large-scale networks rather than on single regions in isolation. Thus, our previous study found that a brain network-level imbalance, involving default mode network (DMN), salience network (SN), emotion regulation network (ERN), basal ganglia network (BGN), could account for the pain-related dysfunctions in different outcomes of HZ patients(Wu et al., 2022). In the present study, we continued investigating the differences in within- and cross-network connectivities between different HZ periods, encompassing acute herpes zoster (AHZ), PHN, and recuperation from herpes zoster (RHZ) patients, in order to identify the specific network connectivity in the process of HZ chronification.

In addition, brain functional and structural changes not only play a role in the initial development of pain chronification, but also can be used as biomarkers to predict future disease symptoms in several chronic pain conditions, including chronic back pain and urologic chronic pelvic pain syndrome(Baliki et al., 2012; Kutch et al., 2017; Mansour et al., 2013; Vachon-Presseau et al., 2016). Previous research has not yet determined the relationship between brain abnormalities in the acute period and the transition to different outcomes of HZ patients. Accordingly, we began exploring the relationship between the abnormal network connectivity of AHZ patients and disease prognosis. This may provide a new neuroimaging biomarker that can predict the longitudinal outcome of HZ and permit the timely treatment and reduce the incidence of PHN transition. If a biological neuroimaging factor predicting the outcome of HZ can be identified, novel treatments directed toward this risk factor may have enhanced potential to promote more sustained symptom abatement in HZ patients.

Subjects

In this observational study, 28 AHZ patients, 31 PHN patients, and 33 RHZ patients were recruited from Second Affiliated Hospital of Zhejiang University, School of Medicine and signed informed consent forms in accordance with the approval by the Ethics Committe. A pain specialist diagnosed PHN according to the criteria of the International Association for the Study of Pain (IASP), with spontaneous pain intensity of at least 4 on a Visual Analog Scale (VAS) [ranging from 0 (no pain) to 10 (the worst pain imaginable)] and with disease duration ≥ 3 months after the acute trunk or acral shingles episode(Scholz et al., 2019). In contrast, RHZ patients were defined with a VAS < 4, at least 3 months after acute shingles, and no administration of medication. AHZ patients were diagnosed with a VAS ≥ 4 in conjunction with trunk or acral shingles in the first clinical visit. All subjects were right-handed.

For exclusion criteria, we ruled out any subjects who had the following condition: (1) atypical HZ (such as the eye, ear, visceral HZ, asymptomatic HZ); (2) current ongoing acute or chronic pain condition, for example, arthritis, toothaches, headaches, and lumbar or cervical spondylopathy; (3) any psychiatric or neurological disorder like epilepsy, history of head injury, mood disorders; (4) any major severe diseases, such as renal insufficiency or severe cardiovascular diseases with unstable hemodynamics; (5) receipt of any other ongoing medications (with the exception of the medications for PHN treatment); (6) any MRI contraindications.

All participants were given questionnaire assessment 1 hour prior to brain scanning, including short-form of the McGill Pain Questionnaire (MPQ), VAS (a total score of pain intensity, ranging from 0 to 10), present pain intensity (PPI, the present score of pain intensity before scanning), ID pain score (a score assessing neuropathic pain), Hamilton Depression Scale (HAMD) and Hamilton Anxiety Scale (HAMA), Positive Affect Negative Affect Score (PANAS), short-form health survey (SF-36) questionnaire (including the following assessment: physical functioning, role-physical, bodily pain, general health, vitality, social functioning, role-emotional, mental health, reported health transition), the same as our previous study(Wu et al., 2022).

Imaging acquisition

All MRI scans were performed using a 3.0-Tesla MRI scanner (GE Discovery 750) equipped with an 8-channel head coil in the Department of Radiology. During the MRI scan, each patient lay supine with their head firmly restrained using foam pads; earplugs were provided to reduce the noise during the scan. High-resolution structural T1-weighted images were acquired using a fast spoiled gradient recalled sequence using the following parameters: repetition time (TR)/echo time (TE) = 7.3/3.0 ms, flip angle = 11°, field of view (FOV) = 260 × 260 mm², matrix size = 256 × 256, slice thickness = 1.2 mm, 196 continuous sagittal slices. Resting-state fMRI images were acquired with a gradient recalled echo (GRE)-echo planar imaging (EPI) sequence: TR/TE 2000/30 ms, flip angle = 77°, matrix = 64 × 64, FOV = 240 × 240 mm², slice thickness = 4 mm, slice gap = 0 mm; 38 interleaved axial slices, scan time = 5 min 20 s.

Resting-state fMRI Imaging preprocessing

Data preprocessing was performed using the Resting-State fMRI Data Analysis Toolkit (Rest, V1.8; http://www.restfmri.net) and Statistical Parametric Mapping (SPM12; http://www.fil.ion.ucl.ac.uk/spm) based on the MATLAB platform (MathWorks, Natick, MA, USA). The first 10 volumes of each functional time series were discarded to avoid transient signal changes before the magnetic field reached steady-state and to allow subjects to get adjusted to the scanning environment. Imging preprocessing included slice timing, head motion correction and spatial normalization to Montreal Neurological Institute (MNI) space. The thresholds for translation (mm) and rotation (degrees) for each participant were obtained in six parameters (three for translation and three for rotation) for each scan. No slices were discarded in the PHN, RHZ and AHZ groups due to head motion, considered more than 2 mm of displacement or 2° of rotation. Functional data were spatially smoothed by using a Gaussian kernel of 6 mm × 6mm × 6mm full width at half maximum (FWHM). The nuisance covariate regression was removed, including head motion parameters, global mean signal, white matter signal, cerebrospinal fluid signal, and other covariates. A detrending and bandpass temporal filtering (0.01–0.08 Hz) was then performed.

Definition of resting-state networks (RSNs) and within- and cross-network functional connectivity (FC) analyses

According to our previous study, four well-established large-scale networks (DMN, SN, ERN, BGN) with a total of 36 target regions of interest (ROIs) were chosen, which are deemed to actively participate in the pain-related sensory, emotion processing and cognitive functions(Wu et al., 2022). FC was quantified using the correlation between the average time series in each ROI with other ROI time series in four networks using Pearson’s correlation coefficient (r). Next, we converted r values to Z scores using Fisher’s r-to-Z transformation. Finally, according to a previous study(Nusslock et al., 2019), the Z scores of all internal pairwise connections were averaged to compute a total network value reflecting the within-network FC. To assess the strength of cross-network FC, the Fisher-transformed correlation between each node in one network and the nodes in another network were calculated. Then, we averaged the Fisher-transformed Pearson correlations of all connections to obtain a single value representing cross-network connectivities. The FC matrix was used to visualize the pairwise correlations between ROIs of different RSNs (Wu et al., 2022).

Statistical analysis

Group differences in demographic and clinical variables among the PHN, RHZ, and AHZ groups were statistically examined by using SPSS 24.0 software. Data for continuous variables are expressed as mean ± SD and were tested for normality using the Shapiro-Wilk test. The comparisons among groups in variables with no evidence against normality were assessed by independent one-way ANOVA for significance. The comparisons among groups in categorical variables were assessed using a χ² test or Fisher's exact test. A p-value < 0.05 was considered statistically significant.

The network FC differences among three groups were assessed by one-way ANOVA, with age, educational level, and head motion parameters as covariates. The p values were adjusted for multiple comparisons with FDR correction (10 times, comprising 4 within-network comparisons and 6 cross-network comparisons). Post hoc testing was performed to compare the pairwise difference between each pair of groups (Wu et al., 2022).

The partial correlation analyses were performed in each group to investigate the correlations between abnormal network FC and clinical characteristics, eliminating the effect of age, years of education, and head motion parameters. The p values < 0.05 were considered statistically significant with FDR correction (55 times, from 11 clinical measures across 5 network connectivities).

Finally, the outcomes of AHZ patients were determined by telephone interview, which served as the outcome variable. With the abnormal network connectivity of AHZ patients as the test variable, a receiver operating characteristic (ROC) curve with the value of sensitivity and specificity were shown to quantify the predictive value of PHN prognosis by using brain network connectivity in AHZ patients.

Demographic and clinical characteristics

In this study, 92 subjects were finally included, including 31 PHN patients, 33 RHZ patients, and 28 AHZ patients. The demographic and clinical characteristics of the participants are presented in Table 1. There were statistically significant differences among the PHN, RHZ, and AHZ patients in terms of age (years, mean ± SD: PHN, 65.52 ± 7.78; RHZ, 61.21 ± 6.17; AHZ, 61.50 ± 8.33) and educational level (years, mean ± SD: PHN, 7.56 ± 3.71; RHZ, 8.97 ± 3.41; AHZ, 7.82 ± 4.45). There were no statistically significant differences in gender (males/females: PHN, 21/10; RHZ, 16/20; AHZ, 18/10) between three groups. PHN patients had similar clinical symptoms to AHZ patients, including pain, emotional, and life quality (SF-36) scores, with the exception of a lower ID pain score. Compared with PHN and AHZ patients, RHZ patients showed lower pain-related and negative emotional scores and a higher life quality score.

Table 1

Demographic and clinical variables of postherpetic neuralgia(PHN), recuperation from herpes zoster (RHZ) and acute herpes zoster (AHZ) patients.
	PHN (n = 31)	RHZ (n = 33)	AHZ (n = 28)	F	p value
Demographic data
Age (years, mean ± SD)	65.52 ± 7.78	61.21 ± 6.17	61.50 ± 8.33	3.25	0.043*
Gender (males/females)	21/10	16/20	18/10	4.37	0.112
Education (years,mean ± SD)	7.56 ± 3.71	8.97 ± 3.41	7.82 ± 4.45	1.21	0.03*
Duration from first onset (months, mean ± SD)	14.92 ± 18.01^a	8.52 ± 5.53 ^b	0.41 ± 0.11	12.89	<0.001*
Clinical data Laterality (Left/Right) Rash location Cervical Thoracic Lumbar Sacral	16/15 4 25 1 1	18/15 6 26 1 0	16/15 4 16 5 3	0.405	0.817
Pain VAS(0–10) (mean ± SD)	5.35 ± 1.02 ^c	0.82 ± 0.98 ^b	5.67 ± 1.25	199.65	<0.001*
MPQ sensory (mean ± SD)	7.16 ± 2.7 ^c	0.61 ± 0.74 ^b	8.1 ± 2.81	103.80	<0.001*
MPQ affective (mean ± SD)	2.03 ± 1.08 ^c	0.12 ± 0.33 ^b	2.18 ± 1.52	36.86	<0.001*
PPI (mean ± SD)	2.52 ± 0.57 ^c	0.52 ± 0.51 ^b	2.61 ± 0.5	159.78	<0.001*
ID pain (mean ± SD)	2.42 ± 1.09 ^a,c	0.33 ± 0.48 ^b	3.36 ± 0.91	101.19	<0.001*
Psychological data
HAMD (mean ± SD)	6.55 ± 3.09 ^c	2.55 ± 2.41 ^b	7.00 ± 3.81	19.54	<0.001*
HAMA (mean ± SD)	4.13 ± 2.42 ^c	1.42 ± 1.56 ^b	3.93 ± 2.07	17.43	<0.001*
PANAS positive (mean ± SD)	12.32 ± 1.56 ^c	15.52 ± 2.91 ^b	12.89 ± 1.95	18.52	<0.001*
PANAS negative (mean ± SD)	16.16 ± 3.13 ^c	10.64 ± 1.32 ^b	16.46 ± 3.79	41.16	<0.001*
SF-36 (mean ± SD)	114.19 ± 7.44 ^c	127.94 ± 5.77 ^b	110.01 ± 7.56	57.27	<0.001*
Head motion parameters
Translation(mm,mean ± SD)	0.27 ± 0.14	0.27 ± 0.11	0.28 ± 0.12	0.134	0.85
Translation(mm,mean ± SD)	0.39 ± 0.23	0.38 ± 0.25	0.39 ± 0.22	0.023	0.97
* Significant difference between three groups
^a Significant difference between AHZ and PHN
^b Significant difference between AHZ and RHZ
^c Significant difference between PHN and RHZ
PHN, postherpetic neuralgia; RHZ, recuperation from herpes zoster;AHZ, acute herpes zoster ; VAS, Visual Analogous Scale; MPQ, McGill pain questionnaire; PPI, present pain intensity; HAMD, Hamilton Depression Scale; HAMA, Hamilton Anxiety Scale; PANAS, Positive Affect Negative Affect Score; SF-36, 36-item short form from health survey.

Comparison of network FCs among PHN, RHZ, and AHZ patients

We determined the within- and cross-network FC matrices of AHZ (Fig. 1A), PHN (Fig. 1B), and RHZ (Fig. 1C) patients. One-way ANOVA identified significant abnormal connectivities among PHN, RHZ, and AHZ patients (FDR-corrected, p < 0.05), including within- BGN (p = 0.0174) (Fig. 2A) and DMN-BGN (p = 0.0135) connectivities (Fig. 2B). There were no significant differences in the other network connectivities among the three groups (Supplemental Table 1).

RHZ patients showed a significant pairwise difference in within-BGN connectivity compared with AHZ (p = 0.0136) and PHN (p = 0.0054) patients (Fig. 2A). PHN patients showed a significant pairwise difference in DMN-BGN connectivity compared with AHZ (p = 0.026) and RHZ (p = 0.0076) patients (Fig. 2B). In short, DMN-BGN and within-BGN connectivities may provide some clues for HZ chronification.

Correlation patterns of network connectivities with clinical symptoms

Partial correlation analysis showed that DMN-BGN connectivity was positively associated with the ID pain score (r = 0.432, p = 0.027) in AHZ patients, no other significance in PHN and RHZ groups (Fig. 2C). However, this finding did not survive multiple comparison correction at an FDR of 5%. As reported previously(Cao et al., 2020), this correlation could provide a heuristic cue that DMN-BGN connectivity potentially accounted for neuropathic pain in the acute period. There were no other significant correlations between within-BGN connectivity and clinical variables in AHZ patients (Supplemental Table 2). There were no significant correlations between other FCs and clinical variables in PHN and RHZ patients.

Network connectivity predicts pain chronification

Finally, we tracked the outcomes of AHZ patients by telephone interview. An ROC curve was plotted to quantify the predictive value for HZ prognosis. The DMN-BGN connectivity of AHZ patients was able to predict the outcome of HZ patients with a sensitivity and specificity of 77.8% and 63.2%, respectively. The area under the curve (AUC) was 0.708 (Fig. 2D).

In this study, we revealed a relatively comprehensive description of the mechanism of HZ chronification. First, we identified within-BGN and DMN-BGN connectivities by HZ patient outcome, with PHN patients showing increased DMN-BGN connectivity compared with AHZ and RHZ patients, and RHZ patients showing increased within-BGN connectivity compared with AHZ and PHN patients. Moreover, DMN-BGN connectivity was associated with the ID pain score in the AHZ patients and could act as a neuroimaging biomarker to predict HZ patient outcomes.

It is increasingly being recognized that higher-grade brain functions rely on the collaboration of large-scale networks rather than on single regions in isolation(Bassett and Sporns, 2017; Fox and Raichle, 2007; Smith et al., 2013). Researchers have reported the involvement of some RSNs in other multiple chronic pain populations and emotional disorders, such as chronic orofacial neuropathic pain, temporomandibular disorder, spondyloarthritis, and major depressive disorder(Alshelh et al., 2018; Hemington et al., 2018; Kucyi et al., 2014; Yu et al., 2019). Our previous research also showed that PHN patients could be characterized by abnormal DMN-BGN and within-BGN connectivity compared with RHZ patients(Wu et al., 2022), which are also the key points of this present study. So we continue to discover the mechanism of HZ pain chronification from the angle of RSNs connectivity.

The principal anatomical component of BGN is the striatum, which is divided into the caudate, putamen, and nucleus accumbens (NAc). The BGN receives afferent inputs from different cortical regions and projects to the cortex via the thalamus, forming a thalamo-cortico-striatal loop(Borsook et al., 2010; Luscher and Malenka, 2011). This neuronal loop serves as the basis of pain processing, which encompasses sensory-discriminative, emotional/affective, and cognitive dimensions of pain(Borsook et al., 2010; Chudler and Dong, 1995; Cropley et al., 2006). In particular, the pivotal role of the NAc, a central node of the mesocorticolimbic system, in reward-aversion processing makes it a key component in the emotional pain network(Baliki et al., 2013). The striatum of the BGN (especially NAc) is rich in dopaminergic neurons and is considered a hedonic node, involving reward, addiction, and euphoric mood(Serafini et al., 2020). Studies have demonstrated that the prominent connectivity between NAc and dorsal striatum (caudate/putamen) is related to emotional learning(Chang et al., 2014; Voorn et al., 2004). NAc structural and functional abnormalities exist in pain and depression independently and within comorbid states(Pittenger and Duman, 2008; Serafini et al., 2020; Sheng et al., 2017). The strength of the within-BGN connectivity may be one of the central mechanisms involved in the pain-depression comorbidity. Our finding of a higher strength of within-BGN connectivity and positive affective scores in RHZ patients suggested that positive emotion may contribute to the process of pain recovery, which can be partly accounted by the within-BGN dopaminergic neurons. In contrast, the lower-level within-BGN connectivity and higher negative emotional scores in AHZ and PHN patients suggested that the persistent low-level within-BGN connectivity may be involved in the pain-depression vicious circle and may promote pain development and maintenance. But unfortunately, the correlation between within-BGN connectivity and clinical scores did not survive, mainly bacause the pain and emotional scores in same group are over centralized and can’t widen the gap, meanwhile the times of correlation analysis are too many to achieve significant value. But as reported previously(Cao et al., 2020), the similar tendency of within-BGN connectivity and affective score may provide a heuristic cue that within-BGN connectivity potentially accounted for condition of pain alleviating in RHZ group.

Another network emphasized in our results is DMN, which is involved in internally directed processes, including theory of mind and memory, attention, and mind-wandering(Peer et al., 2015; Raichle, 2015; Spreng and Grady, 2010). Previous studies showed that the DMN dynamic was disrupted in several chronic pain diseases(Baliki et al., 2008; Kucyi et al., 2013), such as migraine(Xue et al., 2012), fibromyalgia(Napadow et al., 2010), complex regional pain syndrome(Bolwerk et al., 2013), and chronic back pain(Loggia et al., 2013). The imbalanced DMN pattern of these diseases is probably associated with pain attention, rumination, and pain-related negative emotion(Baliki et al., 2008; Erpelding and Davis, 2013; Kucyi et al., 2013; Kucyi et al., 2014). In addition, abnormal DMN activity has been linked to the difficulty in regulating negative thinking in depressive disorders(Malhi et al., 2018; Malhi et al., 2019; Rogers and Joiner, 2017), indirectly suggesting that the DMN is involved in the integration of pain and depression. In general, these findings indicate that chronic pain does indeed have a widespread effect on the DMN balances and suggested that DMN disruptions may underlie the emotional and cognitive impairments accompanying chronic pain(Baliki et al., 2008). Furthermore, several human and animal studies have also identified the role of DMN-BGN (medial prefrontal cortex-NAc, mPFC-NAc) circuitry in chronic pain persistence and its emotional regulation, which have also been emphasized in our study(Chang et al., 2014; Lee et al., 2015; Ren et al., 2016; Vachon-Presseau et al., 2016; Zhou et al., 2018). Our current results showed higher DMN-BGN connectivity in PHN patients compared with AHZ patients, suggesting that the frequent information exchange between emotion-related BGN and advanced cognition-related DMN during the disease course of PHN play an important role in pain chronification, probably partly by aggravating the pain rumination degree and pain-related negative mood. Our study is consistent with longitudinal study showing a variation in the mPFC-NAc connectivity in a chronic back pain population(Baliki et al., 2012). The authors also elucidated the mechanisms of the reorganizational properties of NAc, demonstrating that local neural networks from the NAc to the dorsal striatum and cortex showed minimal disruption at day 5 in spared nerve injury (SNI) animals and more extensive reorganization at day 28 post-injury(Baliki et al., 2014). This limbic-cortical connectivity in the SNI model was linked to dopamine receptor gene expression and the degree of tactile allodynia, which together mediated the development of chronic pain(Chang et al., 2014). These suggested that the limbic dopaminergic system is part of the rearrangement of cortical circuits associated with the transition to chronic pain.

Further research revealed that the limbic-cortical functional and anatomical connection not only plays a role in the initial development of pain chronification, but also can be a biomarker to predict future disease symptoms in chronic back pain and urologic chronic pelvic pain syndrome(Baliki et al., 2012; Vachon-Presseau et al., 2016). In our study, the DMN-BGN connectivity was positively correlated with ID pain scores and was also able to predict the outcome of AHZ patients, with sensitivity and specificity of 77.8% and 63.2%, respectively. This further confirmed the significance of the role of limbic-cortical connectivity in HZ pain chronification and may provide a neuroimaging basis for the early and active treatment of acute HZ patients and may reduce the incidence of refractory PHN.

Limitations

Although these are the first findings of network-level connectivities in HZ patients of different disease courses, the present study still has some limitations. First, due to a lack of sufficient follow-up data from acute HZ patients and the difficulty of a longitudinal study, we simply compared the network connectivity among different populations of different HZ disease courses and did not follow the alterations in the same AHZ population. Future longitudinal study would emphasise the connectivity imbalance within the same acute population. Second, some other networks (such as the sensorimotor network, ascending and descending pain modulation pathways) and their within-/cross-networks connectivities could also be studied and we are presently exploring these options. In addition, we could re-run the analyses by using the independent component analysis (ICA) method to verify our results. These questions would be addressed with prospective longitudinal imaging studies.

Overall, the current findings can be placed in a broader context indicating that a brain network imbalance in the acute HZ period, especially the limbic-cortical connection can represent a risk factor for PHN development. This study may provide a neuroimaging basis for the early and active treatment of acute HZ patients and help to reduce the incidence of refractory PHN.

AHZ, acute herpes zoster;

BGN, basal ganglia network;

DMN, default mode network;

EPI, echo planar imaging;

ERN, emotion regulation network;

fALFF, fractional aptitude of low-frequency fluctuation

FC, functional connectivity;

FDR, False Discovery Rate;

fMRI, functional magnetic resonance imaging;

FOV, field of view;

FWHM, full width at half maximum;

GRE, gradient recalled echo;

HAMA, Hamilton Anxiety Scale;

HAMD, Hamilton Depression Scale;

HZ, herpes zoster;

IASP, International Association for the Study of Pain;

ICA,independent components analysis;

MNI, Montreal Neurological Institute;

mPFC, medial prefrontal cortex;

MPQ, McGill pain questionnaire;

NAc, nucleus accumbens;

PANAS, positive Affect Negative Affect Score;

PHN, postherpetic neuralgia;

PPI, present pain intensity;

ReHo, regional homogeneity;

RHZ, recuperation from herpes zoster;

ROC, receiver operating characteristic;

ROI, regions of interest;

RSN, resting state network;

SF-36, Medical Outcomes Study 36-item short form from health survey;

SN, salience network;

SNI, spared nerve injury;

TE, echo time;

TR, repetition time;

VAS, Visual Analog Scale;

VBM, voxel-based morphometry.

Acknowledgements

We thank the authors of the original study for making the data available and all patients contributing data to this study.

Funding

This work was supported by the National Clinical Key Specialty Construction Project of China, 2021.

Conflict of interest

None of the authors have a conflict of interest to declare.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the Ethics Committee of Second Affiliated Hospital of Zhejiang University, School of Medicine.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Authors Contribution:

Ying Wu : literature search, statistical analysis, manuscript preparation, manuscript editing and manuscript review

Chao Wang : literature search, clinical studies

Wei Qian : data acquisition, data analysis, statistical analysis

Lieju Wang : data acquisition, data analysis, statistical analysis

Lina Yu : concept, design, definition of intellectual content

Minming Zhang : concept, design, definition of intellectual content

Min Yan : concept, design, definition of intellectual content

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Default mode network-basal ganglia network connectivity predicts the transition to postherpetic neuralgia

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Abstract

Background

Methods

Results

Conclusions

Figures

Introduction

Methods

Subjects

Imaging acquisition

Resting-state fMRI Imaging preprocessing

Definition of resting-state networks (RSNs) and within- and cross-network functional connectivity (FC) analyses

Statistical analysis

Results

Demographic and clinical characteristics

Comparison of network FCs among PHN, RHZ, and AHZ patients

Correlation patterns of network connectivities with clinical symptoms

Network connectivity predicts pain chronification

Discussion

Limitations

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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