Computation and regulation of pain dynamics in the human central nervous system

Lead Research Organisation: University of Cambridge
Department Name: Engineering

Abstract

Chronic pain affects one in five people and is the leading cause of disability in the world; musculoskeletal pain alone costs the UK healthcare system £10.2 billion per year. There are unresolved questions that urgently require answers to treat pain effectively, a crucial one being how the feeling of pain arises from brain activity. The brain does not passively receive information from the nerves but rather interprets it based on what it already knows, anticipating and trying to adjust its responses to what will happen next. It remains to be discovered how the brain accomplishes these fundamental functions when we are in pain. This is a critical concern, as our interpretations and expectations of pain determine not only how much pain we feel but also how well we cope with it.

I hypothesise that the brain tries to learn how pain fluctuates over time; by doing so, it generates a 'mental' template of the temporal evolution of pain. Even if pain fluctuates randomly, the brain could learn that information. The brain then uses the template it has learned to predict how pain is likely to evolve in the future. These predictions may regulate the response to pain in the brain and spinal cord, modifying how much pain we feel; indeed, previous work has shown that expecting pain to worsen enhances the brain and spinal-cord response to pain, whereas expecting relief reduces the brain and spinal response to pain (as in the nocebo/placebo effects). I will test these hypotheses using a combination of mathematical models, behavioural and neuroimaging experiments in humans.

If the brain cannot accurately guess how pain evolves over time, it may fail to regulate its response to pain effectively, resulting in unnecessarily amplified pain (much like a false alarm). Why would the brain fail to learn the temporal evolution of pain? There may be many reasons, but anxiety is likely to be the most relevant to the risk of developing a great variety of chronic pain disorders. Thus, I will test whether the computations that the brain uses in attempting to learn and regulate the temporal evolution of pain are dysfunctional in persons who are anxious about pain. Importantly, I will develop a new, non-pharmacological intervention to improve how the brain computes and tries to control the temporal evolution of pain. In the future, this intervention could be used to ease the anxiety's grip on pain in vulnerable individuals, improving pain management and wellbeing.

My research will provide a set of quantitative measures that capture how the brain computes and tries to control the temporal evolution of pain. These measures could be used to examine whether they are dysfunctional in chronic pain disorders, such as back pain. This new research avenue could advance our understanding of chronic pain, help individualise treatments and monitor their outcomes. The research path opened by this project will ultimately contribute to reducing the societal and financial burden of pain.

Technical Summary

Persistent pain is a major clinical problem, but our understanding of the neural mechanisms underpinning pain is insufficient to treat persistent pain effectively. I focus on the human brain mechanisms that mediate pain perception and behaviour. My approach to the problem draws from recent evidence that the brain interprets (i.e. actively infers) incoming sensory signals based on internal representations (i.e. models) learned from past experience. These neural inferences shape perception and behaviour. It is not known how the brain learns to infer how pain fluctuates in intensity over time, but this question is central to understanding the brain's response to pain.

Taking this perspective, I hypothesise that, when receiving persistent nociceptive inputs, the brain computes an internal model of how pain changes over time by learning the temporal statistics of these signals. When new input is detected, this model is used to infer whether pain is likely to increase or decrease in the future. These neural inferences could regulate the transmission of nociceptive inputs from the spinal cord to the brain (as conscious expectations do), thereby controlling pain perception. I will test this new hypothesis using computational modelling, behavioural and neuroimaging experiments in humans.

If the brain cannot learn to accurately predict nociceptive inputs, it follows that it would fail to regulate its responses effectively, amplifying pain and pain-related anxiety. I will test this clinical prediction in anxious individuals with a tendency to catastrophize pain, a risk factor for developing chronic pain. Finally, I will develop and test a cognitive intervention to help people more accurately make inferences about pain states; this intervention might be used for the secondary/tertiary prevention and management of chronic pain in vulnerable individuals.

Planned Impact

This work will impact:

1) Persons at risk of developing chronic pain and persons suffering from chronic pain conditions - My research takes a hypothesis-driven, computational neuroscience approach to identify the quantifiable computational principles that mediate how the brain represents and regulates the temporal evolution of pain. This approach assumes that the brain processes information by taking inputs and forming outputs in order to adapt to the environment. In this framework, a dysfunction can be defined as a set of parameters in this input/output relationship that is related to an individual's characteristics and that leads to maladaptive pain processing in the brain and in the spinal cord. Dysfunctional brain computations of the temporal evolution of pain are likely to mediate the vicious loop that interlocks pain and anxiety; anxiety is a risk factor for many chronic pain disorders, and pain is a risk factor for anxiety and mood disorders. Understanding and correcting neural computations of pain dynamics could help break the maladaptive cycle that maintains pain and anxiety in vulnerable individuals, thereby improving the secondary/tertiary prevention and management of pain.
This research will: (a) provide a set of behavioural and brain measures that can be used to determine whether they predict the risk and severity of chronic pain and the exacerbation of pain-related anxiety (in 5-10 years), thereby helping individualise treatments and monitor their outcomes; (b) improve the prevention and management of chronic pain, thanks to a new cognitive intervention that aims to break the vicious cycle between pain and anxiety; (c) promote pain education, through public engagement and dissemination of informational materials summarising the results of this research.

2) Pain medicine clinicians and health psychologists - by: providing the quantitative measures that can be used to assess, and potentially treat, dysfunctions in brain evaluation and regulation of pain signals; developing a new cognitive intervention to diminish the mutually-worsening effects of pain and anxiety.

3) Staff on the project - by providing interdisciplinary quantitative training and developing skills transferrable to non-academic employment sectors (including computational modelling, problem-solving, public communication).

4) Health-care policymakers - by writing policy briefings and engaging policymakers in a roundtable discussion about possible strategies to break the vicious cycle between pain and anxiety and to reduce the number of people slipping into pain chronicity.

5) Society and economy - This research will contribute to reducing the societal and financial burden of pain, in the timeframe of 8-15 years, by improving the secondary/tertiary prevention, assessment and treatment of chronic pain.

Publications

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