A Reward-Based Neurosignature Predicts the Comorbidity between Chronic Pain and Affective Disorders.
Creators: name: Scarlatti, Francesco
name: Löffler, Martin
name: Emanuel, Schwarz
name: Flor, Herta
Publisher: name: Open Science Framework
Year: 2022
Subjects: subject: Mental Disorders
subject: Cognitive Neuroscience
subject: Psychiatry and Psychology
subject: Psychiatric and Mental Health
subject: Medicine and Health Sciences
subject: Life Sciences
subject: Neuroscience and Neurobiology
subject: Mental and Social Health
subject: Psychological Phenomena and Processes
subject: affective disorders
subject: anxiety
subject: biomarkers
subject: comorbidity
subject: depression
subject: fMRI
subject: MID task
subject: mood disorders
subject: multi-task learning
subject: neurosignature
subject: pain
subject: reward

description: Affective disorders are frequently observed in chronic pain, with major depressive disorder being the first most common comorbidity and generalized anxiety being the second (Radat et al., 2013). The relationship between chronic pain and affective disorders is bidirectional and patients suffering from either condition are more likely to develop the other one than the general population (Antioch et al., 2020; Bair et al., 2003; Bondesson et al., 2018; Viana et al., 2018). The comorbidity is associated with worse outcomes and poorer treatment response (Bair et al., 2003). Hence, there is a need to understand the mechanisms characterizing this comorbidity and to find biomarkers associated with it. The reward system is altered in chronic pain, depression, and anxiety, and it has been proposed that deficits in the different aspects of reward, such as reward learning, appetitive approach behaviour, and response to consummation, can underlie the development of both chronic pain and affective disorders (Rizvi et al., 2021). To test this hypothesis, we will analyse task-fMRI data as well as the behavioural reward-related data of 698 subjects from the IMAGEN dataset, who performed a Monetary Incentive Delay (MID) task. The fMRI data collected at the age of 19 will be used to predict the change in the level of pain, depression, and anxiety at the age of 22, assessed through self-reported questionnaires. Multi-Task Learning will be used to train a model that can contemporarily predict both pain and depression or anxiety. In fact, studies have shown that some alterations are specific to the comorbidities, and they are not present in the disorders that compose them when taken alone (Zhang et al., 2021; Zhou et al., 2019; Zhu et al., 2019). The aim is to identify specific neurosignatures of the comorbidity using different contrasts, and evaluate their predictive importance compared to behavioural variables. For this reason, the neurosignatures showing a significant predictive accuracy will be used to build a second-level model, together with reward-related behavioural variables. The neurosignatures will be examined in another dataset (CBP-PREDICT) containing patients with subacute back pain (SABP), chronic back pain (CBP), and healthy controls. We will focus on brain regions associated with reward, trying to understand if reward-associated variables can be used to characterize the comorbidity between chronic pain and affective disorders.