Network Analysis for Better Understanding the Complex Psycho-Biological Mechanisms behind Fibromyalgia Syndrome

Abstract

The aim of this study was to assess potential associations between sensory, cognitive, health-related, and physical variables in women with fibromyalgia syndrome (FMS) using a network analysis for better understanding the complexity of psycho-biological mechanisms. Demographic, clinical, pressure pain threshold (PPT), health-related, physical, and psychological/cognitive variables were collected in 126 women with FMS. A network analysis was conducted to quantify the adjusted correlations between the modeled variables and to assess the centrality indices (i.e., the degree of connection with other symptoms in the network and the importance in the system modeled as a network. This model showed several local associations between the variables. Multiple positive correlations between PPTs were observed, being the strongest weight between PPTs over the knee and tibialis anterior (ρ: 0.28). Catastrophism was associated with higher hypervigilance (ρ: 0.23) and lower health-related EuroQol-5D (ρ: −0.24). The most central variables were PPT over the tibialis anterior (the highest strength centrality), hand grip (the highest harmonic centrality) and Time Up and Go (the highest betweenness centrality). This study, applying network analysis to understand the complex mechanisms of women with FMS, supports a model where sensory-related, psychological/cognitive, health-related, and physical variables are connected. Implications of the current findings, e.g., developing treatments targeting these mechanisms, are discussed.

Keywords: clinical decision rules; fibromyalgia; function; network analysis; pain.

Figure 1

Location of pressure pain threshold measurements: mastoid process, upper trapezius muscle, lateral epicondyle, second metacarpal, posterosuperior iliac spine, greater trochanter, pes anserine, and tibialis anterior muscle.

Figure 2

Network analysis of the association between demographic, clinical, cognitive, psycho-physical, health-related, and physical measures. Edges represent connections between two nodes and are interpreted as the existence of an association between two nodes, adjusted for all other nodes. Each edge in the network represents either positive regularized adjusted associations (green edges) or negative regularized adjusted associations (red edges). The thickness and color saturation of an edge denotes its weight (the strength of the association between two nodes).

Figure 3

Bootstrapped 95% quantile confidence interval of the estimated edge weights of the network. “Bootstrap mean” reflects the average magnitude of edge weights across the bootstrapped samples. “Sample” reflects the magnitude of edge weights of the original network built on the entire input dataset.

Figure 4

Centrality measures of strength and betweenness of each node in the network. A centrality value of 1 indicates maximal importance, and 0 indicates no importance.

Figure 5

Harmonic centrality measure of each node in the network. A centrality value of 1 indicates maximal importance, and 0 indicates no importance.

Figure 6

Average correlations between centrality indices of networks sampled with persons dropped and networks built on the entire input dataset at all follow-up time points. Lines indicate the means and areas indicate the range from the 2.5th quantile to the 97.5th quantile.

Figure 7

Clusters identified by the Louvain community detection algorithm. Green cluster: psycho-physical (PPTs) variables; purple cluster: pain-related, cognitive, health-related, and physical variables; blue cluster: pain extent variables; red cluster: sociodemographic variables; yellow cluster: diagnostic variables. Numbers represent the same nodes reported in Figure 2.