Exploring altered oscillatory activity in the anterior cingulate cortex after nerve injury: Insights into mechanisms of neuropathic allodynia.

While neural oscillations play a critical role in sensory perception, it remains unclear how these rhythms function under conditions of neuropathic allodynia. Recent studies demonstrated that the anterior cingulate cortex (ACC) is associated with the affective-aversive component of pain, and plasticity changes in this region are closely linked to abnormal allodynic sensations. Here, to study the mechanisms of allodynia, we recorded local field potentials (LFPs) in the bilateral ACC of awake-behaving rats and compared the spectral power and center frequency of brain oscillations between healthy and CCI (chronic constriction injury) induced neuropathic pain conditions. Our results indicated that activation of the ACC occurs bilaterally in the presence of neuropathic pain, similar to the healthy condition. Furthermore, CCI affects both spontaneous and stimulus-induced activity of ACC neurons. Specifically, we observed an increase in spontaneous beta activity after nerve injury compared to the healthy condition. By stimulating operated or unoperated paws, we found more intense event-related desynchronization (ERD) responses in the theta, alpha, and beta frequency bands and faster alpha center frequency after CCI compared to before CCI. Although the behavioral manifestation of allodynia was more pronounced in the operated paw than the unoperated paw following CCI, there was no significant difference in the center frequency and ERD responses observed in the ACC between stimulation of the operated and unoperated limbs. Our findings offer evidence supporting the notion that aberrancies in ACC oscillations may contribute to the maintenance and development of neuropathic allodynia.

Interhemispheric neural characteristics of noxious mechano-nociceptive stimulation in the anterior cingulate cortex.

Background: Pain is an unpleasant sensory and emotional experience. One of the most critical regions of the brain for pain processing is the anterior cingulate cortex (ACC). Several studies have examined the role of this region in thermal nociceptive pain. However, studies on mechanical nociceptive pain have been very limited to date. Although several studies have investigated pain, the interactions between the two hemispheres are still not clear. This study aimed to investigate nociceptive mechanical pain in the ACC bilaterally. Methods: Local field potential (LFP) signals were recorded from seven male Wistar rats' ACC regions of both hemispheres. Mechanical stimulations with two intensities, high-intensity noxious (HN) and non-noxious (NN) were applied to the left hind paw. At the same time, the LFP signals were recorded bilaterally from awake and freely moving rats. The recorded signals were analyzed from different perspectives, including spectral analysis, intensity classification, evoked potential (EP) analysis, and synchrony and similarity of two hemispheres. Results: By using spectro-temporal features and support vector machine (SVM) classifier, HN vs. no-stimulation (NS), NN vs. NS, and HN vs. NN were classified with accuracies of 89.6, 71.1, and 84.7%, respectively. Analyses of the signals from the two hemispheres showed that the EPs in the two hemispheres were very similar and occurred simultaneously; however, the correlation and phase locking value (PLV) between the two hemispheres changed significantly after HN stimulation. These variations persisted for up to 4 s after the stimulation. In contrast, variations in the PLV and correlation for NN stimulation were not significant. Conclusions: This study showed that the ACC area was able to distinguish the intensity of mechanical stimulation based on the power activities of neural responses. In addition, our results suggest that the ACC region is activated bilaterally due to nociceptive mechanical pain. Additionally, stimulations above the pain threshold (HN) significantly affect the synchronicity and correlation between the two hemispheres compared to non-noxious stimuli.

Investigating the application of graph theory features in hand movement directions decoding using EEG signals.

In recent years, functional analysis of brain networks based on graph theory properties has attracted considerable attention. This approach has usually been exploited for structural and functional brain analysis, while its potential in motor decoding tasks has remained unexplored. This study aimed to investigate the feasibility of using graph-based features in hand direction decoding in movement execution and preparation intervals. Hence, EEG signals were recorded from nine healthy subjects while performing a four-target center-out reaching task. The functional brain network was calculated based on the magnitude-squared coherence (MSC) at six frequency bands. Then, the features based on eight graph theory metrics were extracted from brain networks. The classification was performed with a support vector machine classifier. The results revealed that in four-class direction discrimination, the mean accuracy of the graph-based method surpassed 63% and 53% on movement and pre-movement data, respectively. Additionally, a feature fusion approach that combines the graph theory features with power features was proposed. The fusion method raised the classification accuracy to 70.8% and 61.2% for movement and pre-movement intervals, respectively. This work has verified the feasibility of using graph theory properties and their superiority over band power features in a hand movement decoding task.