FaPIA V.1

• Research type

  Research Study

• Full title

  Neural and behavioural biomarkers of fatigue and pain in patients with inflammatory arthritis. V.1

• IRAS ID

  216259

• Contact name

  Michael Lee

• Contact email

  ml404@cam.ac.uk

• Sponsor organisation

  Cambridge University Hospitals NHS Foundation Trust and the University of Cambridge

• Duration of Study in the UK

  3 years, 0 months, 1 days

• Research summary

  Research Summary
  This project aims to establish a behavioural and a brain-based indicator, or ‘biomarker’, for fatigue in inflammatory arthritis, and study its relationship with pain and inflammation. We will use new behavioural analysis and brain scanning methods in a study of rheumatoid and psoriatic arthritis patients and controls. Additionally, we will look at the relationship between joint inflammation that is only picked up on an ultrasound scan and ongoing pain. We will study the different characteristics that patients have, such as their mood and daily function.\n\nPatients and healthy controls will complete questionnaires assessing pain, fatigue, sleep and mood. They will undergo musculoskeletal ultrasound of the joints in order to objectively grade the level of peripheral joint inflammation. Blood tests will be taken and markers of inflammation measured. Volunteers will then undergo functional MRI brain scan at rest and during a simple cognitive task that involves decision making. We aim to compare the brain biomarker with brain scans in an animal inflammatory arthritis model in a partnered project in Japan. The results should provide new clues into the nature of fatigue, and provide the first objective and clinically feasible biomarker of fatigue in arthritis patients and how this impacts on daily activities.\n

  Summary of Results
  : In normal circumstances, learning processes play a fundamental role in how we respond to pain, by allowing us to avoid or minimise harm. For instance, after injury, adaptive learning processes occur at all levels of the nervous system and tune behaviour to optimise recovery. In the brain, changes in motivation (i.e. value-based learning and decision making) manifest as increased avoidance and a reduced tendency to seek rewards/relief, explore alternative solutions and energy conservation (Seymour, 2019). Value-based learning can be quantified and studied using computational models of learning in the brain (Seymour and Mancini, 2020), offering a mechanistic account of how specific neural circuits drive behaviour in response to pain and injury. A key insight from these models is an understanding of how this leads to direct modulation and amplification of pain through endogenous control circuits, illustrating how the brain acts as the core control center for pain and behavioural homeostasis (Heinricher et al, 2009).
  This theoretical framework predicts that misaligned or maladaptive learning might lead to dysfunctional endogenous pain regulation – either independently; by interacting with coincident ‘maladaptive’ processes at a peripheral or spinal level (Arendt-Nielsen, 2015; Woolf, 2011); or indirectly as in the fear-avoidance model (Vlaeyen et al., 2016). This idea is attractive because it offers consilience between peripheral and central ‘theories’ of chronic pain, whilst accommodating biopsychosocial determinants (Mansour et al, 2014). It would also explain comorbidity with anxiety and depression through shared susceptibility in value-based learning. And importantly, it would reveal precise targets for therapeutic intervention via behavioural and technology-based approaches that are specifically targeted to modulate learning.

  We have tested this hypothesis using an established value-based learning task, i.e. a 4 armed bandit task, in 30 patients with chronic inflammatory arthritis (rheumatoid and psoriatic arthritis) and 30 healthy, age-matched controls. Participants were asked to play this instrumental learning task whilst we imaged brain haemodynamic activity in the scanner. The task consisted in a 4-alternative choice probabilistic instrumental (operant) learning with mixed, non-stationary, independent rewards/losses (4-armed bandit task). On each trial, subjects chose to play a slot machine and observed an outcome (reward, loss), which changed over time. The task assessed reward and punishment sensitivity, exploration, learning/forgetting rates, and attention, and was modelled with reinforcement learning algorithms (Seymour et al., 2012). We found that patients with chronic inflammatory arthritis had higher sensitivity to punishments in this task and made more lapses. Neural activity associated with a punishment prediction error was increased in the insula, and this also also reflected in disrupted brain connectivity. This work shows that the neural processing of punishment signals is enhanced in a sample of patients with chronic arthritis.

• REC name

  South West - Central Bristol Research Ethics Committee

• REC reference

  17/SW/0113

• Date of REC Opinion

  18 May 2017

• REC opinion

  Favourable Opinion