Scientific Understanding of Consciousness
Cognitive Processes Influence on Pain Perception
Science 04 Nov 2016: Vol. 354, Issue 6312, pp. 584-587
Deconstructing the sensation of pain: The influence of cognitive processes on pain perception
Katja Wiech, et.al.
Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK.
Nuffield Department of Clinical Neurosciences, Nuffield Division Anaesthetics, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK.
Phenomena such as placebo analgesia or pain relief through distraction highlight the powerful influence cognitive processes and learning mechanisms have on the way we perceive pain. Although contemporary models of pain acknowledge that pain is not a direct readout of nociceptive input, the neuronal processes underlying cognitive modulation are not yet fully understood. Modern concepts of perception—which include computational modeling to quantify the influence of cognitive processes—suggest that perception is critically determined by expectations and their modification through learning. Research on pain has just begun to embrace this view. Insights into these processes promise to open up new avenues to pain prevention and treatment by harnessing the power of the mind.
The search for a “signature of pain in the brain”
A modulatory influence of cognitive factors on the perception of pain has been documented for a number of processes including attention, anticipation, catastrophizing, (re-)appraisal, and perceived control over pain. Undoubtedly, the most impressive and most extensively studied example is a placebo analgesic response. Patients with agonizing levels of pain can report complete pain relief after administration of a sugar pill they think is a powerful painkiller.
The network of brain regions involved in pain processing (“pain matrix”) has been divided into sensory-discriminative and cognitive-affective systems. The sensory-discriminative system, which includes the lateral thalamus and primary and secondary somatosensory cortex (SI and SII, respectively), was thought to process nociceptive input, including its intensity, localization, and quality. In contrast, the cognitive-affective system, comprising regions such as the anterior insula and anterior cingulate cortex, was implicated in psychological aspects of pain.
Studies using a decoding approach (i.e., the prediction of pain perception based on activation patterns in the brain) demonstrated that the prediction is significantly improved when the different brain regions are considered together. In the so-far most rigorous attempt to characterize the “neurological pain signature” (NPS), Researchers have used a machine-learning algorithm to predict the perceived intensity of experimentally induced heat pain in healthy volunteers. The identified network—comprising brain regions such as thalamus, SI, SII, anterior insula, and anterior cingulate cortex—afforded a specificity of about 90% in discriminating physical pain from related phenomena.
Descending pain control system: Top-down modulation of pain
Descending pain control network comprises regions such as the dorsolateral prefrontal cortex (DLPFC), rostral anterior cingulate cortex, and periaqueductal gray (PAG). Activation and functional connectivity between these regions are positively correlated with the level of pain relief reported. The engagement of this modulatory control network has been linked to reduced activation in other pain-related brain regions. Furthermore, the top-down influence has been shown to alter responses in the spinal dorsal horn, which suggests that it can modulate nociceptive processing at an early stage
Descending pain inhibition is largely mediated through endogenous opioids. There is evidence for the contribution of other neurotransmitters, including cannabinoids and dopamine. Taken together, research on the descending pain control system has described a network that is sensitive to cognitive manipulations and can interact with other brain regions involved in pain processing.
Frontostriatal system: Valuation of nociceptive input and higher-order integration of different aspects of pain
Directly compared the modulation of pain through different intensity levels of heat and through cognitive self-regulation of pain in the same individuals. Self-regulation had no effect on the NPS but was associated with changes in functional connectivity (i.e., the crosstalk between brain regions) of mesolimbic brain structures, including the ventromedial prefrontal cortex (vmPFC) and nucleus accumbens (NAc). This finding is remarkable for two reasons. First, it challenges the concept of the NPS as a universal signature of pain in the brain. If the NPS is to be established as an objective readout of pain, it is expected to reflect changes in pain irrespective of the type of modulation that led to the change in perception. Second, it highlights the contribution of the mesolimbic network that has been implicated in learning and valuation rather than in pain processing as such. It could therefore be speculated that this network translates sensory, cognitive, and affective aspects of pain into a “common currency” to integrate them and give rise to one unified pain experience. In a longitudinal study involving brain imaging, functional and structural characteristics of vmPFC and NAc have been shown to predict the development of chronic pain. On the basis of these findings, it has been postulated that the frontostriatal system is key not only for the conversion of nociception into the perception of pain but also for the transition from acute to chronic pain
Attention and the influence of spontaneous brain activity on pain perception
Changes in pain perception have been linked to spontaneous fluctuations in attention to pain that depend on dynamic changes in resting state activity in three distinct brain circuitries: (i) the “salience network,” which is involved in the detection of biologically relevant stimuli and events and comprises brain regions such as mid-cingulate cortex, anterior insula, temporoparietal junction, and DLPFC; (ii) the default mode network (DMN), which shows a reduced signal level during activity compared with a relaxed nontask state and includes regions such as the medial prefrontal cortex, the posterior cingulate cortex and precuneus, the lateral posterior lobe, and the medial temporal lobe; and (iii) the descending pain control system
Construction of a pain experience: “Perception as inference”
Perception is conceptualized as an inferential process in which prior information is used to generate expectations about future perception and to interpret sensory input. During the perceptual process, incoming sensory information is compared against a “template” or expectation that reflects prior information. The concept of predictive coding acknowledges that we are more likely to perceive sensory information in accordance with our template than with competing interpretations. Perception is thereby understood as a process that favors expected outcomes and weighs down information that is incongruent with the expectation. Evidence for such biased perception in the context of pain comes from studies using explicit cues to signal the intensity or onset of an upcoming noxious stimulus or, in more complex paradigms, the predictability or controllability of the stimulation. Moreover, the generation of expectations is a shared feature of most cognitive processes that have been related to pain modulation. Biased perceptual inference has recently been postulated to contribute to various diseases, including functional motor and sensory syndromes and psychiatric disorders
Learning and updating internal models about pain
Our representations of reality should first and foremost enable us to successfully navigate the environment with minimal costs, which renders delusional ideations impractical. A substantial deviation of our expectations from reality should therefore lead to course correction—an updating or revision of our expectations. Predictive coding and learning models rooted in this approach assume that when expected and observed sensory information diverge, a “prediction error message” is generated in the brain that serves as a teaching signal for model updating
Conclusions and outlook
Although the descending pain-control system is commonly interpreted as a top-down influence, its constituting brain regions have reciprocal connections, which allow for up- and downward projections of information. They therefore suggest that—in line with the concept of predictive coding—the descending system could be part of a larger recurrent network exchanging PE signals at all levels of the neuraxis. Several findings point at a critical role of the DLPFC in orchestrating this network. As described above, the DLPFC is part of various networks that are involved in cognitive pain modulation. It plays a key role in evidence accumulation during perceptual decision-making, as described in the context of perception as inference and learning. The integration of these different strands of research with respect to DLPFC functioning and its governance of learning networks can be expected to provide the much-needed unifying model of neural mechanisms underlying cognitive pain modulation.
The concept of pain as an actively constructed experience that is determined by expectations and their modification through learning has far-reaching implications for pain treatment and prevention. Treatment success is known to be critically depending on patients’ expectations, not only in the context of placebos but also with active interventions such as analgesic drugs. Expectations—in turn—are shaped by the information that is provided by health care practitioners. How could information be designed to optimally guide expectations for maximum treatment outcome? How could aberrant information processing be addressed using the framework of predictive coding? Future research should explore the translation of research on the inferential process underlying the perception of pain into clinical practice to optimally inform pain prevention and treatment strategies.
Patients’ complaints about pain that persists despite numerous treatment attempts are often dismissed as being “all in their head.” Modern pain research has shown that this notion is in fact true for any kind of pain, acute or chronic, easy to treat or resistant to all treatments currently available. We are only beginning to understand that the head (or the brain, for that matter) also holds the key to new ways to help patients conquer their pain.
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