A Predictive Coding Model for Evoked and Spontaneous Pain Perception

Altered evoked low-frequency connectivity from SI to ACC following nerve injury in rats

Objective. Despite decades of research on central processing of pain, there are still several unanswered questions, in particular regarding the brain regions that may contribute to this alerting sensation. Since it is generally accepted that more than one cortical area is responsible for pain processing, there is an increasing focus on the interaction between areas known to be involved.Approach. In this study, we aimed to investigate the bidirectional information flow from the primary somatosensory cortex (SI) to the anterior cingulate cortex (ACC) in an animal model of neuropathic pain.19 rats (nine controls and ten intervention) had an intracortical electrode implanted with six pins in SI and six pins in ACC, and a cuff stimulation electrode around the sciatic nerve. The intervention rats were subjected to the spared nerve injury (SNI) after baseline recordings. Electrical stimulation at three intensities of both noxious and non-noxious stimulation was used to record electrically evoked cortical potentials. To investigate information flow, two connectivity measures were used: phase lag index (PLI) and granger prediction (GP). The rats were anesthetized during the entire study.Main results. Immediately after the intervention (<5 min after intervention), the high frequency (γandγ+) PLI was significantly decreased compared to controls. In the last recording cycle (3-4 h after intervention), the GP increased consistently in the intervention group. Peripheral nerve injury, as a model of neuropathic pain, resulted in an immediate decrease in information flow between SI and ACC, possibly due to decreased sensory input from the injured nerve. Hours after injury, the connectivity between SI and ACC increased, likely indicating hypersensitivity of this pathway.Significance. We have shown that both a directed and non-directed connectivity between SI and ACC approach can be used to show the acute changes resulting from the SNI model.

Predictive coding models for pain perception

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 propose a predictive coding paradigm to characterize evoked and non-evoked 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 use predictive coding to investigate the temporal coordination of oscillatory activity between the S1 and ACC. Specifically, we develop a phenomenological predictive coding model to describe the macroscopic dynamics of bottom-up and top-down activity. Supported by recent experimental data, we also develop a biophysical neural mass model to describe the mesoscopic neural 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 prediction about the impact of the model parameters on the physiological or behavioral read-out-thereby yielding mechanistic insight into the uncertainty of expectation, placebo or nocebo effect, and chronic pain.

Nerve Injury Decreases Hyperacute Resting-State Connectivity Between the Anterior Cingulate and Primary Somatosensory Cortex in Anesthetized Rats

A better understanding of neural pain processing and of the development of pain over time, is critical to identify objective measures of pain and to evaluate the effect of pain alleviation therapies. One issue is, that the brain areas known to be related to pain processing are not exclusively responding to painful stimuli, and the neuronal activity is also influenced by other brain areas. Functional connectivity reflects synchrony or covariation of activation between groups of neurons. Previous studies found changes in connectivity days or weeks after pain induction. However, less in known on the temporal development of pain. Our objective was therefore to investigate the interaction between the anterior cingulate cortex (ACC) and primary somatosensory cortex (SI) in the hyperacute (minute) and sustained (hours) response in an animal model of neuropathic pain. Intra-cortical local field potentials (LFP) were recorded in 18 rats. In 10 rats the spared nerve injury model was used as an intervention. The intra-cortical activity was recorded before, immediately after, and three hours after the intervention. The interaction was quantified as the calculated correlation and coherence. The results from the intervention group showed a decrease in correlation between ACC and SI activity, which was most pronounced in the hyperacute phase but a longer time frame may be required for plastic changes to occur. This indicated that both SI and ACC are involved in hyperacute pain processing.

Granger causality analysis of rat cortical functional connectivity in pain

OBJECTIVE

The primary somatosensory cortex (S1) and the anterior cingulate cortex (ACC) are two of the most important cortical brain regions encoding the sensory-discriminative and affective-emotional aspects of pain, respectively. However, the functional connectivity of these two areas during pain processing remains unclear. Developing methods to dissect the functional connectivity and directed information flow between cortical pain circuits can reveal insight into neural mechanisms of pain perception.

APPROACH

We recorded multichannel local field potentials (LFPs) from the S1 and ACC in freely behaving rats under various conditions of pain stimulus (thermal versus mechanical) and pain state (naive versus chronic pain). We applied Granger causality (GC) analysis to the LFP recordings and inferred frequency-dependent GC statistics between the S1 and ACC.

MAIN RESULTS

We found an increased information flow during noxious pain stimulus presentation in both S1[Formula: see text]ACC and ACC[Formula: see text]S1 directions, especially at theta and gamma frequency bands. Similar results were found for thermal and mechanical pain stimuli. The chronic pain state shares common observations, except for further elevated GC measures especially in the gamma band. Furthermore, time-varying GC analysis revealed a negative correlation between the direction-specific and frequency-dependent GC and animal's paw withdrawal latency. In addition, we used computer simulations to investigate the impact of model mismatch, noise, missing variables, and common input on the conditional GC estimate. We also compared the GC results with the transfer entropy (TE) estimates.

SIGNIFICANCE

Our results reveal functional connectivity and directed information flow between the S1 and ACC during various pain conditions. The dynamic GC analysis support the hypothesis of cortico-cortical information loop in pain perception, consistent with the computational predictive coding paradigm.