Enhanced motor network engagement during reward gain anticipation in fibromyalgia.

Reward motivation is essential in shaping human behavior and cognition. Both reward motivation and reward brain circuits are altered in chronic pain conditions, including fibromyalgia. In this study of fibromyalgia patients, we used a data-driven independent component analysis (ICA) approach to investigate how brain networks contribute to altered reward processing. From females with fibromyalgia (N = 24) and female healthy controls (N = 24), we acquired fMRI data while participants performed a monetary incentive delay (MID) reward task. After analyzing the task-based fMRI data using ICA to identify networks, we analyzed 3 networks of interest: motor network (left), value-driven attention network, and basal ganglia network. Then, we evaluated correlation coefficients between each network timecourse versus a task-based timecourse which modeled gain anticipation. Compared to controls, the fibromyalgia cohort demonstrated significantly stronger correlation between the left motor network timecourse and the gain anticipation timecourse, indicating the left motor network was more engaged with gain anticipation in fibromyalgia. In an exploratory analysis, we compared motor network engagement during early versus late phases of gain anticipation. Across cohorts, greater motor network engagement (i.e., stronger correlation between network and gain anticipation) occurred during the late timepoint, which reflected enhanced motor preparation immediately prior to response. Consistent with the main results, patients exhibited greater engagement of the motor network during both early and late phases compared with healthy controls. Visual-attention and basal ganglia networks revealed similar engagement in the task across groups. As indicated by post-hoc analyses, motor network engagement was positively related to anxiety and negatively related to reward responsiveness. In summary, we identified enhanced reward-task related engagement of the motor network in fibromyalgia using a novel data-driven ICA approach. Enhanced motor network engagement in fibromyalgia may relate to impaired reward motivation, heightened anxiety, and possibly to altered motor processing, such as restricted movement or dysregulated motor planning.

Altered Functional Networks during Gain Anticipation in Fibromyalgia.

Reward motivation is essential in shaping human behavior and cognition. Previous studies have shown altered reward motivation and reward brain circuitry in chronic pain conditions, including fibromyalgia. Fibromyalgia is a chronic disorder characterized by widespread musculoskeletal pain, fatigue, cognitive problems, and mood-related symptoms. In this study, we analyzed brain reward networks in patients with fibromyalgia by using a data-driven approach with task-based fMRI data. fMRI data from 24 patients with fibromyalgia and 24 healthy controls were acquired while subjects performed a monetary incentive delay (MID) reward task. Functional networks were derived using independent component analysis (ICA) focused on the gain anticipation phase of the reward task. Functional activity in the motor, value-driven attention, and basal ganglia networks was evaluated during gain anticipation in both patient and healthy control groups. Compared to controls, the motor network was more engaged during gain anticipation in patients with fibromyalgia. Our findings suggest that reward motivation may lead to hyperactivity in the motor network, possibly related to altered motor processing, such as restricted movement or dysregulated motor planning in fibromyalgia. As an exploratory analysis, we compared levels of motor network engagement during early and late timepoints of the gain anticipation phase. Both groups showed greater motor network engagement during the late timepoint (i.e., closer to response), which reflected motor preparation prior to target response. Importantly, compared to controls and consistent with the initial findings described above, patients exhibited greater engagement of the motor network during both early and late timepoints. In summary, by using a novel data-driven ICA approach to analyze task-based fMRI data, we identified elevated motor network engagement during gain anticipation in fibromyalgia.