Altered brain network centrality in patients with trigeminal neuralgia: a resting-state fMRI study

Abstract

Background

Neuroimaging studies revealed that trigeminal neuralgia was related to alternations in brain anatomical function and regional function. However, the functional characteristics of network organization in the whole brain is unknown.

Purpose

The aim of the present study was to analyze potential functional network brain-activity changes and their relationships with clinical features in patients with trigeminal neuralgia via the voxel-wise degree centrality method.

Material and Methods

This study involved a total of 28 trigeminal neuralgia patients (12 men, 16 women) and 28 healthy controls matched in sex, age, and education. Spontaneous brain activity was evaluated by degree centrality. Correlation analysis was used to examine the correlations between behavioral performance and average degree centrality values in several brain regions.

Results

Compared with healthy controls, trigeminal neuralgia patients had significantly higher degree centrality values in the right lingual gyrus, right postcentral gyrus, left paracentral lobule, and bilateral inferior cerebellum. Receiver operative characteristic curve analysis of each brain region confirmed excellent accuracy of the areas under the curve. There was a positive correlation between the mean degree centrality value of the right postcentral gyrus and VAS score (r = 0.885, P < 0.001).

Conclusions

Trigeminal neuralgia causes abnormal brain network activity in multiple brain regions, which may be related to underlying disease mechanisms.

Keywords

Trigeminal neuralgia voxel-wise degree centrality brain activity changes

Introduction

Trigeminal neuralgia (TN) is a chronic neuralgia and the most common facial pain characterized by severe recurrent paroxysmal pain in the trigeminal nerve region of the face. The reported lifetime prevalence rate of TN worldwide is 0.7–0.3% (1 –3). TN is usually unilateral but can be bilateral in rare cases. It often happens spontaneously, mostly affecting middle-aged individuals and women. TN can be divided into typical and atypical types according to clinical symptoms (4). Typical TN is characterized by sudden tingling or electric shock-like pain without facial numbness; there are no symptoms during pain-free intervals. In addition to typical pain, atypical TN can include a variety of pain sensations such as burning, soreness, and others. It is widely believed that microvascular structures may compress the trigeminal nerve, leading to nerve irritation or demyelination of the trigeminal nerve pathway, which results in unnatural conduction of nerve sensory impulses (5 –10). There were some reported cases of TN as the first symptom of diabetes. Uchino found that the incidence of TN in people with hypertension was higher than that of normal individuals; the pathological basis was nerve demyelination (11,12). Primary TN has no obvious nervous system symptoms, but secondary TN may include other symptoms in the trigeminal nerve area, such as sensory degeneration, numbness, corneal reflex retardation or disappearance, persistent pain, and hearing loss (13,14).

Medical history provides powerful evidence for a diagnosis of primary TN, but recent studies have shown that magnetic resonance imaging (MRI) improves the diagnostic ability of secondary TN (15). It can identify microvascular compression of ganglion and TN caused by multiple sclerosis, as well as reveal the compressing blood vessels, making it a useful modality to guide treatment. In fact, Obermann et al. demonstrated reduced gray matter volume in several regions including the somatosensory cortex and cerebellum in TN patients compared to pain-free controls (16). In addition, Desouza demonstrated white matter alterations in TN patients and these were reversed after treatment (17,18). Moreover, Blatow et al. demonstrated that TN patients have reduced primary and secondary somatosensory functional activations in response to tactile stimulation (19). Neuroimaging can also be used to predict treatment outcomes. As an example, DeSouza showed that changes in diffusion tensor imaging (DTI) metrics correlate with pain relief after surgery, suggesting that neuroimaging findings could be used as biomarkers for treatment prediction (18).

With the development of functional MRI (fMRI) technology, researchers have found some changes in gray matter volume and cortical thickness of brain regions in TN patients that perceive and regulate pain, such as the primary and secondary somatosensory regions, insula, thalamus, cingulate cortex, orbital frontal cortex, and others (20). Few studies have examined changes in the brain functional network in patients with TN. Resting state fMRI (RS-fMRI) is widely used in brain functional connectivity research (21,22). It can detect spontaneous neuronal activity in the resting brain and minimize interference caused by behavioral variability. Other advantages include the lack of radioactive tracer, accurate positioning, and combined functional and structural imaging (21,22). Therefore, resting-state functional network connectivity analysis is important for understanding disease pathophysiology. Specifically, degree centrality (DC), as a graph-theoretical measure, quantifies functional connectivity of human brain connections at the voxel level. It evaluates importance by the connection between each node and each voxel in the brain network. Compared with amplitude of low-frequency fluctuation (ALFF) and regional homogeneity (ReHo) technologies, DC can directly reflect the number of direct connections in the voxel and reacts to functional connection in the brain without early selection of the area of interest. DC has been applied to studies of acute unilateral open injury, obesity, and Parkinson’s disease but not TN (23 –26). To further understand the neurophysiological mechanism of TN, our group used DC to analyze alterations in the cerebral functional connection hub of TN patients by using the overall connection matrix of the whole-brain functional network.

Methods

Participants

Twenty-eight TN patients (12 men, 16 women; not all treated with drugs) were recruited from a large general hospital. Inclusion criteria were: (i) pain involved the maxillary (V2) and/or mandibular (V3) branches of the trigeminal nerve and was unilateral; (ii) sharp, intense, paroxysmal stabbing or superficial pain caused by trigger zones or trigger factors; (iii) stereotyped attacks; (iv) no intracranial anatomical abnormalities on routine MRI; (v) no other neurological or psychiatric disorders; (vi) no previous TN treatment; (vii) right-handed; and (viii) ability to undergo MRI examination. Exclusion criteria were: (i) other pain symptoms; (ii) family history of neurological or psychiatric conditions or headache; (iii) co-morbid disease that may affect outcome; and (iv) contraindications to MRI.

We also recruited 28 HCs (12 men, 16 women) who were sex-, age-, and education-matched individuals. Inclusion criteria were: (i) not TN patients; (ii) no serious organic diseases such as heart disease, hypertension, etc.; (iii) no family history of neurological or psychiatric conditions or headache; (iv) no pain symptoms; (v) right-handed; and (vi) no contraindications to MRI.

MRI data acquisition

All participants underwent MRI on a Siemens Trio 3-T scanner as described previously (28). The scanning parameters were as follows: repetition time =2000 ms; echo time = 40 ms; flip angle = 90°; slice thickness/gap = 4.0/1 mm; field of view = 240 × 240 mm; in-plane resolution = 64 × 64; 30 axial slices; and 240 volumes. The patients were instructed to close their eyes during scanning.

fMRI data preprocessing

All functional data were prefiltered with MRIcro (www.MRIcro.com) and preprocessed using SPM8 (http://www.fil.ion.ucl.ac.uk/spm), DPARSFA (http://rfmri.org/DPARSF), and the Resting-state Data Analysis Toolkit (REST, http://www.restfmri.net). After removing the first 10 time-points, the remaining 230 volumes were collected. Volumes with the x, y, or z directions > 2° were excluded. Global signal regression is necessary, otherwise it will affect resting bold signal (27). More details were described in a previous study (28).

Degree centrality

Based on the individual voxel-wise functional network, DC was calculated by counting the number of significant suprathreshold correlations (or the degree of the binarized adjacency matrix) for each individual. The voxel-wise DC map for each participant was converted into a z-score map using the following equation: Z_i = DC_i − mean_all/std_all, where Z_i is z score of the ith voxel, DC_i is the DC value of the ith voxel, mean_all is the mean DC value of all voxels in brain mask, and std_all is the standard deviation of DC values of all voxels in the brain mask (28).

Pain evaluation

The visual analogue scale (VAS) was used to measure TN pain before MRI. Patients were asked to rate their pain on a scale of 0–10 using a 10-cm ruler. The rating of “0” represented no pain and “10” meant severe and extremely intolerable pain (29).

Statistical analysis

For clinical measurements and demographic data, IBM SPSS for Windows, version 20.0 (IBM Corp, Armonk, NY, USA) was used to calculate differences in clinical characteristics between the HCs and TNs via independent two-sample t-tests. For DC analyses, each group was subjected to one-sample t-tests to identify the hubs of the whole-brain functional network. We regarded sex and age as nuisance covariates and performed independent two-sample t-tests within the default gray matter mask to appraise differences between the two groups in the voxel-wise DC with the REST V1.8 (two-tailed, voxel-level: P < 0.001, Gaussian random field (GRF) correction, cluster-level: P < 0.05) (30). Correlation analysis was performed to assess the between-group relationship of behavioral performance and mean DC values.

Receiver operating characteristic (ROC) curve

We speculated that DC value differences could be potentially used as biomarkers for diagnostics and predicting treatment outcome. The ROC curve method was used to test this hypothesis. Accuracy was considered low or high if the area under the curve (AUC) was 0.5–0.7 or 0.7–0.9, respectively.

Brain-behavior correlation analysis

Clinical data such as disease course, VAS score, and pain site were collected for TN patients. Correlations between mean DC values of abnormal regions and clinical symptoms were assessed in the patient group.

Clinical data analysis

For behavioral performance, IBM SPSS for Windows, version 20.0 was used to analyze continuous data (significance level of P < 0.05) with two-sample Student’s t-tests.

Results

Demographics and visual measurements

The mean disease duration of TN patients was 3.733 ± 4.102 years. There were no obvious differences in age (P = 0.787) between the TN and HC groups (Table 1).

Table 1.

Demographic and clinical characteristics of the participants.

Condition	TN	HCs	t	P value
Sex (men/women)	12/16	12/16	N/A	> 0.99
Age (years)	51.392 9.372	51.357 9.302	0.297	0.769
Duration (years)	3.733 4.102	N/A	N/A	N/A
VAS scores	6.321 1.442	N/A	N/A	N/A

Independent t-tests comparing two groups (P < 0.05 significant differences).

TN: trigeminal neuralgia; HC: healthy control; N/A: not applicable; VAS: visual analog scale.

DC differences

We examined voxel morphometry to explore differences between patients with TN and HCs. We found that the DC significantly increased in the right lingual gyrus, right postcentral gyrus, left paracentral lobule, left inferior cerebellum, and right inferior cerebellum (Table 2, Figs. 1 and 2). Fig. 1a shows the change of mean DC in TN patients in the horizontal plane. Fig. 2a and 2b display the changes in mean DCs from the coronal and sagittal planes, respectively. Fig. 1b depicts their stereoscopic forms. Fig. 3 shows the average changes in altered DC between the two groups.

Fig. 1.

Voxel-wise comparison of DC in the TN and HC groups. Significant increase in DC were observed in the right lingual gyrus, right postcentral gyrus, left paracentral lobule, left inferior cerebellum, and right inferior cerebellum. The yellow areas denote higher DC values (two-tailed, voxel-level: P < 0.001, GRF correction, cluster-level: P < 0.05). (a) The change of DC mean of TN patients from the horizontal plane. (b) Their stereoscopic form. DC: degree centrality; TN: trigeminal neuralgia; HC: healthy controls.

Fig. 2.

Clusters showing DC differences between TN and HC groups in (a) axial and (b) sagittal slices. The yellow areas mean denote higher DC values (P < 0.05, AlphaSim corrected). Brain images are displayed in radiology convention (e.g. the left in the figure represents the right side of patients’ brains and vice versa). (a, b) The changes in the mean DCs from coronal and sagittal planes, respectively. DC: degree centrality; TN: trigeminal neuralgia.

Fig. 3.

Brain regions with significant differences in DC between TN patients and HCs. DC: degree centrality; HC: healthy control; RIC: right inferior cerebellum; LIC: left inferior cerebellum; LPL: left paracentral lobule; RLG: right lingual gyrus; RPG: right postcentral gyrus; TN: trigeminal neuralgia.

Table 2.

Brain regions with significant differences in DC between TN patients and HCs.

TN patients and HCs				MNI coordinates
Brain areas	BA	Peak T-values	Voxels	x	y	z
Right lingual gyrus	19,18	5.0329	49	21	−72	−6
Right postcentral gyrus	6	7.7566	225	12	−9	78
Left paracentral lobule	6	6.1843	148	−12	−33	84
Left inferior cerebellum	–	7.0492	85	−33	−57	−57
Right inferior cerebellum	–	4.9791	28	27	−63	−60

The statistical was set at voxel with P < 0.01 for multiple comparisons using a false discovery rate (P < 0.01, cluster > 20 voxels).

DC: degree centrality; HC: healthy control; MNI: Montreal Neurological Institute; BA: Brodmann area; TN: trigeminal neuralgia.

ROC curve

The individual AUCs of DC values in different regions were as follows: right inferior cerebellum (0.960, P < 0.001), left inferior cerebellum (0.957, P < 0.001), right lingual gyrus (0.944, P < 0.001), right postcentral gyrus (0.886, P < 0.001), and left paracentral lobule (0.873, P < 0.001) (Fig. 4a).

Fig. 4.

(a) ROC curve analysis of the mean DC values for altered brain regions. (b) Correlations between the mean DC value of right postcentral gyrus and the VSA scores. (a) The area under the ROC curve were: RIC = 0.960, (P < 0.001; 95% confidence interval [CI] = 0.899–1.000); LIC = 0.957 (P < 0.001; 95% CI = 0.893–1.000); RLG = 0.944 (P < 0.001; 95% CI = 0.877–1.000); RPG = 0.886 (P < 0.001; 95% CI = 0.777–0.994); LPL = 0.873 (P < 0.001; 95% CI = 0.762–0.985). (b) In TN patients, the DC values in the right postcentral gyrus showed a positive correlation with VAS (r = 0.885, P < 0.001). DC: degree centrality; ROC: receiver operating characteristic; RIC: right inferior cerebellum; LIC: left inferior cerebellum; RLG: right lingual gyrus; RPG: right postcentral gyrus; LPL: left paracentral lobule.

Correlation analysis

In TN patients, DC values in the right postcentral gyrus positively correlated with VAS scores (r = 0.885, P < 0.001) (Fig. 4b).

Discussion

The purpose of our study was to explore functional connectivity in the brains of TN patients using DC, a data-driven analysis technique. With fMRI, the brain can be decomposed into different functional networks. Although the brain areas covered by various functional networks are structurally unconnected, the functional connections that reflect the correlation of brain activity are closely linked. Compared with the HC group, patients with TN showed DC value changes in the right lingual gyrus, right postcentral gyrus, left paracentral lobule, left inferior cerebellum, and right inferior cerebellum (Table 2, Fig. 5). We found a pattern of change similar to those described for other chronic pain states (31). This seems to reflect adaptation of the cerebral cortex to frequent, long-term pain (31). The abnormal brain areas identified in our study may be closely related to the development and maintenance of chronic pain in TN patients. Our results highlight local pain synchronization in TN patients and the brain damage associated with central pain management.

Fig. 5.

The DC results of brain activity in the TN patients. Compared with HCs, the following regions showed significantly increased DC values to various extents:1 = lingual gyrus (R) (BA 18/19, t = 5.0329); 2 = postcentral gyrus (R) (BA6, t = 7.7566); 3 = paracentral lobule (L) (BA6, t = 6.1843); 4 = inferior cerebellum (L) (t = 7.0492); and 5 = inferior cerebellum (R) (t = 4.9791) in TN patients. The sizes of the spots denote the degree of quantitative changes. DC: degree centrality; TN: trigeminal neuralgia; HC: healthy control; R: right; L: left; BA: Brodmann area.

DC values in the TN group were elevated in the left and right inferior cerebellum. The cerebellum is traditionally considered the motor regulation center, which receives signals transmitted from the cortex and participates in coordinating voluntary movements and controlling balance. Studies have shown that the cerebellum can receive extensive somatosensory input through the spinocerebellar pathway, so the cerebellum may also be a sensory organ (32). Yuan found that primary TN patients had significantly altered ReHo and fractional ALFF values in the right posterior lobe of the cerebellum, indicating functional change (33). This is similar to our results and suggests that increased cerebellar functional connections may be associated with chronic and high-frequency sensory input associated with TN.

The lingual gyrus is part of the primary visual cortex, receiving direct input from the ipsilateral geniculate nucleus. Its blood supply is from the posterior cerebral artery (34). Previous studies reported that the right lingual gyrus may be connected to mental illnesses such as depression, anxiety, and panic (35,36). The surface volume of the right lingual gyrus is reduced in adolescents with depression, which may be related to visual memory and attention deficit. Gray matter density in the right lingual gyrus is considered to be a predictor of antidepressant response. That is, patients with high density may have a better outcome after the initial treatment, indicating that this brain area is associated with antidepressant responses and cognitive function in depressive patients (37). For patients with TN, chronic pain is one of the main causes of physical and mental pain. Mačianskytė conducted self-assessments of anxiety and depression in 30 patients with TN and chronic facial pain and found that their scores were significantly higher than those with atypical pain (38). There is evidence that patients with chronic pain, including oral and facial pain, have a higher risk of anxiety and depression than the general population (38,39). The change in right lingual functional connectivity in TN patients may be related to depression induced by chronic pain.

In addition, we also found abnormal functional connectivity in the right posterior central gyrus and left lobulus paracentralis in comparison with HCs. The central posterior gyrus is the primary somatosensory cortex, receiving most of the somatosensory information relayed from the thalamus, and posterior central gyrus injury can lead to contralateral somatosensory disorder (40). The front of the lobulus paracentralis is a somatosensory area, which is mainly responsible for the skeletal muscle movement of the and bilateral trunk. The posterior central gyrus and posterior lobulus paracentralis are somatosensory functional areas that mainly receive somatosensory information from the contralateral side (41). Mckiernan proposed “resource allocation theory,” in which the brain’s “resting state” is actually organized functional brain activity. When these activities are disturbed by external tasks, the related processing resources are transferred to the brain area related to the task (42). Therefore, we speculate that when pain occurs, functional activities are enhanced in the brain regions closely related to sensation.

ROC curves provide a standardized and statistically meaningful method to distinguish between TN patients and from HCs. Values < 0.2, 0.2–0.4, 0.4–0.6, 0.6–0.8, and > 0.8 were considered to have poor, fair, moderate, substantial, and almost perfect accuracy, respectively. ROC curve analysis revealed that the AUCs of DC values in different regions were as follows: right inferior cerebellum (0.960, P < 0.001), left inferior cerebellum (0.957, P < 0.001), right lingual gyrus (0.944, P < 0.001), right postcentral gyrus (0.886, P < 0.001), and left paracentral lobule (0.873, P < 0.001). The AUCs of each brain region were > 0.8, indicating that we can accurately diagnose TN by increasing DC values in these brain regions. In brief, our findings demonstrate that the DC method might be a sensitive classification for fMRI, suggesting that the lingual gyrus, postcentral gyrus, paracentral lobule, and inferior cerebellum might be useful diagnostic markers for TN patients. Additionally, we also observed a strong positive correlation between TN patient VAS scores and DC values in the right postcentral gyrus (r = 0.885, P < 0.001). Increasing degree DC values in the right postcentral gyrus might therefore be useful for evaluating the degree of pain, which can be used to predict the effect of TN treatment (decrease in DC value when treatment is effective).

It is important to note that our study focused on existing functional changes in the brains of TN patients, without examining structural abnormalities. It is not clear whether chronic pain causes functional changes in these areas or if functional changes predispose individuals to TN. Longitudinal studies may help clarify the pathogenetic mechanisms of TN.

In conclusion, our results showed abnormal brain function networks in multiple brain regions, which might reflect abnormal activities of the somatosensory cortex and other sensory-related brain regions in TN.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research is supported by National Natural Science Foundation of China (Nos. 81660158, 81160118, 81400372, 81460092, and 81500742), Natural Science research Foundation of Guangdong Province (Nos. 2017A030313614, 2017A020215187, and 2018A030313117), and Medical Science Foundation of Guangdong Province (No. A2016184).

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