The mu-opioid receptor (OPRM1) A118G polymorphism underpins different pain sensitivity and opioid-analgesic outcome with unclear effect on the descending pain modulatory system (DPMS). Primary dysmenorrhea (PDM), the most prevalent gynecological problem with clear painful and pain free conditions, serves as a good clinical model of spontaneous pain. The objective of this imaging genetics study was therefore to explore if differences in functional connectivity (FC) of the DPMS between the OPRM1 A118G polymorphisms could provide a possible explanation for the differences in pain experience. Sixty-one subjects with PDM and 65 controls participated in the current study of resting-state functional magnetic resonance imaging (fMRI) during the menstruation and peri-ovulatory phases; blood samples were taken for genotyping. We studied 3 aspects of pain experience, namely, mnemonic pain (recalled overall menstrual pain), present pain (spontaneous menstrual pain), and experimental pain (thermal pain) intensities. We report that G allele carriers, in comparison to AA homozygotes, exhibited functional hypo-connectivity between the anterior cingulate cortex (ACC) and periaqueductal gray (PAG). Furthermore, G allele carriers lost the correlation with spontaneous pain experience and exhibited dysfunctional DPMS by means of PAG-seeded FC dynamics. This OPRM1 A118G-DPMS interaction is one plausible neurological mechanism underlying the individual differences in pain experience.

We report here that otherwise healthy PDMs are associated with high prevalence of normal variants but not brain abnormalities. Our observations invite further epidemiological and neuroscientific studies.

Numerous studies have reported that long‐term musical training can affect brain functionality and induce structural alterations in the brain. Singing is a form of vocal musical expression with an unparalleled capacity for communicating emotion; however, there has been relatively little research on neuroplasticity at the network level in vocalists (i.e., noninstrumental musicians). Our objective in this study was to elucidate changes in the neural network architecture following long‐term training in the musical arts. We employed a framework based on graph theory to depict the connectivity and efficiency of structural networks in the brain, based on diffusion‐weighted images obtained from 35 vocalists, 27 pianists, and 33 nonmusicians. Our results revealed that musical training (both voice and piano) could enhance connectivity among emotion‐related regions of the brain, such as the amygdala. We also discovered that voice training reshaped the architecture of experience‐dependent networks, such as those involved in vocal motor control, sensory feedback, and language processing. It appears that vocal‐related changes in areas such as the insula, paracentral lobule, supramarginal gyrus, and putamen are associated with functional segregation, multisensory integration, and enhanced network interconnectivity. These results suggest that long‐term musical training can strengthen or prune white matter connectivity networks in an experience‐dependent manner.

No abstract