Investigating the BOLD spectral power of the intrinsic connectivity networks in fibromyalgia patients: A resting-state fMRI study

Recent advances in multivariate statistical analysis of blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) have provided novel insights into the network organization of the human brain. Here, we applied group independent component analysis, a well-established approach for detecting brain intrinsic connectivity networks, to examine the spontaneous BOLD fluctuations in patients with fibromyalgia and healthy controls before and after exposure to a stressor. The BOLD spectral power characteristics of component time courses were calculated using the fast Fourier transform (FFT) algorithm, and group comparison was performed at six frequency bins between 0 and 0.24 Hz at 0.04 Hz intervals. Relative to controls, patients with fibromyalgia displayed significant BOLD spectral power differences in the default-mode, salience, and subcortical networks at the baseline level (P
_{Bon ferroni-corrected}
<; 0.05). Multivariate analysis of covariance (MANCOVA) further revealed significant effects of the cold water temperature, and pain rating on the spectral power of the sensorimotor, salience, and prefrontal networks, while the diagnosis of fibromyalgia influenced the BOLD spectral power of the salience and subcortical networks (P
_{FDR-corrected}
<; 0.05). Since the BOLD spectral power reflects the degree of fluctuations within a network, future studies of the correlation between BOLD spectral power and pain processing can cast additional light on the nature of the central nervous system dysfunction in patients with chronic pain syndromes.