Objective To investigate the impact of chronic pain on brain dynamics at rest. Methods Functional connectivity was examined in patients with fibromyalgia (FM) (n = 9) and healthy controls (n = 11) by calculating partial correlations between low-frequency blood oxygen level–dependent fluctuations extracted from 15 brain regions. Results Patients with FM had more positive and negative correlations within the pain network than healthy controls. Patients with FM displayed enhanced functional connectivity of the anterior cingulate cortex (ACC) with the insula (INS) and basal ganglia (p values between .01 and .05), the secondary somatosensory area with the caudate (CAU) (p = .012), the primary motor cortex with the supplementary motor area (p = .007), the globus pallidus with the amygdala and superior temporal sulcus (both p values < .05), and the medial prefrontal cortex with the posterior cingulate cortex (PCC) and CAU (both p values < .05). Functional connectivity of the ACC with the amygdala and periaqueductal gray (PAG) matter (p values between .001 and .05), the thalamus with the INS and PAG (both p values < .01), the INS with the putamen (p = .038), the PAG with the CAU (p = .038), the secondary somatosensory area with the motor cortex and PCC (both p values < .05), and the PCC with the superior temporal sulcus (p = .002) was also reduced in FM. In addition, significant negative correlations were observed between depression and PAG connectivity strength with the thalamus (r = −0.64, p = .003) and ACC (r = −0.60, p = .004). Conclusions These findings demonstrate that patients with FM display a substantial imbalance of the connectivity within the pain network during rest, suggesting that chronic pain may also lead to changes in brain activity during internally generated thought processes such as occur at rest. Abbreviations BOLD = blood oxygen level–dependent; FM = fibromyalgia; HC = healthy control; WHYMPI = West Haven-Yale Multidimensional Pain Inventory; fMRI = functional magnetic resonance imaging; ACC = anterior cingulate cortex; PCC = posterior cingulate cortex; AMYG = amygdala; CAU = caudate; PUT = putamen; INS = insula; M1 = primary motor area; SMA = supplementary motor area; SI = primary somatosensory area; SII = secondary somatosensory area; mPFC = medial prefrontal cortex; PAG = periaqueductal gray; STS = superior temporal sulcus; THA = thalamus

Facial expression is one of the most relevant nonverbal behaviors in the communication of pain. However, little is known about brain processing of pain expressions in comparison with other affective facial expressions. The present experiment aimed to examine the effects of pain expression intensity on affective ratings and brain dynamics by recording electroencephalography (EEG) from 20 female healthy volunteers 18-24 years of age. Participants were asked to rate the affective characteristics of 144 stimuli depicting facial expressions of pain and anger with 3 level of intensities (high, mild, and low), as well as neutral faces. Results indicated that pain faces were judged as more unpleasant and arousing than anger and neutral faces for all intensity levels. EEG results further showed that facial expressions of pain elicited more enhanced amplitudes of the visual evoked potentials than anger and neutral faces in the latency between 350 and 550 milliseconds after stimulus onset; whereas anger faces elicited greater P200 amplitudes than pain and neutral faces. In addition, more increased theta activity in the latency of 200 to 400 milliseconds after stimulus onset was observed to high-intense as compared with low-intense facial expressions. These findings indicate that brain activity elicited by affective faces is modulated by the intensity of facial expressions and suggest the involvement of different brain mechanisms during the processing and recognition of facial expressions of pain and anger in healthy volunteers.

The neurovisceral integration model proposes a neuronal network that is related to heart rate activity and cognitive performance. The aim of this study was to determine whether heart rate variability (HRV) and variability in electroencephalographic (EEG) functional connectivity in the resting state are related to cognitive flexibility. Thirty-eight right-handed students completed the CAMBIOS test, and their heart and EEG activity was recorded during 6 min in the resting state with their eyes open. We calculated correlations, partial correlations and multiple linear regressions among HRV indices, functional brain connectivity variability and CAMBIOS scores. Furthermore, the sample was divided into groups according to CAMBIOS performance, and one-way ANOVA was applied to evaluate group differences. Our results show direct and inverse correlations among cognitive flexibility, connectivity (positive and negative task networks) and heartbeat variability. Partial correlations and multiple linear regressions suggest that the relation between HRV and CAMBIOS performance is mediated by neuronal oscillations. ANOVA confirms that HRV and variability in functional brain connectivity is related to cognitive performance. In conclusion, the levels of brain signal variability might predict cognitive flexibility in a cognitive task, while HRV might predict cognitive flexibility only when it is mediated by neuronal oscillations.