Predictive coding models for pain perception

Abstract

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...

The temporal and spectral characteristics of expectations and prediction errors in pain and thermoception

“…A recent study in rodents has suggested an information flow between S1 gamma and ACC (Anterior Cingulate Cortex) beta activity during spontaneous pain ( Xiao et al, 2019 ). Based on these data, the authors have proposed a predictive coding model including a bottom-up (gamma) and top-down (beta) component ( Song et al, 2019 ). Finally, in humans, a recent EEG study showed that the sensorimotor cortex is more strongly connected to the medial prefrontal cortex at alpha frequencies during tonic pain, suggesting alpha band activity in tonic pain to be associated with bottom-up instead of top-down signaling ( Nickel et al, 2020 ).…”

Hierarchical predictive coding in distributed pain circuits

“…Like any other research field, a theory is useful since it helps clarify and motivate thinking associated with observational studies (Levenstein et al, 2023). Similarly, algorithmic inference and high-level computational modeling may reveal insight into computational mechanisms of hierarchical predictive coding in pain studies (Alexander and Brown, 2018;Seymour and Mancini, 2020;Song et al, 2021). However, validation or refinement of this theory still requires further systematic investigations.…”

“…A recent report showed that elevated 4 Hz ACC→VTA signaling is associated with anticipatory decision making ("prediction"), whereas error-related feedback integration is associated with increased VTA→ACC signaling ("PE"), which is also predictive of subsequent choice adaptation (Elston et al, 2019). Recently, it has been suggested that the ACC may engage in a top-down prediction in pain perception through alpha/beta oscillations (Song et al, 2021).…”

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

“…Previous research has found increased, mostly at high frequencies, oscillatory activity in either SI, ACC, or both [10,12,13,18,28,38] shortly after intervention with an animal model of pain. Increases in oscillatory activity in one or more cortical areas do not equal changes in connectivity which is why restingstate connectivity is decreased in the minutes or hours following and capsaicin [11] and days or weeks after peripheral nerve injury [11,15].…”