Pain, an aversive experience signaling bodily harm, motivates behavior and drives neuroplastic changes in the central nervous system. However, our understanding of pain's brain bases remains incomplete, particularly regarding the neural representations of different pain types. Most studies focus on cutaneous somatic pain, neglecting visceral pain, which is a common and clinically significant type of pain. Visceral pain differs from somatic pain in its sensory characteristics (poor localization, weak correlation with stimulus intensity, referred pain) and potentially stronger affective dimension. Although some studies suggest separate processing in 'lateral' and 'medial' pain systems, definitive evidence is lacking. This study aimed to investigate whether the Neurologic Pain Signature (NPS), a multivariate brain measure predicting somatic pain intensity, generalizes to visceral pain and whether there are distinct neural signatures for visceral versus somatic pain.

Previous research has used functional magnetic resonance imaging (fMRI) to identify neural correlates of pain. Studies have focused primarily on cutaneous somatic pain, using primarily thermal stimuli applied to the hand or foot. While some studies have directly compared visceral and somatic pain using fMRI, the findings have been mixed, showing both similarities and differences in brain activation patterns across various cortical and subcortical regions. The lack of consistent findings underscores the need for larger-scale studies employing advanced multivariate analytical techniques to identify common and distinct neural representations of these pain types. The development of the NPS by Wager et al. provided a robust multivariate measure for predicting somatic pain intensity, opening up the possibility of investigating its generalizability and specificity across various pain modalities.

This study utilized a retrospective meta-analysis of fMRI data from seven studies (N=165 participants). Five types of pain and discomfort were included: esophageal, gastric, and rectal distension; cutaneous thermal stimulation; and vulvar pressure. Data preprocessing involved standard steps including motion correction, spatial smoothing, and normalization to standard brain space. Multivariate pattern analysis (MVPA) was employed. First, the established NPS was evaluated for its sensitivity and generalizability across different pain types by calculating its expression (cosine similarity to the original NPS pattern) during painful stimulation. This was compared with the responses of neural signatures for other affective processes (negative emotion, social rejection, and vicarious pain). The specificity of the NPS was tested using an independent set of studies. Second, a network-based classifier was developed to distinguish between visceral (rectal distension) and somatic (cutaneous thermal stimulation) pain using resting-state networks. Classification accuracy was assessed through cross-validation and tested on independent datasets. Voxel-wise analyses (univariate GLMs) were performed to identify common and distinct brain activations and deactivations between somatic and visceral stimulation.

The Neurologic Pain Signature (NPS) robustly responded to both somatic and visceral pain across all studies. NPS expression significantly correlated with the intensity of perceived visceral pain. The NPS showed higher sensitivity and specificity to pain when compared with neural signatures for other affective processes. A novel network-based classifier distinguished visceral (rectal) from somatic (thermal) stimulation with high accuracy both in cross-validation (AUROC = 0.92) and in independent cohorts (AUROC = 0.84). Other pain types showed intermediate patterns, reflecting mixtures of somatic and visceral activation patterns. Voxel-wise analyses identified overlapping activations in regions consistent with the NPS across somatic and visceral pain, but also revealed distinct patterns. Notably, the somatomotor network was activated more strongly during somatic pain, while the frontoparietal network was more engaged during visceral pain.

This study provides crucial insights into the neural underpinnings of visceral and somatic pain. The results strongly support the existence of a common core pain-related network (NPS) that generalizes across diverse pain modalities. The finding that the NPS predicted visceral pain intensity and showed greater sensitivity and specificity to pain compared to other affective processes counters the common assumption that visceral pain is more emotionally driven. However, distinct neural signatures were also identified, enabling accurate classification of pain types based on brain activation patterns. These findings suggest that the traditional distinction between 'lateral' and 'medial' pain systems is an oversimplification and that a more nuanced understanding of the involved networks is necessary. The high accuracy of the classifier points towards the potential for developing brain-based biomarkers for distinguishing different pain types.

This study validates the Neurologic Pain Signature (NPS) as a reliable biomarker for pain across modalities and introduces a novel network-based classifier to differentiate between visceral and somatic pain. The findings challenge traditional models of pain processing and highlight the potential for developing brain-based diagnostic tools for various types of pain. Future research should focus on refining these classifiers, investigating individual differences in pain processing, and exploring the potential clinical applications of these findings. Further studies are needed to explore the role of individual differences, particularly sex differences, and to address the limitations related to variation in stimulus parameters and study designs across the included datasets.

The study's limitations include variations in stimulation procedures, fMRI task designs, and stimulus intensities across the different studies. This heterogeneity could influence the observed brain responses. The sample sizes, while large compared to previous studies, could be further increased to improve statistical power and robustness of findings. The study also did not explicitly control for all potential confounding factors that may impact pain perception and processing, and didn't analyze sex differences in detail.