Abnormal Intrinsic Brain Activity and Neuroimaging-Based fMRI Classification in Patients With Herpes Zoster and Postherpetic Neuralgia

Parabolic Changes in Pain Scores Among Partial Herpes Zoster Patients: A Retrospective Study

Background

Herpes zoster (HZ) typically manifests in the acute phase with distinct blisters and severe neuropathic pain. Remarkably, a subset of patients initially presents with only a mild skin rash and moderate pain that gradually intensifies, following a parabolic pattern. Despite being frequently observed in clinical settings, the underlying causes of this trajectory and its potential connection with post-herpetic neuralgia (PHN) remain unclear.

Methods

To investigate this phenomenon in-depth, we conducted a meticulous retrospective study involving 529 eligible HZ patients. All these patients sought medical care at the Third Central Hospital of Tianjin, China, between January 2020 and December 2023.

Results

The research identified that 14.6% of the sample (77 patients) experienced pain scores aligning with a parabolic curve. This trend was significantly more prevalent in patients aged 60 and above, accounting for 90.9% of this group, and demonstrated a positive correlation with age. Moreover, 87.0% of these patients had pre-existing medical conditions, highlighting the potential role of comorbidities in influencing the pain trajectory. A concerning 45.5% of patients sought medical attention more than seven days after the onset of symptoms, a delay that could exacerbate neurological damage. Notably, among those following a parabolic pain pattern, 66.2% eventually developed PHN, a considerably higher rate compared to the broader patient population.

Conclusion

We emphasize that healthcare practitioners meticulously assess patients who initially report lower pain scores for high-risk factors potentially leading to parabolic pain increases, including being over 60 years old, having comorbid conditions, and delaying medical consultation beyond seven days from symptom onset. Early implementation of supplementary pain management therapies may mitigate the risk of PHN development and enhance the quality of life for patients. This study furnishes clinicians with a deeper understanding of the variations in HZ-related pain trajectories, promising to improve treatment approaches and prognoses for HZ patients while paving the way for enriched clinical practice in the future.

Transforming medicine: artificial intelligence integration in the peripheral nervous system

In recent years, artificial intelligence (AI) has undergone remarkable advancements, exerting a significant influence across a multitude of fields. One area that has particularly garnered attention and witnessed substantial progress is its integration into the realm of the nervous system. This article provides a comprehensive examination of AI's applications within the peripheral nervous system, with a specific focus on AI-enhanced diagnostics for peripheral nervous system disorders, AI-driven pain management, advancements in neuroprosthetics, and the development of neural network models. By illuminating these facets, we unveil the burgeoning opportunities for revolutionary medical interventions and the enhancement of human capabilities, thus paving the way for a future in which AI becomes an integral component of our nervous system's interface.

Altered gray matter volume and functional connectivity in lung cancer patients with bone metastasis pain

Bone metastasis pain (BMP) is a severe chronic pain condition. Our previous studies on BMP revealed functional brain abnormalities. However, the potential effect of BMP on brain structure and function, especially gray matter volume (GMV) and related functional networks, have not yet been clearly illustrated. Voxel-based morphometry and functional connectivity (FC) analysis methods were used to investigate GMV and intrinsic FC differences in 45 right-handed lung cancer patients with BMP(+), 37 lung cancer patients without BMP(-), and 45 healthy controls (HCs). Correlation analysis was performed thereafter with all clinical variables by Pearson correlation. Compared to HCs, BMP(+) group exhibited decreased GMV in medial frontal gyrus (MFG) and right middle temporal gyrus (MTG). Compared with BMP(-) group, BMP(+) group exhibited reduced GMV in cerebelum_6_L and left lingual gyrus. However, no regions with significant GMV differences were found between BMP(-) and HCs groups. Receiver operating characteristic analysis indicated the potential classification power of these aberrant regions. Correlation analysis revealed that GMV in the right MTG was positively associated with anxiety in BMP(+) group. Further FC analysis demonstrated enhanced interactions between MFG/right MTG and cerebellum in BMP(+) patients compared with HCs. These results showed that BMP was closely associated with cerebral alterations, which may induce the impairment of pain moderation circuit, deficits in cognitive function, dysfunction of emotional control, and sensorimotor processing. These findings may provide a fresh perspective and further neuroimaging evidence for the possible mechanisms of BMP. Furthermore, the role of the cerebellum in pain processing needs to be further investigated.

Abnormal brain activity in lumbar disc herniation patients with chronic pain is associated with their clinical symptoms

Introduction

Lumbar disc herniation, a chronic degenerative disease, is one of the major contributors to chronic low back pain and disability. Although many studies have been conducted in the past on brain function in chronic low back pain, most of these studies did not classify chronic low back pain (cLBP) patients according to their etiology. The lack of etiologic classification may lead to inconsistencies between findings, and the correlation between differences in brain activation and clinical symptoms in patients with cLBP was less studied in the past.

Methods

In this study, 36 lumbar disc herniation patients with chronic low back pain (LDHCP) and 36 healthy controls (HCs) were included to study brain activity abnormalities in LDHCP. Visual analogue scale (VAS), oswestry disability index (ODI), self-rating anxiety scale (SAS), self-rating depression scale (SDS) were used to assess clinical symptoms.

Results

The results showed that LDHCP patients exhibited abnormally increased and diminished activation of brain regions compared to HCs. Correlation analysis showed that the amplitude of low frequency fluctuations (ALFF) in the left middle frontal gyrus is negatively correlated with SAS and VAS, while the right superior temporal gyrus is positively correlated with SAS and VAS, the dorsolateral left superior frontal gyrus and the right middle frontal gyrus are negatively correlated with VAS and SAS, respectively.

Conclusion

LDHCP patients have brain regions with abnormally increased and abnormally decreased activation compared to healthy controls. Furthermore, some of the abnormally activated brain regions were correlated with clinical pain or emotional symptoms.

Identifying the neural marker of chronic sciatica using multimodal neuroimaging and machine learning analyses

INTRODUCTION

Sciatica is a pain disorder often caused by the herniated disk compressing the lumbosacral nerve roots. Neuroimaging studies have identified functional abnormalities in patients with chronic sciatica (CS). However, few studies have investigated the neural marker of CS using brain structure and the classification value of multidimensional neuroimaging features in CS patients is unclear.

METHODS

Here, structural and resting-state functional magnetic resonance imaging (fMRI) was acquired for 34 CS patients and 36 matched healthy controls (HCs). We analyzed cortical surface area, cortical thickness, amplitude of low-frequency fluctuation (ALFF), regional homogeneity (REHO), between-regions functional connectivity (FC), and assessed the correlation between neuroimaging measures and clinical scores. Finally, the multimodal neuroimaging features were used to differentiate the CS patients and HC individuals by support vector machine (SVM) algorithm.

RESULTS

Compared to HC, CS patients had a larger cortical surface area in the right banks of the superior temporal sulcus and rostral anterior cingulate; higher ALFF value in the left inferior frontal gyrus; enhanced FCs between somatomotor and ventral attention network. Three FCs values were associated with clinical pain scores. Furthermore, the three multimodal neuroimaging features with significant differences between groups and the SVM algorithm could classify CS patients and HC with an accuracy of 90.00%.

DISCUSSION

Together, our findings revealed extensive reorganization of local functional properties, surface area, and network metrics in CS patients. The success of patient identification highlights the potential of using artificial intelligence and multimodal neuroimaging markers in chronic pain research.

Characterization of plasma metabolites and proteins in patients with herpetic neuralgia and development of machine learning predictive models based on metabolomic profiling

Herpes zoster (HZ) is a localized, painful cutaneous eruption that occurs upon reactivation of the herpes virus. Postherpetic neuralgia (PHN) is the most common chronic complication of HZ. In this study, we examined the metabolomic and proteomic signatures of disease progression in patients with HZ and PHN. We identified differentially expressed metabolites (DEMs), differentially expressed proteins (DEPs), and key signaling pathways that transition from healthy volunteers to the acute or/and chronic phases of herpetic neuralgia. Moreover, some specific metabolites correlated with pain scores, disease duration, age, and pain in sex dimorphism. In addition, we developed and validated three optimal predictive models (AUC > 0.9) for classifying HZ and PHN from healthy individuals based on metabolic patterns and machine learning. These findings may reveal the overall metabolomics and proteomics landscapes and proposed the optimal machine learning predictive models, which provide insights into the mechanisms of HZ and PHN.

Predicting response to tVNS in patients with migraine using functional MRI: A voxels-based machine learning analysis

Background

Migraine is a common disorder, affecting many patients. However, for one thing, lacking objective biomarkers, misdiagnosis, and missed diagnosis happen occasionally. For another, though transcutaneous vagus nerve stimulation (tVNS) could alleviate migraine symptoms, the individual difference of tVNS efficacy in migraineurs hamper the clinical application of tVNS. Therefore, it is necessary to identify biomarkers to discriminate migraineurs as well as select patients suitable for tVNS treatment.

Methods

A total of 70 patients diagnosed with migraine without aura (MWoA) and 70 matched healthy controls were recruited to complete fMRI scanning. In study 1, the fractional amplitude of low-frequency fluctuation (fALFF) of each voxel was calculated, and the differences between healthy controls and MWoA were compared. Meaningful voxels were extracted as features for discriminating model construction by a support vector machine. The performance of the discriminating model was assessed by accuracy, sensitivity, and specificity. In addition, a mask of these significant brain regions was generated for further analysis. Then, in study 2, 33 of the 70 patients with MWoA in study 1 receiving real tVNS were included to construct the predicting model in the generated mask. Discriminative features of the discriminating model in study 1 were used to predict the reduction of attack frequency after a 4-week tVNS treatment by support vector regression. A correlation coefficient between predicted value and actual value of the reduction of migraine attack frequency was conducted in 33 patients to assess the performance of predicting model after tVNS treatment. We vislized the distribution of the predictive voxels as well as investigated the association between fALFF change (post-per treatment) of predict weight brain regions and clinical outcomes (frequency of migraine attack) in the real group.

Results

A biomarker containing 3,650 features was identified with an accuracy of 79.3%, sensitivity of 78.6%, and specificity of 80.0% (p < 0.002). The discriminative features were found in the trigeminal cervical complex/rostral ventromedial medulla (TCC/RVM), thalamus, medial prefrontal cortex (mPFC), and temporal gyrus. Then, 70 of 3,650 discriminative features were identified to predict the reduction of attack frequency after tVNS treatment with a correlation coefficient of 0.36 (p = 0.03). The 70 predictive features were involved in TCC/RVM, mPFC, temporal gyrus, middle cingulate cortex (MCC), and insula. The reduction of migraine attack frequency had a positive correlation with right TCC/RVM (r = 0.433, p = 0.021), left MCC (r = 0.451, p = 0.016), and bilateral mPFC (r = 0.416, p = 0.028), and negative with left insula (r = −0.473, p = 0.011) and right superior temporal gyrus/middle temporal gyrus (r = −0.684, p < 0.001), respectively.

Conclusions

By machine learning, the study proposed two potential biomarkers that could discriminate patients with MWoA and predict the efficacy of tVNS in reducing migraine attack frequency. The pivotal features were mainly located in the TCC/RVM, thalamus, mPFC, and temporal gyrus.

The Curative Effect of Pregabalin in the Treatment of Postherpetic Neuralgia Analyzed by Deep Learning-Based Brain Resting-State Functional Magnetic Resonance Images

This work aimed to investigate the brain resting-state functional magnetic resonance imaging (fMRI) technology based on the depth autoencoders algorithm and to evaluate the clinically curative effect of pregabalin in the treatment of postherpetic neuralgia (PHN). In this study, 40 patients with PHN were selected and rolled randomly into a treatment group and a control group (20 cases in each group). Then, a depth autoencoders algorithm was constructed and applied in the brain resting-state fMRI technology. The brains of 40 patients with PHN treated with pregabalin were scanned, and the time curve extracted from MRI images was convolved by linear drift removal bandpass filtering to reduce low-frequency drift and high-frequency noise, so the low-frequency amplitude was calculated. Based on the low-frequency amplitude method, the calculated low-frequency signal energy was eventually divided by the total power of the entire frequency band to obtain the low-frequency amplitude rate value. The amplitude of low-frequency fluctuation (ALFF) and fractional ALFF (f-ALFF) before and after treatment were compared between the treatment group and the control group, and the visual analog scale (VAS) after treatment was also observed. After 4 weeks of taking the drug, the VAS scores of patients from the treatment group in the first week (6.5 ± 0.8 points), the second week (6.5 ± 0.8 points), the third week (3.1 ± 0.3 points), and the fourth week (2.3 ± 0.4 points) after treatment were lower steeply than the scores before treatment (8.3 ± 1.1 points) (P < 0.05). Resting-state fMRI images showed that the f-ALFF of the 4 brain areas in the treatment group was higher than that of the control group, mainly including the bilateral frontal lobes, bilateral parietal lobes, left parietal lobes, and right posterior cerebellar lobes. Besides, the f-ALFF of the 6 brain areas in the treatment group was lower than that of the control group, mainly including the right frontal lobe, right parietal lobe, right middle frontal gyrus, precuneus, left frontal lobe, and superior frontal gyrus. In conclusion, the resting-state fMRI technology based on the depth autoencoders algorithm could efficiently display the brain area characteristic changes of patients with PHN before and after treatment, thereby providing a reference for the diagnosis of the patient's condition.

Clinical Characteristics, Treatment Effectiveness, and Predictors of Response to Pharmacotherapeutic Interventions Among Patients with Herpetic-Related Neuralgia: A Retrospective Analysis

BACKGROUND

The treatment for herpetic-related neuralgia focuses on symptom control by use of antiviral drugs, anticonvulsants, and tricyclic antidepressants. We aimed to explore the clinical characteristics associated with medication responsiveness, and to build a classifier for identification of patients who have risk of inadequate pain management.

METHODS

We recruited herpetic-related neuralgia patients during a 3-year period. Patients were stratified into a medication-resistant pain (MRP) group when the pain decrease in the visual analogue scale (VAS) is < 3 points, and otherwise a medication-sensitive pain (MSP) group. Multivariate logistic regression was performed to determine the factors associated with MRP. We fitted four machine learning (ML) models, namely logistic regression, random forest, supporting vector machines (SVM), and naïve Bayes with clinical characteristics gathered at admission to identify patients with MRP.

RESULTS

A total of 213 patients were recruited, and 132 (61.97%) patients were diagnosed with MRP. Subacute herpes zoster (HZ) (vs. acute, OR 8.95, 95% CI 3.15-29.48, p = 0.0001), severe lesion (vs. mild lesion, OR 3.84, 95% CI 1.44-10.81, p = 0.0084), depressed mood (unit increase OR 1.10, 95% CI 1.00-1.20, p = 0.0447), and hypertension (hypertension, vs. no hypertension, OR 0.36, 95% CI 0.14-0.87, p = 0.0266) were significantly associated with MRP. Among four ML models, SVM had the highest accuracy (0.917) and receiver operating characteristic-area under the curve (0.918) to discriminate MRP from MSP. Phase of disease is the most important feature when fitting ML models.

CONCLUSIONS

Clinical characteristics collected before treatment could be adopted to identify patients with MRP.