Cortical Pain Processing in the Rat Anterior Cingulate Cortex and Primary Somatosensory Cortex

Xiao, Zhengdong; Martinez, Erik; Kulkarni, Prathamesh M.; Zhang, Qiaosheng; Hou, Qianning; Rosenberg, David; Talay, Robert; Shalot, Leor; Zhou, Haocheng; Wang, Jing; Chen, Zhe

doi:10.3389/fncel.2019.00165

Cited by 54 publications

(51 citation statements)

References 56 publications

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“…Shih and colleagues (2019) have recently demonstrated that ACC theta power is decreased, while motor cortex theta power is increased following lesion of the medial thalamic nuclei, a region thought to convey affective elements of pain. Likewise, decreases in Theta and Alpha power following application of a noxious, mechanical probe are evident in the ACC but not S1 (Xiao et al, 2019). Thus, discrepancies in the direction of oscillatory changes may reflect real, physiological differences in how distinct brain regions respond to persistent pain.…”

Section: Discussionmentioning

confidence: 99%

Cortical 6-9 Hz Oscillation are a Reliable Biomarker of Persistent Pain in Rats

Raver

et al. 2020

Preprint

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Neural biomarkers of chronic pain offer a potential tool for improving the speed of diagnosis and delivery of treatment for this devastating disease. Here, we tested the hypothesis that pain states are associated with distinct changed in cortical brain waves. We induced neuropathic orofacial pain in female rats by chronic constriction injury of the infraorbital nerve (CCI-ION). In most animals, this resulted in lasting reductions in mechanical sensitivity thresholds, and in lasting increases in facial grimace scores. We recorded electrocortigraphy (ECoG) signals over the neocortex of these rats, before and after CCI-ION, and analyzed these signals with a novel, spectral modelling approach. Consistent with our hypothesis, power in the 6-9 Hz bandwidth of the ECoG was differentially modulated in animals displaying signs of chronic pain. Specifically, development of mechanical hypersensitivity correlated with a decrease in 6-9 Hz power. Furthermore, we show that changes in the power of this oscillation after injury, obtained at the individual animal level, provide a more sensitive marker of pain presence than do traditional between animal comparisons of post-injury oscillatory power. Identification of animals demonstrating chronic-pain behaviors was more accurate when estimates of post-injury oscillatory power were compared against each animal's own pre-injury baseline than when compared against postinjury power estimates from animals not developing chronic pain. These results highlight the need for establishing individual-specific, "pain-free" baselines from which oscillation disturbances can be measured and which may constitute a reliable, low-cost approach not only for diagnosing chronic pain, but also for identifying individuals likely to transition from acute to chronic pain.

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Section: Discussionmentioning

confidence: 99%

Cortical 6-9 Hz Oscillation are a Reliable Biomarker of Persistent Pain in Rats

Raver

et al. 2020

Preprint

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“…4d), suggesting that while S1 encodes the sensory‐discriminative components of pain during noxious and non‐noxious peripheral stimulation, it may not play a role in pain in the absence of a stimulus in this model of neuropathy. The S1 may be implicated in spontaneous somatosensory sensations in the absence of noxious stimuli since an S1 lesion in the rat alters pain affect and cortical recordings indicate event‐related potentials in the S1 during spontaneous pain episodes . Nevertheless, studies are needed to fully elucidate the role of S1 in non‐evoked pain due to neuropathic injury.…”

Section: Discussionmentioning

confidence: 99%

Changes in Neuronal Activity in the Anterior Cingulate Cortex and Primary Somatosensory Cortex With Nonlinear Burst and Tonic Spinal Cord Stimulation

Quindlen

Kent²,

Anda

et al. 2020

Neuromodulation: Technology at the Neural Interface

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Introduction: Although nonlinear burst and tonic SCS are believed to treat neuropathic pain via distinct pain pathways, the effectiveness of these modalities on brain activity in vivo has not been investigated. This study compared neuronal firing patterns in the brain after nonlinear burst and tonic SCS in a rat model of painful radiculopathy. Methods: Neuronal activity was recorded in the ACC or S1 before and after nonlinear burst or tonic SCS on day 7 following painful cervical nerve root compression (NRC) or sham surgery. The amplitude of nonlinear burst SCS was set at 60% and 90% motor threshold to investigate the effect of lower amplitude SCS on brain activity. Neuronal activity was recorded during and immediately following light brush and noxious pinch of the paw. Change in neuron firing was measured as the percent change in spikes post-SCS relative to pre-SCS baseline. Results: ACC activity decreases during brush after 60% nonlinear burst compared to tonic (p < 0.05) after NRC and compared to 90% nonlinear burst (p < 0.04) and pre-SCS baseline (p < 0.03) after sham. ACC neuron activity decreases (p < 0.01) during pinch after 60% and 90% nonlinear burst compared to tonic for NRC. The 60% of nonlinear burst decreases (p < 0.02) ACC firing during pinch in both groups compared to baseline. In NRC S1 neurons, tonic SCS decreases (p < 0.01) firing from baseline during light brush; 60% nonlinear burst decreases (p < 0.01) firing from baseline during brush and pinch. Conclusions: Nonlinear burst SCS reduces firing in the ACC from a painful stimulus; a lower amplitude nonlinear burst appears to have the greatest effect. Tonic and nonlinear burst SCS may have comparable effects in S1.

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“…During the inter-trial intervals, we examined the rat’s behavior to identify putative spontaneous pain episodes (such as twitch, lifting/flicking, paw withdrawal and paw licking). Due to the lack of ground truth, we referred to those events as spontaneous pain-like episodes [26].…”

Section: Methodsmentioning

confidence: 99%

“…For multisite recordings, it is important to investigate the inter-regional local field potential (LFP) oscillatory coordination [22], as interareal oscillatory synchronization plays an important role in top-down neocortical processing [23]. In a series of rodent pain experiments, we collected various in vivo neurophysiological data (spikes and LFP) from single or two brain regions in freely behaving rats [7, 24–26]. These data have established the foundation towards improved understanding of pain perception as well as empirical evidence for computational modeling.…”

Section: Introductionmentioning

confidence: 99%

Predictive coding models for pain perception

Song

Yao

Kemprecos

et al. 2019

Preprint

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Pain is a complex, multidimensional experience that involves dynamic interactions between sensory-discriminative and affective-emotional processes. Pain experiences have a high degree of variability depending on their context and prior anticipation. Viewing pain perception as a perceptual inference problem, we use a predictive coding paradigm to characterize both evoked and spontaneous pain. We record the local field potentials (LFPs) from the primary somatosensory cortex (S1) and the anterior cingulate cortex (ACC) of freely behaving rats-two regions known to encode the sensory-discriminative and affective-emotional aspects of pain, respectively. We further propose a framework of predictive coding to investigate the temporal coordination of oscillatory activity between the S1 and ACC. Specifically, we develop a high-level, empirical and phenomenological model to describe the macroscopic dynamics of bottom-up and top-down activity. Supported by recent experimental data, we also develop a mechanistic mean-field model to describe the mesoscopic population neuronal dynamics in the S1 and ACC populations, in both naive and chronic pain-treated animals. Our proposed predictive coding models not only replicate important experimental findings, but also provide new mechanistic insight into the uncertainty of expectation, placebo or nocebo effect, and chronic pain. Author SummaryPain perception in the mammalian brain is encoded through multiple brain circuits.The experience of pain is often associated with brain rhythms or neuronal oscillations at different frequencies. Understanding the temporal coordination of neural oscillatory activity from different brain regions is important for dissecting pain circuit mechanisms and revealing differences between distinct pain conditions. Predictive coding is a general computational framework to understand perceptual inference by integrating bottom-up sensory information and top-down expectation. Supported by experimental data, we propose a predictive coding framework for pain perception, and develop empirical and biologically-constrained computational models to characterize oscillatory dynamics of neuronal populations from two cortical circuits-one for the sensory-discriminative PLOS 2/49 experience and the other for affective-emotional experience, and further characterize their temporal coordination under various pain conditions. Our computational study of biologically-constrained neuronal population model reveals important mechanistic insight on pain perception, placebo analgesia, and chronic pain. Introduction 1Pain is a basic experience that is subjective and multidimensional. Pain processing 2 involves sensory, affective, and cognitive processing across distributed neural 3 circuits [1-5]. However, a complete understanding of pain perception and cortical pain 4 processing has remained elusive. Given the same nociceptive stimuli, the context 5 matters for pain percept. Human neuroimaging studies have shown that among many 6 brain regions, the primary somatosensory cortex (S1) a...

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Cortical Pain Processing in the Rat Anterior Cingulate Cortex and Primary Somatosensory Cortex

Cited by 54 publications

References 56 publications

Cortical 6-9 Hz Oscillation are a Reliable Biomarker of Persistent Pain in Rats

Cortical 6-9 Hz Oscillation are a Reliable Biomarker of Persistent Pain in Rats

Changes in Neuronal Activity in the Anterior Cingulate Cortex and Primary Somatosensory Cortex With Nonlinear Burst and Tonic Spinal Cord Stimulation

Predictive coding models for pain perception

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