Pain
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Decoding pain: uncovering the factors that affect the performance of neuroimaging-based pain models.
Neuroimaging-based pain biomarkers, when combined with machine learning techniques, have demonstrated potential in decoding pain intensity and diagnosing clinical pain conditions. However, a systematic evaluation of how different modeling options affect model performance remains unexplored. This study presents the results from a comprehensive literature survey and benchmark analysis. ⋯ Specifically, incorporating more pain-related brain regions, increasing sample sizes, and averaging less data during training and more data during testing improved performance. These findings offer useful guidance for developing neuroimaging-based biomarkers, underscoring the importance of strategic selection of modeling approaches to build better-performing neuroimaging pain biomarkers. However, the generalizability of these findings to clinical pain requires further investigation.
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Menstrual pain is associated with deficits in central pain processing, yet neuroimaging studies to date have all been limited by focusing on group comparisons of adult women with vs without menstrual pain. This study aimed to investigate the role of the triple network model (TNM) of brain networks in adolescent girls with varied menstrual pain severity ratings. One hundred participants (ages 13-19 years) completed a 6-min resting state functional magnetic resonance imaging (fMRI) scan and rated menstrual pain severity, menstrual pain interference, and cumulative menstrual pain exposure. ⋯ In addition, menstrual pain interference was positively associated with connectivity within the left CEN, whereas connectivity both within the right CEN and between the right CEN and cortical areas outside the network (including the insula) were negatively associated with menstrual pain interference. Cumulative menstrual pain exposure shared a strong negative association with connectivity between the default mode network and other widespread regions associated with large-scale brain networks. These findings support a key role for the involvement of TNM brain networks in menstrual pain characteristics and suggest that alterations in pain processing exist in adolescents with varying levels of menstrual pain.
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Spinal cord injury leads to hyperexcitability and dysfunction in spinal sensory processing. As hyperexcitable circuits can become epileptiform, we explored whether such activity emerges in a thoracic spinal cord injury (SCI) contusion model of neuropathic pain. Recordings from spinal sensory axons in multiple below-lesion segmental dorsal roots demonstrated that SCI facilitated the emergence of spontaneous ectopic burst spiking in afferent axons, which were correlated across multiple adjacent dorsal roots. ⋯ We conclude that spinal cord injury promotes the emergence of epileptiform activity in spinal sensory networks that promote profound corruption of sensory signaling. This includes hyperexcitability and bursting by ectopic spiking in afferent axons that propagate bidirectionally by reentrant central and peripheral projections as well as sensory circuit hypoexcitability during the burst refractory period. More broadly, the work links circuit hyperexcitability to epileptiform circuit emergence, further strengthening it as a conceptual basis to understand features of sensory dysfunction and neuropathic pain.
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Na v 1.9 is of interest to the pain community for a number of reasons, including the human mutations in the gene encoding Na v 1.9, SCN11a , that are associated with both pain and loss of pain phenotypes. However, because much of what we know about the biophysical properties of Na v 1.9 has been learned through the study of rodent sensory neurons, and there is only 76% identity between human and rodent homologs of SCN11a , there is reason to suggest that there may be differences in the biophysical properties of the channels in human and rodent sensory neurons, and consequently, the contribution of these channels to the control of sensory neuron excitability, if not pain. Thus, the purpose of this study was to characterize Na v 1.9 currents in human sensory neurons and compare the properties of these currents with those in rat sensory neurons recorded under identical conditions. ⋯ However, we noted a number of potentially important differences between the currents in human and rat sensory neurons including a lower threshold for activation, higher threshold for inactivation, slower deactivation, and faster recovery from slow inactivation. Human Na v 1.9 was inhibited by inflammatory mediators, whereas rat Na v 1.9 was potentiated. Our results may have implications for the role of Na v 1.9 in sensory, if not nociceptive signaling.
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Cold allodynia is a common complaint of patients suffering from neuropathic pain initiated by peripheral nerve injury. However, the mechanisms that drive neuropathic cold pain remain elusive. In this study, we show that the interleukin (IL)-33/ST2 signaling in the dorsal root ganglion (DRG) is a critical contributor to neuropathic cold pain by interacting with the cold sensor transient receptor potential melastatin 8 (TRPM8). ⋯ Co-immunoprecipitation assays further reveal that ST2 interacts with TRPM8 in DRG neurons. Importantly, rIL-33-induced cold allodynia is abolished by pharmacological inhibition of TRPM8 and genetic ablation of the TRPM8-expressing neurons. Thus, our findings suggest that the IL-33/ST2 signaling mediates neuropathic cold pain through downstream cold-sensitive TRPM8 channels, thereby identifying a potential analgesic target for the treatment of neuropathic cold pain.