Resilience in Juvenile Fibromyalgia: Links with Symptoms, Affective Distress, and Brain Connectivity
Poster No:
1712
Submission Type:
Abstract Submission
Authors:
Maria Suñol1, Jon Dudley2, Michael Payne3, Han Tong2, Tracy Ting2, Susmita Kashikar-Zuck2, Robert Coghill2, Marina López-Solà1
Institutions:
1University of Barcelona, Barcelona, Catalunya, 2Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 3Cincinnati Children's Hospital Medical Center, Cincinnati , OH
First Author:
Co-Author(s):
Introduction:
Juvenile fibromyalgia (JFM) is a chronic pain syndrome predominantly affecting adolescent females (1,2). In JFM, resilience has been associated with reduced pain, disability, symptom severity, and suicidality, and with increased energy levels and quality of life (3,4). Thus, resilience may be a protective factor in coping with pain, reducing affective burden, and promoting a positive outlook. We study, for the first time, differences between higher vs. lower resilience JFM adolescents on core disease symptoms, affective traits, and brain functional connectivity.
Methods:
We recruited 41 adolescent females with JFM and 40 matched pain-free adolescent females. Participants completed a resting-state fMRI exam and validated questionnaires on resilience (Shift-and-Persist Scale), core JFM symptoms (Fibromyalgia Symptom Severity Questionnaire, Widespread Pain index, Functional Disability Inventory), and affective traits (Children's Depression Inventory, Screen for Child Anxiety-Related Disorders, Self-Compassion Scale).
Based on Shift-and-Persist scores, we performed a hierarchical clustering analysis in the JFM group, which landed two patient subgroups (higher vs lower resilience). We performed two principal component analyses (varimax rotation and component' eigenvalues>1) to reduce the dimensionality of 1) core JFM symptom-related variables, and 2) affective traits. Next, using two-sample t-tests, we assessed differences in the principal components of core JFM symptoms and affective traits between JFM patients with higher vs. lower resilience.
Imaging data were preprocessed and denoised using MATLAB-R2021a and the CONN Toolbox-20.b. We estimated resting-state functional connectivity strength with the Intrinsic Connectivity Contrast Approach (5) and computed average path length and clustering coefficient across the nodes of the cortical CONN network atlas. Connectivity strength and network topology differences between higher vs. lower resilience JFM groups were assessed with two-sample t-tests. For completion, we compared these subgroups of patients with pain-free controls in terms of clinical and connectivity measures.
Based on Shift-and-Persist scores, we performed a hierarchical clustering analysis in the JFM group, which landed two patient subgroups (higher vs lower resilience). We performed two principal component analyses (varimax rotation and component' eigenvalues>1) to reduce the dimensionality of 1) core JFM symptom-related variables, and 2) affective traits. Next, using two-sample t-tests, we assessed differences in the principal components of core JFM symptoms and affective traits between JFM patients with higher vs. lower resilience.
Imaging data were preprocessed and denoised using MATLAB-R2021a and the CONN Toolbox-20.b. We estimated resting-state functional connectivity strength with the Intrinsic Connectivity Contrast Approach (5) and computed average path length and clustering coefficient across the nodes of the cortical CONN network atlas. Connectivity strength and network topology differences between higher vs. lower resilience JFM groups were assessed with two-sample t-tests. For completion, we compared these subgroups of patients with pain-free controls in terms of clinical and connectivity measures.
Results:
JFM patients with higher vs. lower resilience did not differ in the principal component summarizing core JFM symptoms (T=1.02; p=.314). In contrast, the groups differed in the component summarizing affective traits (T=4.03; p<.001). Considering component loadings, the higher resilience JFM group showed less depressive and anxiety symptoms and higher self-compassion.
The high resilience JFM group (vs. the low resilience JFM group) showed increased connectivity strength in the posterior cingulate cortex (T=6.22; pFDR=.025), angular gyri (T's>3.9; pFDR's<.024), superior frontal (T>5.2, pFDR's<.017) and inferior temporal cortices (T's>4.7; pFDR<.015) (Figure 1). They also showed lower average path length (i.e., higher efficiency) in nodes of the visual, dorsal attention, and default-mode networks (T's>2.6, pFDR's<.041). We found no differences in clustering coefficient.
Compared to controls, JFM patients with higher resilience had reduced connectivity strength in a single cluster located in the paracentral lobule (T=4.18; pFDR=.013), whereas JFM patients with lower resilience had a broader pattern of reduced connectivity encompassing sensorimotor, visual, attentional, and self-referential regions (T's>4 ; pFDR's<.04) (Figure 2).
The high resilience JFM group (vs. the low resilience JFM group) showed increased connectivity strength in the posterior cingulate cortex (T=6.22; pFDR=.025), angular gyri (T's>3.9; pFDR's<.024), superior frontal (T>5.2, pFDR's<.017) and inferior temporal cortices (T's>4.7; pFDR<.015) (Figure 1). They also showed lower average path length (i.e., higher efficiency) in nodes of the visual, dorsal attention, and default-mode networks (T's>2.6, pFDR's<.041). We found no differences in clustering coefficient.
Compared to controls, JFM patients with higher resilience had reduced connectivity strength in a single cluster located in the paracentral lobule (T=4.18; pFDR=.013), whereas JFM patients with lower resilience had a broader pattern of reduced connectivity encompassing sensorimotor, visual, attentional, and self-referential regions (T's>4 ; pFDR's<.04) (Figure 2).
Conclusions:
Our results emphasize the clinical relevance of resilience in mitigating affective suffering and shaping neural networks, particularly the default-mode network. The higher resilience JFM group (vs. lower resilience JFM group) had similar levels of core JFM symptoms, reduced affective suffering, increased connectivity strength in key nodes of the DMN, and lower average path lengths in cortical networks. We found that the connectivity strength pattern of higher resilience JFM patients resembled that of pain-free controls more than the pattern observed in the lower resilience JFM group.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling 1
Task-Independent and Resting-State Analysis
Novel Imaging Acquisition Methods:
BOLD fMRI
Perception, Attention and Motor Behavior:
Perception: Pain and Visceral 2
Keywords:
Anxiety
DISORDERS
Emotions
FUNCTIONAL MRI
Pain
PEDIATRIC
Pediatric Disorders
Other - fibromyalgia, resilience
1|2Indicates the priority used for review
Provide references using author date format
2. Kashikar-Zuck, S., King, C., Ting, T. V., & Arnold, L. M. (2016). Juvenile Fibromyalgia: Different from the Adult Chronic Pain Syndrome?. Current rheumatology reports, 18(4), 19. https://doi.org/10.1007/s11926-016-0569-9
3. Gmuca, S., Xiao, R., Urquhart, A., Weiss, P. F., Gillham, J. E., Ginsburg, K. R., Sherry, D. D., & Gerber, J. S. (2019). The Role of Patient and Parental Resilience in Adolescents with Chronic Musculoskeletal Pain. The Journal of pediatrics, 210, 118–126.e2. https://doi.org/10.1016/j.jpeds.2019.03.006
4. Gmuca, S., Sonagra, M., Xiao, R., Miller, K. S., Thomas, N. H., Young, J. F., Weiss, P. F., Sherry, D. D., & Gerber, J. S. (2021). Suicidal risk and resilience in juvenile fibromyalgia syndrome: a cross-sectional cohort study. Pediatric rheumatology online journal, 19(1), 3. https://doi.org/10.1186/s12969-020-00487-w
5. Martuzzi, R., Ramani, R., Qiu, M., Shen, X., Papademetris, X., & Constable, R. T. (2011). A whole-brain voxel based measure of intrinsic connectivity contrast reveals local changes in tissue connectivity with anesthetic without a priori assumptions on thresholds or regions of interest. NeuroImage, 58(4), 1044–1050. https://doi.org/10.1016/j.neuroimage.2011.06.075