Pain‐related reorganization in the primary somatosensory cortex of patients with postherpetic neuralgia

Abstract

Studies on functional and structural changes in the primary somatosensory cortex (S1) have provided important insights into neural mechanisms underlying several chronic pain conditions. However, the role of S1 plasticity in postherpetic neuralgia (PHN) remains elusive. Combining psychophysics and magnetic resonance imaging (MRI), we investigated whether pain in PHN patients is linked to S1 reorganization as compared with healthy controls. Results from voxel-based morphometry showed no structural differences between groups. To characterize functional plasticity, we compared S1 responses to noxious laser stimuli of a fixed intensity between both groups and assessed the relationship between S1 activation and spontaneous pain in PHN patients. Although the intensity of evoked pain was comparable in both groups, PHN patients exhibited greater activation in S1 ipsilateral to the stimulated hand. Painrelated activity was identified in contralateral superior S1 (SS1) in controls as expected, but in bilateral inferior S1 (IS1) in PHN patients with no overlap between SS1 and IS1. Contralateral SS1 engaged during evoked pain in controls encoded spontaneous pain in patients, suggesting functional S1 reorganization in PHN.Resting-state fMRI data showed decreased functional connectivity between left and right SS1 in PHN patients, which scaled with the intensity of spontaneous pain.Finally, multivariate pattern analyses (MVPA) demonstrated that BOLD activity and resting-state functional connectivity of S1 predicted within-subject variations of evoked and spontaneous pain intensities across groups. In summary, functional reorganization in S1 might play a key role in chronic pain related to PHN and could be a potential treatment target in this patient group.

Uncovering Brain Functional Connectivity Disruption Patterns of Lung Cancer-related Pain

“…A study reported that decreased connectivity in the posterior domains of the DMN in lung cancer patients after chemotherapy (Simó et al, 2018). Besides, chemotherapy-induced disturbances of interactions within and between networks also were discovered, which mainly include executive control, sensorimotor, attentional, and auditory networks (Hu et al, 2022). Consistent with these studies, we found widespread hypo-FCs across important functional networks in lung cancer patients, which may be related to chemotherapeutic agents easily causing demyelination and neuro-in ammation in the brain directly or indirectly (Janelsins, Kesler, Ahles, & Morrow, 2014).…”

“…The SOM, including somatosensory and motor regions, plays a critical role in the eventual processing of sensation and the execution of motor functions (Stinear, Coxon, & Byblow, 2009). Previous studies have demonstrated that FCs in the somatosensory cortex were disrupted in response to pain stimulation (Chang, Fabrizi, & Fitzgerald, 2022; Lee, Lee, Lee, & Woo, 2022; Li et al, 2022). Similarly, we found that hyper-connectivity between the somatomotor cortex was associated with cancer pain, and the connectivity strength increased with pain duration.…”

“…Recent studies have manifested that brain areas outside the pain matrix are also involved in several pain disorders; hence, it is necessary to investigate the brain as a whole (Mayr, Jahn, Deak, et al, 2022;Meeker et al, 2022;Xu et al, 2022). In addition, several studies have con rmed the abnormal connectivities of pain may be network-speci c, which usually involves the somatosensory network (Li et al, 2022), default mode network (van Ettinger-Veenstra et al, 2019), and salience network (Xu et al, 2022). Herein, we tried to categorize the abnormal connectivities into several various functional modules, which may help to better understand the impairment of the brain network in the presence of cancer pain.…”

The role of ascending arousal network in patients with chronic insomnia disorder

“…Indeed, many fields, from oncology and cardiology to internal medicine, have developed biomarkers that indicate specific pathophysiological mechanisms. In recent years, the combination of machine‐learning‐based MVPA approaches and neuroimaging data features has yielded some encouraging diagnostic and predictive models for individual‐level disease diagnosis and efficacy prediction (H. Li et al, 2022 ; Pang et al, 2022 ; Zhang et al, 2022 ). However, it is still in the early stages of brain biomarker development; future applications should extend our predictive model to multiple time‐visits longitudinal experimental designs, such as predicting the effect of pharmacological and non‐pharmacological therapies on insomnia by collecting data from two time points observed before and after treatment.…”

“…from oncology and cardiology to internal medicine, have developed biomarkers that indicate specific pathophysiological mechanisms. In recent years, the combination of machine-learning-based MVPA approaches and neuroimaging data features has yielded some encouraging diagnostic and predictive models for individual-level disease diagnosis and efficacy prediction (H Li et al, 2022;Pang et al, 2022;Zhang et al, 2022)…”