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

Abstract

Objective: Neuroimaging studies on neuropathic pain have discovered abnormalities in brain structure and function. However, the brain pattern changes from herpes zoster (HZ) to postherpetic neuralgia (PHN) remain unclear. The present study aimed to compare the brain activity between HZ and PHN patients and explore the potential neural mechanisms underlying cognitive impairment in neuropathic pain patients. Methods: Resting-state functional magnetic resonance imaging (MRI) was carried out among 28 right-handed HZ patients, 24 right-handed PHN patients, and 20 healthy controls (HC), using a 3T MRI system. The amplitude of low-frequency fluctuation (ALFF) was analyzed to detect the brain activity of the patients. Correlations between ALFF and clinical pain scales were assessed in two groups of patients. Differences in brain activity between groups were examined and used in a support vector machine (SVM) algorithm for the subjects' classification. Results: Spontaneous brain activity was reduced in both patient groups. Compared with HC, patients from both groups had decreased ALFF in the precuneus, posterior cingulate cortex, and middle temporal gyrus. Meanwhile, the neural activities of angular gyrus and middle frontal gyrus were lowered in HZ and PHN patients, respectively. Reduced ALFF in these regions was associated with clinical pain scales in PHN patients only. Using SVM algorithm, the decreased brain activity in these regions allowed for the classification of neuropathic pain patients (HZ and PHN) and HC. Moreover, HZ and PHN patients are also roughly classified by the same model. Conclusion: Our study indicated that mean ALFF values in these pain-related regions can be used as a functional MRI-based biomarker for the classification of subjects with different pain conditions. Altered brain activity might contribute to PHN-induced pain.

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

“…Furthermore, we fitted logistic regression, RF, SVM, and NB machine learning models to identify MRP by inputting basic clinical characteristics. We found that the SVM model had the highest accuracy to discriminate MRP from MSP, with a prediction accuracy comparable to previous imaging or EEG-based approaches [ 13 , 18 ].…”

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

“…Furthermore, restoring arginine bioavailability through exogenous arginine supplementation might be a beneficial approach for treating PHN (Bakshi and Morris, 2016). Notably, a recent study proposed a novel classification method for patients with HZ and PHN based on functional magnetic resonance imaging (fMRI), which indicated that decreased brain activity via the SVM algorithm could be used to classify individuals with different pain conditions (Huang et al, 2020). Notably, ML models also provide a novel identification 10.3389/fnmol.2022.1009677 method for patients with herpetic neuralgia who are at risk of inadequate pain management.…”

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

“…In recent years, SVM techniques combined with neuroimaging metrics have been applied to differentiate pain patients from HCs and to predict the outcome of certain interventions ( Bagarinao et al, 2014 ; Zeng et al, 2019 ; Huang et al, 2020 ; Tu et al, 2020b ; Gui et al, 2021 ; Wei et al, 2022 ). The patients with neuropathic pain and the HC were classified by the mean ALFF values of the frontal gyrus and the precuneus using the linear SVM classifier, and the classification accuracy was 86.36% between the PHN patients and HC ( Huang et al, 2020 ). A study identified a neural marker with abnormal FC within the SMN and FPN that could discriminate MwoA patients from HC with a 91.4% accuracy rate ( Tu et al, 2020b ).…”

“…Given these neuroimaging findings on chronic pain, we speculated that patients with CS may also have abnormal changes in structural properties or between-regions FC. In addition, previous studies have applied machine learning techniques to distinguish patients with post-herpetic neuralgia (PHN) and HC using the amplitude of low-frequency fluctuation (ALFF) values ( Huang et al, 2020 ). However, few studies have classified neuropathic pain patients from HC by multimodal neuroimaging features.…”