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Post-injury pain and behaviour: a control theory perspective - PubMed

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Post-injury pain and behaviour: a control theory perspective

Ben Seymour et al. Nat Rev Neurosci. 2023 Jun.

Abstract

Injuries of various types occur commonly in the lives of humans and other animals and lead to a pattern of persistent pain and recuperative behaviour that allows safe and effective recovery. In this Perspective, we propose a control-theoretic framework to explain the adaptive processes in the brain that drive physiological post-injury behaviour. We set out an evolutionary and ethological view on how animals respond to injury, illustrating how the behavioural state associated with persistent pain and recuperation may be just as important as phasic pain in ensuring survival. Adopting a normative approach, we suggest that the brain implements a continuous optimal inference of the current state of injury from diverse sensory and physiological signals. This drives the various effector control mechanisms of behavioural homeostasis, which span the modulation of ongoing motivation and perception to drive rest and hyper-protective behaviours. However, an inherent problem with this is that these protective behaviours may partially obscure information about whether injury has resolved. Such information restriction may seed a tendency to aberrantly or persistently infer injury, and may thus promote the transition to pathological chronic pain states.

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Figures

FIGURE 1.
FIGURE 1.. A control perspective for injury and pain.

a) Schematic diagram of hierarchical control loop for nociception, action selection and action execution, and metacontrol. At the heart of this are two control loops: one for action selection, and one for action control. Sitting above this is a meta-control loop which monitors and modulates the ‘lower’ control loops. This illustrates the computational difference between pain and nociception: with nociception reflecting the sensory signal communicating afferent information, and pain representing the control signal that governs learning and response execution. b) A general flowchart of closed-loop control for inferring the latent injured or pain state x(t). The system receives bottom-up input s(t), subject to an endogenous control input u(t), and produces a behavioral output. y(t). The behavioral output also produces feedback (such as an efference copy of action) to influence the bottom-up or top-down processes. The controllability of the plant (dashed box) induces two different outcomes: high level of controllability leads to adaptive emotional and behavioral responses, followed by pain recovery, whereas low perceived controllability leads to maladaptive emotional and behavioral responses which causes chronic pain. c) An implementation view of Bayesian inference through a feedforward architecture, where neural firing rates representing belief distributions are encoded independently and summed toward an output. The generative model that produces a Bayesian prediction estimate from multisensory inputs through a nonlinear mapping F(⋅) can be implemented by a recurrent neural network of excitatory and inhibitory neurons-.

FIGURE 2.
FIGURE 2.. Neural implementation and representations of injury and persistent pain.

a) Information flows and afferent (left) and efferent (right) pathways for insula-centered injury-state inference and effector control. At the heart of this is an insula-centered hierarchy with successively higher latent abstractions of the injury state. Afferent pathways feed various inputs to the hub, from subcortical and cortico-cortical projections; and efferent routes can implement different types of responses. This includes the multiple afferent pathways that ascend the spinal cord to various brainstem nuclei, such as the parabrachial, periaqueductal gray (PAG), dorsal respiratory group (DRG), locus coeruleus (LC) and others, forming the bidirectional brainstem-subcortical network,. b) Schematic illustration of an insula-hub perspective for injury state representations in more details, including the different types of sensory information important for inference. The anterior, mid, and posterior segments of the insula have distinct and complementary functional roles. Note that the injury state inference may be shared with broader cortical areas, including the ACC and VMPFC, which are omitted here.

FIGURE 3.
FIGURE 3.. Brain oscillations that encode post-injury or pain state.

a) Neuropathic patients showed augmented MEG theta/alpha power (8-10 Hz) in multiple brain regions (thalamus, posterior insula, and primary somatosensory cortex or S1) of the ascending nociceptive pathway compared to age-matched healthy control (adapted from REF, Wolter Kuwer Health, Inc). b) Noxious stimuli generated a reversible shift in the S1 LFP theta-peak frequency from a formalin-induced mouse model of pain. The scatter plots illustrate the association between theta-peak frequency and theta-peak height, where each dot represents a sample computed from a 2-min moving temporal window. This shift in theta-peak frequency was observed during nociceptive phases but not during the baseline or recovery period and was inversely correlated with instantaneous pain intensity. This result suggest that dynamics of theta oscillations may represent the ongoing status of injury or pain state (adapted from REF, CC-BY-4.0). c) Changes in pre-stimulus theta activity in the insula modulate human pain perception. Left: From EEG recordings of healthy subjects, time-frequency representations showed the difference between pain and non-pain trials averaged across two temporal electrodes (T7/TF7 vs T8/TF8). Solid outline indicates the significant pre-stimulus effect identified by permutation testing. EEG source localization revealed two generators at the bilateral insula cortex for the scalp theta power differences, especially at the contralateral insula site. Right: Comparison of brain topographies in relative power differences (dB) between pain and non-pain trials during the pre- and post-stimulus periods (adapted from REF, Society for Neuroscience). d) LFP gamma oscillations (40-90 Hz) are enhanced in response to phasic pain. Left: Magnitude of gamma-band oscillations elicited by nociceptive stimuli in the contralateral left or right insula from insular LFP recordings of epileptic patients. The size of circles represents the magnitude of post-stimulus change in the gamma band magnitude (150-300 ms). Right: Comparison of time-frequency representation of the changes in oscillatory power (40-90 Hz) elicited by nociceptive, vibrotactile, auditory and visual stimuli at the insula (adapted from REF, Oxford University Press).

FIGURE 4.
FIGURE 4.. Information restriction model of chronic pain.

a) Illustration of pain through a timeline from injury through to recovery. The solid line reflects the normal, adaptive profile of pain; whereas the dashed line reflects the transition to chronic pain, mediated (at least partially, since there are other factors involved in chronic pain) by various mechanisms of information restriction. b) Illustration of the four key mechanisms that seed information restriction, which effectively reduces the ability of the brain to recognize that an injury has resolved. This results in an internal model of a peripheral injury that is persistent, and which also continues to drive the physiological and behavioural responses appropriate to a state of true injury. We frame these mechanisms as ‘maladaptive’ to emphasize that the nature of the mechanism, when ‘ill-tuned’, leads to a sub-optimal outcome.

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