scholarly journals Predictive Coding Models for Pain Perception

2019 ◽  
Author(s):  
Yuru Song ◽  
Mingchen Yao ◽  
Helen Kemprecos ◽  
Áine Byrne ◽  
Zhengdong Xiao ◽  
...  
Keyword(s):  
Experimental Data ◽  
Chronic Pain ◽  
Pain Perception ◽  
Predictive Coding ◽  
Oscillatory Activity ◽  
Top Down ◽  
Perceptual Inference ◽  
Bottom Up ◽  
Temporal Coordination ◽  
Mechanistic Insight

AbstractPain 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 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.

scholarly journals Signature patterns for top-down and bottom-up information processing via cross-frequency coupling in macaque auditory cortex

2018 ◽  
Author(s):  
Christian D. Márton ◽  
Makoto Fukushima ◽  
Corrie R. Camalier ◽  
Simon R. Schultz ◽  
Bruno B. Averbeck
Keyword(s):  
Information Processing ◽  
Auditory Cortex ◽  
Predictive Coding ◽  
Low Frequency ◽  
Information Need ◽  
Top Down ◽  
Bottom Up ◽  
Low Frequencies ◽  
Frequency Coupling ◽  
Cross Frequency Coupling

AbstractPredictive coding is a theoretical framework that provides a functional interpretation of top-down and bottom up interactions in sensory processing. The theory has suggested that specific frequency bands relay bottom-up and top-down information (e.g. “γ up, β down”). But it remains unclear whether this notion generalizes to cross-frequency interactions. Furthermore, most of the evidence so far comes from visual pathways. Here we examined cross-frequency coupling across four sectors of the auditory hierarchy in the macaque. We computed two measures of cross-frequency coupling, phase-amplitude coupling (PAC) and amplitude-amplitude coupling (AAC). Our findings revealed distinct patterns for bottom-up and top-down information processing among cross-frequency interactions. Both top-down and bottom-up made prominent use of low frequencies: low-to-low frequency (θ, α, β) and low frequency-to-high γ couplings were predominant top-down, while low frequency-to-low γ couplings were predominant bottom-up. These patterns were largely preserved across coupling types (PAC and AAC) and across stimulus types (natural and synthetic auditory stimuli), suggesting they are a general feature of information processing in auditory cortex. Moreover, our findings showed that low-frequency PAC alternated between predominantly top-down or bottom-up over time. Altogether, this suggests sensory information need not be propagated along separate frequencies upwards and downwards. Rather, information can be unmixed by having low frequencies couple to distinct frequency ranges in the target region, and by alternating top-down and bottom-up processing over time.1SignificanceThe brain consists of highly interconnected cortical areas, yet the patterns in directional cortical communication are not fully understood, in particular with regards to interactions between different signal components across frequencies. We employed a a unified, computationally advantageous Granger-causal framework to examine bi-directional cross-frequency interactions across four sectors of the auditory cortical hierarchy in macaques. Our findings extend the view of cross-frequency interactions in auditory cortex, suggesting they also play a prominent role in top-down processing. Our findings also suggest information need not be propagated along separate channels up and down the cortical hierarchy, with important implications for theories of information processing in the brain such as predictive coding.

scholarly journals The Landscape of Chronic Pain: Broader Perspectives

Medicina ◽  
2019 ◽  
Vol 55 (5) ◽  
pp. 182 ◽  
Author(s):  
Mark I. Johnson
Keyword(s):  
Chronic Pain ◽  
Health Care Policy ◽  
Pain Perception ◽  
Population Level ◽  
World Health ◽  
Health Concern ◽  
Perceptual Inference ◽  
International Classification Of Disease ◽  
And Shifts ◽  
Scholarly Discourse

Chronic pain is a global health concern. This special issue on matters related to chronic pain aims to draw on research and scholarly discourse from an eclectic mix of areas and perspectives. The purpose of this non-systematic topical review is to précis an assortment of contemporary topics related to chronic pain and its management to nurture debate about research, practice and health care policy. The review discusses the phenomenon of pain, the struggle that patients have trying to legitimize their pain to others, the utility of the acute–chronic dichotomy, and the burden of chronic pain on society. The review describes the introduction of chronic primary pain in the World Health Organization’s International Classification of Disease, 11th Revision and discusses the importance of biopsychosocial approaches to manage pain, the consequences of overprescribing and shifts in service delivery in primary care settings. The second half of the review explores pain perception as a multisensory perceptual inference discussing how contexts, predictions and expectations contribute to the malleability of somatosensations including pain, and how this knowledge can inform the development of therapies and strategies to alleviate pain. Finally, the review explores chronic pain through an evolutionary lens by comparing modern urban lifestyles with genetic heritage that encodes physiology adapted to live in the Paleolithic era. I speculate that modern urban lifestyles may be painogenic in nature, worsening chronic pain in individuals and burdening society at the population level.

scholarly journals The cutaneous borders of interoception: Active and social inference of pain and pleasure on the skin

2018 ◽  
Author(s):  
Mariana von Mohr ◽  
Aikaterini Fotopoulou
Keyword(s):  
Social Factors ◽  
Predictive Coding ◽  
Active Inference ◽  
Top Down ◽  
Bottom Up ◽  
Social Inference ◽  
Brain Levels ◽  
The Brain

Pain and pleasant touch have been recently classified as interoceptive modalities. This reclassification lies at the heart of long-standing debates questioning whether these modalities should be defined as sensations on their basis of neurophysiological specificity at the periphery or as homeostatic emotions on the basis of top-down convergence and modulation at the spinal and brain levels. Here, we outline the literature on the peripheral and central neurophysiology of pain and pleasant touch. We next recast this literature within a recent Bayesian predictive coding framework, namely active inference. This recasting puts forward a unifying model of bottom-up and top-down determinants of pain and pleasant touch and the role of social factors in modulating the salience of peripheral signals reaching the brain.

Racial prototypicality cues exacerbate racial bias in pain perception from the bottom up and the top down

2020 ◽  
Author(s):  
Alexis Drain ◽  
Azaadeh Goharzad ◽  
Jennie Qu-Lee ◽  
Jingrun Lin ◽  
Peter Mende-Siedlecki
Keyword(s):  
Pain Perception ◽  
Racial Bias ◽  
Meta Analysis ◽  
Structural Features ◽  
Facial Structure ◽  
Top Down ◽  
Bottom Up ◽  
Race And Gender ◽  
Pain Outcomes ◽  
And Gender

Racial disparities in pain care may stem, in part, from a perceptual source. While perceptual disruptions in recognizing painful expressions on Black faces have been demonstrated under tightly-controlled conditions (e.g., controlling for low-level stimulus differences in luminance and facial structure, using all male stimuli), these effects may be exacerbated by cues to racial prototypicality. Indeed, both bottom-up (e.g., skin tone, facial structure) and top-down (e.g., stereotype associations between race and gender) factors related to racial prototypicality moderate social perception, with some evidence pointing towards deleterious consequences in the domain of health. Here, we assessed whether these factors shape racial bias in pain perception: we examined the effect of racially prototypical features in Experiments 1 and 2 and target gender in a meta-analysis across five additional experiments. Overall, darker skin tones were associated with more stringent pain perception and more conservative treatment, while racially prototypic structural features exacerbated racial bias in pain outcomes. Moreover, target gender reliably moderated the effect of race on pain outcomes: racial biases in both pain perception and treatment were larger for male (versus female) targets. Taken together, these data demonstrate the overall robustness of racial bias in pain perception and its facilitation of gaps in treatment, but also the extent to which these biases are moderated by both bottom-up and top-down factors related to racial prototypicality.

scholarly journals Hierarchical Frequency Tagging reveals neural markers of predictive coding under varying uncertainty

2016 ◽  
Author(s):  
Noam Gordon ◽  
Roger Koenig-Robert ◽  
Naotsugu Tsuchiya ◽  
Jeroen van Boxtel ◽  
Jakob Hohwy
Keyword(s):  
Sensory Input ◽  
Predictive Coding ◽  
Prediction Errors ◽  
The Novel ◽  
Top Down ◽  
Bottom Up ◽  
Visual Hierarchy ◽  
Core Elements ◽  
Neural Markers ◽  
Eeg Recordings

AbstractUnderstanding the integration of top-down and bottom-up signals is essential for the study of perception. Current accounts of predictive coding describe this in terms of interactions between state units encoding expectations or predictions, and error units encoding prediction error. However, direct neural evidence for such interactions has not been well established. To achieve this, we combined EEG methods that preferentially tag different levels in the visual hierarchy: Steady State Visual Evoked Potential (SSVEP at 10Hz, tracking bottom-up signals) and Semantic Wavelet-Induced Frequency Tagging (SWIFT at 1.3Hz tracking top-down signals). Importantly, we examined intermodulation components (IM, e.g., 11.3Hz) as a measure of integration between these signals. To examine the influence of expectation and predictions on the nature of such integration, we constructed 50-second movie streams and modulated expectation levels for upcoming stimuli by varying the proportion of images presented across trials. We found SWIFT, SSVEP and IM signals to differ in important ways. SSVEP was strongest over occipital electrodes and was not modified by certainty. Conversely, SWIFT signals were evident over temporo- and parieto-occipital areas and decreased as a function of increasing certainty levels. Finally, IMs were evident over occipital electrodes and increased as a function of certainty. These results link SSVEP, SWIFT and IM signals to sensory evidence, predictions, prediction errors and hypothesis-testing - the core elements of predictive coding. These findings provide neural evidence for the integration of top-down and bottom-up information in perception, opening new avenues to studying such interactions in perception while constraining neuronal models of predictive coding.SIGNIFICANCE STATEMENTThere is a growing understanding that both top-down and bottom-up signals underlie perception. But how do these signals interact? And how does this process depend on the signals’ probabilistic properties? ‘Predictive coding’ theories of perception describe this in terms how well top-down predictions fit with bottom-up sensory input. Identifying neural markers for such signal integration is therefore essential for the study of perception and predictive coding theories in particular. The novel Hierarchical Frequency Tagging method simultaneously tags top-down and bottom-up signals in EEG recordings, while obtaining a measure for the level of integration between these signals. Our results suggest that top-down predictions indeed integrate with bottom-up signals in a manner that is modulated by the predictability of the sensory input.

scholarly journals Bottom‐up or top‐down?: The role of child and parent chronic pain and anxiety in the context of parental catastrophizing and solicitousness

2019 ◽  
Vol 24 (3) ◽  
pp. 604-616 ◽  
Author(s):  
Maren K. Wallrath ◽  
Julian Rubel ◽  
Isgard Ohls ◽  
Cüneyt Demiralay ◽  
Tanja Hechler
Keyword(s):  
Chronic Pain ◽  
Top Down ◽  
Bottom Up

scholarly journals Circular Causality of Emotions in Moving Pictures

2021 ◽  
Vol 20 (1) ◽  
pp. 86-110
Author(s):  
Mircea Valeriu Deaca
Keyword(s):  
Visual Information ◽  
Internal State ◽  
Predictive Coding ◽  
The Body ◽  
Subjective Feeling ◽  
Top Down ◽  
Perceptual Inference ◽  
Feeling States ◽  
Circular Causality ◽  
Cognitive Inference

Abstract In the framework of predictive coding, as explained by Giovanni Pezzulo in his article Why do you fear the bogeyman? An embodied predictive coding model of perceptual inference (2014), humans construct instances of emotions by a double arrow of explanation of stimuli. Top-down cognitive models explain in a predictive fashion the emotional value of stimuli. At the same time, feelings and emotions depend on the perception of internal changes in the body. When confronted with uncertain auditory and visual information, a multimodal internal state assigns more weight to interoceptive information (rather than auditory and visual information) like visceral and autonomic states as hunger or thirst (motivational conditions). In short, an emotional mood can constrain the construction of a particular instance of emotion. This observation suggests that the dynamics of generative processes of Bayesian inference contain a mechanism of bidirectional link between perceptual and cognitive inference and feelings and emotions. In other words, “subjective feeling states and emotions influence perceptual and cognitive inference, which in turn produce new subjective feeling states and emotions” as a self-fulfilling prophecy (Pezzulo 2014, 908). This article focuses on the short introductory scene from Steven Spielberg’s Jaws (1975), claiming that the construction / emergence of the fear and sadness emotions are created out of the circular causal coupling instantiated between cinematic bottom-up mood cues and top-down cognitive explanations.

scholarly journals Expectation and attention increase the integration of top-down and bottom-up signals in perception through different pathways

2018 ◽  
Author(s):  
Noam Gordon ◽  
Naotsugu Tsuchiya ◽  
Roger Koenig-Robert ◽  
Jakob Hohwy
Keyword(s):  
Predictive Coding ◽  
Sensory Information ◽  
Phase Coherence ◽  
Signal Integration ◽  
Physiological Measures ◽  
Top Down ◽  
Bottom Up ◽  
Visual Hierarchy ◽  
Linear Signal ◽  
The Brain

AbstractPerception results from the integration of incoming sensory information with pre-existing information available in the brain. In this EEG (electroencephalography) study we utilised the Hierarchical Frequency Tagging method to examine how such integration is modulated by expectation and attention. Using intermodulation (IM) components as a measure of non-linear signal integration, we show in three different experiments that both expectation and attention enhance integration between top-down and bottom-up signals. Based on multispectral phase coherence, we present two direct physiological measures to demonstrate the distinct yet related mechanisms of expectation and attention. Specifically, our results link expectation to the modulation of prediction signals and the integration of top-down and bottom-up information at lower levels of the visual hierarchy. Meanwhile, they link attention to the propagation of ascending signals and the integration of information at higher levels of the visual hierarchy. These results are consistent with the predictive coding account of perception.

scholarly journals Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Stephan Thaler ◽  
Julija Zavadlav
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Coarse Graining ◽  
Md Simulations ◽  
Coarse Grained ◽  
Thermodynamic Perturbation Theory ◽  
Top Down ◽  
Bottom Up ◽  
Coarse Grained Model ◽  
Gradient Computation

AbstractIn molecular dynamics (MD), neural network (NN) potentials trained bottom-up on quantum mechanical data have seen tremendous success recently. Top-down approaches that learn NN potentials directly from experimental data have received less attention, typically facing numerical and computational challenges when backpropagating through MD simulations. We present the Differentiable Trajectory Reweighting (DiffTRe) method, which bypasses differentiation through the MD simulation for time-independent observables. Leveraging thermodynamic perturbation theory, we avoid exploding gradients and achieve around 2 orders of magnitude speed-up in gradient computation for top-down learning. We show effectiveness of DiffTRe in learning NN potentials for an atomistic model of diamond and a coarse-grained model of water based on diverse experimental observables including thermodynamic, structural and mechanical properties. Importantly, DiffTRe also generalizes bottom-up structural coarse-graining methods such as iterative Boltzmann inversion to arbitrary potentials. The presented method constitutes an important milestone towards enriching NN potentials with experimental data, particularly when accurate bottom-up data is unavailable.

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