Nociception, Pain, Negative Moods, and Behavior Selection

Abstract

Recent neuroimaging studies suggest that the brain adapts with pain, as well as imparts risk for developing chronic pain. Within this context, we revisit the concepts for nociception, acute and chronic pain, and negative moods relative to behavior selection. We redefine nociception as the mechanism protecting the organism from injury, while acute pain as failure of avoidant behavior, and a mesolimbic threshold process that gates the transformation of nociceptive activity to conscious pain. Adaptations in this threshold process are envisioned to be critical for development of chronic pain. We deconstruct chronic pain into four distinct phases, each with specific mechanisms, and outline current state of knowledge regarding these mechanisms: the limbic brain imparting risk, and the mesolimbic learning processes reorganizing the neocortex into a chronic pain state. Moreover, pain and negative moods are envisioned as a continuum of aversive behavioral learning, which enhance survival by protecting against threats.

Copyright © 2015 Elsevier Inc. All rights reserved.

Figures

Figure 1

Descartes’s concept of sensation illustrates the pain system. In addition reorganization of its components are superimposed based on modern rodent model physiology and human brain imaging studies. The Cartesian illustration is explicit regarding an impinging stimulus being transduced and transmitted to a specific brain region where perception takes place. The additional panels emphasize the modern evidence that all components of this system undergo reorganization following an injury that gives rise to a persistent or chronic pain state. End organ injury gives rise to changes in the local milieu, inflammatory soup, and in afferent response properties; collectively described as peripheral sensitization (adapted from (Julius and Basbaum, 2001)). Additionally, spinal cord circuitry undergoes a large number of changes resulting in central sensitization (adapted from (Scholz and Woolf, 2002)), which includes enhanced glutamatergic signaling, changes in second order messenger processes and activation of microglia. At the level of the brain, human neuroimaging studies indicate anatomical and functional reorganization.

Figure 2

Brain circuitry and temporal dynamics for the threshold phenomenon, θ, which determines conversion of nociception to conscious pain perception. a. The block diagram indicates θ is the output of the limbic brain. Internal states of the limbic brain, relative to neocortical memories determining current state of the organism (value, expectation, and salience), as well as the afferent nociceptive drive control θ. Other similar threshold processes in turn modulate the state of the organism through learning mechanisms, thus modifying values, expectations, and salience. b. More detailed circuit diagram emphasizing the interaction between limbic circuits, θ, and behavior selection. Diagram is adapted from studies of the reward/aversion circuitry, regarding striatal-cortical control loops (based on illustrations in (Luscher and Malenka, 2011; Nakanishi et al., 2014; Russo and Nestler, 2013)). Dense glutamatergic inputs from amygdala, hippocampus, and prefrontal cortex (mPFC) control the affective and motivational properties of accumbens (NAc) that responds to novel reward/aversion-related stimuli. Dopaminergic-GABAergic loops between accumbens and ventral tegmental area (VTA) provide the resultant value for θ, which through GP/SNr and thalamocortical circuits modulates behavior. Dopaminergic projections control synaptic properties and thus the affective state of the organism. c. Corticobasal ganglia-cortical loops conveying limbic, associative, and sensorimotor information. These loops are generally envisioned as a series of parallel projections. However the relay points, especially in the basal ganglia, provide opportunities for interactions between the loops. This organization enables the functional propagation of the limbic threshold phenomenon to influence goal-directed and habitual behaviors. (panle adapted from (Redgrave et al., 2010)). d. Conscious experience of acute painful events (P) depends on nociception (N) and the corticolimbic threshold, θ. e. Transitioning from subacute to chronic pain also depends on the individuals’ θ. Left panel depicts the classic viewpoint where nociceptive signal amplitude controls transition to chronic pain. Right panel is the view advanced here: For a similar injury, with equivalent nociception relayed to the brain, individuals with corticolimbic risk factors will persist to chronic pain while resilient ones will recover.

Figure 3

Constructing the brain acute pain representation map from resting state brain activity. a. Brain regions identified for the reverse inference for the term “pain”, which identifies 311 PubMed studies in the Neurosynth meta-analysis tool (Yarkoni et al., 2011). The map is thresholded for z-values larger than 3.0. Highest confidence activations (z-values > 8.0) are localized to six brain regions: bilateral secondary somatosensory cortex (S2), anterior cingulate (ACC), bilateral anterior and posterior insula (aINS, pINS), thalamus (TH), and periaqueductal grey (PAG). b. Resting state functional connectivity networks for the six main nodes most robustly associated with the term “pain”. Functional connectivity is derived from resting state activity from 1000 subjects (Biswal et al., 2010), generated in Neurosynth (thresholded at correlation values > 0.3, approximately corresponding to > 3 standard deviations from baseline). Essentially the same network is identified when ACC, aINS, or S2 are used as seeds. The pINS seed identifies bilateral pINS as well as posterior cingulate/supplementary motor area. The TH network is limited to bilateral thalamus, and PAG seed only shows connectivity limited to itself. c. Overlap between the map for the term “pain” and sum of six resting state networks. Blue is the same map shown in panel a. Red is the sum of all functional connections identified in panel b. The overlap between red and blue maps is 72% of the blue map.

Figure 4

Transition to chronic pain may be deconstructed to four component phases: Predisposition, injury or inciting event, a transition period, and a maintenance phase. Brain circuitry and their interactions across the phases are illustrated in human brain imaging studies, a–h. a. Specific brain white matter regional properties (red) impart risk for developing chronic pain following an acute episode of back pain (Mansour et al., 2013). b. Limbic brain structural properties may also impart risk for pain chronification (e.g. shape and/or size of the hippocampus) (Mutso et al., 2012). c. In the transition phase, strength of information exchange between the prefrontal cortex and accumbens, after an end organ injury, determines long-term pain chronification (Baliki et al., 2012). d. The transition process is the influence of predisposing brain factors in combination with the injury-induced nociceptive signals that control mesolimbic learning mechanisms, altogether determining extent of prefrontal-accumbens information exchange (modulating θ in figure 2). Chronification of pain gives rise to: e. condition specific subjective pain-related brain activity patterns (Baliki et al., 2006; Hashmi et al., 2013; Parks et al., 2011), f. increased information exchange within the hippocampus and between the hippocampus and the cortex (Mutso et al., 2013), g. reorganization of brain grey matter regional similarity (Baliki et al., 2011), and h. distortions in information sharing in resting state brain activity, specifically brain activity phase relationship between the default mode network and the rest of the brain shows chronic pain type-specific patterns (Baliki et al., 2014b). In rodent models for persistent pain, the four phases are better conceptualized as pre-injury manipulations that influence post-injury pain-like behavior, and early and late post-injury consequences. Supraspinal circuits implicated in the rodent four phases of pain persistence are highlighted in 1–9: 1. Bilateral lesion of the rat basolateral amygdala (BLA) diminishes post-injury tactile allodynia for 28 days after neuropathy (Li et al., 2013). 2. Lidocaine infusion within accumbens in the rat diminishes post-injury tactile allodynia for the duration of infusion (14 days), after a neuropathic injury (spared nerve injury, SNI) (Chang et al., 2014). 3. Hours following induction of an arthritis model in the rat, amygdala neurons become hyperexcitable (Neugebauer et al., 2003). 4. Five days after SNI neuropathy in the rat, accumbens covariance of receptor gene expression is upregulated (Chang et al., 2014), and 5. dendritic size and branchings of prefrontal pyramidal neurons are expanded (Metz et al., 2009). 6. Fifteen days after SNI neuropathy adult hippocampal neurogenesis is downregulated (Mutso et al., 2012). 7. Accumbens medium spiny neurons with dopamine D2 receptors show decreased AMPA/NMDA ratio in neuropathic injured rodents (Schwartz et al., 2014). 8. Resting state whole-brain functional network in the anesthetized rat shows increased (red) and decreased (blue) functional connections 28 days after SNI neuropathy relative to sham injury (Baliki et al., 2014a). 9. Six months following neuropathic injury prefrontal (PFC) cortical grey matter volume is decreased in the rat (Seminowicz et al., 2009). Overall, the human data illustrates brain risk factors for, and brain reorganization with, chronification of pain. The rodent results show persistent painlike behavior is dependent on, and in turn reorganizes, limbic brain circuitry.

Figure 5

Nociception, pain, and negative moods constitute a continuum imparting inhibition of behavior through negative affect, based on expected or apparent inputs across varying spatial and temporal dimensions. The four landscapes (a–d) illustrate negative emotional value assignment relative to the individual (the contemplative Cartesian self). Zero on this space-time plane represents either the body in relation to sensory inputs, or equivalently the self within the arena or the global neural workspace of consciousness, where accumulated or experienced aversiveness is assigned for varying space-time relationships that dictate behavioral selection. Hot colored valleys represent negative affective states or valuations, blue-white undulations signify emotionally more neutral states. a. In the absence of an experienced or expected threat (e.g. while kneeling to smell the roses) nociception in the absence of negative affect subconsciously protects the organism from injury by constraining behavioral repertoires (delimiting bodily positions or postures). b. Failure of nociception results in conscious pain (burning the skin of the Cartesian self by the fire), associated with a rapid withdrawal from the environment. Thus, pain evokes conscious negative affect and behavioral modification at the scale of the immediate body vicinity (aversion at zero space-time). c. When the threat is a learnt association, and is expected to be encountered at a distance or time removed from the body, then the subject experiences anxiety or stress. d. If instead the threat is experienced as, or expected to be, pervasive the associated negative mood is more abstract, described as depression, and the behavioral inhibition is generalized across scales of time and space. As pain is a primary reinforcer, its presence or persistence can rapidly become associated with expanded aversive landscapes, incorporating various combinations of the landscapes b–d, which is complimentary to the imprecision model recently proposed for chronic pain (Moseley and Vlaeyen, 2015). In this framework, we posit that the four phases of transition to chronic pain (illustrated in figure 3) also apply to chronification of negative moods. Both specific chronic pain conditions and the variety of types of chronic negative moods are expected to have unique limbic predisposition signatures and long-term brain adaptations. Computations needed for constructing these cognitive aversiveness maps are variants of Sutton and Barto’s (Sutton and Barto, 1981) temporal difference algorithm, applied to dopaminergic activity for assimilating reward prediction error to induce approach behavior (Schultz et al., 1997), which can also be recast as a Bayesian inference that optimizes energy based on model evidence (Friston et al., 2014).